Merge branch 'main' of github.com:microsoft/agent-lightning into apo-algorithm

add room tasks
add variant03
2025-10-14 09:03:39 +08:00 · 2025-10-14 09:03:20 +08:00 · 2025-10-14 09:01:29 +08:00 · 2025-10-14 01:04:22 +08:00 · 2025-10-14 01:01:35 +08:00 · 2025-10-14 00:39:35 +08:00
453 changed files with 9061 additions and 113393 deletions
@@ -1,14 +0,0 @@
-.venv
-**/.venv
-__pycache__
-.git
-.gitignore
-**/node_modules
-dist
-build
-.env
-docker
-.pytest_cache
-.vscode
-**/*.log
-examples/**/data
@@ -1,32 +0,0 @@
-name: Backport Merged Pull Request
-on:
-  pull_request_target:
-    types: [closed]
-permissions:
-  contents: write
-  issues: write
-  pull-requests: write
-
-# NOTE:
-# Microsoft requires rotating BOT_PAT every 3 months.
-# Log onto agent-lightning-bot account and rotate the PAT if needed.
-
-jobs:
-  backport:
-    name: Backport pull request
-    runs-on: ubuntu-latest
-    # Don't run on closed unmerged pull requests
-    if: github.event.pull_request.merged
-    steps:
-      - uses: actions/checkout@v4
-      - name: Create backport pull requests
-        uses: korthout/backport-action@v3
-        with:
-          branch_name: 'backport/${pull_number}/${target_branch}'
-          label_pattern: ^(stable/[^ ]+)$
-          github_token: ${{ secrets.BOT_PAT }}
-          add_labels: backport
-          add_author_as_assignee: true
-          git_committer_name: agent-lightning-bot
-          # This email address is not monitored.
-          git_committer_email: agl.msft@outlook.com
@@ -1,29 +0,0 @@
-name: Badge - APO
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - APO
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-apo.yml', label: 'apo', variants: ['legacy', 'stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Azure
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Azure
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-azure.yml', label: 'azure', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Calc-X
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Calc-X
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-calc-x.yml', label: 'calc-x', variants: ['legacy', 'stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Claude Code
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Claude Code
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-claude-code.yml', label: 'claude-code', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Compatibility
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Backward Compatibility
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-compat.yml', label: 'examples-compat', variants: ['legacy', 'stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,41 +0,0 @@
-name: Badge - Examples
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Calc-X
-      - Examples - Spider
-      - Examples - APO
-      - Examples - Unsloth
-      - Examples - Tinker
-      - Examples - Azure
-      - Examples - Claude Code
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-calc-x.yml', label: 'examples-calc-x.stable', variants: ['stable'] },
-              { workflow: 'examples-spider.yml', label: 'examples-spider.stable', variants: ['stable'] },
-              { workflow: 'examples-apo.yml', label: 'examples-apo.stable', variants: ['stable'] },
-              { workflow: 'examples-unsloth.yml', label: 'examples-unsloth.stable', variants: ['stable'] },
-              { workflow: 'examples-tinker.yml', label: 'examples-tinker.stable', variants: ['stable'] },
-              { workflow: 'examples-azure.yml', label: 'examples-azure.stable', variants: ['stable'] },
-              { workflow: 'examples-claude-code.yml', label: 'examples-claude-code.stable', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,37 +0,0 @@
-name: Badge - Latest
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Calc-X
-      - Examples - Spider
-      - Examples - APO
-      - Examples - Unsloth
-      - GPU Test
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-calc-x.yml', label: 'calc-x.latest', variants: ['latest'] },
-              { workflow: 'examples-spider.yml', label: 'spider.latest', variants: ['latest'] },
-              { workflow: 'examples-apo.yml', label: 'apo.latest', variants: ['latest'] },
-              { workflow: 'examples-unsloth.yml', label: 'unsloth.latest', variants: ['latest'] },
-              { workflow: 'tests-full.yml', label: 'tests-full.latest', variants: ['latest'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Spider
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Spider
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-spider.yml', label: 'spider', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Tinker
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Tinker
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-tinker.yml', label: 'tinker', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,31 +0,0 @@
-name: Badge - Unit Test
-
-on:
-  workflow_run:
-    workflows:
-      - CPU Test
-      - GPU Test
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'tests-full.yml', label: 'tests-full', variants: ['legacy', 'stable'] },
-              { workflow: 'tests.yml', label: 'tests', variants: ['legacy', 'stable', 'Lint', 'documentation', 'JavaScript'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,29 +0,0 @@
-name: Badge - Unsloth
-
-on:
-  workflow_run:
-    workflows:
-      - Examples - Unsloth
-    types: [completed]
-
-  workflow_dispatch:
-
-permissions:
-  actions: read
-  contents: read
-
-jobs:
-  badge:
-    if: ${{ github.event_name == 'workflow_dispatch' || (github.event_name == 'workflow_run' && github.event.workflow_run.head_branch == 'main') }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/github-script@v8
-        with:
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-          script: |
-            const badgeAggregation = require('./scripts/badge_aggregation.js');
-            const dependencies = [
-              { workflow: 'examples-unsloth.yml', label: 'examples-unsloth.stable', variants: ['stable'] },
-            ];
-            await badgeAggregation({ github, context, core, dependencies });
@@ -1,334 +0,0 @@
-name: Benchmark
-permissions:
-  contents: read
-on:
-  workflow_dispatch:
-
-jobs:
-  benchmark:
-    name: Benchmark (${{ matrix.backend.id }}, ${{ matrix.scenario.display }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-cpu]
-    timeout-minutes: 60
-    strategy:
-      fail-fast: false
-      matrix:
-        backend:
-          - id: memory
-            compose_file: compose.prometheus-memory-store.yml
-          - id: mongo
-            compose_file: compose.prometheus-mongo-store.yml
-        scenario:
-          - id: minimal-production
-            display: Minimal production scale
-            store_workers: 4
-            args: >-
-              --mode batch
-              --total-tasks 4096
-              --batch-size 256
-              --n-runners 32
-              --max-rounds 6
-              --sleep-seconds 0.5
-          - id: medium-production
-            display: Medium production scale
-            store_workers: 16
-            args: >-
-              --mode batch
-              --total-tasks 10000
-              --batch-size 1000
-              --n-runners 100
-              --max-rounds 10
-              --sleep-seconds 0.1
-          - id: large-batch
-            display: Large batch waves
-            store_workers: 32
-            args: >-
-              --mode batch
-              --total-tasks 100000
-              --batch-size 8192
-              --n-runners 256
-              --max-rounds 6
-              --sleep-seconds 0.1
-          - id: long-queues
-            display: Long rollout queues
-            store_workers: 32
-            args: >-
-              --mode batch_partial
-              --total-tasks 100000
-              --batch-size 1024
-              --n-runners 256
-              --remaining-tasks 4096
-              --max-rounds 4
-              --sleep-seconds 0.1
-          - id: high-concurrency
-            display: High-throughput concurrent requests
-            store_workers: 32
-            args: >-
-              --mode single
-              --total-tasks 100000
-              --concurrency 2048
-              --n-runners 256
-              --max-rounds 2
-              --sleep-seconds 0.1
-          - id: heavy-traces
-            display: Heavy rollouts with deep traces
-            store_workers: 64
-            args: >-
-              --mode batch_partial
-              --total-tasks 10000
-              --batch-size 1024
-              --remaining-tasks 256
-              --n-runners 512
-              --max-rounds 20
-              --sleep-seconds 1.0
-    env:
-      STORE_URL: http://localhost:4747
-      STORE_API_URL: http://localhost:4747/v1/agl
-      PROM_URL: http://localhost:9090
-      SCENARIO_ID: ${{ matrix.scenario.id }}
-      BACKEND_ID: ${{ matrix.backend.id }}
-      ARTIFACT_DIR: artifacts/${{ matrix.scenario.id }}-${{ matrix.backend.id }}
-      COMPOSE_FILE: ${{ matrix.backend.compose_file }}
-      AGL_STORE_N_WORKERS: ${{ matrix.scenario.store_workers }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: '3.12'
-
-      - name: Sync dependencies
-        run: uv sync --frozen --extra mongo --group core-stable --group dev
-
-      - name: Check disk space
-        run: df -h
-
-      - name: Reset benchmark data directories
-        run: |
-          set -euo pipefail
-          cd docker
-          rm -rf data
-          bash setup.sh
-
-      - name: Launch ${{ matrix.backend.id }} Prometheus stack
-        run: |
-          set -euo pipefail
-          cd docker
-          docker compose -f "$COMPOSE_FILE" down -v || true
-          docker compose -f "$COMPOSE_FILE" up -d --quiet-pull
-
-      - name: Wait for store readiness
-        run: |
-          set -euo pipefail
-          for attempt in {1..60}; do
-            if curl -fsS "$STORE_API_URL/health" >/dev/null 2>&1; then
-              exit 0
-            fi
-            sleep 1
-          done
-          echo "Store did not become ready in time" >&2
-          docker compose -f "$COMPOSE_FILE" logs app
-          exit 1
-
-      - name: Prepare artifact directory
-        run: mkdir -p "$ARTIFACT_DIR"
-
-      - name: Record benchmark start
-        run: echo "BENCHMARK_START=$(date -u +%FT%TZ)" >> "$GITHUB_ENV"
-
-      - name: Run ${{ matrix.scenario.display }} workload
-        run: |
-          set -euo pipefail
-          uv run --locked --no-sync python -m tests.benchmark.benchmark_store \
-            --store-url "$STORE_URL" \
-            ${{ matrix.scenario.args }}
-
-      - name: Record benchmark end
-        if: ${{ always() }}
-        run: echo "BENCHMARK_END=$(date -u +%FT%TZ)" >> "$GITHUB_ENV"
-
-      - name: Run benchmark analysis
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          mkdir -p "$ARTIFACT_DIR"
-          if [ -z "${BENCHMARK_START:-}" ] || [ -z "${BENCHMARK_END:-}" ]; then
-            echo "Analysis skipped: benchmark window not recorded." > "$ARTIFACT_DIR/analysis.txt"
-            exit 1
-          fi
-          uv run --locked --no-sync python -m tests.benchmark.analysis \
-            --prom-url "$PROM_URL" \
-            --store-url "$STORE_API_URL" \
-            --start "$BENCHMARK_START" \
-            --end "$BENCHMARK_END" \
-            | tee "$ARTIFACT_DIR/analysis.txt"
-
-      - name: Stop ${{ matrix.backend.id }} Prometheus stack
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          cd docker
-          docker compose -f "$COMPOSE_FILE" down -v || true
-
-      - name: Archive Prometheus metrics
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          mkdir -p "$ARTIFACT_DIR"
-          if [ -d docker/data/prometheus ]; then
-            tar -C docker/data -czf "$ARTIFACT_DIR/prometheus-${SCENARIO_ID}-${BACKEND_ID}.tar.gz" prometheus
-          fi
-          if docker compose -f "$COMPOSE_FILE" ps --format '{{.Name}}' >/dev/null 2>&1; then
-            docker compose -f "$COMPOSE_FILE" logs app > "$ARTIFACT_DIR/docker-${SCENARIO_ID}-${BACKEND_ID}.log" || true
-          fi
-
-      - name: Upload benchmark artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: benchmark-${{ matrix.scenario.id }}-${{ matrix.backend.id }}
-          path: ${{ env.ARTIFACT_DIR }}
-          if-no-files-found: error
-
-  micro-benchmark:
-    name: Micro-benchmark (${{ matrix.backend.id }}, ${{ matrix.mode.display }})
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    strategy:
-      fail-fast: false
-      matrix:
-        backend:
-          - id: memory
-            compose_file: compose.prometheus-memory-store.yml
-          - id: mongo
-            compose_file: compose.prometheus-mongo-store.yml
-        mode:
-          - id: worker
-            display: Update worker throughput
-            cli: worker
-          - id: dequeue-empty
-            display: Dequeue empty throughput
-            cli: dequeue-empty
-          - id: rollout
-            display: Rollout + span throughput
-            cli: rollout
-    env:
-      STORE_URL: http://localhost:4747
-      STORE_API_URL: http://localhost:4747/v1/agl
-      PROM_URL: http://localhost:9090
-      BACKEND_ID: ${{ matrix.backend.id }}
-      MODE_ID: ${{ matrix.mode.id }}
-      ARTIFACT_DIR: artifacts/micro-${{ matrix.mode.id }}-${{ matrix.backend.id }}
-      COMPOSE_FILE: ${{ matrix.backend.compose_file }}
-      AGL_STORE_N_WORKERS: 8
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: '3.12'
-
-      - name: Sync dependencies
-        run: uv sync --frozen --extra mongo --group core-stable --group dev
-
-      - name: Reset benchmark data directories
-        run: |
-          set -euo pipefail
-          cd docker
-          rm -rf data
-          bash setup.sh
-
-      - name: Launch ${{ matrix.backend.id }} Prometheus stack
-        run: |
-          set -euo pipefail
-          cd docker
-          docker compose -f "$COMPOSE_FILE" down -v || true
-          docker compose -f "$COMPOSE_FILE" up -d --quiet-pull
-
-      - name: Wait for store readiness
-        run: |
-          set -euo pipefail
-          for attempt in {1..60}; do
-            if curl -fsS "$STORE_API_URL/health" >/dev/null 2>&1; then
-              exit 0
-            fi
-            sleep 1
-          done
-          echo "Store did not become ready in time" >&2
-          cd docker && docker compose -f "$COMPOSE_FILE" logs app
-          exit 1
-
-      - name: Prepare artifact directory
-        run: mkdir -p "$ARTIFACT_DIR"
-
-      - name: Record micro benchmark start
-        run: echo "BENCHMARK_START=$(date -u +%FT%TZ)" >> "$GITHUB_ENV"
-
-      - name: Run ${{ matrix.mode.display }}
-        run: |
-          set -euo pipefail
-          mkdir -p "$ARTIFACT_DIR"
-          uv run --locked --no-sync python -m tests.benchmark.micro_benchmark \
-            --store-url "$STORE_URL" \
-            --summary-file "$ARTIFACT_DIR/summary-${MODE_ID}.txt" \
-            "${{ matrix.mode.cli }}" | tee "$ARTIFACT_DIR/micro-${MODE_ID}.txt"
-
-      - name: Record micro benchmark end
-        if: ${{ always() }}
-        run: echo "BENCHMARK_END=$(date -u +%FT%TZ)" >> "$GITHUB_ENV"
-
-      - name: Run micro benchmark analysis
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          mkdir -p "$ARTIFACT_DIR"
-          if [ -z "${BENCHMARK_START:-}" ] || [ -z "${BENCHMARK_END:-}" ]; then
-            echo "Analysis skipped: benchmark window not recorded." > "$ARTIFACT_DIR/analysis-${MODE_ID}.txt"
-            exit 1
-          fi
-          uv run --locked --no-sync python -m tests.benchmark.analysis \
-            --prom-url "$PROM_URL" \
-            --store-url "$STORE_API_URL" \
-            --start "$BENCHMARK_START" \
-            --end "$BENCHMARK_END" \
-            | tee "$ARTIFACT_DIR/analysis-${MODE_ID}.txt"
-
-      - name: Show micro benchmark summary
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          summary_file="$ARTIFACT_DIR/summary-${MODE_ID}.txt"
-          if [ -f "$summary_file" ]; then
-            echo "Micro benchmark summary ($MODE_ID/$BACKEND_ID):"
-            cat "$summary_file"
-          else
-            echo "Summary file not found: $summary_file"
-          fi
-
-      - name: Stop ${{ matrix.backend.id }} Prometheus stack
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          cd docker
-          docker compose -f "$COMPOSE_FILE" down -v || true
-
-      - name: Archive Prometheus metrics
-        if: ${{ always() }}
-        run: |
-          set -euo pipefail
-          mkdir -p "$ARTIFACT_DIR"
-          if [ -d docker/data/prometheus ]; then
-            tar -C docker/data -czf "$ARTIFACT_DIR/prometheus-micro-${MODE_ID}-${BACKEND_ID}.tar.gz" prometheus
-          fi
-          if docker compose -f "$COMPOSE_FILE" ps --format '{{.Name}}' >/dev/null 2>&1; then
-            docker compose -f "$COMPOSE_FILE" logs app > "$ARTIFACT_DIR/docker-micro-${MODE_ID}-${BACKEND_ID}.log" || true
-          fi
-
-      - name: Upload micro benchmark artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: micro-benchmark-${{ matrix.mode.id }}-${{ matrix.backend.id }}
-          path: ${{ env.ARTIFACT_DIR }}
-          if-no-files-found: error
@@ -1,33 +0,0 @@
-name: Dashboard
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 5 AM UTC+8
-    - cron: '0 21 * * *'
-
-  workflow_dispatch:
-
-  push:
-    branches: [ main, stable/**/* ]
-
-jobs:
-  dashboard:
-    name: Chromatic
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Run Chromatic
-        uses: chromaui/action@v13
-        with:
-          projectToken: ${{ secrets.CHROMATIC_PROJECT_TOKEN }}
-          workingDir: dashboard
-          exitZeroOnChanges: false
@@ -8,10 +8,6 @@ on:
      - 'v*'
  workflow_dispatch:

-concurrency:
-  group: docs-deploy
-  cancel-in-progress: false
-
 permissions:
  contents: write
  pages: write
@@ -24,14 +20,15 @@ jobs:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
-      - uses: actions/setup-python@v6
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-      - name: Sync dependencies
-        run: uv sync --frozen --no-default-groups --group dev
+
+      - name: Install dependencies
+        run: |
+          ./scripts/setup_stable.sh

      - name: Configure Git
        run: |
@@ -54,11 +51,11 @@ jobs:
      - name: Deploy versioned docs
        if: startsWith(github.ref, 'refs/tags/')
        run: |
-          uv run --locked --no-sync mike deploy --push --update-aliases ${{ steps.version.outputs.version }} stable
+          mike deploy --push --update-aliases ${{ steps.version.outputs.version }} stable

      - name: Deploy dev docs
        if: github.ref == 'refs/heads/main'
        run: |
-          uv run --locked --no-sync mike deploy --push latest
+          mike deploy --push latest
          # Always set stable to default
-          uv run --locked --no-sync mike set-default --push stable
+          mike set-default --push stable
@@ -1,116 +0,0 @@
-name: Examples - APO
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 3 AM UTC+8
-    - cron: '0 19 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-apo, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'APO - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('APO - {0}', github.event_name) }}
-
-jobs:
-  apo:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-apo' ||
-      github.event.action == 'ci-all'
-    name: APO (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    # This job is run on GitHub hosted runners rather than self-hosted runners because it needs no GPU.
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: |
-          uv sync --frozen --no-default-groups --extra apo \
-            --group dev --group experiment --group agents --group core-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable & legacy)
-        run: |
-          uv sync --frozen --no-default-groups --extra apo \
-            --group dev --group experiment --group agents --group core-${{ matrix.setup-script }}
-        if: matrix.setup-script != 'latest'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-apo-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: APO custom algorithm
-        run: |
-          set -ex
-          cd examples/apo
-          uv run apo_custom_algorithm_trainer.py | tee _ci_apo.log
-          # Check whether the log contains "Best prompt found:"
-          grep "Best prompt found:" _ci_apo.log
-        env:
-          # New versions follow OPENAI_BASE_URL instead of OPENAI_API_BASE
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-      - name: APO custom algorithm debugger
-        run: |
-          set -ex
-          cd examples/apo
-          uv run apo_debug.py --mode runner
-          uv run apo_debug.py --mode hook
-          uv run apo_debug.py --mode trainer
-        env:
-          # New versions follow OPENAI_BASE_URL instead of OPENAI_API_BASE
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-
-      - name: APO built-in algorithm
-        run: |
-          set -ex
-          cd examples/apo
-          uv run room_selector_apo.py
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-        if: matrix.setup-script != 'legacy'
@@ -1,98 +0,0 @@
-name: Examples - Azure
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 4 AM UTC+8
-    - cron: '0 20 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-azure, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Azure - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Azure - {0}', github.event_name) }}
-
-jobs:
-  azure:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-azure' ||
-      github.event.action == 'ci-all'
-    name: Azure (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-cpu]
-    timeout-minutes: 400
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.12'
-          setup-script: 'stable'
-      fail-fast: false
-    steps:
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies
-        run: |
-          uv sync --frozen --no-default-groups \
-            --group dev --group experiment --group agents --group core-stable
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-azure-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Azure Login
-        run: |
-          az login --identity
-        shell: bash
-
-      - name: Azure OpenAI Sanity Check
-        run: |
-          source .venv/bin/activate
-          cd examples/azure
-          python capital_agent.py
-        shell: bash
-        env:
-          AZURE_OPENAI_ENDPOINT: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-        id: azure_openai_sanity_check
-
-      - name: Azure OpenAI Supervised Fine-tuning
-        run: |
-          source .venv/bin/activate
-          cd examples/azure
-          python train_capital_agent.py --n-iterations 2 --cleanup
-        shell: bash
-        env:
-          AZURE_OPENAI_ENDPOINT: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-          AZURE_SUBSCRIPTION_ID: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
-          AZURE_OPENAI_API_VERSION: 2025-04-01-preview
-          AZURE_RESOURCE_GROUP: ${{ secrets.AZURE_RESOURCE_GROUP }}
-          AZURE_RESOURCE_NAME: ${{ secrets.AZURE_RESOURCE_NAME }}
-        id: azure_openai_finetune
@@ -1,340 +0,0 @@
-name: Examples - Calc-X
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 3 AM UTC+8
-    - cron: '0 19 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-calc-x, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Calc-X - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Calc-X - {0}', github.event_name) }}
-
-jobs:
-  calc-x-perf:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-calc-x' ||
-      github.event.action == 'ci-all'
-    name: Calc-X Performance (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 90
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable & legacy)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script != 'latest'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-calc-x-performance-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: Prepare Calc-X dataset
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run gdown --fuzzy https://drive.google.com/file/d/1FQMyKLLd6hP9dw9rfZn1EZOWNvKaDsqw/view
-          unzip calc-x-data.zip -d data
-          rm calc-x-data.zip
-
-      - name: Calc-X MCP sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run tests/test_mcp_calculator.py
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-      - name: Calc-X sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run legacy_calc_agent_debug.py
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-
-      # Calc-X training suddenly works after running the sanity check.
-      # And it has to be run before Spider training.
-      # The client side used to hang in many of my attempts.
-      # Don't ask why. Don't touch this.
-      - name: Calc-X training
-        run: |
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-          sleep 5
-          python train_calc_agent.py --val-file data/test_mini.parquet --ci
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train
-
-      - name: Validate Calc-X training
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train.outputs.project_name }} ${{ steps.calc_x_train.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-
-  calc-x-variants:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-calc-x' ||
-      github.event.action == 'ci-all'
-    name: Calc-X Variants (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 90
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable & legacy)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script != 'latest'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-calc-x-variants-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: Prepare Calc-X dataset
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run gdown --fuzzy https://drive.google.com/file/d/1FQMyKLLd6hP9dw9rfZn1EZOWNvKaDsqw/view
-          unzip calc-x-data.zip -d data
-          rm calc-x-data.zip
-
-      - name: Calc-X MCP sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run tests/test_mcp_calculator.py
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-      - name: Calc-X sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run legacy_calc_agent_debug.py
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-
-      - name: Training with local model
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-          sleep 5
-          hf download Qwen/Qwen2.5-0.5B-Instruct --local-dir data/qwen_model
-          PYTHONUNBUFFERED=1 python train_calc_agent.py --val-file data/test_mini.parquet --ci-fast --model $(realpath data/qwen_model)
-          sleep 10
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train_local_model
-
-      - name: Validate training with local model
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train_local_model.outputs.project_name }} ${{ steps.calc_x_train_local_model.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-
-      - name: Training with LLM Proxy
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-          sleep 5
-          PYTHONUNBUFFERED=1 python train_calc_agent.py --val-file data/test_mini.parquet --ci-fast --llm-proxy
-          sleep 10
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train_llm_proxy
-
-      - name: Validate training with LLM Proxy
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train_llm_proxy.outputs.project_name }} ${{ steps.calc_x_train_llm_proxy.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-
-      - name: Training with external store
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-
-          agl store --port 4747 &
-          sleep 5
-          AGL_MANAGED_STORE=0 AGL_CURRENT_ROLE=runner python train_calc_agent.py --external-store-address http://localhost:4747 --val-file data/test_mini.parquet --ci-fast &
-          sleep 5
-          AGL_MANAGED_STORE=0 AGL_CURRENT_ROLE=algorithm python train_calc_agent.py --external-store-address http://localhost:4747 --val-file data/test_mini.parquet --ci-fast
-
-          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
-          while pgrep -f agl; do
-            echo "Waiting for agl to finish..."
-            sleep 5
-          done
-          pkill -f train_calc_agent.py && echo "SIGTERM sent to train_calc_agent.py" || echo "No train_calc_agent.py process found"
-          while pgrep -f train_calc_agent.py; do
-            echo "Waiting for train_calc_agent.py to finish..."
-            sleep 5
-          done
-          echo "train_calc_agent.py has finished."
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train_external_store
-
-      - name: Validate training with external store
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train_external_store.outputs.project_name }} ${{ steps.calc_x_train_external_store.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-
-      - name: Training with role-based environment variables
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-
-          PYTHONUNBUFFERED=1 AGL_SERVER_HOST=127.0.0.1 AGL_SERVER_PORT=5858 AGL_CURRENT_ROLE=runner python train_calc_agent.py --val-file data/test_mini.parquet --ci-fast &
-          sleep 5
-          PYTHONUNBUFFERED=1 AGL_SERVER_HOST=0.0.0.0 AGL_SERVER_PORT=5858 AGL_CURRENT_ROLE=algorithm python train_calc_agent.py --val-file data/test_mini.parquet --ci-fast
-
-          pkill -f train_calc_agent.py && echo "SIGTERM sent to train_calc_agent.py" || echo "No train_calc_agent.py process found"
-          while pgrep -f train_calc_agent.py; do
-            echo "Waiting for train_calc_agent.py to finish..."
-            sleep 5
-          done
-          echo "train_calc_agent.py has finished."
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train_role_based_env_var
-
-      - name: Validate training with role-based environment variables
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train_role_based_env_var.outputs.project_name }} ${{ steps.calc_x_train_role_based_env_var.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
@@ -1,151 +0,0 @@
-name: Examples - Claude Code
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 4 AM UTC+8
-    - cron: "0 20 * * *"
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-claude-code, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Claude Code - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Claude Code - {0}', github.event_name) }}
-
-jobs:
-  claude-code:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-claude-code' ||
-      github.event.action == 'ci-all'
-    name: Claude Code (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 60
-    strategy:
-      matrix:
-        include:
-          - python-version: "3.12"
-            setup-script: "stable"
-          - python-version: "3.13"
-            setup-script: "latest"
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies
-        run: |
-          uv sync --frozen --no-default-groups \
-            --group dev --group experiment --group agents --group torch-gpu-stable
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-claude-code-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Download model
-        run: |
-          source .venv/bin/activate
-          python -c "from transformers import AutoModelForCausalLM; AutoModelForCausalLM.from_pretrained('Qwen/Qwen3-Coder-30B-A3B-Instruct')"
-
-      - name: Launch vLLM server
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct \
-            --max-model-len 131072 \
-            --enable-auto-tool-choice \
-            --tool-call-parser qwen3_coder \
-            --port 45993 &
-
-          VLLM_READY=0
-          for i in {1..100}; do
-            if curl -sSf http://localhost:45993/v1/models > /dev/null 2>&1; then
-              echo "vLLM server is ready!"
-              VLLM_READY=1
-              break
-            fi
-            echo "Waiting for vLLM server to be ready... (${i})"
-            sleep 5
-          done
-          if [[ "$VLLM_READY" != "1" ]]; then
-            echo "vLLM server failed to start!"
-            exit 1
-          fi
-
-      - name: Claude Code sanity check with vLLM models
-        run: |
-          source .venv/bin/activate
-          cd examples/claude_code
-          python claude_code_agent.py vllm --backend-model-high Qwen/Qwen3-Coder-30B-A3B-Instruct --backend-model-low Qwen/Qwen3-Coder-30B-A3B-Instruct --base-url http://localhost:45993/v1 --debug
-        shell: bash
-
-      - name: Upload sanity check artifacts for vLLM
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: claude-code-sanity-check-vllm-${{ matrix.setup-script }}
-          path: |
-            examples/claude_code/data/
-            examples/claude_code/logs/
-          if-no-files-found: error
-
-      - name: Cleanup vLLM
-        run: |
-          set -euo pipefail
-          pkill -f vllm
-          for i in {1..60}; do
-            if ! pgrep -f vllm; then
-              break
-            fi
-            sleep 5
-          done
-          rm -rf examples/claude_code/data/
-          rm -rf examples/claude_code/logs/
-
-      - name: Claude Code sanity check with OpenAI models
-        run: |
-          source .venv/bin/activate
-          cd examples/claude_code
-          python claude_code_agent.py openai --backend-model-high gpt-5.1-codex-mini --backend-model-low gpt-4.1-mini --debug
-        shell: bash
-        env:
-          OPENAI_BASE_URL: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-
-      - name: Upload sanity check artifacts for OpenAI
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v4
-        with:
-          name: claude-code-sanity-check-openai-${{ matrix.setup-script }}
-          path: |
-            examples/claude_code/data/
-            examples/claude_code/logs/
-          if-no-files-found: error
@@ -1,151 +0,0 @@
-name: Examples - Backward Compatibility
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 6 AM UTC+8
-    - cron: '0 22 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-compat, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Backward Compatibility - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Backward Compatibility - {0}', github.event_name) }}
-
-jobs:
-  backward-compatibility:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-compat' ||
-      github.event.action == 'ci-all'
-    name: Backward Compatibility (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 30
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Sync dependencies
-        run: |
-          uv sync --frozen --no-default-groups --extra apo --extra verl \
-            --group dev --group experiment --group agents --group torch-gpu-${{ matrix.setup-script }}
-      - name: Override VERL (stable)
-        run: |
-          uv pip install verl==0.5.0 vllm==0.10.2
-        if: matrix.setup-script == 'stable'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-backward-compatibility-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-      - name: Prepare Calc-X dataset
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run gdown --fuzzy https://drive.google.com/file/d/1FQMyKLLd6hP9dw9rfZn1EZOWNvKaDsqw/view
-          unzip calc-x-data.zip -d data
-          rm calc-x-data.zip
-
-      - name: APO example (legacy client-server style)
-        run: |
-          set -ex
-          cd examples/apo
-          uv run legacy_apo_client.py &
-          sleep 3  # Wait for the client to be up
-          uv run legacy_apo_server.py
-          pkill -f legacy_apo_client.py && echo "SIGTERM sent to legacy_apo_client.py" || echo "No legacy_apo_client.py process found"
-          while pgrep -f legacy_apo_client.py; do
-            echo "Waiting for legacy_apo_client.py to finish..."
-            sleep 5
-          done
-          echo "legacy_apo_client.py has finished."
-          sleep 10
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-
-      - name: Calc-X MCP sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run tests/test_mcp_calculator.py
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-      - name: Calc-X sanity check
-        run: |
-          set -ex
-          cd examples/calc_x
-          uv run legacy_calc_agent_debug.py
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-
-      - name: Calc-X training (legacy client-server style)
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/calc_x
-          ../../scripts/restart_ray.sh
-          sleep 5
-          PYTHONUNBUFFERED=1 python legacy_calc_agent.py &
-          bash legacy_train.sh
-          pkill -f legacy_calc_agent.py && echo "SIGTERM sent to legacy_calc_agent.py" || echo "No legacy_calc_agent.py process found"
-          while pgrep -f legacy_calc_agent.py; do
-            echo "Waiting for legacy_calc_agent.py to finish..."
-            sleep 5
-          done
-          echo "legacy_calc_agent.py has finished."
-          sleep 10
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: calc_x_train
-
-      - name: Validate Calc-X training
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.calc_x_train.outputs.project_name }} ${{ steps.calc_x_train.outputs.run_name }}
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
@@ -1,179 +0,0 @@
-name: Examples - RAG
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 6 AM UTC+8
-    - cron: '0 22 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-rag, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'RAG - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('RAG - {0}', github.event_name) }}
-
-jobs:
-  rag:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-rag' ||
-      github.event.action == 'ci-all'
-    name: RAG (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 60
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group rag --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable & legacy)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group rag --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script != 'latest'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-spider-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: Prepare RAG dataset
-        run: |
-          set -euo pipefail
-          cd examples/rag
-          mkdir -p data
-          uv run gdown --fuzzy "https://drive.google.com/file/d/1Pq4Ag8zVoN8gUtLu0LcBfY35Dm5zL0hq/view?usp=drive_link" -O data/dataset_tiny.parquet
-          uv run gdown --fuzzy "https://drive.google.com/file/d/1REXCpRLbeZu1KfWWKhIGEQe_WNHUOBkS/view?usp=drive_link" -O data/chunks_candidate_tiny.pkl
-          uv run gdown --fuzzy "https://drive.google.com/file/d/1f6P-h_8KSRhe5pqDHWbRQWvUhTygfZ-c/view?usp=drive_link" -O data/index_hnsw_faiss_n32e40_tiny.index
-
-      - name: Run WIKI Retriever MCP Server
-        run: |
-          set -euo pipefail
-          cd examples/rag
-          uv run python wiki_retriever_mcp.py &
-          for i in {1..20}; do
-            sleep 5
-            if nc -z localhost 8099; then
-              echo "MCP server is up!"
-              exit 0
-            else
-              echo "Waiting for MCP server to start..."
-            fi
-          done
-          echo "MCP server failed to start within expected time."
-          exit 1
-
-      - name: Run vLLM Server
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          vllm serve Qwen/Qwen2.5-1.5B-Instruct \
-            --enable-auto-tool-choice \
-            --tool-call-parser hermes \
-            --port 8000 &
-
-          VLLM_READY=0
-          for i in {1..100}; do
-            if curl -sSf http://localhost:8000/v1/models > /dev/null 2>&1; then
-              echo "vLLM server is ready!"
-              VLLM_READY=1
-              break
-            fi
-            echo "Waiting for vLLM server to be ready... (${i})"
-            sleep 5
-          done
-          if [[ "$VLLM_READY" != "1" ]]; then
-            echo "vLLM server failed to start!"
-            exit 1
-          fi
-
-      - name: Run RAG Sanity check
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/rag
-          uv run python rag_agent.py
-        shell: bash
-
-      - name: Stop vLLM Server
-        run: |
-          set -euo pipefail
-          pkill -f vllm
-          for i in {1..60}; do
-            if ! pgrep -f vllm; then
-              break
-            fi
-            sleep 5
-          done
-
-      - name: RAG training
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/rag
-          ../../scripts/restart_ray.sh
-          sleep 5
-          PYTHONUNBUFFERED=1 python train_rag.py fast
-          sleep 10
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: rag_train
-
-      - name: Validate RAG training
-        run: |
-          set -ex
-          # Allow up to 5 rollouts to fail to produce rewards
-          uv run scripts/validate_example_wandb.py ${{ steps.rag_train.outputs.project_name }} ${{ steps.rag_train.outputs.run_name }} --reward-tolerance 5
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
@@ -1,126 +0,0 @@
-name: Examples - Spider
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 4 AM UTC+8
-    - cron: '0 20 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-spider, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Spider - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Spider - {0}', github.event_name) }}
-
-jobs:
-  spider:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-spider' ||
-      github.event.action == 'ci-all'
-    name: Spider (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 60
-    strategy:
-      matrix:
-        include:
-        # legacy is omitted because langchain doesn't work with legacy vllm versions
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group langchain --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable)
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group agents --group langchain --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script == 'stable'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-spider-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: Prepare Spider dataset
-        run: |
-          set -ex
-          cd examples/spider
-          uv run gdown --fuzzy https://drive.google.com/file/d/1oi9J1jZP9TyM35L85CL3qeGWl2jqlnL6/view
-          unzip -q spider-data.zip -d data
-          rm spider-data.zip
-
-      - name: Spider sanity check
-        run: |
-          set -ex
-          cd examples/spider
-          uv run sql_agent.py
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-        if: success() || failure()
-
-      - name: Spider training
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/spider
-          ../../scripts/restart_ray.sh
-          sleep 5
-          PYTHONUNBUFFERED=1 python train_sql_agent.py fast
-          sleep 10
-        shell: bash
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-        id: spider_train
-
-      - name: Validate Spider training
-        run: |
-          set -ex
-          uv run scripts/validate_example_wandb.py ${{ steps.spider_train.outputs.project_name }} ${{ steps.spider_train.outputs.run_name }} --reward-tolerance 5
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
@@ -1,160 +0,0 @@
-name: Examples - Tinker
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 3 AM UTC+8
-    - cron: '0 19 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-tinker, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Tinker - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Tinker - {0}', github.event_name) }}
-
-jobs:
-  tinker:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-tinker' ||
-      github.event.action == 'ci-all'
-    name: Tinker (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-cpu]
-    timeout-minutes: 150
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies
-        run: |
-          uv sync --frozen --no-default-groups \
-            --group dev --group experiment --group agents --group torch-cpu --group core-stable --group tinker
-      - name: Freeze dependencies
-        run: |
-          set -euo pipefail
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-tinker-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Tinker LLM sanity check
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          # TODO: Currently only test the client tracer implementation.
-          python -m tests.test_tinker_llm
-        shell: bash
-        env:
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
-
-      - name: Tinker Hello
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          python hello.py oneclick --ci
-        shell: bash
-        env:
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
-
-      - name: Tinker Q20 Evaluate (GPT-4.1)
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          mkdir -p logs
-          python q20_evaluate.py --ci --model gpt-4.1 --output-file logs/q20_evaluate_gpt-4.1.jsonl
-        shell: bash
-        env:
-          OPENAI_BASE_URL: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-          CREWAI_DISABLE_TELEMETRY: true
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
-
-      - name: Tinker Q20 Evaluate (Qwen3-30B-A3B-Instruct-2507)
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          python q20_evaluate.py --ci --model Qwen/Qwen3-30B-A3B-Instruct-2507 --output-file logs/q20_evaluate_qwen3-30b-a3b.jsonl
-        shell: bash
-        env:
-          OPENAI_BASE_URL: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-          CREWAI_DISABLE_TELEMETRY: true
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
-
-      - name: Tinker Q20 Training Dry Run
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          python q20_train.py dryrun --model qwen4b
-        shell: bash
-        env:
-          OPENAI_BASE_URL: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-          CREWAI_DISABLE_TELEMETRY: true
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
-
-      - name: Tinker Q20 Training
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/tinker
-          agl store --port 4747 &
-          sleep 5
-          python q20_train.py runner --n-runners 4 &
-          sleep 5
-          python q20_train.py algo --model qwen4b --ci
-          sleep 5
-
-          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
-          while pgrep -f agl; do
-            echo "Waiting for agl to finish..."
-            sleep 5
-          done
-          pkill -f q20_train.py && echo "SIGTERM sent to q20_train.py" || echo "No q20_train.py process found"
-          while pgrep -f q20_train.py; do
-            echo "Waiting for q20_train.py to finish..."
-            sleep 5
-          done
-          echo "q20_train.py has finished."
-        shell: bash
-        env:
-          OPENAI_BASE_URL: ${{ secrets.AZURE_OPENAI_ENDPOINT_SWEDEN }}
-          OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY_SWEDEN }}
-          CREWAI_DISABLE_TELEMETRY: true
-          TINKER_API_KEY: ${{ secrets.TINKER_API_KEY }}
@@ -1,129 +0,0 @@
-name: Examples - Unsloth
-permissions:
-  contents: read
-on:
-  schedule:
-    # Every day at 5 AM UTC+8
-    - cron: '0 21 * * *'
-
-  workflow_dispatch:
-
-  repository_dispatch:
-    types: [ci-unsloth, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'Unsloth - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('Unsloth - {0}', github.event_name) }}
-
-jobs:
-  unsloth:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-unsloth' ||
-      github.event.action == 'ci-all'
-    name: Unsloth (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 60
-    strategy:
-      matrix:
-        # Legacy versions are not supported for Unsloth examples.
-        include:
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - name: Check disk space
-        run: df -h
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies
-        run: |
-          uv sync --frozen --no-default-groups --extra verl \
-            --group dev --group experiment --group trl --group agents --group torch-gpu-stable
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-unsloth-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Prepare Unsloth model
-        run: |
-          set -ex
-          cd examples/unsloth
-          rm -rf models
-          uv run hf download unsloth/Qwen3-4B-Instruct-2507 --local-dir models/version_0
-
-      - name: Unsloth SFT example
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/unsloth
-
-          agl store --port 4747 &
-          sleep 5
-          python sft_rollout_runners.py &
-          sleep 5
-          python sft_algorithm.py
-
-          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
-          while pgrep -f agl; do
-            echo "Waiting for agl to finish..."
-            sleep 5
-          done
-          pkill -f sft_rollout_runners.py && echo "SIGTERM sent to sft_rollout_runners.py" || echo "No sft_rollout_runners.py process found"
-          while pgrep -f sft_rollout_runners.py; do
-            echo "Waiting for sft_rollout_runners.py to finish..."
-            sleep 5
-          done
-          echo "sft_rollout_runners.py has finished."
-          sleep 10
-
-          # Check models/version_2 must exist
-          if [ ! -d "models/version_2" ]; then
-            echo "models/version_2 does not exist"
-            exit 1
-          fi
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
-
-      - name: Unsloth SFT example all-in-one
-        run: |
-          set -ex
-          source .venv/bin/activate
-          cd examples/unsloth
-          rm -rf models/version_1 models/version_2
-
-          python sft_allinone.py
-          if [ ! -d "models/version_2" ]; then
-            echo "models/version_2 does not exist"
-            exit 1
-          fi
-        env:
-          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
-          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
@@ -0,0 +1,318 @@
+name: Examples Test
+permissions:
+  contents: read
+on:
+  schedule:
+    # Every day at 3 AM UTC+8
+    - cron: '0 19 * * *'
+
+  workflow_dispatch:
+
+jobs:
+  examples:
+    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
+    timeout-minutes: 90
+    strategy:
+      matrix:
+        setup: [stable, latest]
+      fail-fast: false
+    steps:
+      - name: Check GPU status
+        run: nvidia-smi
+      - name: Check disk space
+        run: df -h
+      - uses: actions/checkout@v4
+      - name: Create a virtual environment
+        run: python3 -m venv .venv
+      - name: Install dependencies (${{ matrix.setup }})
+        run: |
+          . .venv/bin/activate
+          ./scripts/setup_${{ matrix.setup }}_gpu.sh
+      - name: Freeze dependencies
+        run: |
+          . .venv/bin/activate
+          which python
+          which pip
+          which uvx
+          pip list | tee requirements-freeze.txt
+      - name: Upload dependencies artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: dependencies-${{ matrix.setup }}
+          path: requirements-freeze.txt
+          compression-level: 0
+
+      - name: Launch LiteLLM Proxy
+        run: |
+          set -ex
+          . .venv/bin/activate
+          litellm --config scripts/litellm_ci.yaml --port 12306 &
+          sleep 10  # Wait for the proxy to be up
+        env:
+          AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }}
+          AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
+
+      - name: Verify LiteLLM Proxy
+        run: |
+          set -ex
+          . .venv/bin/activate
+          python scripts/litellm_sanity_check.py
+        env:
+          OPENAI_BASE_URL: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+
+      - name: Prepare Unsloth model
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/unsloth
+          rm -rf models
+          hf download unsloth/Qwen3-4B-Instruct-2507 --local-dir models/version_0
+
+      - name: Prepare Spider dataset
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/spider
+          gdown --fuzzy https://drive.google.com/file/d/1oi9J1jZP9TyM35L85CL3qeGWl2jqlnL6/view
+          unzip -q spider-data.zip -d data
+          rm spider-data.zip
+      - name: Prepare Calc-X dataset
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/calc_x
+          gdown --fuzzy https://drive.google.com/file/d/1FQMyKLLd6hP9dw9rfZn1EZOWNvKaDsqw/view
+          unzip calc-x-data.zip -d data
+          rm calc-x-data.zip
+
+      # APO Examples test
+      - name: APO example (legacy)
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/apo
+          python legacy_apo_client.py &
+          sleep 3  # Wait for the client to be up
+          python legacy_apo_server.py
+          pkill -f legacy_apo_client.py && echo "SIGTERM sent to legacy_apo_client.py" || echo "No legacy_apo_client.py process found"
+          while pgrep -f legacy_apo_client.py; do
+            echo "Waiting for legacy_apo_client.py to finish..."
+            sleep 5
+          done
+          echo "legacy_apo_client.py has finished."
+          sleep 10
+        env:
+          OPENAI_API_BASE: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+      - name: APO example
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/apo
+          python apo.py | tee _ci_apo.log
+          # Check whether the log contains "Best prompt found:"
+          grep "Best prompt found:" _ci_apo.log
+        env:
+          # New versions follow OPENAI_BASE_URL instead of OPENAI_API_BASE
+          OPENAI_BASE_URL: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+      - name: APO example debug sanity check
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/apo
+          python apo_debug.py --mode runner
+          python apo_debug.py --mode trainer
+        env:
+          # New versions follow OPENAI_BASE_URL instead of OPENAI_API_BASE
+          OPENAI_BASE_URL: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+
+      - name: Spider sanity check
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/spider
+          python sql_agent.py --trainer.n-workers 1 --trainer.dev true --trainer.max-tasks 2
+        env:
+          VERL_API_BASE: http://localhost:9999/
+          OPENAI_API_BASE: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+      - name: Calc-X MCP sanity check
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/calc_x
+          python tests/test_mcp_calculator.py
+        env:
+          OPENAI_API_BASE: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+      - name: Calc-X sanity check
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/calc_x
+          python calc_agent_dev.py
+        env:
+          OPENAI_API_BASE: http://localhost:12306/
+          OPENAI_API_KEY: dummy
+
+      # Calc-X training suddenly works after running the sanity check.
+      # And it has to be run before Spider training.
+      # The client side used to hang in many of my attempts.
+      # Don't ask why. Don't touch this.
+      - name: Calc-X training v0.1
+        run: |
+          set -ex
+          source .venv/bin/activate
+          cd examples/calc_x
+          ../../scripts/restart_ray.sh
+          sleep 5
+          PYTHONUNBUFFERED=1 python calc_agent.py &
+          bash train_ci.sh
+          pkill -f calc_agent.py && echo "SIGTERM sent to calc_agent.py" || echo "No calc_agent.py process found"
+          while pgrep -f calc_agent.py; do
+            echo "Waiting for calc_agent.py to finish..."
+            sleep 5
+          done
+          echo "calc_agent.py has finished."
+          sleep 10
+        shell: bash
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        id: calc_x_train
+        if: success() || failure()
+
+      - name: Validate Calc-X training
+        run: |
+          set -ex
+          . .venv/bin/activate
+          python scripts/validate_example_wandb.py ${{ steps.calc_x_train.outputs.project_name }} ${{ steps.calc_x_train.outputs.run_name }}
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+
+      - name: Calc-X training v0.2
+        run: |
+          set -ex
+          source .venv/bin/activate
+          cd examples/calc_x
+          ../../scripts/restart_ray.sh
+          sleep 5
+          PYTHONUNBUFFERED=1 python calc_agent_v0_2.py
+          sleep 10
+        shell: bash
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        id: calc_x_train_v0_2
+        if: success() || failure()
+
+      - name: Calc-X training v0.2 LLM Proxy
+        run: |
+          set -ex
+          source .venv/bin/activate
+          cd examples/calc_x
+          ../../scripts/restart_ray.sh
+          sleep 5
+          PYTHONUNBUFFERED=1 python calc_agent_v0_2_llm_proxy.py
+          sleep 10
+        shell: bash
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        id: calc_x_train_v0_2_llm_proxy
+        if: success() || failure()
+
+      - name: Spider training
+        run: |
+          set -ex
+          source .venv/bin/activate
+          cd examples/spider
+          ../../scripts/restart_ray.sh
+          sleep 5
+          PYTHONUNBUFFERED=1 python sql_agent.py --trainer.n-workers 10 &
+          bash train_ci.sh
+          pkill -f sql_agent.py && echo "SIGTERM sent to sql_agent.py" || echo "No sql_agent.py process found"
+          while pgrep -f sql_agent.py; do
+            echo "Waiting for sql_agent.py to finish..."
+            sleep 5
+          done
+          echo "sql_agent.py has finished."
+          sleep 10
+        shell: bash
+        env:
+          VERL_API_BASE: http://localhost:9991/
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        id: spider_train
+        if: success() || failure()
+
+      - name: Validate Spider training
+        run: |
+          set -ex
+          . .venv/bin/activate
+          python scripts/validate_example_wandb.py ${{ steps.spider_train.outputs.project_name }} ${{ steps.spider_train.outputs.run_name }}
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+
+      # Unsloth Examples test
+      - name: Unsloth SFT example
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/unsloth
+
+          agl store --port 4747 &
+          sleep 5
+          python sft_rollout_runners.py &
+          sleep 5
+          python sft_algorithm.py
+
+          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
+          while pgrep -f agl; do
+            echo "Waiting for agl to finish..."
+            sleep 5
+          done
+          pkill -f sft_rollout_runners.py && echo "SIGTERM sent to sft_rollout_runners.py" || echo "No sft_rollout_runners.py process found"
+          while pgrep -f sft_rollout_runners.py; do
+            echo "Waiting for sft_rollout_runners.py to finish..."
+            sleep 5
+          done
+          echo "sft_rollout_runners.py has finished."
+          sleep 10
+
+          # Check models/version_2 must exist
+          if [ ! -d "models/version_2" ]; then
+            echo "models/version_2 does not exist"
+            exit 1
+          fi
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        if: ${{ (success() || failure()) && matrix.setup == 'latest' }}
+
+      - name: Unsloth SFT example all-in-one
+        run: |
+          set -ex
+          . .venv/bin/activate
+          cd examples/unsloth
+          rm -rf models/version_1 models/version_2
+
+          python sft_allinone.py
+          if [ ! -d "models/version_2" ]; then
+            echo "models/version_2 does not exist"
+            exit 1
+          fi
+        env:
+          WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
+          WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
+        if: matrix.setup == 'latest'
+
+      # Cleanup
+      - name: Cleanup
+        run: ./scripts/cleanup.sh
+        if: success() || failure()
@@ -1,309 +0,0 @@
-name: Issue Comment
-
-on:
-  issue_comment:
-    types: [created]
-
-permissions:
-  pull-requests: write
-  issues: write
-  contents: write
-  actions: read
-
-jobs:
-  dispatch:
-    # Only run for comments on pull requests AND when the comment starts with "/ci"
-    if: >
-      github.event.issue.pull_request != null &&
-      startsWith(github.event.comment.body, '/ci')
-    runs-on: ubuntu-latest
-    outputs:
-      dispatched: ${{ steps.dispatch.outputs.dispatched }}
-      event_types: ${{ steps.dispatch.outputs.event_types }}
-      correlation_id: ${{ steps.dispatch.outputs.correlation_id }}
-      trigger_comment_id: ${{ steps.dispatch.outputs.trigger_comment_id }}
-      ack_comment_id: ${{ steps.ack.outputs.comment_id }}
-    steps:
-      - name: Guardrail — allow only members/collaborators
-        id: guard
-        uses: actions/github-script@v8
-        with:
-          script: |
-            const allowed = ['MEMBER','OWNER','COLLABORATOR'];
-            const assoc = context.payload.comment.author_association;
-            if (!allowed.includes(assoc)) {
-              core.notice(`Ignoring /ci from ${context.payload.comment.user.login} (author_association=${assoc}).`);
-              core.setOutput('skip', 'true');
-            }
-
-      - name: Trigger repository dispatch
-        id: dispatch
-        if: steps.guard.outputs.skip != 'true'
-        uses: actions/github-script@v8
-        with:
-          script: |
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const pull_number = context.payload.issue.number;
-            const comment = context.payload.comment;
-
-            // Fetch current PR state
-            const { data: pr } = await github.rest.pulls.get({ owner, repo, pull_number });
-
-            // Add reaction so folks know we saw it
-            try {
-              await github.rest.reactions.createForIssueComment({
-                owner,
-                repo,
-                comment_id: comment.id,
-                content: 'rocket'
-              });
-            } catch (e) {
-              core.info('Could not add reaction (likely due to permissions). Continuing.');
-            }
-
-            const labels = (pr.labels ?? []).map(label => label.name);
-            const directCiLabels = labels.filter(label => label.startsWith('ci-'));
-            const hasCiAll = directCiLabels.includes('ci-all');
-            const dedupe = new Set(
-              directCiLabels.filter(label => label !== 'ci-all')
-            );
-
-            if (!hasCiAll && dedupe.size === 0) {
-              core.notice('No ci-* labels found on the pull request; nothing to dispatch.');
-              core.setOutput('dispatched', 'false');
-              core.setOutput('event_types', '');
-              return;
-            }
-
-            const correlation_id = `id-${comment.id}-${Date.now().toString(36)}`;
-
-            const clientPayload = {
-              correlation_id,
-              pull_number,
-              pr_ref: `refs/pull/${pull_number}/merge`,
-              pr_head_ref: pr.head.ref,
-              pr_head_sha: pr.head.sha,
-              pr_base_ref: pr.base.ref,
-              pr_base_sha: pr.base.sha,
-              trigger_comment_id: comment.id,
-              trigger_comment_user: comment.user.login,
-            };
-
-            const eventTypes = hasCiAll
-              ? ['ci-all']
-              : Array.from(dedupe);
-            for (const eventType of eventTypes) {
-              await github.rest.repos.createDispatchEvent({
-                owner,
-                repo,
-                event_type: eventType,
-                client_payload: { ...clientPayload, ci_label: eventType }
-              });
-              core.notice(`Dispatched '${eventType}' event for PR #${pull_number}.`);
-            }
-
-            core.setOutput('dispatched', 'true');
-            core.setOutput('event_types', eventTypes.join(','));
-            core.setOutput('correlation_id', correlation_id);
-            core.setOutput('trigger_comment_id', String(comment.id));
-
-      - name: Acknowledge in thread (optional)
-        if: steps.guard.outputs.skip != 'true' && steps.dispatch.outputs.dispatched == 'true'
-        id: ack
-        uses: actions/github-script@v8
-        env:
-          EVENT_TYPES: ${{ steps.dispatch.outputs.event_types }}
-          CORRELATION_ID: ${{ steps.dispatch.outputs.correlation_id }}
-        with:
-          script: |
-            const eventTypes = (process.env.EVENT_TYPES || '')
-              .split(',')
-              .map(label => label.trim())
-              .filter(Boolean);
-            const formatted = eventTypes.map(label => `\`repository_dispatch:${label}\``).join(', ');
-            const { owner, repo } = context.repo;
-            const issue_number = context.payload.issue.number;
-            const body = [
-              `✅ CI trigger requested by @${context.payload.comment.user.login}.`,
-              `Fired ${formatted}.`,
-              '',
-              `_Collecting run links for correlation \`${process.env.CORRELATION_ID}\`…_`
-            ].join('\n');
-            const { data: comment } = await github.rest.issues.createComment({
-              owner, repo, issue_number,
-              body
-            });
-            core.setOutput('comment_id', String(comment.id));
-
-      - name: Notify missing ci label
-        if: steps.guard.outputs.skip != 'true' && steps.dispatch.outputs.dispatched != 'true'
-        uses: actions/github-script@v8
-        with:
-          script: |
-            const { owner, repo } = context.repo;
-            const issue_number = context.payload.issue.number;
-            await github.rest.issues.createComment({
-              owner,
-              repo,
-              issue_number,
-              body: `⚠️ CI trigger ignored because the pull request has no \`ci-*\` labels (e.g. \`ci-apo\`, \`ci-calc-x\`). Add the desired labels and try \`/ci\` again.`
-            });
-
-  watch:
-    needs: dispatch
-    if: needs.dispatch.outputs.dispatched == 'true'
-    runs-on: ubuntu-latest
-    timeout-minutes: 180
-    steps:
-      - name: Track dispatched runs and update comment
-        uses: actions/github-script@v8
-        env:
-          CORRELATION_ID: ${{ needs.dispatch.outputs.correlation_id }}
-          ACK_COMMENT_ID: ${{ needs.dispatch.outputs.ack_comment_id }}
-          TRIGGER_COMMENT_ID: ${{ needs.dispatch.outputs.trigger_comment_id }}
-        with:
-          script: |
-            const owner = context.repo.owner;
-            const repo = context.repo.repo;
-            const correlationId = process.env.CORRELATION_ID;
-            if (!correlationId) {
-              core.warning('No correlation id supplied; nothing to watch.');
-              return;
-            }
-
-            const ackCommentId = Number(process.env.ACK_COMMENT_ID || 0);
-            if (!ackCommentId) {
-              core.warning('No comment id available for updates; skipping watch.');
-              return;
-            }
-            const triggerCommentId = Number(process.env.TRIGGER_COMMENT_ID || 0);
-            if (!triggerCommentId) {
-              core.warning('No trigger comment id available; skipping watch.');
-              return;
-            }
-
-            const prefix = `🚀 CI Watcher for correlation ${correlationId} triggered by comment ${triggerCommentId}`;
-            core.notice(`Watching workflow runs for correlation '${correlationId}' using comment ${ackCommentId}.`);
-
-            function fmt(run) {
-              const status = run.status;
-              const conclusion = run.conclusion;
-              const badge = status === 'completed'
-                ? (conclusion === 'success' ? '🟢' : conclusion === 'failure' ? '🔴' : '🟡')
-                : (status === 'in_progress' ? '🟣' : '⚪️');
-              const title = run.display_title || run.name || `run ${run.id}`;
-              const statusText = status === 'completed' ? `${status}/${conclusion}` : status;
-              return `- ${badge} [${title}](${run.html_url}) — \`${statusText}\``;
-            }
-
-            const signatureOf = runs =>
-              runs
-                .map(run => `${run.id}:${run.status}/${run.conclusion || ''}`)
-                .sort()
-                .join('|');
-
-            const deadlineMs = Date.now() + 175 * 60 * 1000; // 175 minutes
-            let found = [];
-
-            async function searchOnce() {
-              const runs = await github.paginate(
-                github.rest.actions.listWorkflowRunsForRepo,
-                { owner, repo, event: 'repository_dispatch', per_page: 100 }
-              );
-              const cutoff = new Date(Date.now() - 60 * 60 * 1000); // last hour
-              return runs.filter(run => {
-                const createdAt = new Date(run.created_at);
-                const title = String(run.display_title || run.name || '');
-                return createdAt >= cutoff && title.includes(correlationId);
-              });
-            }
-
-            while (Date.now() < deadlineMs) {
-              found = await searchOnce();
-              if (found.length > 0) {
-                core.notice(`Discovered ${found.length} workflow run(s) for correlation '${correlationId}'.`);
-                break;
-              }
-              core.notice(`No runs found yet for correlation '${correlationId}'; retrying shortly.`);
-              await new Promise(res => setTimeout(res, 10000));
-            }
-
-            if (found.length === 0) {
-              core.notice(`Watcher timed out with no runs for correlation '${correlationId}'; notifying thread.`);
-              await github.rest.issues.updateComment({
-                owner,
-                repo,
-                comment_id: ackCommentId,
-                body: [
-                  prefix,
-                  `⚠️ I couldn't find any workflow runs for correlation \`${correlationId}\`.`,
-                  `They may be delayed or misconfigured.`
-                ].join('\n')
-              });
-              return;
-            }
-
-            const runIds = new Set(found.map(run => run.id));
-            let lastSignature = '';
-
-            async function refreshRuns() {
-              const ids = Array.from(runIds);
-              const refreshed = [];
-              for (const id of ids) {
-                const { data } = await github.rest.actions.getWorkflowRun({
-                  owner,
-                  repo,
-                  run_id: id
-                });
-                refreshed.push(data);
-              }
-              return refreshed;
-            }
-
-            async function updateCommentIfChanged(runs, allDone) {
-              const signature = signatureOf(runs);
-              if (signature === lastSignature) {
-                // Run statuses unchanged; skipping comment update.
-                return;
-              }
-              lastSignature = signature;
-              core.notice(`Updating comment ${ackCommentId} with ${runs.length} run status entries (allDone=${allDone}).`);
-              await github.rest.issues.updateComment({
-                owner,
-                repo,
-                comment_id: ackCommentId,
-                body: [
-                  prefix,
-                  `🏃‍♀️ Tracking ${runs.length} workflow run(s):`,
-                  '',
-                  ...runs.map(fmt),
-                  '',
-                  allDone ? '✅ All runs completed.' : '_Still running…_'
-                ].join('\n')
-              });
-            }
-
-            await updateCommentIfChanged(found, found.every(run => run.status === 'completed'));
-
-            while (Date.now() < deadlineMs) {
-              const latest = await searchOnce();
-              for (const run of latest) {
-                if (!runIds.has(run.id)) {
-                  runIds.add(run.id);
-                  core.notice(`Detected additional run ${run.id} (${run.name || run.display_title || 'unnamed'}) for correlation '${correlationId}'.`);
-                }
-              }
-              const current = await refreshRuns();
-              const allDone = current.every(run => run.status === 'completed');
-              await updateCommentIfChanged(current, allDone);
-              if (allDone) {
-                core.notice(`All runs for correlation '${correlationId}' completed; stopping watcher.`);
-                break;
-              }
-              await new Promise(res => setTimeout(res, 60000));
-            }
-
-            if (Date.now() >= deadlineMs) {
-              core.warning(`Watcher hit the deadline while monitoring correlation '${correlationId}'.`);
-            }
@@ -2,8 +2,8 @@ name: PyPI Nightly Build

 on:
  schedule:
-    # Run daily at 6:00 AM UTC+8
-    - cron: '0 22 * * *'
+    # Run daily at 6:00 AM UTC
+    - cron: '0 6 * * *'
  workflow_dispatch:  # Allow manual trigger

 jobs:
@@ -14,25 +14,18 @@ jobs:
      contents: read

    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - uses: actions/setup-python@v6
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-      - name: Sync dependencies
-        run: uv sync --frozen --no-default-groups --group dev

-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Build dashboard
-        run: cd dashboard && npm run build
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .[dev]

      - name: Get current version
        id: get_version
@@ -51,9 +44,16 @@ jobs:

      - name: Build package
        run: |
-          uv build
+          hatch build

      - name: Publish to Test PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
          repository-url: https://test.pypi.org/legacy/
+
+      - name: Test installation from Test PyPI
+        run: |
+          # Wait a bit for the package to be available
+          sleep 30
+          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ agentlightning
+          python -c "import agentlightning; print('Package installed successfully')"
@@ -48,34 +48,34 @@ jobs:
      contents: read

    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - uses: actions/setup-python@v6
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-      - name: Sync dependencies
-        run: uv sync --frozen --no-default-groups --group dev

-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Build dashboard
-        run: cd dashboard && npm run build
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .[dev]

      - name: Build package
        run: |
-          uv build
+          hatch build

      - name: Verify package contents
        run: |
-          uv run --locked --no-sync python -m tarfile -l dist/*.tar.gz
-          uv run --locked --no-sync python -m zipfile -l dist/*.whl
+          python -m tarfile -l dist/*.tar.gz
+          python -m zipfile -l dist/*.whl

      - name: Publish to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
+
+      - name: Test installation from PyPI
+        run: |
+          # Wait a bit for the package to be available
+          sleep 30
+          pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ agentlightning
+          python -c "import agentlightning; print('Package installed successfully')"
@@ -8,344 +8,62 @@ on:

  workflow_dispatch:

-  repository_dispatch:
-    types: [ci-gpu, ci-all]
-
-run-name: >-
-  ${{ github.event_name == 'repository_dispatch'
-      && format(
-        'GPU Test - PR #{0} - {1} - {2}',
-        github.event.client_payload.pull_number,
-        github.event.client_payload.ci_label,
-        github.event.client_payload.correlation_id
-      )
-      || format('GPU Test - {0}', github.event_name) }}
-
 jobs:
  tests-full:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-gpu' ||
-      github.event.action == 'ci-all'
-    name: GPU Test with Python ${{ matrix.python-version }} (${{ matrix.setup-script }})
-
    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
    timeout-minutes: 30
    strategy:
      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
+        setup: [stable, latest]
      fail-fast: false
    steps:
      - name: Check GPU status
        run: nvidia-smi
      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group langchain --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable)
-        run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group langchain --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script == 'stable'
-      # Don't install langchain for legacy dependency because it has conflicts with torch.
-      - name: Sync dependencies (legacy)
-        run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group torch-gpu-legacy
-        if: matrix.setup-script == 'legacy'
+      - name: Create a virtual environment
+        run: python3 -m venv .venv
+      - name: Install dependencies (${{ matrix.setup }})
+        run: |
+          . .venv/bin/activate
+          ./scripts/setup_${{ matrix.setup }}_gpu.sh
      - name: Freeze dependencies
        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
+          . .venv/bin/activate
+          which python
+          which pip
+          which uvx
+          pip list | tee requirements-freeze.txt
      - name: Upload dependencies artifact
        uses: actions/upload-artifact@v4
        with:
-          name: dependencies-tests-full-${{ matrix.python-version }}-${{ matrix.setup-script }}
+          name: dependencies-${{ matrix.setup }}
          path: requirements-freeze.txt
          compression-level: 0

-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Build dashboard
-        run: cd dashboard && npm run build
-
-      - name: Setup Docker environments
-        run: |
-          set -euo pipefail
-
-          cd docker
-
-          # Setup data directories
-          ./setup.sh
-
-          # Start Dockers
-          docker compose -f compose.mongo.yml up -d
-
-          SERVICE_NAME=mongo
-          TIMEOUT=60   # seconds
-          SLEEP=2
-
-          cid="$(docker compose -f compose.mongo.yml ps -q "$SERVICE_NAME")"
-          if [ -z "$cid" ]; then
-            echo "Service $SERVICE_NAME is not running"
-            exit 1
-          fi
-
-          echo "Waiting for $SERVICE_NAME to become healthy..."
-          end=$((SECONDS + TIMEOUT))
-
-          while [ "$SECONDS" -lt "$end" ]; do
-            status="$(docker inspect -f '{{.State.Health.Status}}' "$cid")"
-            echo "Current status: $status"
-
-            if [ "$status" = "healthy" ]; then
-              echo "$SERVICE_NAME is healthy ✅"
-              exit 0
-            elif [ "$status" = "unhealthy" ]; then
-              echo "$SERVICE_NAME is unhealthy ❌"
-              docker logs "$cid" || true
-              exit 1
-            fi
-
-            sleep "$SLEEP"
-          done
-
-          echo "Timed out waiting for $SERVICE_NAME to become healthy after ${TIMEOUT}s"
-          docker logs "$cid" || true
-          exit 1
-        shell: bash
-
      - name: Launch LiteLLM Proxy
        run: |
-          ./scripts/litellm_run.sh
+          set -ex
+          . .venv/bin/activate
+          litellm --config scripts/litellm_ci.yaml --port 12306 &
+          sleep 10  # Wait for the proxy to be up
        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
+          AZURE_API_BASE: ${{ secrets.AZURE_API_BASE }}
+          AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
+      - name: Verify LiteLLM Proxy
+        run: |
+          set -ex
+          . .venv/bin/activate
+          python scripts/litellm_sanity_check.py
+        env:
+          OPENAI_BASE_URL: http://localhost:12306/
+          OPENAI_API_KEY: dummy

      - name: Run tests
        run: |
-          uv run pytest -v --durations=0 tests
+          set -ex
+          . .venv/bin/activate
+          pytest -v --durations=0 tests
        env:
          PYTEST_ADDOPTS: "--color=yes"
          OPENAI_BASE_URL: http://localhost:12306/
          OPENAI_API_KEY: dummy
-          AGL_TEST_MONGO_URI: mongodb://localhost:27017/?replicaSet=rs0
-
-
-  minimal-examples:
-    if: >
-      github.event_name != 'repository_dispatch' ||
-      github.event.action == 'ci-gpu' ||
-      github.event.action == 'ci-all'
-    name: Minimal Examples with Python ${{ matrix.python-version }} (${{ matrix.setup-script }})
-
-    runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
-    timeout-minutes: 30
-    strategy:
-      matrix:
-        include:
-        - python-version: '3.10'
-          setup-script: 'legacy'
-        - python-version: '3.12'
-          setup-script: 'stable'
-        - python-version: '3.13'
-          setup-script: 'latest'
-      fail-fast: false
-    steps:
-      - name: Check GPU status
-        run: nvidia-smi
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event_name == 'repository_dispatch' && github.event.client_payload.pr_ref || (github.event.pull_request.number && format('refs/pull/{0}/merge', github.event.pull_request.number)) || github.ref }}
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: uv sync --frozen --no-default-groups --extra apo --group dev --group agents --group langchain --group torch-gpu-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable)
-        run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group langchain --group torch-gpu-${{ matrix.setup-script }}
-        if: matrix.setup-script == 'stable'
-      # Don't install langchain for legacy dependency because it has conflicts with torch.
-      - name: Sync dependencies (legacy)
-        run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group torch-gpu-legacy
-        if: matrix.setup-script == 'legacy'
-      - name: Freeze dependencies
-        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
-      - name: Upload dependencies artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: dependencies-minimal-examples-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
-          compression-level: 0
-
-      - name: Launch LiteLLM Proxy
-        run: |
-          ./scripts/litellm_run.sh
-        env:
-          AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
-          AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
-
-      - name: Write Traces via Otel Tracer
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          python write_traces.py otel
-          sleep 5
-
-      - name: Write Traces via AgentOps Tracer
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          python write_traces.py agentops
-          sleep 5
-
-      - name: Write Traces via Otel Tracer with Client
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          agl store --port 45993 --log-level DEBUG &
-          sleep 5
-          python write_traces.py otel --use-client
-          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
-          while pgrep -f agl; do
-            echo "Waiting for agl to finish..."
-            sleep 5
-          done
-
-      - name: Write Traces via AgentOps Tracer with Client
-        env:
-          OPENAI_BASE_URL: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          agl store --port 45993 --log-level DEBUG &
-          sleep 5
-          python write_traces.py agentops --use-client
-          pkill -f agl && echo "SIGTERM sent to agl" || echo "No agl process found"
-          while pgrep -f agl; do
-            echo "Waiting for agl to finish..."
-            sleep 5
-          done
-
-      - name: vLLM Server
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          python vllm_server.py Qwen/Qwen2.5-0.5B-Instruct
-
-      - name: LLM Proxy (OpenAI backend)
-        env:
-          OPENAI_API_BASE: http://localhost:12306/
-          OPENAI_API_KEY: dummy
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-
-          python llm_proxy.py openai gpt-4.1-mini &
-
-          LLM_PROXY_READY=0
-          for attempt in $(seq 1 30); do
-            if curl -sSf http://localhost:43886/health > /dev/null 2>&1; then
-              LLM_PROXY_READY=1
-              break
-            fi
-            sleep 2
-          done
-          if [[ "$LLM_PROXY_READY" != "1" ]]; then
-            echo "LLM proxy failed to become healthy" >&2
-            exit 1
-          fi
-
-          python llm_proxy.py test gpt-4.1-mini
-
-          pkill -f llm_proxy.py && echo "SIGTERM sent to llm_proxy.py" || echo "No llm_proxy.py process found"
-          while pgrep -f llm_proxy.py; do
-            echo "Waiting for llm_proxy.py to finish..."
-            sleep 5
-          done
-
-      - name: LLM Proxy (vLLM backend)
-        if: matrix.setup-script != 'legacy'  # Skip if return_token_ids is not supported
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          python llm_proxy.py vllm Qwen/Qwen2.5-0.5B-Instruct &
-
-          LLM_PROXY_READY=0
-          for attempt in $(seq 1 30); do
-            if curl -sSf http://localhost:43886/health > /dev/null 2>&1; then
-              LLM_PROXY_READY=1
-              break
-            fi
-            sleep 2
-          done
-          if [[ "$LLM_PROXY_READY" != "1" ]]; then
-            echo "LLM proxy failed to become healthy" >&2
-            exit 1
-          fi
-
-          python llm_proxy.py test Qwen/Qwen2.5-0.5B-Instruct
-
-          pkill -f llm_proxy.py && echo "SIGTERM sent to llm_proxy.py" || echo "No llm_proxy.py process found"
-          while pgrep -f llm_proxy.py; do
-            echo "Waiting for llm_proxy.py to finish..."
-            sleep 5
-          done
-
-      - name: MultiMetrics backend example
-        run: |
-          set -euo pipefail
-          source .venv/bin/activate
-          cd examples/minimal
-          python write_metrics.py --duration 8 --prom-port 9105 --prom-host 0.0.0.0 2>&1 | tee metrics.log &
-          pid=$!
-
-          for attempt in $(seq 1 20); do
-            if curl -sSf http://localhost:9105/metrics | grep -q minimal_requests_total; then
-              echo "Metrics endpoint responding"
-              wait $pid
-              cat metrics.log
-              exit 0
-            fi
-            sleep 1
-          done
-
-          echo "Metrics endpoint did not respond"
-          exit 1
@@ -5,9 +5,9 @@ permissions:

 on:
  push:
-    branches: [ main, stable/**/* ]
+    branches: [ main ]
  pull_request:
-    branches: [ main, stable/**/* ]
+    branches: [ main ]
  workflow_dispatch:

  schedule:
@@ -16,96 +16,70 @@ on:

 jobs:

-  lint:
-    strategy:
-      matrix:
-        setup: [fast, slow]
-    name: Lint - ${{ matrix.setup }}
+  lint-fast:
+    name: Lint - Fast
    runs-on: ubuntu-latest
    timeout-minutes: 10
    steps:
-      - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v7
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
        with:
-          enable-cache: true
          python-version: '3.12'
-      - name: Sync dependencies (fast)
-        run: uv sync --frozen --group dev --no-default-groups
-        if: matrix.setup == 'fast'
-      - name: Sync dependencies (slow)
+      - name: Install dependencies
        run: |
-          uv sync --frozen \
-            --extra apo \
-            --extra verl \
-            --extra mongo \
-            --group dev \
-            --group torch-cpu \
-            --group torch-stable \
-            --group trl \
-            --group tinker \
-            --group agents \
-            --group langchain \
-            --no-default-groups
-        if: matrix.setup == 'slow'
-      # This pre-commit skips JavaScript on purpose.
+          python -m pip install --upgrade pip
+          pip install -e .[dev]
      - name: Run pre-commit
        uses: pre-commit/action@v3.0.1
      - name: Check Python headers
-        run: uv run --locked --no-sync scripts/check_headers.py
+        run: |
+          python scripts/check_python_headers.py
      - name: Run Black
-        run: uv run --locked --no-sync black --check .
+        run: black --check .
      - name: Run isort
-        run: uv run --locked --no-sync isort --check-only .
-      - name: Run pyright (fast)
-        run: uv run --locked --no-sync pyright -p pyrightconfig.fast.json
-        if: matrix.setup == 'fast'
-      - name: Run pyright (slow)
-        run: uv run --locked --no-sync pyright -p pyrightconfig.json
-        if: matrix.setup == 'slow'
+        run: isort --check-only .
+      - name: Run pyright
+        run: pyright -p pyrightconfig.fast.json

-  lint-js:
-    name: Lint - JavaScript
+  lint-slow:
+    name: Lint - Slow
    runs-on: ubuntu-latest
    timeout-minutes: 10
    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-node@v6
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
        with:
-          node-version: '22'
+          python-version: '3.12'
      - name: Install dependencies
-        run: cd dashboard && npm ci
-      - name: Run ESLint
-        run: cd dashboard && npm run eslint
-      - name: Run Prettier
-        run: cd dashboard && npm run prettier
-      - name: Run Stylelint
-        run: cd dashboard && npm run stylelint
-      - name: Run Typecheck
-        run: cd dashboard && npm run typecheck
-      - name: Verify build
-        run: cd dashboard && npm run build
+        run: |
+          ./scripts/setup_type_checking.sh
+      - name: Run Black
+        run: black --check .
+      - name: Run isort
+        run: isort --check-only .
+      - name: Run pyright
+        run: pyright -p pyrightconfig.json

  docs:
    name: Build documentation
    runs-on: ubuntu-latest
    timeout-minutes: 10
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
        with:
          fetch-depth: 0
-      - uses: actions/setup-python@v6
+      - uses: actions/setup-python@v4
        with:
          python-version: '3.12'
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-      - name: Sync dependencies
-        run: uv sync --frozen --no-default-groups --group dev
+      - name: Install documentation dependencies
+        run: |
+          ./scripts/setup_stable.sh
      - name: Set source commit for docs
        run: |
          echo "SOURCE_COMMIT=${{ github.sha }}" >> $GITHUB_ENV
      - name: Build documentation
-        run: uv run --locked --no-sync mkdocs build --strict
+        run: |
+          mkdocs build --strict
      - name: Upload docs artifact
        uses: actions/upload-artifact@v4
        with:
@@ -118,78 +92,35 @@ jobs:
      matrix:
        include:
          - python-version: '3.10'
-            setup-script: 'legacy'
-          - python-version: '3.11'
            setup-script: 'stable'
          - python-version: '3.12'
-            setup-script: 'stable'
-          - python-version: '3.13'
            setup-script: 'latest'
+          - python-version: '3.12'
+            setup-script: 'stable'
      fail-fast: false

    name: Test with Python ${{ matrix.python-version }} (${{ matrix.setup-script }})
    runs-on: ubuntu-latest
    timeout-minutes: 15
    steps:
-      - uses: actions/checkout@v4
-      - uses: astral-sh/setup-uv@v7
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
        with:
-          enable-cache: true
          python-version: ${{ matrix.python-version }}
-      - name: Upgrade dependencies (latest)
-        run: uv lock --upgrade
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (latest)
-        run: uv sync --frozen --no-default-groups --extra apo --group dev --group agents --group langchain --group core-stable
-        if: matrix.setup-script == 'latest'
-      - name: Sync dependencies (stable & legacy)
-        run: uv sync --frozen --no-default-groups --extra apo --group dev --group agents --group langchain --group core-${{ matrix.setup-script }}
-        if: matrix.setup-script != 'latest'
+      - name: Install dependencies
+        run: |
+          ./scripts/setup_${{ matrix.setup-script }}.sh
      - name: Freeze dependencies
        run: |
-          set -ex
-          uv pip freeze | tee requirements-freeze.txt
-          echo "UV_LOCKED=1" >> $GITHUB_ENV
-          echo "UV_NO_SYNC=1" >> $GITHUB_ENV
+          pip list | tee requirements-freeze-${{ matrix.python-version }}-${{ matrix.setup-script }}.txt
      - name: Upload dependencies artifact
        uses: actions/upload-artifact@v4
        with:
-          name: dependencies-${{ matrix.python-version }}-${{ matrix.setup-script }}
-          path: requirements-freeze.txt
+          name: dependencies-python-${{ matrix.python-version }}-${{ matrix.setup-script }}
+          path: requirements-freeze-${{ matrix.python-version }}-${{ matrix.setup-script }}.txt
          compression-level: 0
-
-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Build dashboard
-        run: cd dashboard && npm run build
-
      - name: Run tests
        run: |
-          uv run pytest -v --durations=0 tests -m "not mongo"
+          pytest -v --durations=0 tests
        env:
          PYTEST_ADDOPTS: "--color=yes"
-
-  test-js:
-    name: Test - JavaScript
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-      - uses: astral-sh/setup-uv@v7
-        with:
-          enable-cache: true
-          python-version: '3.12'
-      - name: Sync Python dependencies
-        run: uv sync --frozen --no-default-groups --extra apo --group dev --group agents --group core-stable
-      - name: Install JavaScript dependencies
-        run: cd dashboard && npm ci
-      - name: Run vitest
-        run: cd dashboard && npm run vitest
@@ -189,9 +189,6 @@ cython_debug/
 #  you could uncomment the following to ignore the enitre vscode folder
 .vscode/

-# Emacs backup files
-*~
-
 # Ruff stuff:
 .ruff_cache/

@@ -207,12 +204,3 @@ cython_debug/

 # Claude
 .claude/*.local.json
-
-# Dashboard generated files
-agentlightning/dashboard/**/*.css
-agentlightning/dashboard/**/*.js
-agentlightning/dashboard/**/*.html
-agentlightning/dashboard/**/*.svg
-
-# Docker data
-docker/data/
@@ -8,8 +8,6 @@ repos:
        exclude: ^mkdocs\.yml$
      - id: check-toml
      - id: check-added-large-files
-        args: ["--maxkb=1024"]
-        exclude: (^uv\.lock$)|(^docs/assets/.*\.svg$)
      - id: check-shebang-scripts-are-executable
      - id: detect-private-key
  - repo: https://github.com/pycqa/isort
@@ -24,53 +22,3 @@ repos:
        pass_filenames: false
        always_run: true
        args: ["."]
-
-  - repo: local
-    hooks:
-    - id: prettier
-      name: prettier (dashboard)
-      language: system
-      pass_filenames: false
-      always_run: true
-      entry: >
-        bash -c '
-          cd dashboard || exit 1
-          if [ -d node_modules ]; then
-            echo "✅ node_modules already exists"
-            npx prettier --cache --write "**/*.{ts,tsx,mjs,cjs}"
-          else
-            echo "⚠️ node_modules not found — npx is not reliable. Skipping."
-          fi
-        '
-
-    - id: eslint
-      name: eslint (dashboard)
-      language: system
-      pass_filenames: false
-      always_run: true
-      entry: >
-        bash -c '
-          cd dashboard || exit 1
-          if [ -d node_modules ]; then
-            echo "✅ node_modules already exists"
-            npx eslint --cache --fix .
-          else
-            echo "⚠️ node_modules not found — npx is not reliable. Skipping."
-          fi
-        '
-
-    - id: stylelint
-      name: stylelint (dashboard)
-      language: system
-      pass_filenames: false
-      always_run: true
-      entry: >
-        bash -c '
-          cd dashboard || exit 1
-          if [ -d node_modules ]; then
-            echo "✅ node_modules already exists"
-            npx stylelint --cache --fix "**/*.css"
-          else
-            echo "⚠️ node_modules not found — npx is not reliable. Skipping."
-          fi
-        '
@@ -1 +0,0 @@
-3.12
@@ -1,14 +1,13 @@
-<p align="center">
-  <img src="docs/assets/readme-banner.svg" alt="Agent-lightning-banner" style="width:600px"/>
-</p>
+<div style="text-align:center; margin-bottom:20px;">
+  <img src="docs/assets/readme-banner.png" alt="Agent-lightning-banner" style="max-width:600px"/>
+</div>

 # Agent Lightning⚡

-[![Unit Tests](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml)
-[![Documentation](https://img.shields.io/badge/GitHub%20Pages-Documentation-blue)](https://microsoft.github.io/agent-lightning/)
+[![CPU Test](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml)
+[![GPU Test](https://github.com/microsoft/agent-lightning/actions/workflows/examples.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples.yml)
 [![PyPI version](https://badge.fury.io/py/agentlightning.svg)](https://badge.fury.io/py/agentlightning)
 [![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)
-[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/microsoft/agent-lightning)
 [![Discord](https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white)](https://discord.gg/RYk7CdvDR7)

 **The absolute trainer to light up AI agents.**
@@ -18,36 +17,14 @@ Join our [Discord community](https://discord.gg/RYk7CdvDR7) to connect with othe
 ## ⚡ Core Features

 - Turn your agent into an optimizable beast with **ZERO CODE CHANGE** (almost)! 💤
- Build with **ANY** agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖
+- Build with **ANY** agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, ...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖
 - **Selectively** optimize one or more agents in a multi-agent system. 🎯
- Embraces **Algorithms** like Reinforcement Learning, Automatic Prompt Optimization, Supervised Fine-tuning and more. 🤗
+- Embraces Reinforcement Learning, Automatic Prompt Optimization and more **algorithms**. 🤗

-Read more on our [documentation website](https://microsoft.github.io/agent-lightning/).
+![Agent-Lightning-code-diff](docs/assets/readme-diff.png)

-<p align="center">
-  <img src="docs/assets/readme-diff.svg" alt="Agent-Lightning Core Quickstart" style="width:100%"/>
-</p>
+## ⚡ Resources

-## ⚡ Installation
-
-```bash
-pip install agentlightning
-```
-
-For the latest nightly build (cutting-edge features), you can install from Test PyPI:
-
-```bash
-pip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ agentlightning
-```
-
-Please refer to our [installation guide](https://microsoft.github.io/agent-lightning/stable/tutorials/installation/) for more details.
-
-To start using Agent-lightning, check out our [documentation](https://microsoft.github.io/agent-lightning/) and [examples](./examples).
-
-## ⚡ Articles
-
- 11/4/2025 [Tuning ANY AI agent with Tinker ✕ Agent-lightning](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-1-1d8c9a397f0e) Medium. See also [Part 2](https://medium.com/@yugez/tuning-any-ai-agent-with-tinker-agent-lightning-part-2-332c5437f0dc).
- 10/22/2025 [No More Retokenization Drift: Returning Token IDs via the OpenAI Compatible API Matters in Agent RL](https://blog.vllm.ai/2025/10/22/agent-lightning.html) vLLM blog. See also [Zhihu writeup](https://zhuanlan.zhihu.com/p/1965067274642785725).
 - 8/11/2025 [Training AI Agents to Write and Self-correct SQL with Reinforcement Learning](https://medium.com/@yugez/training-ai-agents-to-write-and-self-correct-sql-with-reinforcement-learning-571ed31281ad) Medium.
 - 8/5/2025 [Agent Lightning: Train ANY AI Agents with Reinforcement Learning](https://arxiv.org/abs/2508.03680) arXiv paper.
 - 7/26/2025 [We discovered an approach to train any AI agent with RL, with (almost) zero code changes.](https://www.reddit.com/r/LocalLLaMA/comments/1m9m670/we_discovered_an_approach_to_train_any_ai_agent/) Reddit.
@@ -58,28 +35,114 @@ To start using Agent-lightning, check out our [documentation](https://microsoft.
 - [DeepWerewolf](https://github.com/af-74413592/DeepWerewolf) — A case study of agent RL training for the Chinese Werewolf game built with AgentScope and Agent Lightning.
 - [AgentFlow](https://agentflow.stanford.edu/) — A modular multi-agent framework that combines planner, executor, verifier, and generator agents with the Flow-GRPO algorithm to tackle long-horizon, sparse-reward tasks.

+## ⚡ Installation
+
+First, let's get your environment set up. We'll be using `/path/to/agentlightning` to refer to the directory containing this README file.
+
+### 1. Set Up Your Environment
+
+We strongly recommend creating a new virtual environment to avoid conflicts with other packages. You can use either `conda` or `venv`. **Python 3.10 or later** is recommended.
+
+### 2. Install Core Training Dependencies (Optional)
+
+If you are running RL with Agent-Lightning, the next step is to install the essential packages: `PyTorch`, `FlashAttention`, `vLLM` and `VERL`. The following versions and installation order have been tested and are confirmed to work.
+
+```bash
+pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
+pip install flash-attn --no-build-isolation
+pip install vllm==0.9.2
+pip install verl==0.5.0
+```
+
+See `scripts/setup_stable_gpu.sh` for a full installation script.
+
+### 3. Install Agent Lightning
+
+Now, you're ready to install Agent Lightning itself.
+
+```bash
+pip install agentlightning
+```
+
+### 4. Install Agent Frameworks (Optional)
+
+If you plan to use other agent frameworks, you can install them with the following commands. If you don't need these, feel free to skip this step.
+We recommend doing this as the final step to avoid dependency versions being overwritten by mistake.
+
+```bash
+# AutoGen (Recommended to install first)
+pip install "autogen-agentchat" "autogen-ext[openai]"
+
+# LiteLLM
+pip install "litellm[proxy]"
+
+# MCP
+pip install mcp
+
+# UV
+pip install uv
+
+# OpenAI Agents
+pip install openai-agents
+
+# LangChain
+pip install langgraph "langchain[openai]" langchain-community langchain-text-splitters
+
+# SQL-related dependencies
+pip install sqlparse nltk
+```
+
+Don't worry if dependency conflicts arise during this step. Follow the installation order above and the conflicts generally do not matter.
+
+## ⚡ Examples
+
+For more detailed examples, please see the `examples` folder:
+
+1. [calc_x](examples/calc_x): An agent built with AutoGen with calculator tool use, trained on Calc-X dataset with Reinforcement Learning.
+2. [spider](examples/spider): A write-check-rewrite looped agent with LangGraph with SQL execution; selectively optimize write and rewrite on Spider dataset with Reinforcement Learning.
+3. [apo](examples/apo): An example to customize an optimization algorithm: Automatic Prompt Optimization.
+
+## ⚡ Important Caveats
+
+1.  **AgentOps Integration**: Agent Lightning uses [AgentOps](https://github.com/AgentOps-AI/agentops) for agent tracking by default. If you're already using AgentOps in your own code, you'll need to disable our managed AgentOps client by modifying the `tracer` parameter of trainer.
+2.  **Debugging Traces**: If you encounter issues with tracing, you can visualize the trace tree using `tracer.last_trace().visualize("tree_graph")`. Please note that this API is experimental and may change in future releases.
+3.  **Launching the Server and Agents**: Currently, the training server and agent clients must be launched in separate processes. You can open two terminal windows or run one of them in the background. The launching order generally doesn't matter.
+4.  **Environment Variables**: The environment variables and working directory at the time of `ray init` are important. If you run into "file not found" errors, try restarting Ray from your current working directory.
+5.  **Handling Timeouts**: The training server may hang if samples fail or time out on the agent side. To prevent this, we recommend setting limits on the prompt and response lengths, as this is the most common cause of failures.
+6.  **VERL Failures**: Save checkpoints frequently, as VERL with vLLM may sometimes experience out-of-memory issues. If you encounter a VERL failure, you can resume training from the last checkpoint.
+
 ## ⚡ Architecture

-Agent Lightning keeps the moving parts to a minimum so you can focus on your idea, not the plumbing. Your agent continues to run as usual; you can still use any agent framework you like; you drop in the lightweight `agl.emit_xxx()` helper, or let the tracer collect every prompt, tool call, and reward. Those events become structured spans that flow into the LightningStore, a central hub that keeps tasks, resources, and traces in sync.
+Currently, Agent Lightning is built around a **training server** and one or multiple **agents**.

-On the other side of the store sits the algorithm you choose, or write yourself. The algorithm reads spans, learns from them, and posts updated resources such as refined prompt templates or new policy weights. The Trainer ties it all together: it streams datasets to runners, ferries resources between the store and the algorithm, and updates the inference engine when improvements land. You can either stop there, or simply let the same loop keep turning.
+* The **server** manages the training data, prepares samples for the agents, and provides the LLM endpoint.
+* **Agents** retrieve samples from the server, process them (which may involve interacting with the LLM), and send the results back. These results, or "trajectories," are lists of prompts and responses from the LLM.
+* The **server** then collects these trajectories and computes the losses to optimize the language models.

-No rewrites, no lock-in, just a clear path from first rollout to steady improvement.
+![Agent-Lightning-architecture](docs/assets/readme-architecture.png)

-<p align="center">
-  <img src="docs/assets/readme-architecture.svg" alt="Agent-lightning Architecture" style="width:100%"/>
-</p>
+## ⚡ Development Instructions

-## ⚡ CI Status
+Install with development dependencies:

-| Workflow | Status |
-|----------|--------|
-| CPU Tests | [![tests workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/tests.yml) |
-| Full Tests | [![tests summary workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unit.yml) |
-| UI Tests | [![UI Tests](https://github.com/microsoft/agent-lightning/actions/workflows/dashboard.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/dashboard.yml) |
-| Examples Integration | [![examples summary workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-examples.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-examples.yml) |
-| Latest Dependency Compatibility | [![latest summary workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-latest.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-latest.yml) |
-| Legacy Examples Compatibility | [![compat summary workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-compat.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-compat.yml) |
+```
+git clone https://github.com/microsoft/agent-lightning
+cd agent-lightning
+pip install -e .[dev]
+```
+
+Please run pre-commit hooks before checking in code:
+
+```
+pre-commit install
+pre-commit run --all-files --show-diff-on-failure --color=always
+```
+
+Serve documentation locally:
+
+```bash
+mkdocs serve
+```

 ## ⚡ Citation

@@ -99,7 +162,7 @@ If you find Agent Lightning useful in your research or projects, please cite our

 ## ⚡ Contributing

-This project welcomes contributions and suggestions. Start by reading the [Contributing Guide](docs/community/contributing.md) for recommended contribution points, environment setup, branching conventions, and pull request expectations. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

 When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

@@ -1,22 +1,23 @@
 # Copyright (c) Microsoft. All rights reserved.

-__version__ = "0.3.0"
+__version__ = "0.2.0"

-from .adapter import *
-from .algorithm import *
-from .client import AgentLightningClient, DevTaskLoader  # deprecated  # type: ignore
-from .config import *
-from .emitter import *
-from .env_var import *
-from .execution import *
+from .client import AgentLightningClient, DevTaskLoader
+from .config import lightning_cli
 from .litagent import *
-from .llm_proxy import *
-from .logging import configure_logger  # deprecated  # type: ignore
-from .logging import setup as setup_logging  # type: ignore
-from .logging import setup_module as setup_module_logging  # type: ignore
-from .runner import *
-from .server import AgentLightningServer  # deprecated  # type: ignore
-from .store import *
-from .tracer import *
-from .trainer import *
+from .logging import configure_logger
+from .reward import reward
+from .server import AgentLightningServer
+from .trainer import Trainer
 from .types import *
+
+__all__ = [
+    "AgentLightningClient",
+    "DevTaskLoader",
+    "lightning_cli",
+    "configure_logger",
+    "reward",
+    "AgentLightningServer",
+    "Trainer",
+    "__version__",
+]
@@ -1,15 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.

-from .base import Adapter, OtelTraceAdapter, TraceAdapter
-from .messages import TraceToMessages
-from .triplet import LlmProxyTraceToTriplet, TracerTraceToTriplet, TraceToTripletBase
+from .base import Adapter, TraceAdapter
+from .triplet import BaseTraceTripletAdapter, LlmProxyTripletAdapter, TraceTripletAdapter

-__all__ = [
-    "TraceAdapter",
-    "OtelTraceAdapter",
-    "Adapter",
-    "TraceToTripletBase",
-    "TracerTraceToTriplet",
-    "LlmProxyTraceToTriplet",
-    "TraceToMessages",
-]
+__all__ = ["TraceAdapter", "Adapter", "BaseTraceTripletAdapter", "TraceTripletAdapter", "LlmProxyTripletAdapter"]
@@ -1,6 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.

-from typing import Generic, Sequence, TypeVar
+from typing import Generic, List, TypeVar

 from opentelemetry.sdk.trace import ReadableSpan

@@ -13,20 +13,15 @@ T_to = TypeVar("T_to")
 class Adapter(Generic[T_from, T_to]):
    """Base class for synchronous adapters that convert data from one format to another.

-    The class defines a minimal protocol so that adapters can be treated like callables while
-    still allowing subclasses to supply the concrete transformation logic.
+    This class defines a simple protocol for transformation:
+    - The `__call__` method makes adapters callable, so they can be used like functions.
+    - Subclasses must implement the `adapt` method to define the actual conversion logic.

-    !!! note
-        Subclasses must override [`adapt()`][agentlightning.Adapter.adapt] to provide
-        the actual conversion.
+    Type parameters:
+        T_from: The source data type (input).
+        T_to: The target data type (output).

-    Type Variables:
-
-        T_from: Source data type supplied to the adapter.
-
-        T_to: Target data type produced by the adapter.
-
-    Examples:
+    Example:
        >>> class IntToStrAdapter(Adapter[int, str]):
        ...     def adapt(self, source: int) -> str:
        ...         return str(source)
@@ -39,9 +34,8 @@ class Adapter(Generic[T_from, T_to]):
    def __call__(self, source: T_from, /) -> T_to:
        """Convert the data to the target format.

-        This method delegates to [`adapt()`][agentlightning.Adapter.adapt] so that an
-        instance of [`Adapter`][agentlightning.Adapter] can be used like a standard
-        function.
+        This method delegates to `adapt` and allows the adapter
+        to be invoked as a function.

        Args:
            source: Input data in the source format.
@@ -54,27 +48,36 @@ class Adapter(Generic[T_from, T_to]):
    def adapt(self, source: T_from, /) -> T_to:
        """Convert the data to the target format.

-        Subclasses must override this method with the concrete transformation logic. The base
-        implementation raises `NotImplementedError` to make the requirement explicit.
+        Subclasses should override this method with the concrete
+        transformation logic.

        Args:
            source: Input data in the source format.

        Returns:
            Data converted to the target format.
+
+        Raises:
+            NotImplementedError: If the method is not implemented
+            in a subclass.
        """
        raise NotImplementedError("Adapter.adapt() is not implemented")


-class OtelTraceAdapter(Adapter[Sequence[ReadableSpan], T_to], Generic[T_to]):
+class OtelTraceAdapter(Adapter[List[ReadableSpan], T_to], Generic[T_to]):
    """Base class for adapters that convert OpenTelemetry trace spans into other formats.

-    This specialization of [`Adapter`][agentlightning.Adapter] expects a list of
-    `opentelemetry.sdk.trace.ReadableSpan` instances and produces any target format, such as
-    reinforcement learning trajectories, structured logs, or analytics-ready payloads.
+    This class specializes `Adapter` for working with OpenTelemetry `ReadableSpan`
+    objects. It expects a list of spans as input and produces a custom target format
+    (e.g., reinforcement learning training data, SFT datasets, logs, metrics).

-    Examples:
-        >>> class TraceToDictAdapter(OtelTraceAdapter[dict]):
+    Subclasses should override `adapt` to define the desired conversion.
+
+    Type parameters:
+        T_to: The target data type that spans should be converted into.
+
+    Example:
+        >>> class TraceToDictAdapter(TraceAdapter[dict]):
        ...     def adapt(self, spans: List[ReadableSpan]) -> dict:
        ...         return {"count": len(spans)}
        ...
@@ -84,11 +87,10 @@ class OtelTraceAdapter(Adapter[Sequence[ReadableSpan], T_to], Generic[T_to]):
    """


-class TraceAdapter(Adapter[Sequence[Span], T_to], Generic[T_to]):
+class TraceAdapter(Adapter[List[Span], T_to], Generic[T_to]):
    """Base class for adapters that convert trace spans into other formats.

-    This class specializes [`Adapter`][agentlightning.Adapter] for working with
-    [`Span`][agentlightning.Span] instances emitted by Agent Lightning instrumentation.
-    Subclasses receive entire trace slices and return a format suited for the downstream consumer,
-    for example reinforcement learning training data or observability metrics.
+    This class specializes `Adapter` for working with trace spans. It expects a list of
+    Agent-lightning spans as input and produces a custom target format
+    (e.g., reinforcement learning training data, SFT datasets, logs, metrics).
    """
@@ -1,70 +1,44 @@
 # Copyright (c) Microsoft. All rights reserved.

-from __future__ import annotations
-
 import json
 from collections import defaultdict
-from typing import TYPE_CHECKING, Any, Dict, Generator, Iterable, List, Optional, Sequence, TypedDict, Union, cast
+from typing import Any, Dict, Generator, List, Optional, Sequence, TypedDict, Union, cast

-from pydantic import TypeAdapter
+from openai.types.chat.chat_completion_function_tool_param import ChatCompletionFunctionToolParam
+from openai.types.chat.chat_completion_message import ChatCompletionMessage
+from openai.types.chat.chat_completion_message_function_tool_call import ChatCompletionMessageFunctionToolCall, Function
+from openai.types.chat.chat_completion_message_param import ChatCompletionMessageParam
+from openai.types.shared_params import FunctionDefinition
+from pydantic import BaseModel, TypeAdapter

 from agentlightning.types import Span

 from .base import TraceAdapter

-if TYPE_CHECKING:
-    from openai.types.chat import (
-        ChatCompletionFunctionToolParam,
-        ChatCompletionMessageFunctionToolCallParam,
-        ChatCompletionMessageParam,
-    )

-
-class OpenAIMessages(TypedDict):
-    """OpenAI-style chat messages with optional tool definitions.
-
-    Attributes:
-        messages: Ordered chat messages that describe the conversation.
-        tools: Tool specifications available to the assistant, if any.
-    """
-
-    messages: List[ChatCompletionMessageParam]
-    tools: Optional[List[ChatCompletionFunctionToolParam]]
+class OpenAIMessages(BaseModel):
+    messages: List[Union[ChatCompletionMessage, ChatCompletionMessageParam]]
+    tools: Optional[List[ChatCompletionFunctionToolParam]] = None


 class _RawSpanInfo(TypedDict):
-    """Intermediate representation parsed from a span.
-
-    Attributes:
-        prompt: Prompt messages reconstructed from span attributes.
-        completion: Assistant completions following tool invocations.
-        request: Request payload recorded in the trace.
-        response: Response payload recorded in the trace.
-        tools: Tool call metadata extracted from child spans.
-    """
-
    prompt: List[Dict[str, Any]]
    completion: List[Dict[str, Any]]
    request: Dict[str, Any]
    response: Dict[str, Any]
-    tools: List[Dict[str, Any]]


 def group_genai_dict(data: Dict[str, Any], prefix: str) -> Union[Dict[str, Any], List[Any]]:
-    """Convert flattened trace attributes into nested structures.
-
-    Attributes emitted by the tracing pipeline often arrive as dotted paths (for example
-    `gen_ai.prompt.0.role`). This helper groups those keys into nested dictionaries or lists so that
-    downstream processing can operate on structured data.
+    """
+    Convert a flat dict with keys like 'gen_ai.prompt.0.role'
+    into structured nested dicts or lists under the given prefix.

    Args:
-        data: Flat dictionary whose keys are dotted paths.
-        prefix: Top-level key (for example `gen_ai.prompt`) that determines which attributes are
-            grouped.
+        data: Flat dictionary (keys are dotted paths).
+        prefix: Top-level key to extract (e.g., 'gen_ai.prompt').

    Returns:
-        A nested dictionary (no numeric index detected) or list (numeric indices detected) containing
-        the grouped values.
+        A nested dict (if no index detected) or list (if indexed).
    """
    result: Union[Dict[str, Any], List[Any]] = {}

@@ -103,31 +77,22 @@ def group_genai_dict(data: Dict[str, Any], prefix: str) -> Union[Dict[str, Any],
    return result


-def convert_to_openai_messages(prompt_completion_list: List[_RawSpanInfo]) -> Generator[OpenAIMessages, None, None]:
-    """Convert raw trace payloads into OpenAI-style chat messages.
-
-    The function consumes an iterable produced by
-    [`TraceToMessages.adapt()`][agentlightning.TraceToMessages.adapt] and yields
-    structures that match the OpenAI fine-tuning JSONL schema, including tool definitions.
-
-    Args:
-        prompt_completion_list: Raw prompt/completion/tool payloads extracted from a trace.
-
-    Returns:
-        A generator that yields [`OpenAIMessages`][agentlightning.adapter.messages.OpenAIMessages]
-        entries compatible with the OpenAI Functions fine-tuning format.
+def convert_to_openai_messages(
+    prompt_completion_list: List[_RawSpanInfo], tool_requests: List[Dict[str, Any]]
+) -> Generator[OpenAIMessages, None, None]:
    """
+    Convert raw tool call traces + prompt/completion list
+    into OpenAI fine-tuning JSONL format (tool calling style).

-    # Import locally to avoid legacy OpenAI version type import errors
-    from openai.types.chat import (
-        ChatCompletionAssistantMessageParam,
-        ChatCompletionFunctionToolParam,
-        ChatCompletionMessageFunctionToolCallParam,
-        ChatCompletionMessageParam,
-    )
+    Since promopt-completions sometimes do not contain the generated tool calls,
+    the tool call requests need to be provided separately.
+    The tool calls are then matched in a first-come-first-served basis to the tool call requests.

+    https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/fine-tuning-functions
+    """
    for pc_entry in prompt_completion_list:
-        messages: List[ChatCompletionMessageParam] = []
+        messages: List[Union[ChatCompletionMessage, ChatCompletionMessageParam]] = []
+        tools: List[ChatCompletionFunctionToolParam] = []

        # Extract messages
        for msg in pc_entry["prompt"]:
@@ -135,18 +100,17 @@ def convert_to_openai_messages(prompt_completion_list: List[_RawSpanInfo]) -> Ge

            if role == "assistant" and "tool_calls" in msg:
                # Use the tool_calls directly
-                # This branch is usually not used in the wild.
-                tool_calls: List[ChatCompletionMessageFunctionToolCallParam] = [
-                    ChatCompletionMessageFunctionToolCallParam(
-                        id=call["id"],
-                        type="function",
-                        function={"name": call["name"], "arguments": call["arguments"]},
+                tool_calls: Sequence[ChatCompletionMessageFunctionToolCall] = []
+                for call in msg["tool_calls"]:
+                    function = Function(name=call["name"], arguments=call["arguments"])
+                    tool_calls.append(
+                        ChatCompletionMessageFunctionToolCall(
+                            id=call["id"],
+                            type="function",
+                            function=function,
+                        )
                    )
-                    for call in msg["tool_calls"]
-                ]
-                messages.append(
-                    ChatCompletionAssistantMessageParam(role="assistant", content=None, tool_calls=tool_calls)
-                )
+                messages.append(ChatCompletionMessage(role="assistant", tool_calls=list(tool_calls)))
            else:
                # Normal user/system/tool content
                message = cast(
@@ -160,92 +124,81 @@ def convert_to_openai_messages(prompt_completion_list: List[_RawSpanInfo]) -> Ge
        # Extract completions (assistant outputs after tool responses)
        for comp in pc_entry["completion"]:
            if comp.get("role") == "assistant":
-                content = comp.get("content")
-                if pc_entry["tools"]:
-                    tool_calls = [
-                        ChatCompletionMessageFunctionToolCallParam(
-                            id=tool["call"]["id"],
-                            type=tool["call"]["type"],
-                            function={"name": tool["name"], "arguments": tool["parameters"]},
-                        )
-                        for tool in pc_entry["tools"]
-                    ]
-                    messages.append(
-                        ChatCompletionAssistantMessageParam(role="assistant", content=content, tool_calls=tool_calls)
+                if comp.get("content"):
+                    message = ChatCompletionMessage(role="assistant", content=comp["content"])
+                    messages.append(message)
+                elif comp.get("finish_reason") == "tool_calls":
+                    if len(tool_requests) == 0:
+                        raise ValueError("No tool requests available for tool_calls completion")
+                    tool_req = tool_requests.pop(0)
+                    # TODO: this is a hack because tracing frameworks did not report the tool call properly (?)
+                    message = ChatCompletionMessage(
+                        role="assistant",
+                        tool_calls=[
+                            ChatCompletionMessageFunctionToolCall(
+                                id=tool_req["call"]["id"],
+                                type=tool_req["call"]["type"],
+                                function=Function(name=tool_req["name"], arguments=tool_req["parameters"]),
+                            )
+                        ],
                    )
+                    messages.append(message)
                else:
-                    messages.append(ChatCompletionAssistantMessageParam(role="assistant", content=content))
+                    raise ValueError(f"Unsupported assistant completion: {comp}")

        # Build tools definitions (if available)
        if "functions" in pc_entry["request"]:
-            tools = [
-                ChatCompletionFunctionToolParam(
-                    type="function",
-                    function={
-                        "name": fn["name"],
-                        "description": fn.get("description", ""),
-                        "parameters": (
-                            json.loads(fn["parameters"]) if isinstance(fn["parameters"], str) else fn["parameters"]
+            for fn in pc_entry["request"]["functions"]:
+                tools.append(
+                    ChatCompletionFunctionToolParam(
+                        type="function",
+                        function=FunctionDefinition(
+                            name=fn["name"],
+                            description=fn.get("description", ""),
+                            parameters=(
+                                json.loads(fn["parameters"]) if isinstance(fn["parameters"], str) else fn["parameters"]
+                            ),
                        ),
-                    },
+                    )
                )
-                for fn in pc_entry["request"]["functions"]
-            ]
            yield OpenAIMessages(messages=messages, tools=tools)
        else:
-            yield OpenAIMessages(messages=messages, tools=None)
+            yield OpenAIMessages(messages=messages, tools=tools)


-class TraceToMessages(TraceAdapter[List[OpenAIMessages]]):
-    """Convert trace spans into OpenAI-compatible conversation messages.
+class TraceMessagesAdapter(TraceAdapter[List[OpenAIMessages]]):
+    """
+    Adapter that converts OpenTelemetry trace spans into OpenAI-compatible message format.

-    The adapter reconstructs prompts, completions, tool calls, and function definitions from
-    `gen_ai.*` span attributes. The resulting objects match the JSONL structure expected by the
-    OpenAI fine-tuning pipeline.
+    This adapter processes trace spans containing LLM conversation data and transforms them
+    into structured OpenAI message format suitable for fine-tuning or analysis. It extracts
+    prompts, completions, tool calls, and function definitions from trace attributes and
+    reconstructs the conversation flow.

-    !!! warning
-        The adapter assumes all spans share a common trace and that tool call spans are direct
-        children of the associated completion span.
+    The adapter handles:
+    - Converting flat trace attributes into structured message objects
+    - Extracting and matching tool calls with their corresponding requests
+    - Building proper OpenAI ChatCompletionMessage objects with roles, content, and tool calls
+    - Generating function definitions for tools used in conversations
+
+    Returns:
+        List[OpenAIMessages]: A list of structured message conversations with associated tools
    """

-    def get_tool_calls(self, completion: Span, all_spans: Sequence[Span], /) -> Iterable[Dict[str, Any]]:
-        """Yield tool call payloads for a completion span.
-
-        Args:
-            completion: The completion span whose descendants should be inspected.
-            all_spans: The complete span list belonging to the trace.
-
-        Yields:
-            Dictionaries describing tool calls with identifiers, names, and arguments.
-
-        Raises:
-            ValueError: If a candidate tool span cannot be converted into a dictionary.
-        """
-        # Get all the spans that are children of the completion span
-        children = [span for span in all_spans if span.parent_id == completion.span_id]
-        # Get the tool calls from the children
-        for maybe_tool_call in children:
-            tool_call = group_genai_dict(maybe_tool_call.attributes, "tool")
-            if not isinstance(tool_call, dict):
-                raise ValueError(f"Extracted tool call from trace is not a dict: {tool_call}")
-            if tool_call:
-                yield tool_call
-
-    def adapt(self, source: Sequence[Span], /) -> List[OpenAIMessages]:
-        """Transform trace spans into OpenAI chat payloads.
-
-        Args:
-            source: Spans containing `gen_ai.*` attributes emitted by the tracing pipeline.
-
-        Returns:
-            A list of [`OpenAIMessages`][agentlightning.adapter.messages.OpenAIMessages] entries that
-            capture prompts, completions, tools, and metadata.
-        """
+    def adapt(self, source: List[Span], /) -> List[OpenAIMessages]:
+        raw_tool_calls: List[Dict[str, Any]] = []
        raw_prompt_completions: List[_RawSpanInfo] = []

        for span in source:
            attributes = {k: v for k, v in span.attributes.items()}

+            # Otherwise we strip all the tool calls and prompts and responses
+            tool_call = group_genai_dict(dict(attributes), "tool")
+            if not isinstance(tool_call, dict):
+                raise ValueError(f"Extracted tool call from trace is not a dict: {tool_call}")
+            if tool_call:
+                raw_tool_calls.append(tool_call)
+
            # Get all related information from the trace span
            prompt = group_genai_dict(attributes, "gen_ai.prompt") or []
            completion = group_genai_dict(attributes, "gen_ai.completion") or []
@@ -260,11 +213,8 @@ class TraceToMessages(TraceAdapter[List[OpenAIMessages]]):
            if not isinstance(response, dict):
                raise ValueError(f"Extracted response from trace is not a dict: {response}")
            if prompt or completion or request or response:
-                tools = list(self.get_tool_calls(span, source)) or []
                raw_prompt_completions.append(
-                    _RawSpanInfo(
-                        prompt=prompt or [], completion=completion, request=request, response=response, tools=tools
-                    )
+                    _RawSpanInfo(prompt=prompt or [], completion=completion, request=request, response=response)
                )

-        return list(convert_to_openai_messages(raw_prompt_completions))
+        return list(convert_to_openai_messages(raw_prompt_completions, raw_tool_calls))
@@ -3,31 +3,23 @@
 from __future__ import annotations

 import json
-import logging
 import re
 from enum import Enum
-from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
+from typing import Any, Dict, List, Optional, Tuple, Union, cast

 from opentelemetry.sdk.trace import ReadableSpan
 from pydantic import BaseModel

-from agentlightning.emitter.reward import get_reward_value
-from agentlightning.types import Span, Triplet
+from agentlightning.types import SpanNames, Triplet
+from agentlightning.types.tracer import Span

 from .base import TraceAdapter

-logger = logging.getLogger(__name__)
-

 class Transition(BaseModel):
-    """A single transition within a reinforcement learning trajectory.
-
-    Attributes:
-        state: Token identifiers describing the model input state.
-        action: Token identifiers representing the model output.
-        response_id: Identifier of the LLM response used to deduplicate spans.
-        agent_name: Human-readable agent name captured from the trace.
-        reward: Scalar reward associated with the transition, if available.
+    """
+    Transition class representing one transition in a trajectory.
+    State and action are a list of token IDs.
    """

    state: List[int]
@@ -39,27 +31,22 @@ class Transition(BaseModel):


 class RewardMatchPolicy(str, Enum):
-    """Strategies for matching rewards to LLM call spans.
-
-    !!! note
-        Each reward span must expose a payload shaped like `{"type": "reward", "value": <float>|None}`
-        as described in `reward.py`.
+    """How to find the reward for each transition from the trace.
+    In all cases, the reward must have data `{"type": "reward", "value": <float>|None}`,
+    as defined in `reward.py`.
    """

    FIRST_SIBLING = "first_sibling"
-    """Use the first sibling in the current trace subtree as the reward unless another LLM call match is found."""
+    """Use the first sibling in the current trace subtree as the reward, except another LLM call match is found."""

    FIRST_OCCURRENCE = "first_occurrence"
-    """Use the first reward encountered in chronological order after the current LLM call match."""
+    """Use the first occurrence of the reward (in start time order) that occur after the current LLM call match.
+    """


 class TraceTree:
-    """Tree representation of a trace span and its descendants.
-
-    Attributes:
-        id: Unique identifier for the span node.
-        span: [`Span`][agentlightning.Span] backing this node.
-        children: Child nodes connected to the current span.
+    """
+    A trace item, along with its span and children.
    """

    def __init__(
@@ -93,16 +80,10 @@ class TraceTree:
        self.children.append(child)

    def visualize(self, filename: str, interested_span_match: str | None = None) -> None:
-        """Render the trace tree with Graphviz for debugging purposes.
-
-        Args:
-            filename: Base filename for the generated `.png` diagram.
-            interested_span_match: Optional regular expression used to keep only matching spans
-                (and their ancestors) in the output.
-
-        !!! note
-            The method requires the optional `graphviz` dependency to be available in the runtime
-            environment.
+        """
+        Visualize the trace tree using graphviz.
+        For debugging purposes only.
+        Use `interested_span_match` to filter the spans (and its ancesters) to be visualized.
        """
        import graphviz

@@ -144,11 +125,9 @@ class TraceTree:
        dot.render(filename, format="png", cleanup=True)  # type: ignore

    def names_tuple(self) -> Tuple[str, List[Any]]:
-        """Return the span name alongside nested child names.
-
-        Returns:
-            A tuple of the current span name and a list of tuples for each child containing the
-            child name and its descendants.
+        """Return the span name, and a list of children.
+        Each child is also a tuple of span name and a list of children.
+        Useful for debugging and testing.
        """
        name = self.span.name
        agent_name = self.agent_name()
@@ -161,14 +140,15 @@ class TraceTree:
        return name, children_names

    def traverse(self) -> List["TraceTree"]:
-        """Traverse the tree depth first and return every node."""
+        """
+        Traverse the trace tree and return a list of all spans.
+        """
        spans: List["TraceTree"] = [self]
        for child in self.children:
            spans.extend(child.traverse())
        return spans

    def to_json(self) -> dict[str, Any]:
-        """Convert the tree node into a JSON-serialisable structure."""
        if isinstance(self.span, ReadableSpan):
            span_data = json.loads(self.span.to_json())
        else:
@@ -181,17 +161,10 @@ class TraceTree:

    @classmethod
    def from_spans(cls, spans: List[Span]) -> "TraceTree":
-        """Construct a tree from a flat list of spans.
-
-        Args:
-            spans: Spans that collectively form a single trace segment.
-
-        Returns:
-            A [`TraceTree`][agentlightning.adapter.triplet.TraceTree] rooted at either the
-            discovered root span or a synthetic root when multiple roots are present.
-
-        Raises:
-            ValueError: If the span list is empty or no root span can be inferred.
+        """
+        Create a TraceTree from a list of spans.
+        All spans without parents found will be considered as candidate root spans.
+        If multiple root spans are found, a virtual root span will be created as the parent of all root spans.
        """

        if not spans:
@@ -272,11 +245,8 @@ class TraceTree:
            return root_span

    def agent_name(self) -> Optional[str]:
-        """Return the agent name associated with the span, if any.
-
-        Returns:
-            Agent name extracted from known attributes, otherwise `None`.
-        """
+        """Return the name of agent span. Return the agent or None (not an agent at all).
+        Extend this function to support more agent frameworks."""
        attributes = self.span.attributes
        if attributes is None:  # type: ignore
            return None
@@ -309,23 +279,26 @@ class TraceTree:
            return agent_name

    def maybe_reward_dict(self) -> dict[str, Any]:
-        """Return a reward payload if the span encodes one.
+        for key in [
+            "agentops.task.output",  # newer versions of agentops
+            "agentops.entity.output",
+        ]:
+            output = self.span.attributes.get(key)  # type: ignore
+            if output:
+                if isinstance(output, dict):
+                    return output
+                elif isinstance(output, str):
+                    try:
+                        return json.loads(output)
+                    except json.JSONDecodeError:
+                        return {}

-        Returns:
-            Dictionary containing reward metadata, or an empty dictionary when no reward is found.
-        """
-        reward_value = get_reward_value(self.span)
-        if reward_value is not None:
-            return {"type": "reward", "value": reward_value}
-        else:
-            return {}
+        # Latest emit reward format
+        if self.span.name == SpanNames.REWARD.value and self.span.attributes:
+            return {"type": "reward", "value": self.span.attributes.get("reward", None)}
+        return {}

    def is_reward_span(self) -> bool:
-        """Return whether the span explicitly encodes a reward.
-
-        Returns:
-            `True` when the span payload describes a reward, otherwise `False`.
-        """
        maybe_reward = self.maybe_reward_dict()
        return maybe_reward and maybe_reward.get("type") == "reward"  # type: ignore

@@ -339,19 +312,12 @@ class TraceTree:
        within_llm_call: Optional[bool] = None,
        existing_llm_call_response_ids: Optional[set[str]] = None,
    ) -> List[Tuple["TraceTree", str]]:
-        """Find LLM call spans matching the supplied filters.
+        """Find all LLM calls in the trace tree.

-        Args:
-            llm_call_match: Regular expression used to match span names that qualify as LLM calls.
-            agent_match: Optional regular expression that must match the enclosing agent span name.
-            within_matching_subtree: Marker propagated through recursive calls to record matching agents.
-            within_reward: When `True`, suppresses LLM matches under reward spans.
-            within_llm_call: When `True`, prevents duplicate matches for nested LLM calls.
-            existing_llm_call_response_ids: Known response identifiers used to deduplicate spans.
+        The LLM call is defined as a span with type = request and name matching `llm_call_match`.
+        If `agent_match` is not None, it must also reside in an agent span (type = agent) with name matched.

-        Returns:
-            A list of tuples pairing the matching node with the agent subtree label that triggered the
-            match.
+        Return a list of traces and the agent names (why it's selected).
        """
        llm_calls: List[Tuple[TraceTree, str]] = []

@@ -407,26 +373,19 @@ class TraceTree:
        return llm_calls

    def repair_hierarchy(self) -> None:
-        """Repair missing parent-child relationships introduced by mixed tracing systems.
-
-        Some agent frameworks emit spans via multiple subsystems, which can cause LLM completion
-        spans to float directly under the root span instead of being nested under the correct agent.
-        The method re-parents those spans to the closest ancestor that fully envelopes the child in
-        time.
-
-        If we don't, when we want to select the LLM completion span with agent as filter.
-        We will never get the correct span underneath.
        """
-        # If the current node has only one child, recursively repair its hierarchy directly.
-        # This special-case handling is needed because when a trace is manually ended
-        # (via agentops.end_trace), the AgentOps provider automatically wraps all spans
-        # under an extra synthetic root node (e.g., "run_one.session").
-        if len(self.children) == 1:
-            self.children[0].repair_hierarchy()
-            return
+        We find that sometimes the hierarchy is not correct, due to the way the spans are created.
+        The spans within the agent frameworks (e.g., OpenAI Agent SDK) and spans within the LLM frameworks
+        (e.g., Anthropic) are created in two systems.
+        So the inner LLM completion span does not necessarily have an agent span as a parent.
+        Rather they sometimes directly become children of the root span.
+        This becomes a problem when we want to select the LLM completion span with agent as filter.
+        To repair the hierarchy, for each children of the root span, we find a span over the whole tree,
+        with duration covering the current span and being closest to the current span.

+        This function modifies the tree in place.
+        """
        nodes_to_repair = list(self.children)
-
        for repair_node in nodes_to_repair:
            if len(self.children) == 1:
                # If there is only one child, we don't need to repair the hierarchy.
@@ -451,16 +410,7 @@ class TraceTree:
                closest_parent.children.append(repair_node)

    def match_rewards(self, reward_match: str, llm_calls: List["TraceTree"]) -> dict[str, Optional[float]]:
-        """Assign rewards to previously matched LLM calls.
-
-        Args:
-            reward_match: Strategy identifier from
-                [`RewardMatchPolicy`][agentlightning.adapter.triplet.RewardMatchPolicy].
-            llm_calls: Trace nodes representing LLM call spans.
-
-        Returns:
-            Mapping from span identifier to reward value or `None` when no reward is available.
-        """
+        """Match the rewards to the LLM calls."""
        llm_call_ids = set([llm_call.id for llm_call in llm_calls])
        rewards: dict[str, Optional[float]] = {}

@@ -505,30 +455,6 @@ class TraceTree:

        return rewards

-    def span_to_triplet(self, span: Span, agent_name: str) -> Triplet:
-        """Convert a span to a triplet.
-
-        Subclass can override this method to add more fields to the triplet,
-        such as chat messages and tool calls.
-        """
-        prompt_token_ids = span.attributes.get("prompt_token_ids", [])  # type: ignore
-        response_token_ids = span.attributes.get("response_token_ids", [])  # type: ignore
-        response_id = span.attributes.get("gen_ai.response.id", None)  # type: ignore
-
-        logprobs_content = span.attributes.get("logprobs.content", None)  # type: ignore
-        if isinstance(logprobs_content, str):
-            logprobs_content = json.loads(logprobs_content)
-            response: Dict[str, Any] = {"token_ids": response_token_ids, "logprobs": logprobs_content}
-        else:
-            response = {"token_ids": response_token_ids}
-
-        return Triplet(
-            prompt={"token_ids": prompt_token_ids},
-            response=response,
-            reward=None,
-            metadata=dict(response_id=response_id, agent_name=agent_name),
-        )
-
    def to_trajectory(
        self,
        llm_call_match: str = r"openai\.chat\.completion",
@@ -537,21 +463,20 @@ class TraceTree:
        dedup_llm_call: bool = True,
        reward_match: RewardMatchPolicy = RewardMatchPolicy.FIRST_OCCURRENCE,
        final_reward: Optional[float] = None,
-        _skip_empty_token_spans: bool = False,
    ) -> List[Triplet]:
-        """Convert the trace tree into a trajectory of [`Triplet`][agentlightning.Triplet] items.
+        """Convert the trace tree to a trajectory.

-        Args:
-            llm_call_match: Regular expression for LLM call span names.
-            agent_match: Optional regular expression for agent span names.
-            exclude_llm_call_in_reward: When `True`, prevents searching for rewards under the LLM
-                call subtree.
-            dedup_llm_call: When `True`, deduplicates spans using the LLM response identifier.
-            reward_match: Reward matching policy used to associate reward spans with LLM calls.
-            final_reward: Optional reward appended to the final transition when provided.
+        First, we find all the LLM calls (span type = request, `llm_call_match` matching the span name).
+        If the agent match is set, we check, for each LLM call,
+        if it resides in an agent (span type = agent, `agent_match` matching the span name).
+        The above sets the basis for the trajectory, as we use the prompt token IDs and response token IDs for each LLM call,
+        as the state and action of each transition.

-        Returns:
-            A list of [`Triplet`][agentlightning.Triplet] objects ordered by call sequence.
+        Then, we find the reward for each transition.
+        The reward is searched on the trace tree, after the LLM call,
+        until the next LLM call or the end of the tree depending on the policy.
+        It can be enforced to a sibling or the first occurrence in the time order, depending on the policy.
+        If a reward is never found for a transition, it is set to None.
        """
        # Find all LLM calls
        llm_calls = self.find_llm_calls(
@@ -562,23 +487,25 @@ class TraceTree:
            within_llm_call=False if dedup_llm_call else None,
            existing_llm_call_response_ids=set(),
        )
+        id_transitions = [
+            (
+                llm_call.id,
+                Triplet(
+                    prompt={"token_ids": llm_call.span.attributes.get("prompt_token_ids", [])},  # type: ignore
+                    response={"token_ids": llm_call.span.attributes.get("response_token_ids", [])},  # type: ignore
+                    reward=None,
+                    metadata=dict(
+                        response_id=llm_call.span.attributes.get(  # type: ignore
+                            "gen_ai.response.id", None
+                        ),  # it works at least for OpenAI
+                        agent_name=agent_name,
+                    ),
+                ),
+            )
+            for llm_call, agent_name in llm_calls
+        ]

-        id_transitions: List[Tuple[str, Triplet]] = []
-        # We need to filter out the LLM calls with unrecorded token IDs
-        filtered_llm_calls: List[Tuple[TraceTree, str]] = []
-        for llm_call, agent_name in llm_calls:
-            triplet = self.span_to_triplet(llm_call.span, agent_name)
-            # This is a hot-fix for Tinker+CrewAI, which has some anonymous requests outside the trained agent.
-            # TODO: We might need to reconsider this.
-            if _skip_empty_token_spans and (
-                not triplet.prompt.get("token_ids") or not triplet.response.get("token_ids")
-            ):
-                logger.warning(f"Skipping LLM call with unrecorded token IDs: {triplet}")
-                continue
-            filtered_llm_calls.append((llm_call, agent_name))
-            id_transitions.append((llm_call.id, triplet))
-
-        rewards = self.match_rewards(reward_match, [call for call, _ in filtered_llm_calls])
+        rewards = self.match_rewards(reward_match, [call for call, _ in llm_calls])
        transitions = [
            transition.model_copy(update={"reward": rewards.get(id, None)}) for id, transition in id_transitions
        ]
@@ -594,23 +521,23 @@ class TraceTree:
        )


-class TraceToTripletBase(TraceAdapter[List[Triplet]]):
-    """Base class for adapters that emit [`Triplet`][agentlightning.Triplet] trajectories."""
+class BaseTraceTripletAdapter(TraceAdapter[List[Triplet]]):
+    """
+    Base class for trace triplet adapters.
+    """


-class TracerTraceToTriplet(TraceToTripletBase):
-    """Convert tracer-emitted spans into triplet trajectories.
+class TraceTripletAdapter(BaseTraceTripletAdapter):
+    """
+    An adapter to convert OpenTelemetry spans to triplet data.

    Attributes:
-        repair_hierarchy: When `True`, repair the span tree using
-            [`TraceTree.repair_hierarchy()`][agentlightning.adapter.triplet.TraceTree.repair_hierarchy]
-            before matching calls and rewards.
-        llm_call_match: Regular expression pattern that selects LLM call span names.
-        agent_match: Optional regular expression pattern for agent span names. When omitted, spans
-            from any agent are considered.
-        exclude_llm_call_in_reward: When `True`, ignore matches under reward spans while searching
-            for rewards.
-        reward_match: Strategy used to associate rewards with LLM calls.
+        repair_hierarchy: When `repair_hierarchy` is set to True, the trace will be repaired with the time information.
+            See `TraceTree.repair_hierarchy` for more details.
+        llm_call_match: Regular expression pattern to match LLM call span names.
+        agent_match: Optional regular expression pattern to match agent span names. If None, all agents are matched.
+        exclude_llm_call_in_reward: Whether to exclude LLM calls that occur within reward spans.
+        reward_match: Policy for matching rewards to LLM calls.
    """

    def __init__(
@@ -620,14 +547,12 @@ class TracerTraceToTriplet(TraceToTripletBase):
        agent_match: Optional[str] = None,
        exclude_llm_call_in_reward: bool = True,
        reward_match: RewardMatchPolicy = RewardMatchPolicy.FIRST_OCCURRENCE,
-        _skip_empty_token_spans: bool = False,
    ):
        self.repair_hierarchy = repair_hierarchy
        self.llm_call_match = llm_call_match
        self.agent_match = agent_match
        self.exclude_llm_call_in_reward = exclude_llm_call_in_reward
        self.reward_match = reward_match
-        self._skip_empty_token_spans = _skip_empty_token_spans

    def visualize(
        self,
@@ -636,17 +561,16 @@ class TracerTraceToTriplet(TraceToTripletBase):
        filename: str = "trace_tree",
        interested_span_match: str | None = None,
    ) -> TraceTree:
-        """Visualize the trace tree built from the supplied spans.
+        """
+        Visualize the trace tree.

        Args:
-            source: Collection of Agent Lightning [`Span`][agentlightning.Span] objects
-                or raw `opentelemetry.sdk.trace.ReadableSpan` instances.
-            filename: Base filename for the generated image; `.png` is appended automatically.
-            interested_span_match: Optional regular expression used to highlight a subset of spans.
+            source (List[Span]): The list of OpenTelemetry spans to visualize.
+            filename (str): The base filename for the output visualization (default: "trace_tree").
+            interested_span_match (str | None): Optional regular expression pattern to highlight or focus on specific spans in the visualization.

        Returns:
-            The [`TraceTree`][agentlightning.adapter.triplet.TraceTree] built from the provided
-            spans.
+            TraceTree: The constructed trace tree object.
        """
        source_normalized = [
            Span.from_opentelemetry(span, "dummy", "dummy", 0) if isinstance(span, ReadableSpan) else span
@@ -658,15 +582,8 @@ class TracerTraceToTriplet(TraceToTripletBase):
        trace_tree.visualize(filename, interested_span_match=interested_span_match)
        return trace_tree

-    def adapt(self, source: Union[Sequence[Span], Sequence[ReadableSpan]], /) -> List[Triplet]:  # type: ignore
-        """Convert tracer spans into [`Triplet`][agentlightning.Triplet] trajectories.
-
-        Args:
-            source: Agent Lightning spans or raw OpenTelemetry spans that form a trace.
-
-        Returns:
-            Ordered list of trajectory transitions with prompt, response, and reward information.
-        """
+    def adapt(self, source: Union[List[Span], List[ReadableSpan]], /) -> List[Triplet]:  # type: ignore
+        """Convert OpenTelemetry spans to a list of Triplet objects."""
        source_normalized = [
            Span.from_opentelemetry(span, "dummy", "dummy", 0) if isinstance(span, ReadableSpan) else span
            for span in source
@@ -679,29 +596,30 @@ class TracerTraceToTriplet(TraceToTripletBase):
            agent_match=self.agent_match,
            exclude_llm_call_in_reward=self.exclude_llm_call_in_reward,
            reward_match=self.reward_match,
-            _skip_empty_token_spans=self._skip_empty_token_spans,
        )
        return trajectory


-class LlmProxyTraceToTriplet(TraceToTripletBase):
-    """Convert telemetry emitted by the LLM Proxy into triplet trajectories.
+class LlmProxyTripletAdapter(BaseTraceTripletAdapter):
+    """
+    Converting telemetry data emitted by the LLM Proxy to triplet data.
+    This adapter is very experimental. Should only be used when the TraceTripletAdapter does not work at all.

-    !!! warning
-        This adapter is experimental and might be merged with
-        [`TracerTraceToTriplet`][agentlightning.TracerTraceToTriplet] in the future.
-
-    !!! danger
-        Do not rely on timestamps when using this adapter. Proxy spans can originate on different
-        machines with unsynchronised clocks, so `sequence_id` is treated as the sole source of
-        ordering.
+    IMPORTANT: Do NOT rely on timestamps here. Proxy spans can be emitted from different
+    machines with unsynchronized clocks. We therefore treat `sequence_id` as the only
+    reliable ordering primitive and perform "first occurrence" reward matching using
+    sequence order only.

    Strategy:

-    1. Sort spans by `(sequence_id, start_time)` for deterministic processing.
-    2. Extract token identifiers from `litellm_request` or `raw_gen_ai_request` spans.
-    3. Extract rewards from spans exposing AgentOps-style payloads or explicit reward spans.
-    4. Match each reward to the most recent unmatched LLM call whose sequence is smaller.
+    1) Sort spans by (sequence_id, start_time).
+    2) Extract LLM calls that expose prompt/response token IDs from either:
+       - litellm_request         (sometimes only metadata, ignore if no token ids)
+       - raw_gen_ai_request      (llm.hosted_vllm.* stringified fields)
+    3) Extract rewards from spans whose attributes contain an AgentOps-style
+       reward payload or explicit REWARD span.
+    4) For each reward with sequence R, assign it to the most recent *unmatched* LLM call
+       with sequence < R. Ignore timestamps completely.
    """

    def _literal_eval_maybe(self, v: Any) -> Any:
@@ -763,23 +681,34 @@ class LlmProxyTraceToTriplet(TraceToTripletBase):
        return cast(List[int], prompt_ids), cast(List[int], resp_ids)

    def _maybe_reward_value(self, span: Span) -> Optional[float]:
-        """Parse reward from typical AgentOps payloads or explicit reward spans."""
-        return get_reward_value(span)
+        """
+        Parse reward from typical AgentOps payload or explicit REWARD span.
+        """
+        attrs = span.attributes or {}
+
+        # AgentOps new/old keys
+        for k in ("agentops.task.output", "agentops.entity.output"):
+            v = attrs.get(k)
+            v = self._literal_eval_maybe(v)
+            if isinstance(v, dict) and cast(Dict[str, Any], v).get("type") == "reward":
+                rv = cast(Dict[str, Any], v).get("value", None)
+                if rv is None or isinstance(rv, (int, float)):
+                    return None if rv is None else float(rv)
+
+        # Explicit reward span
+        if span.name == SpanNames.REWARD.value:
+            rv = attrs.get("reward", None)
+            if rv is None or isinstance(rv, (int, float)):
+                return None if rv is None else float(rv)
+
+        return None

    def _request_id_from_attrs(self, attrs: Dict[str, Any]) -> Optional[str]:
        # Prefer OpenAI-like id if present, else proxy raw id.
        rid = attrs.get("gen_ai.response.id") or attrs.get("llm.hosted_vllm.id")
        return str(rid) if isinstance(rid, str) and rid else None

-    def adapt(self, source: Sequence[Span], /) -> List[Triplet]:  # type: ignore
-        """Convert LLM Proxy spans into [`Triplet`][agentlightning.Triplet] trajectories.
-
-        Args:
-            source: Spans emitted by the LLM Proxy containing prompt, response, and reward data.
-
-        Returns:
-            Ordered trajectory transitions matched purely by `sequence_id`.
-        """
+    def adapt(self, source: List[Span], /) -> List[Triplet]:  # type: ignore
        # 1) Sort deterministically by (sequence_id, start_time).
        spans = sorted(
            source,
@@ -1,29 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.

-from __future__ import annotations
+from .base import BaseAlgorithm

-from typing import TYPE_CHECKING, Any
-
-from .base import Algorithm
-from .decorator import algo
-from .fast import Baseline, FastAlgorithm
-
-if TYPE_CHECKING:
-    from .apo import APO as APOType
-    from .verl import VERL as VERLType
-
-__all__ = ["Algorithm", "algo", "FastAlgorithm", "Baseline", "APO", "VERL"]
-
-# Shortcuts for usages like algo.APO(...)
-
-
-def APO(*args: Any, **kwargs: Any) -> APOType[Any]:
-    from .apo import APO as APOImplementation
-
-    return APOImplementation(*args, **kwargs)
-
-
-def VERL(*args: Any, **kwargs: Any) -> VERLType:
-    from .verl import VERL as VERLImplementation
-
-    return VERLImplementation(*args, **kwargs)
+__all__ = ["BaseAlgorithm"]
@@ -1,5 +1 @@
 # Copyright (c) Microsoft. All rights reserved.
-
-from .apo import APO
-
-__all__ = ["APO"]
@@ -7,26 +7,23 @@ APO with textual gradients that read rollout spans and outputs to modify the pro
 - rollout: same pattern as your example, but task is a dict (T_task)
 """

-import asyncio
 import logging
 import random
 import time
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Counter, Dict, Generic, Iterator, List, Optional, Sequence, Set, Tuple, TypedDict, TypeVar, cast
+from typing import Any, Dict, Generic, Iterator, List, Optional, Sequence, Tuple, TypedDict, TypeVar, cast

 import poml
 from openai import AsyncOpenAI

-from agentlightning.adapter.messages import TraceToMessages
-from agentlightning.algorithm.base import Algorithm
-from agentlightning.algorithm.utils import batch_iter_over_dataset
+from agentlightning.adapter.messages import TraceMessagesAdapter
+from agentlightning.algorithm.base import BaseAlgorithm
 from agentlightning.reward import find_final_reward
-from agentlightning.types import Dataset, NamedResources, PromptTemplate, Rollout, RolloutMode, RolloutStatus
+from agentlightning.types import Dataset, NamedResources, PromptTemplate, RolloutMode, RolloutStatus, RolloutV2

 logger = logging.getLogger(__name__)

-T_task = TypeVar("T_task")
+T_task = TypeVar("T_task", bound=Dict[str, Any])


 class RolloutResultForAPO(TypedDict):
@@ -35,20 +32,12 @@ class RolloutResultForAPO(TypedDict):
    status: RolloutStatus
    final_reward: Optional[float]
    spans: List[Dict[str, Any]]
-    messages: List[Any]
-
-
-@dataclass
-class VersionedPromptTemplate:
-    version: str
-    prompt_template: PromptTemplate
-    score: Optional[float] = None
+    messages: List[Dict[str, Any]]


 GRADIENT_PROMPT_FILES = [
    Path(__file__).parent / "prompts" / "text_gradient_variant01.poml",
    Path(__file__).parent / "prompts" / "text_gradient_variant02.poml",
-    Path(__file__).parent / "prompts" / "text_gradient_variant03.poml",
 ]

 APPLY_EDIT_PROMPT_FILES = [
@@ -57,7 +46,42 @@ APPLY_EDIT_PROMPT_FILES = [
 ]


-class APO(Algorithm, Generic[T_task]):
+def batch_iter_over_dataset(dataset: Dataset[T_task], batch_size: int) -> Iterator[Sequence[T_task]]:
+    """
+    Create an infinite iterator that yields batches from the dataset.
+
+    When batch_size >= dataset size, yields the entire shuffled dataset repeatedly.
+    When batch_size < dataset size, yields batches of the specified size, reshuffling
+    after each complete pass through the dataset.
+
+    Args:
+        dataset: The dataset to iterate over.
+        batch_size: The desired batch size.
+
+    Yields:
+        Sequences of tasks from the dataset. Each task appears at most once per epoch.
+    """
+    if batch_size >= len(dataset):
+        while True:
+            dataset_copy = [dataset[i] for i in range(len(dataset))]
+            random.shuffle(dataset_copy)
+            yield dataset_copy
+
+    else:
+        current_batch: List[int] = []
+        while True:
+            indices = list(range(len(dataset)))
+            random.shuffle(indices)
+            for index in indices:
+                if index in current_batch:
+                    continue
+                current_batch.append(index)
+                if len(current_batch) == batch_size:
+                    yield [dataset[index] for index in current_batch]
+                    current_batch = []
+
+
+class APO(BaseAlgorithm, Generic[T_task]):
    """Automatic Prompt Optimization (APO) algorithm using textual gradients and beam search.

    APO is an iterative prompt optimization algorithm that uses LLM-generated textual gradients
@@ -65,16 +89,14 @@ class APO(Algorithm, Generic[T_task]):
    computes critiques based on the results, and applies edits to generate improved prompts.

    The algorithm operates in rounds, where each round:
-
    1. Samples parent prompts from the current beam
    2. Generates new prompts by computing textual gradients and applying edits
    3. Evaluates all candidates on a validation set
    4. Selects the top-k prompts for the next round

    Based on the ideas from:
-
-    - [ProTeGi](https://aclanthology.org/2023.emnlp-main.494.pdf)
-    - [TextGrad](https://github.com/zou-group/textgrad)
+    - ProTeGi: https://aclanthology.org/2023.emnlp-main.494.pdf
+    - TextGrad: https://github.com/zou-group/textgrad
    """

    def __init__(
@@ -89,10 +111,7 @@ class APO(Algorithm, Generic[T_task]):
        beam_width: int = 4,
        branch_factor: int = 4,
        beam_rounds: int = 3,
-        rollout_batch_timeout: float = 3600.0,
-        run_initial_validation: bool = True,
-        # Internal flags for debugging
-        _poml_trace: bool = False,
+        rollout_batch_timeout: float = 600.0,
    ):
        """
        Initialize the APO algorithm with configuration parameters.
@@ -109,8 +128,6 @@ class APO(Algorithm, Generic[T_task]):
                by applying textual gradient edits. This controls the expansion of the search tree.
            beam_rounds: Number of beam search rounds to perform.
            rollout_batch_timeout: Maximum time in seconds to wait for rollout batch completion.
-            run_initial_validation: If True, runs validation on the seed prompt before starting
-                optimization to establish a baseline score. Defaults to True.
        """
        self.async_openai_client = async_openai_client
        self.gradient_model = gradient_model
@@ -122,62 +139,9 @@ class APO(Algorithm, Generic[T_task]):
        self.branch_factor = branch_factor
        self.beam_rounds = beam_rounds
        self.rollout_batch_timeout = rollout_batch_timeout
-        self.run_initial_validation = run_initial_validation

        self._history_best_prompt: Optional[PromptTemplate] = None
        self._history_best_score: float = float("-inf")
-        self._history_best_version: Optional[str] = None
-
-        self._version_counter: int = 0
-
-        self._poml_trace = _poml_trace
-
-    def _create_versioned_prompt(
-        self,
-        prompt_template: PromptTemplate,
-        *,
-        score: Optional[float] = None,
-    ) -> VersionedPromptTemplate:
-        """
-        Wrap a prompt template with a new monotonically increasing version identifier.
-        """
-        version = f"v{self._version_counter}"
-        self._version_counter += 1
-        return VersionedPromptTemplate(version=version, prompt_template=prompt_template, score=score)
-
-    def _format_log_prefix(
-        self,
-        *,
-        round_num: Optional[int] = None,
-        beam_idx: Optional[int] = None,
-        branch_idx: Optional[int] = None,
-        prompt_version: Optional[str] = None,
-    ) -> str:
-        """
-        Construct the standardized log prefix.
-        """
-        parts: List[str] = []
-        if round_num is not None:
-            parts.append(f"Round {round_num:02d}")
-        if beam_idx is not None:
-            parts.append(f"Beam {beam_idx:02d}")
-        if branch_idx is not None:
-            parts.append(f"Branch {branch_idx:02d}")
-        if prompt_version is not None:
-            parts.append(f"Prompt {prompt_version}")
-        if not parts:
-            return ""
-        return f"[{' | '.join(parts)}]"
-
-    def _log(self, level: int, message: str, *, prefix: Optional[str] = None) -> None:
-        """
-        Log a message with an optional standardized prefix.
-        """
-        effective_prefix = prefix
-        if effective_prefix:
-            logger.log(level, f"{effective_prefix} {message}")
-        else:
-            logger.log(level, message)

    def get_seed_prompt_template(self) -> Tuple[str, PromptTemplate]:
        """
@@ -200,19 +164,19 @@ class APO(Algorithm, Generic[T_task]):
                return name, resource
        raise ValueError("No prompt template resource found in initial_resources")

-    def get_adapter(self) -> TraceToMessages:
+    def get_adapter(self) -> TraceMessagesAdapter:
        """
        Get the adapter for converting spans to messages.

        Returns:
-            The TraceToMessages instance for this algorithm.
+            The TraceMessagesAdapter instance for this algorithm.

        Raises:
-            ValueError: If the adapter is not a TraceToMessages.
+            ValueError: If the adapter is not a TraceMessagesAdapter.
        """
        adapter = super().get_adapter()
-        if not isinstance(adapter, TraceToMessages):
-            raise ValueError("Adapter must be a TraceToMessages for APO algorithm")
+        if not isinstance(adapter, TraceMessagesAdapter):
+            raise ValueError("Adapter must be a TraceMessagesAdapter for APO algorithm")
        return adapter

    def get_best_prompt(self) -> PromptTemplate:
@@ -231,10 +195,8 @@ class APO(Algorithm, Generic[T_task]):

    async def compute_textual_gradient(
        self,
-        current_prompt: VersionedPromptTemplate,
+        current_prompt: str,
        rollout_results: List[RolloutResultForAPO],
-        *,
-        prefix: Optional[str] = None,
    ) -> Optional[str]:
        """
        Compute a textual gradient (critique) for the current prompt based on rollout results.
@@ -252,60 +214,45 @@ class APO(Algorithm, Generic[T_task]):
        tg_template = random.choice(GRADIENT_PROMPT_FILES)

        if len(rollout_results) < self.gradient_batch_size:
-            self._log(
-                logging.WARNING,
-                f"Only {len(rollout_results)} rollouts available, but {self.gradient_batch_size} are needed. Using all rollouts.",
-                prefix=prefix,
+            logger.warning(
+                f"Only {len(rollout_results)} rollouts available, but {self.gradient_batch_size} are needed. Using all rollouts."
            )
            sampled_rollout_results = rollout_results
        else:
            sampled_rollout_results = random.sample(rollout_results, self.gradient_batch_size)

-        self._log(
-            logging.INFO,
-            f"Gradient will be computed with {self.gradient_model} for {len(sampled_rollout_results)} rollouts with template: {tg_template.name}",
-            prefix=prefix,
+        logger.info(
+            f"Gradient will be computed with {self.gradient_model} for {len(sampled_rollout_results)} rollouts with template: {tg_template}"
        )

        tg_msg = poml.poml(  # type: ignore
            tg_template,
            context={
                "experiments": sampled_rollout_results,
-                "prompt_template": current_prompt.prompt_template.template,
+                "prompt_template": current_prompt,
            },
            format="openai_chat",
        )
-        self._log(
-            logging.DEBUG,
-            f"Gradient computed with {self.gradient_model} prompt: {tg_msg}",
-            prefix=prefix,
-        )
+        logger.debug(f"Gradient computed with {self.gradient_model} prompt: {tg_msg}")
        critique_response = await self.async_openai_client.chat.completions.create(
            model=self.gradient_model,
            messages=tg_msg["messages"],  # type: ignore
            temperature=self.diversity_temperature,
        )
        critique_text = critique_response.choices[0].message.content
-        self._log(
-            logging.INFO,
-            f"Gradient computed with {self.gradient_model} has result: {critique_text}",
-            prefix=prefix,
-        )
+        logger.debug(f"Gradient computed with {self.gradient_model} has result: {critique_text}")

        return critique_text

    async def textual_gradient_and_apply_edit(
        self,
-        current_prompt: VersionedPromptTemplate,
+        current_prompt: str,
        rollout: List[RolloutResultForAPO],
-        *,
-        prefix: Optional[str] = None,
    ) -> Optional[str]:
        """
        Generate an improved prompt by computing a textual gradient and applying an edit.

        This is the main optimization step that:
-
        1. Computes a critique (textual gradient) based on rollout performance
        2. Uses another LLM to apply the critique and generate an improved prompt

@@ -317,30 +264,18 @@ class APO(Algorithm, Generic[T_task]):
            The improved prompt text, or the original prompt if gradient computation fails.
        """
        # 1) Critique
-        critique_text = await self.compute_textual_gradient(
-            current_prompt,
-            rollout,
-            prefix=prefix,
-        )
+        critique_text = await self.compute_textual_gradient(current_prompt, rollout)
        if not critique_text:
-            self._log(
-                logging.ERROR,
-                "Failed to compute critique for prompt.",
-                prefix=prefix,
-            )
-            return current_prompt.prompt_template.template
+            logger.error(f"Failed to compute critique for prompt.")
+            return current_prompt

        # 2) Apply edit
        ae_template = random.choice(APPLY_EDIT_PROMPT_FILES)
-        self._log(
-            logging.INFO,
-            f"Edit will be generated by {self.apply_edit_model} with template: {ae_template.name}",
-            prefix=prefix,
-        )
+        logger.info(f"Edit will be generated by {self.apply_edit_model} with template: {ae_template}")
        ae_msg = poml.poml(  # type: ignore
            ae_template,
            context={
-                "prompt_template": current_prompt.prompt_template.template,
+                "prompt_template": current_prompt,
                "critique": critique_text,
            },
            format="openai_chat",
@@ -353,19 +288,10 @@ class APO(Algorithm, Generic[T_task]):
        )
        new_prompt = ae_response.choices[0].message.content
        if new_prompt:
-            self._log(
-                logging.INFO,
-                f"Edit generated by {self.apply_edit_model}: {new_prompt[:50]}...",
-                prefix=prefix,
-            )
+            logger.info(f"Edit generated by {self.apply_edit_model}: {new_prompt}")
        return new_prompt

-    async def get_rollout_results(
-        self,
-        rollout: List[Rollout],
-        *,
-        prefix: Optional[str] = None,
-    ) -> List[RolloutResultForAPO]:
+    async def get_rollout_results(self, rollout: List[RolloutV2]) -> List[RolloutResultForAPO]:
        """
        Convert completed rollouts to APO-compatible result format.

@@ -388,31 +314,27 @@ class APO(Algorithm, Generic[T_task]):
                status=r.status,
                final_reward=find_final_reward(spans),
                spans=[span.model_dump() for span in spans],
-                messages=messages,
+                messages=[m.model_dump() for m in messages],
            )
-            self._log(
-                logging.DEBUG,
-                f"Rollout result for {r.rollout_id}: status {rollout_result['status']} with final reward {rollout_result['final_reward']}. "
-                f"{len(rollout_result['spans'])} spans and {len(rollout_result['messages'])} messages.",
-                prefix=prefix,
+            logger.info(
+                f"Rollout result for {r.rollout_id}: status {rollout_result['status']} "
+                f"with final reward {rollout_result['final_reward']}. "
+                f"{len(rollout_result['spans'])} spans and {len(rollout_result['messages'])} messages."
            )
            rollout_results.append(rollout_result)
        return rollout_results

    async def evaluate_prompt_on_batch(
        self,
-        prompt: VersionedPromptTemplate,
+        prompt: str,
        resource_name: str,
        dataset: Sequence[T_task],
        mode: RolloutMode,
-        *,
-        prefix: Optional[str] = None,
    ) -> Tuple[List[RolloutResultForAPO], float]:
        """
        Evaluate a prompt on a batch of tasks by running rollouts and computing average reward.

        This method:
-
        1. Adds the prompt as a named resource to the store
        2. Enqueues rollouts for each task in the dataset
        3. Waits for rollouts to complete (with timeout)
@@ -429,56 +351,31 @@ class APO(Algorithm, Generic[T_task]):
            detailed information for each rollout and average_reward is the mean final reward.
        """
        store = self.get_store()
-        preview = prompt.prompt_template.template[:50]
-        self._log(
-            logging.INFO,
-            f'Evaluating prompt "{preview}..." on {len(dataset)} tasks in {mode} mode',
-            prefix=prefix,
-        )
+        logger.info(f'Evaluating prompt "{prompt[:50]}..." on {len(dataset)} tasks in {mode} mode')

        # Install prompt as named resource
-        resources: NamedResources = {resource_name: prompt.prompt_template}
-        resource_update = await store.update_resources(prompt.version, resources)
+        resources: NamedResources = {resource_name: PromptTemplate(template=prompt, engine="f-string")}
+        await store.add_resources(resources)

        rollout_ids: List[str] = []
        for t in dataset:
-            r = await store.enqueue_rollout(input=t, mode=mode, resources_id=resource_update.resources_id)
+            r = await store.enqueue_rollout(input=t, mode=mode)  # task can be any dict processable by the client
            rollout_ids.append(r.rollout_id)

        deadline = time.time() + self.rollout_batch_timeout
-        finished: List[Rollout] = []
+        finished: List[RolloutV2] = []
        while time.time() < deadline:
            finished = await store.wait_for_rollouts(rollout_ids=rollout_ids, timeout=0.0)
            if len(finished) >= len(rollout_ids):
-                self._log(
-                    logging.INFO,
-                    f"All {len(rollout_ids)} rollouts finished within timeout.",
-                    prefix=prefix,
-                )
+                logger.info(f"All {len(rollout_ids)} rollouts finished within timeout.")
                break
-            else:
-                self._log(
-                    logging.DEBUG,
-                    f"Only {len(finished)} rollouts finished within timeout. Waiting for remaining {len(rollout_ids) - len(finished)} rollouts.",
-                    prefix=prefix,
-                )
-                # Sleep to avoid busy-waiting
-                await asyncio.sleep(2.0)

-        rollout_results = await self.get_rollout_results(
-            finished,
-            prefix=prefix,
-        )
+        rollout_results = await self.get_rollout_results(finished)
        final_rewards = [rr["final_reward"] for rr in rollout_results]

        avg = float(sum([r or 0.0 for r in final_rewards]) / max(1, len(final_rewards)))
-        status_counter = Counter([rr["status"] for rr in rollout_results])

-        self._log(
-            logging.INFO,
-            f"Evaluated {len(rollout_results)} rollouts. Statuses: {status_counter}. Rewards: {final_rewards}, average is {avg}",
-            prefix=prefix,
-        )
+        logger.info(f"Evaluated {len(rollout_results)} rollouts. Rewards: {final_rewards}. Average reward: {avg}")
        return rollout_results, avg

    def _initialize_beam(
@@ -515,11 +412,7 @@ class APO(Algorithm, Generic[T_task]):

        return resource_name, seed_prompt, grad_dataset_iterator, val_dataset_iterator

-    def _sample_parent_prompts(
-        self,
-        beam: List[VersionedPromptTemplate],
-        round_num: int,
-    ) -> List[Tuple[int, VersionedPromptTemplate]]:
+    def _sample_parent_prompts(self, beam: List[PromptTemplate], round_num: int) -> List[PromptTemplate]:
        """
        Sample parent prompts from the current beam for generating new candidates.

@@ -533,31 +426,26 @@ class APO(Algorithm, Generic[T_task]):
        Returns:
            List of parent prompts to generate children from.
        """
-        display_round = round_num + 1
        if len(beam) < self.beam_width:
-            prefix = self._format_log_prefix(round_num=display_round)
-            self._log(
-                logging.WARNING,
-                f"Beam width is currently {self.beam_width}, but only {len(beam)} prompts in beam. Replicating all prompts.",
-                prefix=prefix,
+            logger.warning(
+                f"[Round {round_num + 1}] Beam width is currently {self.beam_width}, but only {len(beam)} prompts in beam. "
+                "Replicating all prompts."
            )
-            return [(i % len(beam), beam[i % len(beam)]) for i in range(self.beam_width)]
-
-        selected_indices = random.sample(range(len(beam)), self.beam_width)
-        return [(idx, beam[idx]) for idx in selected_indices]
+            return [beam[i % len(beam)] for i in range(self.beam_width)]
+        else:
+            return random.sample(beam, self.beam_width)

    async def _generate_candidate_prompts(
        self,
-        parent_prompts: List[Tuple[int, VersionedPromptTemplate]],
+        parent_prompts: List[PromptTemplate],
        resource_name: str,
        grad_dataset_iterator: Iterator[Sequence[T_task]],
        round_num: int,
-    ) -> List[VersionedPromptTemplate]:
+    ) -> List[PromptTemplate]:
        """
        Generate new candidate prompts from parents using textual gradients.

        For each parent prompt, generates branch_factor new candidates by:
-
        1. Evaluating the parent on a training batch
        2. Computing textual gradient
        3. Applying edit to generate improved prompt
@@ -571,100 +459,35 @@ class APO(Algorithm, Generic[T_task]):
        Returns:
            List of newly generated prompt templates.
        """
-        display_round = round_num + 1
-        round_prefix = self._format_log_prefix(round_num=display_round)
-        self._log(
-            logging.INFO,
-            f"Applying {self.branch_factor} edits to each of the {len(parent_prompts)} parents based on "
-            "gradients computed on training dataset",
-            prefix=round_prefix,
+        logger.info(
+            f"[Round {round_num + 1}] Applying {self.branch_factor} edits to each of "
+            f"the {len(parent_prompts)} parents on training dataset"
        )

-        parent_prompts_str = [
-            f"{p.version}:{p.score:.3f}" if p.score is not None else p.version for _, p in parent_prompts
-        ]
-        self._log(
-            logging.INFO,
-            f"Parent prompts: {', '.join(parent_prompts_str)}",
-            prefix=round_prefix,
-        )
-
-        candidates: List[VersionedPromptTemplate] = []
-        used_beam_indices: Set[int] = set()
-        for real_beam_idx, (beam_idx, prompt) in enumerate(parent_prompts):
-            if beam_idx in used_beam_indices:
-                beam_prefix = self._format_log_prefix(
-                    round_num=display_round,
-                    beam_idx=beam_idx + 1,
-                    prompt_version=prompt.version,
-                )
-                self._log(
-                    logging.WARNING,
-                    "Duplicated beam index found. Might be caused by beam_width too high. "
-                    + f"The real index of this beam is {real_beam_idx + 1}.",
-                    prefix=beam_prefix,
-                )
-            else:
-                used_beam_indices.add(beam_idx)
-            for branch_idx in range(self.branch_factor):
-                parent_prefix = self._format_log_prefix(
-                    round_num=display_round,
-                    beam_idx=beam_idx + 1,
-                    branch_idx=branch_idx + 1,
-                    prompt_version=prompt.version,
-                )
-                baseline_score = f"{prompt.score:.3f}" if prompt.score is not None else "N/A"
-                self._log(
-                    logging.INFO,
-                    f"Use parent prompt {prompt.version} as a baseline to generate a new prompt. Baseline score: {baseline_score}",
-                    prefix=parent_prefix,
-                )
+        candidates: List[PromptTemplate] = []
+        for prompt in parent_prompts:
+            for _ in range(self.branch_factor):
                grad_samples = next(grad_dataset_iterator)
                rollout_results, _ = await self.evaluate_prompt_on_batch(
-                    prompt,
-                    resource_name,
-                    grad_samples,
-                    mode="train",
-                    prefix=parent_prefix,
-                )
-                new_prompt = await self.textual_gradient_and_apply_edit(
-                    prompt,
-                    rollout_results,
-                    prefix=parent_prefix,
+                    prompt.template, resource_name, grad_samples, mode="train"
                )
+                new_prompt = await self.textual_gradient_and_apply_edit(prompt.template, rollout_results)
                if not new_prompt:
-                    self._log(
-                        logging.ERROR,
-                        f"Failed to compute edit for prompt: {prompt.prompt_template.template}",
-                        prefix=parent_prefix,
-                    )
+                    logger.error(f"[Round {round_num + 1}] Failed to compute edit for prompt: {prompt.template}")
                    continue
                new_prompt_template = PromptTemplate(template=new_prompt, engine="f-string")
-                versioned_candidate = self._create_versioned_prompt(new_prompt_template)
-                self._log(
-                    logging.INFO,
-                    f"New prompt template created from parent {prompt.version}: {versioned_candidate.version}",
-                    prefix=parent_prefix,
-                )
-                candidate_prefix = self._format_log_prefix(
-                    round_num=display_round, prompt_version=versioned_candidate.version
-                )
-                self._log(
-                    logging.INFO,
-                    f"New prompt template created from parent {prompt.version}:\n```\n{new_prompt}\n```",
-                    prefix=candidate_prefix,
-                )
-                candidates.append(versioned_candidate)
+                logger.info(f"[Round {round_num + 1}] New prompt template: {new_prompt_template}")
+                candidates.append(new_prompt_template)

        return candidates

    async def _evaluate_and_select_beam(
        self,
-        candidates: List[VersionedPromptTemplate],
+        candidates: List[PromptTemplate],
        resource_name: str,
        val_dataset_iterator: Iterator[Sequence[T_task]],
        round_num: int,
-    ) -> List[VersionedPromptTemplate]:
+    ) -> List[PromptTemplate]:
        """
        Evaluate all candidate prompts on validation data and select top-k for the beam.

@@ -680,56 +503,30 @@ class APO(Algorithm, Generic[T_task]):
        Raises:
            ValueError: If no candidates remain after evaluation.
        """
-        display_round = round_num + 1
-        round_prefix = self._format_log_prefix(round_num=display_round)
-        self._log(
-            logging.INFO,
-            f"Evaluating {len(candidates)} candidates on validation dataset",
-            prefix=round_prefix,
-        )
+        logger.info(f"[Round {round_num + 1}] Evaluating {len(candidates)} candidates on validation dataset")

        val_batch = next(val_dataset_iterator)
+        scores: List[Tuple[PromptTemplate, float]] = []

-        for prompt in candidates:
-            candidate_prefix = self._format_log_prefix(
-                round_num=display_round,
-                prompt_version=prompt.version,
-            )
-            _, score = await self.evaluate_prompt_on_batch(
-                prompt,
-                resource_name,
-                val_batch,
-                mode="val",
-                prefix=candidate_prefix,
-            )
-            prompt.score = score
-            self._log(
-                logging.INFO,
-                f"Candidate score: {score:.3f}",
-                prefix=candidate_prefix,
-            )
+        for idx, prompt in enumerate(candidates):
+            _, score = await self.evaluate_prompt_on_batch(prompt.template, resource_name, val_batch, mode="val")
+            scores.append((prompt, score))
+            logger.info(f"[Round {round_num + 1}] Candidate {idx} score: {score:.3f}")

        # Sort by score (descending) and select top beam_width
-        sorted_prompts = [p for p in sorted(candidates, key=lambda x: cast(float, x.score), reverse=True)]
-        selected_prompts = sorted_prompts[: self.beam_width]
-        selected_versions = [
-            f"{prompt.version}:{prompt.score:.3f}" if prompt.score is not None else prompt.version
-            for prompt in selected_prompts
-        ]
-        self._log(
-            logging.INFO,
-            f"Top {len(selected_prompts)} candidates on validation dataset: {selected_versions}",
-            prefix=round_prefix,
+        sorted_prompts = [p for p, _ in sorted(scores, key=lambda x: x[1], reverse=True)][: self.beam_width]
+        logger.info(
+            f"[Round {round_num + 1}] Top {len(sorted_prompts)} candidates on validation dataset: {sorted_prompts}"
        )

-        if len(selected_prompts) == 0:
+        if len(sorted_prompts) == 0:
            raise ValueError("No beam candidates any more")

-        return selected_prompts
+        return sorted_prompts

    async def _update_best_prompt(
        self,
-        beam: List[VersionedPromptTemplate],
+        beam: List[PromptTemplate],
        resource_name: str,
        val_dataset: Dataset[T_task],
        round_num: int,
@@ -743,38 +540,18 @@ class APO(Algorithm, Generic[T_task]):
            val_dataset: Full validation dataset.
            round_num: Current round number (for logging, 0-indexed).
        """
-        display_round = round_num + 1
        best_prompt = beam[0]
-        prefix = self._format_log_prefix(round_num=display_round, prompt_version=best_prompt.version)
        _, best_score = await self.evaluate_prompt_on_batch(
-            best_prompt,
-            resource_name,
-            cast(Sequence[T_task], val_dataset),
-            mode="val",
-            prefix=prefix,
-        )
-        self._log(
-            logging.INFO,
-            f"Beam leader score: {best_score:.3f}",
-            prefix=prefix,
+            best_prompt.template, resource_name, cast(Sequence[T_task], val_dataset), mode="val"
        )
+        logger.info(f"[Round {round_num + 1}] Best prompt {best_prompt} has score: {best_score:.3f}")

        if best_score > self._history_best_score:
-            prev = self._history_best_score
-            self._log(
-                logging.INFO,
-                f"Best prompt updated. New best score: {best_score:.3f} (prev: {prev:.3f})",
-                prefix=prefix,
+            logger.info(
+                f"[Round {round_num + 1}] Best prompt updated. New best score: {best_score:.3f} (prev: {self._history_best_score:.3f})"
            )
-            self._history_best_prompt = best_prompt.prompt_template
+            self._history_best_prompt = best_prompt
            self._history_best_score = best_score
-            self._history_best_version = best_prompt.version
-        else:
-            self._log(
-                logging.WARNING,
-                f"Best prompt not updated. Current score: {best_score:.3f} vs. history best: {self._history_best_score:.3f})",
-                prefix=prefix,
-            )

    async def run(
        self,
@@ -785,7 +562,6 @@ class APO(Algorithm, Generic[T_task]):
        Execute the APO algorithm to optimize prompts through beam search with textual gradients.

        The algorithm performs iterative prompt optimization over multiple rounds:
-
        - Each round: samples parent prompts, generates new candidates via textual gradients,
          evaluates all candidates on validation data, and keeps the top performers
        - Tracks the historically best prompt across all rounds
@@ -801,51 +577,15 @@ class APO(Algorithm, Generic[T_task]):
        # Initialize beam search
        resource_name, seed_prompt, grad_iterator, val_iterator = self._initialize_beam(train_dataset, val_dataset)

-        if self._poml_trace:
-            poml.set_trace(trace_dir="pomltrace")
-
        # Validation datasets are guaranteed to be non-None after initialization
        assert val_dataset is not None

        # Start with seed prompt in the beam
-        seed_versioned = self._create_versioned_prompt(seed_prompt)
-        beam: List[VersionedPromptTemplate] = [seed_versioned]
-        self._history_best_prompt = seed_prompt
-        self._history_best_version = seed_versioned.version
-
-        # Optionally evaluate seed prompt on validation set to establish baseline
-        if self.run_initial_validation:
-            seed_prefix = self._format_log_prefix(round_num=0, prompt_version=seed_versioned.version)
-            self._log(
-                logging.INFO,
-                "Evaluating seed prompt on validation dataset before optimization...",
-                prefix=seed_prefix,
-            )
-            _, seed_score = await self.evaluate_prompt_on_batch(
-                seed_versioned,
-                resource_name,
-                cast(Sequence[T_task], val_dataset),
-                mode="val",
-                prefix=seed_prefix,
-            )
-            self._log(
-                logging.INFO,
-                f"Seed prompt baseline score: {seed_score:.3f}",
-                prefix=seed_prefix,
-            )
-            self._history_best_prompt = seed_prompt
-            self._history_best_score = seed_score
-            self._history_best_version = seed_versioned.version
+        beam: List[PromptTemplate] = [seed_prompt]

        # Run beam search for specified number of rounds
        for rnd in range(self.beam_rounds):
-            display_round = rnd + 1
-            round_prefix = self._format_log_prefix(round_num=display_round)
-            self._log(
-                logging.INFO,
-                f"Round {display_round}/{self.beam_rounds}...",
-                prefix=round_prefix,
-            )
+            logger.info(f"[Round {rnd + 1}] Round {rnd + 1}/{self.beam_rounds}...")

            # Sample parent prompts from current beam
            parent_prompts = self._sample_parent_prompts(beam, rnd)
@@ -1,22 +1,22 @@
 <poml>
-  <p>Revise the given prompt template using the critique as constraints and improvement guide.</p>
-  <cp caption="Revision Rules">
-    <list listStyle="decimal">
-      <item>Rewrite or restructure the prompt if critique implies it.</item>
-      <item>Explicitly include any requested output format, structure, or word limit, if requested by the critique.</item>
-      <item>Prioritize mechanism-first phrasing: define what to do, then how to do it.</item>
-      <item>Preserve placeholder variables inside curly brackets.</item>
+  <p>You will revise the current prompt using the critique.</p>
+  <cp caption="Rules">
+    <list>
+      <item>Keep style concise.</item>
+      <item>Add explicit output format if helpful.</item>
+      <item>Prefer mechanism-first.</item>
+      <item>Include word limits if missing.</item>
    </list>
  </cp>
  <output-format>
-    Return only the improved prompt template with placeholders intact. Do not include other explanations on how you did it, or headers and introductory texts.
+    Return only the new prompt template. Keep the placeholders (in curly-brackets) as still placeholders. No fenced code blocks. No other text.
  </output-format>
  <human-msg>
-    <cp caption="Prompt Template">
-      <text whiteSpace="pre">{{ prompt_template }}</text>
+    <cp caption="Current Prompt Template">
+      <text>{{ prompt_template }}</text>
    </cp>
    <cp caption="Critique">
-      <text whiteSpace="pre">{{ critique }}</text>
+      <text>{{ critique }}</text>
    </cp>
  </human-msg>
 </poml>
@@ -1,18 +1,15 @@
-<!-- Conservative Edit Prompt -->
-
 <poml>
-  <p>Revise the prompt to address ONE critique point clearly and effectively. Preserve all variable names in curly-brackets.</p>
-  <p>Do not address more than one critique point. Focus on the single most critical issue.</p>
-  <p>Keep the new prompt close in tone, length, and structure to the original.</p>
+  <p>Revise the prompt to address all critique points. Preserve variable names in curly-brackets.</p>
+  <hint>Add a rubric: mechanism first, 1 support fact, less than 100 words.</hint>
  <output-format>
-    Return only the revised full prompt. Do not include explanations, comparisons, or other text.
+    Return only the new full prompt. No extra explanations, punctuations or diff.
  </output-format>
  <human-msg>
    <cp caption="PROMPT" level="3">
-      <text whiteSpace="pre">{{ prompt_template }}</text>
+      <text>{{ prompt_template }}</text>
    </cp>
    <cp caption="CRITIQUE" level="3">
-      <text whiteSpace="pre">{{ critique }}</text>
+      <text>{{ critique }}</text>
    </cp>
  </human-msg>
 </poml>
@@ -1,7 +1,7 @@
 <poml>
  <p>You optimize a prompt template.</p>
  <cp caption="Original Prompt Template">
-    <text whiteSpace="pre">{{ prompt_template }}</text>
+    <text>{{ prompt_template }}</text>
  </cp>
  <cp caption="Experiments with Original Prompt Template">
    <cp for="experiment in experiments" caption="Experiment {{ loop.index + 1 }}">
@@ -2,7 +2,7 @@
  <role>You are a prompt engineer.</role>
  <task>Analyze where the current prompt failed to elicit the right mechanism.</task>
  <cp caption="Current Prompt Template">
-    <text whiteSpace="pre">{{ prompt_template }}</text>
+    <text>{{ prompt_template }}</text>
  </cp>
  <cp caption="Sample Runs with Current Prompt Template">
    <p>The following are the OpenTelemetry spans collected from the sample runs with the current prompt template. They should contain both prompt, responses and rewards.</p>
@@ -91,7 +91,7 @@

  <human-msg>
    <cp caption="Prompt">
-      <text whiteSpace="pre">{{ prompt_template }}</text>
+      <text>{{ prompt_template }}</text>
    </cp>
    <cp caption="Sample Runs of the Prompts (Historical Messages and Rewards)">
      <cp for="experiment in experiments" caption="Sample Run #{{ loop.index + 1 }}">
@@ -2,14 +2,22 @@

 from __future__ import annotations

+import functools
 import inspect
 import weakref
 from typing import (
    TYPE_CHECKING,
    Any,
    Awaitable,
+    Dict,
+    Generic,
+    Literal,
    Optional,
+    Protocol,
+    TypeVar,
    Union,
+    cast,
+    overload,
 )

 from agentlightning.adapter import TraceAdapter
@@ -22,7 +30,7 @@ if TYPE_CHECKING:
    from agentlightning.trainer import Trainer


-class Algorithm:
+class BaseAlgorithm:
    """Algorithm is the strategy, or tuner to train the agent."""

    _trainer_ref: weakref.ReferenceType[Trainer] | None = None
@@ -160,3 +168,239 @@ class Algorithm:
            The AgentLightningClient instance associated with this algorithm.
        """
        raise NotImplementedError("Subclasses must implement get_client().")
+
+
+class FastAlgorithm(BaseAlgorithm):
+    """Algorithm that can run fast and qualify for dev mode.
+
+    Fast algorithms enable agent developers to quickly iterate on agent development
+    without waiting for a long training to complete.
+    """
+
+
+# Algorithm function signature types
+# We've missed a lot of combinations here.
+# Let's add them in future.
+
+
+class AlgorithmFuncSyncFull(Protocol):
+    def __call__(
+        self,
+        *,
+        store: LightningStore,
+        train_dataset: Optional[Dataset[Any]],
+        val_dataset: Optional[Dataset[Any]],
+        llm_proxy: Optional[LLMProxy],
+        adapter: Optional[TraceAdapter[Any]],
+        initial_resources: Optional[NamedResources],
+    ) -> None: ...
+
+
+class AlgorithmFuncSyncOnlyStore(Protocol):
+    def __call__(self, *, store: LightningStore) -> None: ...
+
+
+class AlgorithmFuncSyncOnlyDataset(Protocol):
+    def __call__(self, *, train_dataset: Optional[Dataset[Any]], val_dataset: Optional[Dataset[Any]]) -> None: ...
+
+
+class AlgorithmFuncAsyncFull(Protocol):
+    def __call__(
+        self,
+        *,
+        store: LightningStore,
+        train_dataset: Optional[Dataset[Any]],
+        val_dataset: Optional[Dataset[Any]],
+        llm_proxy: Optional[LLMProxy],
+        adapter: Optional[TraceAdapter[Any]],
+        initial_resources: Optional[NamedResources],
+    ) -> Awaitable[None]: ...
+
+
+class AlgorithmFuncAsyncOnlyStore(Protocol):
+    def __call__(self, *, store: LightningStore) -> Awaitable[None]: ...
+
+
+class AlgorithmFuncAsyncOnlyDataset(Protocol):
+    def __call__(
+        self, *, train_dataset: Optional[Dataset[Any]], val_dataset: Optional[Dataset[Any]]
+    ) -> Awaitable[None]: ...
+
+
+AlgorithmFuncAsync = Union[AlgorithmFuncAsyncOnlyStore, AlgorithmFuncAsyncOnlyDataset, AlgorithmFuncAsyncFull]
+
+AlgorithmFuncSync = Union[AlgorithmFuncSyncOnlyStore, AlgorithmFuncSyncOnlyDataset, AlgorithmFuncSyncFull]
+
+
+class AlgorithmFuncSyncFallback(Protocol):
+    def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
+
+
+class AlgorithmFuncAsyncFallback(Protocol):
+    def __call__(self, *args: Any, **kwargs: Any) -> Awaitable[Any]: ...
+
+
+AlgorithmFuncSyncLike = Union[AlgorithmFuncSync, AlgorithmFuncSyncFallback]
+AlgorithmFuncAsyncLike = Union[AlgorithmFuncAsync, AlgorithmFuncAsyncFallback]
+
+AlgorithmFunc = Union[AlgorithmFuncSyncLike, AlgorithmFuncAsyncLike]
+
+
+AsyncFlag = Literal[True, False]
+AF = TypeVar("AF", bound=AsyncFlag)
+
+
+class FunctionalAlgorithm(BaseAlgorithm, Generic[AF]):
+    """A BaseAlgorithm that wraps a function-based algorithm implementation.
+
+    This class allows users to define algorithm behavior using a simple function
+    that takes train_dataset and val_dataset parameters, rather than implementing
+    a full BaseAlgorithm subclass.
+    """
+
+    @overload
+    def __init__(self: "FunctionalAlgorithm[Literal[False]]", algorithm_func: AlgorithmFuncSyncLike) -> None: ...
+
+    @overload
+    def __init__(self: "FunctionalAlgorithm[Literal[True]]", algorithm_func: AlgorithmFuncAsyncLike) -> None: ...
+
+    def __init__(self, algorithm_func: Union[AlgorithmFuncSyncLike, AlgorithmFuncAsyncLike]) -> None:
+        """
+        Initialize the FunctionalAlgorithm with an algorithm function.
+
+        Args:
+            algorithm_func: A function that defines the algorithm's behavior.
+                           Can be sync or async with signature:
+                           (train_dataset, val_dataset) -> None
+        """
+        super().__init__()
+        self._algorithm_func = algorithm_func
+        self._sig = inspect.signature(algorithm_func)
+        self._is_async = inspect.iscoroutinefunction(algorithm_func)
+
+        # Copy function metadata to preserve type hints and other attributes
+        functools.update_wrapper(self, algorithm_func)  # type: ignore
+
+    def is_async(self) -> bool:
+        return self._is_async
+
+    @overload
+    def run(
+        self: "FunctionalAlgorithm[Literal[False]]",
+        train_dataset: Optional[Dataset[Any]] = None,
+        val_dataset: Optional[Dataset[Any]] = None,
+    ) -> None: ...
+
+    @overload
+    def run(
+        self: "FunctionalAlgorithm[Literal[True]]",
+        train_dataset: Optional[Dataset[Any]] = None,
+        val_dataset: Optional[Dataset[Any]] = None,
+    ) -> Awaitable[None]: ...
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        return self._algorithm_func(*args, **kwargs)  # type: ignore
+
+    def run(
+        self,
+        train_dataset: Optional[Dataset[Any]] = None,
+        val_dataset: Optional[Dataset[Any]] = None,
+    ) -> Union[None, Awaitable[None]]:
+        """Execute the algorithm using the wrapped function.
+
+        Args:
+            train_dataset: The dataset to train on.
+            val_dataset: The dataset to validate on.
+
+        Returns:
+            None or Awaitable[None] if the function is async.
+        """
+        kwargs: Dict[str, Any] = {}
+        if "store" in self._sig.parameters:
+            kwargs["store"] = self.get_store()
+        if "adapter" in self._sig.parameters:
+            kwargs["adapter"] = self.get_adapter()
+        if "llm_proxy" in self._sig.parameters:
+            kwargs["llm_proxy"] = self.get_llm_proxy()
+        if "initial_resources" in self._sig.parameters:
+            kwargs["initial_resources"] = self.get_initial_resources()
+        if "train_dataset" in self._sig.parameters:
+            kwargs["train_dataset"] = train_dataset
+        elif train_dataset is not None:
+            raise TypeError(
+                f"train_dataset is provided but not supported by the algorithm function: {self._algorithm_func}"
+            )
+        if "val_dataset" in self._sig.parameters:
+            kwargs["val_dataset"] = val_dataset
+        elif val_dataset is not None:
+            raise TypeError(
+                f"val_dataset is provided but not supported by the algorithm function: {self._algorithm_func}"
+            )
+        # both sync and async functions can be called with the same signature
+        result = self._algorithm_func(**kwargs)  # type: ignore[misc]
+        if self._is_async:
+            return cast(Awaitable[None], result)
+        return None
+
+
+@overload
+def algo(func: AlgorithmFuncAsync) -> FunctionalAlgorithm[Literal[True]]: ...
+
+
+@overload
+def algo(func: AlgorithmFuncAsyncFallback) -> FunctionalAlgorithm[Any]: ...
+
+
+@overload
+def algo(func: AlgorithmFuncSync) -> FunctionalAlgorithm[Literal[False]]: ...
+
+
+@overload
+def algo(func: AlgorithmFuncSyncFallback) -> FunctionalAlgorithm[Any]: ...
+
+
+def algo(
+    func: Union[
+        AlgorithmFuncSync,
+        AlgorithmFuncAsync,
+        AlgorithmFuncSyncFallback,
+        AlgorithmFuncAsyncFallback,
+    ],
+) -> Union[FunctionalAlgorithm[Literal[False]], FunctionalAlgorithm[Literal[True]]]:
+    """Create a BaseAlgorithm from a function.
+
+    This decorator allows you to define an algorithm using a simple function
+    instead of creating a full BaseAlgorithm subclass. The returned FunctionalAlgorithm
+    instance is callable, preserving the original function's behavior.
+
+    Args:
+        func: A function that defines the algorithm's behavior with signature:
+              (train_dataset, val_dataset) -> None
+              Can be sync or async.
+
+    Returns:
+        A callable FunctionalAlgorithm instance that preserves the original function's
+        type hints and behavior while providing all algorithm functionality.
+
+    Example:
+        @algo
+        def my_algorithm(train_dataset, val_dataset):
+            # Algorithm logic here
+            for task in train_dataset:
+                # Process training tasks
+                pass
+
+        @algo
+        async def my_async_algorithm(train_dataset, val_dataset):
+            # Async algorithm logic here
+            async for task in train_dataset:
+                # Process training tasks asynchronously
+                pass
+
+        # Function is still callable with original behavior
+        my_algorithm(train_data, val_data)
+
+        # Algorithm methods are also available
+        my_algorithm.run(train_data, val_data)
+    """
+    return FunctionalAlgorithm(func)
@@ -1,264 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import functools
-import inspect
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Awaitable,
-    Dict,
-    Generic,
-    Literal,
-    Optional,
-    Protocol,
-    TypeVar,
-    Union,
-    cast,
-    overload,
-)
-
-from agentlightning.adapter import TraceAdapter
-from agentlightning.store.base import LightningStore
-from agentlightning.types import Dataset, NamedResources
-
-if TYPE_CHECKING:
-    from agentlightning.llm_proxy import LLMProxy
-
-from .base import Algorithm
-
-# Algorithm function signature types
-# We've missed a lot of combinations here.
-# Let's add them in future.
-
-
-class AlgorithmFuncSyncFull(Protocol):
-    def __call__(
-        self,
-        *,
-        store: LightningStore,
-        train_dataset: Optional[Dataset[Any]],
-        val_dataset: Optional[Dataset[Any]],
-        llm_proxy: Optional[LLMProxy],
-        adapter: Optional[TraceAdapter[Any]],
-        initial_resources: Optional[NamedResources],
-    ) -> None: ...
-
-
-class AlgorithmFuncSyncOnlyStore(Protocol):
-    def __call__(self, *, store: LightningStore) -> None: ...
-
-
-class AlgorithmFuncSyncOnlyDataset(Protocol):
-    def __call__(self, *, train_dataset: Optional[Dataset[Any]], val_dataset: Optional[Dataset[Any]]) -> None: ...
-
-
-class AlgorithmFuncAsyncFull(Protocol):
-    def __call__(
-        self,
-        *,
-        store: LightningStore,
-        train_dataset: Optional[Dataset[Any]],
-        val_dataset: Optional[Dataset[Any]],
-        llm_proxy: Optional[LLMProxy],
-        adapter: Optional[TraceAdapter[Any]],
-        initial_resources: Optional[NamedResources],
-    ) -> Awaitable[None]: ...
-
-
-class AlgorithmFuncAsyncOnlyStore(Protocol):
-    def __call__(self, *, store: LightningStore) -> Awaitable[None]: ...
-
-
-class AlgorithmFuncAsyncOnlyDataset(Protocol):
-    def __call__(
-        self, *, train_dataset: Optional[Dataset[Any]], val_dataset: Optional[Dataset[Any]]
-    ) -> Awaitable[None]: ...
-
-
-AlgorithmFuncAsync = Union[AlgorithmFuncAsyncOnlyStore, AlgorithmFuncAsyncOnlyDataset, AlgorithmFuncAsyncFull]
-
-AlgorithmFuncSync = Union[AlgorithmFuncSyncOnlyStore, AlgorithmFuncSyncOnlyDataset, AlgorithmFuncSyncFull]
-
-
-class AlgorithmFuncSyncFallback(Protocol):
-    def __call__(self, *args: Any, **kwargs: Any) -> Any: ...
-
-
-class AlgorithmFuncAsyncFallback(Protocol):
-    def __call__(self, *args: Any, **kwargs: Any) -> Awaitable[Any]: ...
-
-
-AlgorithmFuncSyncLike = Union[AlgorithmFuncSync, AlgorithmFuncSyncFallback]
-AlgorithmFuncAsyncLike = Union[AlgorithmFuncAsync, AlgorithmFuncAsyncFallback]
-
-AlgorithmFunc = Union[AlgorithmFuncSyncLike, AlgorithmFuncAsyncLike]
-
-
-AsyncFlag = Literal[True, False]
-AF = TypeVar("AF", bound=AsyncFlag)
-
-
-class FunctionalAlgorithm(Algorithm, Generic[AF]):
-    """An algorithm wrapper built from a callable implementation.
-
-    Functional algorithms let you provide an ordinary function instead of
-    subclassing [`Algorithm`][agentlightning.Algorithm]. The wrapper inspects
-    the callable signature to supply optional dependencies
-    such as the store, adapter, and LLM proxy.
-    """
-
-    @overload
-    def __init__(self: "FunctionalAlgorithm[Literal[False]]", algorithm_func: AlgorithmFuncSyncLike) -> None: ...
-
-    @overload
-    def __init__(self: "FunctionalAlgorithm[Literal[True]]", algorithm_func: AlgorithmFuncAsyncLike) -> None: ...
-
-    def __init__(self, algorithm_func: Union[AlgorithmFuncSyncLike, AlgorithmFuncAsyncLike]) -> None:
-        """Wrap a function that implements algorithm behaviour.
-
-        Args:
-            algorithm_func: Sync or async callable implementing the algorithm
-                contract. Arguments are detected automatically based on the
-                function signature.
-        """
-        super().__init__()
-        self._algorithm_func = algorithm_func
-        self._sig = inspect.signature(algorithm_func)
-        self._is_async = inspect.iscoroutinefunction(algorithm_func)
-
-        # Copy function metadata to preserve type hints and other attributes
-        functools.update_wrapper(self, algorithm_func)  # type: ignore
-
-    def is_async(self) -> bool:
-        return self._is_async
-
-    @overload
-    def run(
-        self: "FunctionalAlgorithm[Literal[False]]",
-        train_dataset: Optional[Dataset[Any]] = None,
-        val_dataset: Optional[Dataset[Any]] = None,
-    ) -> None: ...
-
-    @overload
-    def run(
-        self: "FunctionalAlgorithm[Literal[True]]",
-        train_dataset: Optional[Dataset[Any]] = None,
-        val_dataset: Optional[Dataset[Any]] = None,
-    ) -> Awaitable[None]: ...
-
-    def __call__(self, *args: Any, **kwargs: Any) -> Any:
-        return self._algorithm_func(*args, **kwargs)  # type: ignore
-
-    def run(
-        self,
-        train_dataset: Optional[Dataset[Any]] = None,
-        val_dataset: Optional[Dataset[Any]] = None,
-    ) -> Union[None, Awaitable[None]]:
-        """Execute the wrapped function with injected dependencies.
-
-        Args:
-            train_dataset: Optional training dataset passed through when the
-                callable declares a `train_dataset` parameter.
-            val_dataset: Optional validation dataset passed through when the
-                callable declares a `val_dataset` parameter.
-
-        Returns:
-            None for sync callables or an awaitable when the callable is async.
-
-        Raises:
-            TypeError: If a dataset is provided but the function signature does
-                not accept the corresponding argument.
-        """
-        kwargs: Dict[str, Any] = {}
-        if "store" in self._sig.parameters:
-            kwargs["store"] = self.get_store()
-        if "adapter" in self._sig.parameters:
-            kwargs["adapter"] = self.get_adapter()
-        if "llm_proxy" in self._sig.parameters:
-            kwargs["llm_proxy"] = self.get_llm_proxy()
-        if "initial_resources" in self._sig.parameters:
-            kwargs["initial_resources"] = self.get_initial_resources()
-        if "train_dataset" in self._sig.parameters:
-            kwargs["train_dataset"] = train_dataset
-        elif train_dataset is not None:
-            raise TypeError(
-                f"train_dataset is provided but not supported by the algorithm function: {self._algorithm_func}"
-            )
-        if "val_dataset" in self._sig.parameters:
-            kwargs["val_dataset"] = val_dataset
-        elif val_dataset is not None:
-            raise TypeError(
-                f"val_dataset is provided but not supported by the algorithm function: {self._algorithm_func}"
-            )
-        # both sync and async functions can be called with the same signature
-        result = self._algorithm_func(**kwargs)  # type: ignore[misc]
-        if self._is_async:
-            return cast(Awaitable[None], result)
-        return None
-
-
-@overload
-def algo(func: AlgorithmFuncAsync) -> FunctionalAlgorithm[Literal[True]]: ...
-
-
-@overload
-def algo(func: AlgorithmFuncAsyncFallback) -> FunctionalAlgorithm[Any]: ...
-
-
-@overload
-def algo(func: AlgorithmFuncSync) -> FunctionalAlgorithm[Literal[False]]: ...
-
-
-@overload
-def algo(func: AlgorithmFuncSyncFallback) -> FunctionalAlgorithm[Any]: ...
-
-
-def algo(
-    func: Union[
-        AlgorithmFuncSync,
-        AlgorithmFuncAsync,
-        AlgorithmFuncSyncFallback,
-        AlgorithmFuncAsyncFallback,
-    ],
-) -> Union[FunctionalAlgorithm[Literal[False]], FunctionalAlgorithm[Literal[True]]]:
-    """Convert a callable into a [`FunctionalAlgorithm`][agentlightning.algorithm.decorator.FunctionalAlgorithm].
-
-    The decorator inspects the callable signature to decide which dependencies
-    to inject at runtime, enabling concise algorithm definitions that still
-    leverage the full training runtime.
-
-    Args:
-        func: Function implementing the algorithm logic. May be synchronous or
-            asynchronous. The function can expect all of, or a subset of the following parameters:
-
-            - `store`: [`LightningStore`][agentlightning.store.base.LightningStore],
-            - `train_dataset`: [`Dataset`][agentlightning.Dataset],
-            - `val_dataset`: [`Dataset`][agentlightning.Dataset],
-            - `llm_proxy`: [`LLMProxy`][agentlightning.LLMProxy],
-            - `adapter`: [`TraceAdapter`][agentlightning.TraceAdapter],
-            - `initial_resources`: [`NamedResources`][agentlightning.NamedResources],
-
-            If the function does not expect a parameter, the wrapper will not inject it into the call.
-            Using `*args` and `**kwargs` will not work and no parameters will be injected.
-
-    Returns:
-        FunctionalAlgorithm that proxies the callable while exposing the
-        `Algorithm` interface.
-
-    Examples:
-        ```python
-        from agentlightning.algorithm.decorator import algo
-
-        @algo
-        def batching_algorithm(*, store, train_dataset, val_dataset):
-            for sample in train_dataset:
-                store.enqueue_rollout(input=sample, mode="train")
-
-        @algo
-        async def async_algorithm(*, store, train_dataset=None, val_dataset=None):
-            await store.enqueue_rollout(input={"prompt": "hello"}, mode="train")
-        ```
-    """
-    return FunctionalAlgorithm(func)
@@ -5,102 +5,55 @@ from __future__ import annotations
 import asyncio
 import logging
 from datetime import datetime
-from typing import Any, List, Literal, Optional
+from typing import Any, List, Optional

-from agentlightning.types import Attempt, Dataset, Rollout, RolloutStatus, Span
+from agentlightning.llm_proxy import ModelConfig
+from agentlightning.types import Dataset, RolloutStatus, RolloutV2

-from .base import Algorithm
+from .base import FastAlgorithm

 logger = logging.getLogger(__name__)

-__all__ = ["FastAlgorithm", "Baseline"]
-
-
-class FastAlgorithm(Algorithm):
-    """Base class for lightweight algorithms optimised for developer workflows.
-
-    Fast algorithms prioritise short feedback loops so an agent developer can run
-    small-scale experiments without waiting for long-running training jobs to
-    finish.
-    """
-

 def _timestamp_to_iso_str(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp).isoformat()


-class Baseline(FastAlgorithm):
-    """Reference implementation that streams the full dataset through the rollout queue.
+class MockAlgorithm(FastAlgorithm):
+    """A dummy implementation of algorithm interface that puts all dataset into the queue, and waits for all rollouts to complete.

-    The baseline algorithm batches task submissions, waits for each rollout to
-    finish, and logs every collected span and reward. It is primarily useful as
-    a smoke test for the platform plumbing rather than a performant trainer.
+    Logs all collected spans and rewards.

    Args:
-        n_epochs: Number of dataset passes to execute for both the train and val
-            splits during developer experiments.
-        train_split: Fraction of the concatenated dataset to treat as training
-            data. Must be strictly between 0 and 1.
-        polling_interval: Interval, in seconds, to poll the store for queue
-            depth and rollout completion.
-        max_queue_length: Number of rollouts allowed to wait in the queue before
-            throttling additional submissions.
-        span_verbosity: Level of detail to include when logging span metadata.
-
-    Raises:
-        ValueError: If `train_split` falls outside the `(0, 1)` interval.
-
-    Examples:
-        ```python
-        from agentlightning.algorithm.fast import Baseline
-
-        algorithm = Baseline(n_epochs=2, train_split=0.8, span_verbosity="key_values")
-        trainer.fit(algorithm, train_dataset=my_train, val_dataset=my_val)
-        ```
+        model_list: Optional list of models to load into the llm proxy.
+            If both model_list and llm_proxy is provided, llm_proxy will be launched.
+            Not implemented yet.
+        n_epochs: Number of epochs to run through the dev dataset.
+        train_split: Fraction of dev dataset to use for training vs validation. Must be between 0 and 1.
+        polling_interval: Time interval (in seconds) to poll the store for queue length and for completed rollouts.
+        max_queue_length: Maximum number of rollouts to keep in the queue at any time.
    """

    def __init__(
        self,
        *,
+        model_list: Optional[List[ModelConfig]] = None,
        n_epochs: int = 1,
        train_split: float = 0.5,
        polling_interval: float = 5.0,
        max_queue_length: int = 4,
-        span_verbosity: Literal["keys", "key_values", "none"] = "keys",
    ) -> None:
        super().__init__()
        self.n_epochs = n_epochs
        self.train_split = train_split
        self.polling_interval = polling_interval
        self.max_queue_length = max_queue_length
-        self.span_verbosity = span_verbosity
        if not (0.0 < self.train_split < 1.0):
            raise ValueError("train_split must be between 0 and 1.")

        self._finished_rollout_count = 0

-    def _span_to_string(self, rollout_id: str, attempt: Attempt, span: Span) -> str:
-        """Format a span for logging based on the configured verbosity."""
-        if self.span_verbosity == "none":
-            return ""
-
-        prefix_msg = f"[Rollout {rollout_id} | Attempt {attempt.attempt_id} | Span {span.span_id}] #{span.sequence_id} ({span.name}) "
-        elapsed = f"{span.end_time - span.start_time:.2f}" if span.start_time and span.end_time else "unknown"
-
-        msg = (
-            prefix_msg
-            + f"From {_timestamp_to_iso_str(span.start_time) if span.start_time else 'unknown'}, "
-            + f"to {_timestamp_to_iso_str(span.end_time) if span.end_time else 'unknown'}, "
-            + f"{elapsed} seconds. "
-        )
-        if self.span_verbosity == "key_values":
-            msg += f"Attributes: {span.attributes}"
-        else:
-            msg += f"Attribute keys: {list(span.attributes.keys())}"
-        return msg
-
-    async def _handle_rollout_finish(self, rollout: Rollout) -> None:
-        """Log attempt metadata and emit adapted traces when a rollout ends."""
+    async def _handle_rollout_finish(self, rollout: RolloutV2) -> None:
        store = self.get_store()

        rollout_id = rollout.rollout_id
@@ -113,37 +66,35 @@ class Baseline(FastAlgorithm):
        attempts = await store.query_attempts(rollout_id)
        for attempt in attempts:
            logger.info(
-                "[Rollout %s | Attempt %s] ID: %s. Status: %s. Worker: %s",
-                rollout_id,
-                attempt.sequence_id,
-                attempt.attempt_id,
-                attempt.status,
-                attempt.worker_id,
+                f"[Rollout {rollout_id} | Attempt {attempt.sequence_id}] ID: {attempt.attempt_id}. Status: {attempt.status}. Worker: {attempt.worker_id}"
            )
            spans = await store.query_spans(rollout_id=rollout_id)
            for span in spans:
-                if self.span_verbosity != "none":
-                    logger.info(self._span_to_string(rollout.rollout_id, attempt, span))
+                prefix_msg = f"[Rollout {rollout_id} | Attempt {attempt.attempt_id} | Span {span.span_id}] #{span.sequence_id} ({span.name}) "
+                elapsed = f"{span.end_time - span.start_time:.2f}" if span.start_time and span.end_time else "unknown"
+                logger.info(
+                    prefix_msg
+                    + f"From {_timestamp_to_iso_str(span.start_time) if span.start_time else 'unknown'}, "
+                    + f"to {_timestamp_to_iso_str(span.end_time) if span.end_time else 'unknown'}, "
+                    + f"{elapsed} seconds. Attributes: {span.attributes}"
+                )

        # Attempts to adapt the spans using the adapter if provided
        try:
            adapter = self.get_adapter()
-        except ValueError:
-            logger.warning("No adapter set for MockAlgorithm. Skipping trace adaptation.")
-            adapter = None
-        if adapter is not None:
            spans = await store.query_spans(rollout_id=rollout_id, attempt_id="latest")
            transformed_data = adapter.adapt(spans)
            logger.info(f"[Rollout {rollout_id}] Adapted data: {transformed_data}")
+        except ValueError:
+            logger.warning("No adapter set for MockAlgorithm. Skipping trace adaptation.")

    async def _enqueue_rollouts(
        self, dataset: Dataset[Any], train_indices: List[int], val_indices: List[int], resources_id: str
    ) -> None:
-        """Submit rollouts while respecting the maximum queue length."""
        store = self.get_store()

        for index in train_indices + val_indices:
-            queuing_rollouts = await store.query_rollouts(status_in=["queuing", "requeuing"])
+            queuing_rollouts = await store.query_rollouts(status=["queuing", "requeuing"])
            if len(queuing_rollouts) <= 1:
                # Only enqueue a new rollout when there is at most 1 rollout in the queue.
                sample = dataset[index]
@@ -153,7 +104,6 @@ class Baseline(FastAlgorithm):
            await asyncio.sleep(self.polling_interval)

    async def _harvest_rollout_spans(self, rollout_id: str):
-        """Poll rollout status updates until completion and log transitions."""
        store = self.get_store()
        last_status: Optional[RolloutStatus] = None

@@ -185,12 +135,11 @@ class Baseline(FastAlgorithm):
        train_dataset: Optional[Dataset[Any]] = None,
        val_dataset: Optional[Dataset[Any]] = None,
    ) -> None:
-        """Execute the baseline loop across the provided datasets."""
        train_dataset_length = len(train_dataset) if train_dataset is not None else 0
        val_dataset_length = len(val_dataset) if val_dataset is not None else 0
        if train_dataset_length == 0 and val_dataset_length == 0:
            logger.error(
-                "MockAlgorithm requires at least one dataset. Provide train_dataset or val_dataset before running."
+                "MockAlgorithm requires at least a train_dataset or val_dataset to run. No train_dataset or val_dataset is provided. Exiting."
            )
            return

@@ -199,8 +148,6 @@ class Baseline(FastAlgorithm):
        ]
        train_indices = list(range(0, train_dataset_length))
        val_indices = list(range(train_dataset_length, train_dataset_length + val_dataset_length))
-        logger.debug(f"Train indices: {train_indices}")
-        logger.debug(f"Val indices: {val_indices}")

        store = self.get_store()

@@ -218,24 +165,18 @@ class Baseline(FastAlgorithm):
            harvest_tasks: List[asyncio.Task[None]] = []
            logger.info(f"Proceeding epoch {epoch + 1}/{self.n_epochs}.")
            for index in train_indices + val_indices:
-                logger.info(
-                    f"Processing index {index}. {len(train_indices)} train indices and {len(val_indices)} val indices in total."
-                )
-                while True:
-                    queuing_rollouts = await store.query_rollouts(status_in=["queuing", "requeuing"])
-                    if len(queuing_rollouts) <= self.max_queue_length:
-                        # Only enqueue a new rollout when there is at most "max_queue_length" rollout in the queue.
-                        sample = concatenated_dataset[index]
-                        mode = "train" if index in train_indices else "val"
-                        rollout = await store.enqueue_rollout(input=sample, mode=mode, resources_id=resources_id)
-                        harvest_tasks.append(asyncio.create_task(self._harvest_rollout_spans(rollout.rollout_id)))
-                        logger.info(f"Enqueued rollout {rollout.rollout_id} in {mode} mode with sample: {sample}")
-                        break
-                    else:
-                        # Sleep a bit and try again later.
-                        await asyncio.sleep(self.polling_interval)
+                queuing_rollouts = await store.query_rollouts(status=["queuing", "requeuing"])
+                if len(queuing_rollouts) <= self.max_queue_length:
+                    # Only enqueue a new rollout when there is at most "max_queue_length" rollout in the queue.
+                    sample = concatenated_dataset[index]
+                    mode = "train" if index in train_indices else "val"
+                    rollout = await store.enqueue_rollout(input=sample, mode=mode, resources_id=resources_id)
+                    harvest_tasks.append(asyncio.create_task(self._harvest_rollout_spans(rollout.rollout_id)))
+                    logger.info(f"Enqueued rollout {rollout.rollout_id} in {mode} mode with sample: {sample}")
+                else:
+                    # Sleep a bit and try again later.
+                    await asyncio.sleep(self.polling_interval)

            # Wait for all harvest tasks to complete
-            logger.info(f"Waiting for {len(harvest_tasks)} harvest tasks to complete...")
            if len(harvest_tasks) > 0:
                await asyncio.gather(*harvest_tasks)
@@ -1,43 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-import random
-from typing import Iterator, List, Sequence, TypeVar
-
-from agentlightning.types import Dataset
-
-T_task = TypeVar("T_task")
-
-
-def batch_iter_over_dataset(dataset: Dataset[T_task], batch_size: int) -> Iterator[Sequence[T_task]]:
-    """
-    Create an infinite iterator that yields batches from the dataset.
-
-    When batch_size >= dataset size, yields the entire shuffled dataset repeatedly.
-    When batch_size < dataset size, yields batches of the specified size, reshuffling
-    after each complete pass through the dataset.
-
-    Args:
-        dataset: The dataset to iterate over.
-        batch_size: The desired batch size.
-
-    Yields:
-        Sequences of tasks from the dataset. Each task appears at most once per epoch.
-    """
-    if batch_size >= len(dataset):
-        while True:
-            dataset_copy = [dataset[i] for i in range(len(dataset))]
-            random.shuffle(dataset_copy)
-            yield dataset_copy
-
-    else:
-        current_batch: List[int] = []
-        while True:
-            indices = list(range(len(dataset)))
-            random.shuffle(indices)
-            for index in indices:
-                if index in current_batch:
-                    continue
-                current_batch.append(index)
-                if len(current_batch) == batch_size:
-                    yield [dataset[index] for index in current_batch]
-                    current_batch = []
@@ -5,91 +5,13 @@ from typing import Any, Optional
 from hydra import compose, initialize
 from omegaconf import OmegaConf

-from agentlightning.algorithm.base import Algorithm
+from agentlightning.algorithm.base import BaseAlgorithm
 from agentlightning.client import AgentLightningClient
 from agentlightning.types import Dataset
 from agentlightning.verl.entrypoint import run_ppo  # type: ignore


-class VERL(Algorithm):
-    """VERL-powered algorithm that delegates training to the VERL PPO runner.
-
-    !!! warning
-        Advanced customisation currently requires copying the VERL source and
-        modifying it directly. Native hooks for overriding training behaviour
-        will land in a future release.
-
-    Args:
-        config: Dictionary mirroring the overrides passed to the VERL CLI. The
-            overrides are merged with VERL's packaged defaults via Hydra before
-            launching training.
-
-    Examples:
-        ```python
-        from agentlightning.algorithm.verl import VERL
-
-        algorithm = VERL(
-            config={
-                "algorithm": {
-                    "adv_estimator": "grpo",
-                    "use_kl_in_reward": False,
-                },
-                "data": {
-                    "train_batch_size": 32,
-                    "max_prompt_length": 4096,
-                    "max_response_length": 2048,
-                },
-                "actor_rollout_ref": {
-                    "rollout": {
-                        "tensor_model_parallel_size": 1,
-                        "n": 4,
-                        "log_prob_micro_batch_size_per_gpu": 4,
-                        "multi_turn": {"format": "hermes"},
-                        "name": "vllm",
-                        "gpu_memory_utilization": 0.6,
-                    },
-                    "actor": {
-                        "ppo_mini_batch_size": 32,
-                        "ppo_micro_batch_size_per_gpu": 4,
-                        "optim": {"lr": 1e-6},
-                        "use_kl_loss": False,
-                        "kl_loss_coef": 0.0,
-                        "entropy_coeff": 0,
-                        "clip_ratio_low": 0.2,
-                        "clip_ratio_high": 0.3,
-                        "fsdp_config": {
-                            "param_offload": True,
-                            "optimizer_offload": True,
-                        },
-                    },
-                    "ref": {
-                        "log_prob_micro_batch_size_per_gpu": 8,
-                        "fsdp_config": {"param_offload": True},
-                    },
-                    "model": {
-                        "path": "Qwen/Qwen2.5-1.5B-Instruct",
-                        "use_remove_padding": True,
-                        "enable_gradient_checkpointing": True,
-                    },
-                },
-                "trainer": {
-                    "n_gpus_per_node": 1,
-                    "val_before_train": True,
-                    "critic_warmup": 0,
-                    "logger": ["console", "wandb"],
-                    "project_name": "AgentLightning",
-                    "experiment_name": "calc_x",
-                    "nnodes": 1,
-                    "save_freq": 64,
-                    "test_freq": 32,
-                    "total_epochs": 2,
-                },
-            }
-        )
-        trainer.fit(algorithm, train_dataset=my_train_dataset)
-        ```
-    """
-
+class VERL(BaseAlgorithm):
    def __init__(self, config: dict[str, Any]):
        super().__init__()

@@ -99,8 +21,6 @@ class VERL(Algorithm):

        # Merge your dict overrides
        override_conf = OmegaConf.create(config)
-        # Allow adding new fields
-        OmegaConf.set_struct(base_cfg, False)
        self.config = OmegaConf.merge(base_cfg, override_conf)

    def run(
@@ -108,17 +28,6 @@ class VERL(Algorithm):
        train_dataset: Optional[Dataset[Any]] = None,
        val_dataset: Optional[Dataset[Any]] = None,
    ) -> None:
-        """Launch the VERL PPO entrypoint with the configured runtime context.
-
-        Args:
-            train_dataset: Optional dataset forwarded to VERL for training.
-            val_dataset: Optional dataset forwarded to VERL for evaluation.
-
-        Raises:
-            ValueError: If required dependencies such as the store, LLM proxy, or
-                adapter have been garbage-collected when using the V1 execution
-                mode.
-        """
        try:
            store = self.get_store()
        except Exception:
@@ -145,10 +54,5 @@ class VERL(Algorithm):
            )

    def get_client(self) -> AgentLightningClient:
-        """Create a client bound to the VERL-managed Agent Lightning server.
-
-        Deprecated:
-            Since v0.2.
-        """
        port = self.config.agentlightning.port
        return AgentLightningClient(endpoint=f"http://localhost:{port}")
@@ -0,0 +1,30 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from __future__ import annotations
+
+import argparse
+import time
+from typing import Iterable
+
+from agentlightning.instrumentation.agentops import AgentOpsServerManager
+
+
+def main(argv: Iterable[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description="Start AgentOps server")
+    parser.add_argument("--daemon", action="store_true", help="Run server as a daemon")
+    parser.add_argument("--port", type=int, default=8002, help="Port to run the server on")
+    args = parser.parse_args(list(argv) if argv is not None else None)
+
+    manager = AgentOpsServerManager(daemon=args.daemon, port=args.port)
+    try:
+        manager.start()
+        # Wait forever
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        manager.stop()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
@@ -6,94 +6,23 @@ from __future__ import annotations

 import argparse
 import asyncio
-import logging
 from typing import Iterable

-from agentlightning import setup_logging
+from agentlightning.logging import configure_logger
 from agentlightning.store.client_server import LightningStoreServer
 from agentlightning.store.memory import InMemoryLightningStore

-logger = logging.getLogger(__name__)
-

 def main(argv: Iterable[str] | None = None) -> int:
    parser = argparse.ArgumentParser(description="Run a LightningStore server")
-    parser.add_argument("--host", default="0.0.0.0", help="Host to bind the server to")
    parser.add_argument("--port", type=int, default=4747, help="Port to run the server on")
-    parser.add_argument(
-        "--cors-origin",
-        dest="cors_origins",
-        action="append",
-        help="Allowed CORS origin. Repeat for multiple origins. Use '*' to allow all origins.",
-    )
-    parser.add_argument(
-        "--log-level",
-        default="INFO",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
-        help="Configure the logging level for the store.",
-    )
-    parser.add_argument(
-        "--prometheus",
-        action="store_true",
-        help="Enable Prometheus metrics.",
-    )
-    parser.add_argument(
-        "--n-workers",
-        default=1,
-        type=int,
-        help=(
-            "Number of workers to run in the server. When it's greater than 1, the server will be run using `mp` launch mode. "
-            "Only applicable for zero-copy stores such as MongoDB backend."
-        ),
-    )
-
-    parser.add_argument(
-        "--backend",
-        choices=["memory", "mongo"],
-        default="memory",
-        help="Backend to use for the store.",
-    )
-    parser.add_argument(
-        "--mongo-uri",
-        default="mongodb://localhost:27017/?replicaSet=rs0",
-        help="MongoDB URI to use for the store. Applicable only if --backend is 'mongo'.",
-    )
-
    args = parser.parse_args(list(argv) if argv is not None else None)

-    setup_logging(args.log_level)
+    configure_logger()

-    if args.backend == "memory":
-        store = InMemoryLightningStore(
-            prometheus=args.prometheus, thread_safe=True
-        )  # Using thread_safe store for server
-    elif args.backend == "mongo":
-        from agentlightning.store.mongo import MongoLightningStore
-
-        store = MongoLightningStore(client=args.mongo_uri, prometheus=args.prometheus)
-    else:
-        raise ValueError(f"Invalid backend: {args.backend}")
-
-    if args.n_workers > 1:
-        logger.info(f"Running the server using `mp` launch mode with {args.n_workers} workers.")
-        launch_mode = "mp"
-    else:
-        logger.info("Running the server using `asyncio` launch mode.")
-        launch_mode = "asyncio"
-    server = LightningStoreServer(
-        store,
-        host=args.host,
-        port=args.port,
-        cors_allow_origins=args.cors_origins,
-        launch_mode=launch_mode,
-        prometheus=args.prometheus,
-        n_workers=args.n_workers,
-    )
-    try:
-        asyncio.run(server.run_forever())
-    except RuntimeError as exc:
-        logger.error("LightningStore server failed to start: %s", exc, exc_info=True)
-        return 1
+    store = InMemoryLightningStore()
+    server = LightningStoreServer(store, host="0.0.0.0", port=args.port)
+    asyncio.run(server.run_forever())
    return 0


@@ -1,47 +1,29 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Utilities for interacting with legacy Agent Lightning servers.
-
-This module contains compatibility shims that speak the deprecated HTTP
-interface used by older Agent Lightning deployments. Modern code should prefer
-the store-based APIs exposed by `agentlightning.store`, but keeping these
-clients available makes it easier to migrate existing workflows incrementally.
-"""
+"""Legacy client for interacting with a legacy Agent Lightning server."""

 import asyncio
 import logging
 import time
 import urllib.parse
-import warnings
 from typing import Any, Dict, List, Optional, Union

 import aiohttp
 import requests

-from .types import NamedResources, ResourcesUpdate, RolloutLegacy, Task, TaskIfAny, TaskInput
+from .types import NamedResources, ResourcesUpdate, Rollout, Task, TaskIfAny, TaskInput

 logger = logging.getLogger(__name__)


 class AgentLightningClient:
-    """Client wrapper for the legacy version-aware Agent Lightning server.
+    """
+    Client for interacting with a version-aware Agent Lightning Server.

-    The client exposes synchronous and asynchronous helpers for polling tasks,
-    retrieving resource bundles, and submitting rollouts. It also maintains a
-    simple in-memory cache keyed by the server-provided resource identifier to
-    avoid redundant network requests.
-
-    !!! warning "Deprecated"
-        [`AgentLightningClient`][agentlightning.client.AgentLightningClient] is part of
-        the legacy client/server stack. New code should rely on the store-based APIs
-        implemented in `agentlightning.store`.
-
-    Attributes:
-        endpoint: Base URL of the Agent Lightning server.
-        poll_interval: Delay in seconds between polling attempts when no task is
-            available.
-        timeout: Timeout in seconds applied to HTTP requests.
-        task_count: Number of tasks claimed during the lifetime of this client.
+    This client handles polling for tasks, fetching specific versions of resources
+    (like model configurations), and posting completed rollouts back to the server.
+    It provides both synchronous and asynchronous methods for these operations and
+    includes a cache for resources.
    """

    _next_task_uri = "/task"
@@ -50,16 +32,13 @@ class AgentLightningClient:
    _report_rollout_uri = "/rollout"

    def __init__(self, endpoint: str, poll_interval: float = 5.0, timeout: float = 10.0):
-        """Initialize the client.
+        """Initializes the AgentLightningClient.

        Args:
-            endpoint: Root URL of the Agent Lightning server.
-            poll_interval: Seconds to wait between polling attempts.
-            timeout: Seconds before a request to the server is considered timed out.
+            endpoint: The root URL of the Agent Lightning server.
+            poll_interval: The interval in seconds to wait between polling for new tasks.
+            timeout: The timeout in seconds for HTTP requests.
        """
-        warnings.warn(
-            "AgentLightningClient is deprecated. Please use LightningStoreClient instead.", DeprecationWarning
-        )
        self.endpoint = endpoint
        self.task_count = 0
        self.poll_interval = poll_interval
@@ -68,13 +47,13 @@ class AgentLightningClient:
        self._default_headers = {"X-AgentLightning-Client": "true"}

    async def _request_json_async(self, url: str) -> Optional[Dict[str, Any]]:
-        """Perform an asynchronous ``GET`` request and parse the JSON payload.
+        """Makes an async GET request to the specified URL and returns the JSON response.

        Args:
-            url: Fully qualified URL to query.
+            url: The URL to request.

        Returns:
-            Parsed JSON body as a dictionary if the request succeeds; otherwise ``None``.
+            The JSON response as a dictionary or None if the request fails.
        """
        timeout = aiohttp.ClientTimeout(total=self.timeout)
        async with aiohttp.ClientSession(timeout=timeout) as session:
@@ -87,14 +66,14 @@ class AgentLightningClient:
                return None

    async def _post_json_async(self, url: str, payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
-        """Perform an asynchronous ``POST`` request with a JSON body.
+        """Makes an async POST request with a JSON payload.

        Args:
-            url: Fully qualified URL that accepts the payload.
-            payload: Dictionary that will be serialized and sent as JSON.
+            url: The URL to post to.
+            payload: The dictionary data to send as JSON.

        Returns:
-            Parsed JSON body as a dictionary if the request succeeds; otherwise ``None``.
+            The JSON response as a dictionary or None if the request fails.
        """
        timeout = aiohttp.ClientTimeout(total=self.timeout)
        async with aiohttp.ClientSession(timeout=timeout) as session:
@@ -107,11 +86,10 @@ class AgentLightningClient:
                return None

    async def poll_next_task_async(self) -> Optional[Task]:
-        """Poll the server asynchronously until a task becomes available.
+        """Polls the server asynchronously for the next task until one is available.

        Returns:
-            The next [`Task`][agentlightning.Task] exposed by the server,
-            or ``None`` if polling fails.
+            A Task object containing the task details.
        """
        url = urllib.parse.urljoin(self.endpoint, self._next_task_uri)
        while True:
@@ -126,15 +104,13 @@ class AgentLightningClient:
            await asyncio.sleep(self.poll_interval)

    async def get_resources_by_id_async(self, resource_id: str) -> Optional[ResourcesUpdate]:
-        """Fetch a specific resource bundle by identifier.
+        """Fetches a specific version of resources by its ID, using a cache.

        Args:
-            resource_id: Identifier sourced from the task metadata.
+            resource_id: The ID of the resources to fetch, usually from a Task's metadata.

        Returns:
-            Cached or freshly downloaded
-            [`ResourcesUpdate`][agentlightning.ResourcesUpdate], or
-            ``None`` when the server returns an error.
+            A ResourcesUpdate object containing the versioned resources, or None if not found.
        """
        if resource_id in self._resource_cache:
            logger.debug(f"Found resources '{resource_id}' in cache.")
@@ -150,11 +126,10 @@ class AgentLightningClient:
        return None

    async def get_latest_resources_async(self) -> Optional[ResourcesUpdate]:
-        """Fetch the most recent resource bundle advertised by the server.
+        """Fetches the latest available resources from the server.

        Returns:
-            [`ResourcesUpdate`][agentlightning.ResourcesUpdate] for the
-            newest version, or ``None`` when unavailable.
+            A ResourcesUpdate object containing the latest resources.
        """
        url = urllib.parse.urljoin(self.endpoint, self._latest_resources_uri)
        response = await self._request_json_async(url)
@@ -165,27 +140,27 @@ class AgentLightningClient:
            return resources_update
        return None

-    async def post_rollout_async(self, rollout: RolloutLegacy) -> Optional[Dict[str, Any]]:
-        """Submit a completed rollout back to the server.
+    async def post_rollout_async(self, rollout: Rollout) -> Optional[Dict[str, Any]]:
+        """Posts a completed rollout to the server asynchronously.

        Args:
-            rollout: Legacy rollout payload produced by the executor.
+            rollout: A Rollout object containing the results of a task.

        Returns:
-            Parsed JSON response returned by the server, or ``None`` when the request fails.
+            The server's JSON response as a dictionary.
        """
        url = urllib.parse.urljoin(self.endpoint, self._report_rollout_uri)
        payload = rollout.model_dump(mode="json")
        return await self._post_json_async(url, payload)

    def _request_json(self, url: str) -> Optional[Dict[str, Any]]:
-        """Perform a blocking ``GET`` request and parse the JSON payload.
+        """Makes a sync GET request to the specified URL and returns the JSON response.

        Args:
-            url: Fully qualified URL to query.
+            url: The URL to request.

        Returns:
-            Parsed JSON body as a dictionary if the request succeeds; otherwise ``None``.
+            The JSON response as a dictionary or None if the request fails.
        """
        try:
            response = requests.get(url, timeout=self.timeout, headers=self._default_headers)
@@ -196,14 +171,14 @@ class AgentLightningClient:
            return None

    def _post_json(self, url: str, payload: Dict[str, Any]) -> Optional[Dict[str, Any]]:
-        """Perform a blocking ``POST`` request with a JSON payload.
+        """Makes a sync POST request with a JSON payload.

        Args:
-            url: Fully qualified URL that accepts the payload.
-            payload: Dictionary that will be serialized and sent as JSON.
+            url: The URL to post to.
+            payload: The dictionary data to send as JSON.

        Returns:
-            Parsed JSON body as a dictionary if the request succeeds; otherwise ``None``.
+            The JSON response as a dictionary or None if the request fails.
        """
        try:
            response = requests.post(url, json=payload, timeout=self.timeout, headers=self._default_headers)
@@ -214,11 +189,10 @@ class AgentLightningClient:
            return None

    def poll_next_task(self) -> Optional[Task]:
-        """Poll the server synchronously until a task becomes available.
+        """Polls the server synchronously for the next task until one is available.

        Returns:
-            The next [`Task`][agentlightning.Task] available for execution, or
-            ``None`` if polling fails.
+            A Task object containing the task details, including the required `resources_id`.
        """
        url = urllib.parse.urljoin(self.endpoint, self._next_task_uri)
        while True:
@@ -233,15 +207,13 @@ class AgentLightningClient:
            time.sleep(self.poll_interval)

    def get_resources_by_id(self, resource_id: str) -> Optional[ResourcesUpdate]:
-        """Fetch a specific resource bundle by identifier.
+        """Fetches a specific version of resources by its ID synchronously, using a cache.

        Args:
-            resource_id: Identifier sourced from the task metadata.
+            resource_id: The ID of the resources to fetch, usually from a Task's metadata.

        Returns:
-            Cached or freshly downloaded
-            [`ResourcesUpdate`][agentlightning.ResourcesUpdate], or
-            ``None`` when the server returns an error.
+            A ResourcesUpdate object containing the versioned resources, or None if not found.
        """
        if resource_id in self._resource_cache:
            logger.debug(f"Found resources '{resource_id}' in cache.")
@@ -257,11 +229,10 @@ class AgentLightningClient:
        return None

    def get_latest_resources(self) -> Optional[ResourcesUpdate]:
-        """Fetch the most recent resource bundle advertised by the server.
+        """Fetches the latest available resources from the server synchronously.

        Returns:
-            [`ResourcesUpdate`][agentlightning.ResourcesUpdate] for the
-            newest version, or ``None`` when unavailable.
+            A ResourcesUpdate object containing the latest resources.
        """
        url = urllib.parse.urljoin(self.endpoint, self._latest_resources_uri)
        response = self._request_json(url)
@@ -271,14 +242,14 @@ class AgentLightningClient:
            return resources_update
        return None

-    def post_rollout(self, rollout: RolloutLegacy) -> Optional[Dict[str, Any]]:
-        """Submit a completed rollout back to the server.
+    def post_rollout(self, rollout: Rollout) -> Optional[Dict[str, Any]]:
+        """Posts a completed rollout to the server synchronously.

        Args:
-            rollout: Legacy rollout payload produced by the executor.
+            rollout: A Rollout object containing the results of a task.

        Returns:
-            Parsed JSON response returned by the server, or ``None`` when the request fails.
+            The server's JSON response as a dictionary.
        """
        url = urllib.parse.urljoin(self.endpoint, self._report_rollout_uri)
        payload = rollout.model_dump(mode="json")
@@ -286,16 +257,14 @@ class AgentLightningClient:


 class DevTaskLoader(AgentLightningClient):
-    """In-memory task loader used for development and integration tests.
+    """A local task manager for development that provides sample tasks and resources.

-    The loader mimics the behavior of the legacy HTTP server by storing tasks and
-    resources locally. Polling methods simply iterate over the provided collection,
-    allowing rapid iteration without provisioning any external infrastructure.
+    This client mocks the server APIs by maintaining a local queue of tasks and resources
+    within the same process. It's designed for development, testing, and scenarios where
+    a full Agent Lightning server is not needed.

-    !!! warning "Deprecated"
-
-        [`DevTaskLoader`][agentlightning.client.DevTaskLoader] is a compatibility shim.
-        Prefer [`Trainer.dev`][agentlightning.Trainer.dev] for new code.
+    The DevTaskLoader overrides the polling and resource fetching methods to return data
+    from local collections instead of making HTTP requests to a remote server.
    """

    def __init__(
@@ -304,19 +273,13 @@ class DevTaskLoader(AgentLightningClient):
        resources: Union[NamedResources, ResourcesUpdate],
        **kwargs: Any,
    ):
-        """Initialize the loader with predefined tasks and resources.
+        """Initializes the DevTaskLoader with pre-defined tasks and resources.

        Args:
-            tasks: Sequence of task inputs or preconstructed tasks that will be served in
-                order.
-            resources: Static resources returned for any `resources_id` query.
-            **kwargs: Additional keyword arguments forwarded to the parent client.
-
-        Raises:
-            ValueError: If no tasks are provided or both [`Task`][agentlightning.Task]
-                and [`TaskInput`][agentlightning.TaskInput] instances are mixed.
+            tasks: Either a List of TaskInput objects or a List of Task objects.
+            resources: Either NamedResources or ResourcesUpdate object.
+            **kwargs: Additional arguments passed to the parent AgentLightningClient.
        """
-        warnings.warn("DevTaskLoader is deprecated. Please use Trainer.dev instead.", DeprecationWarning)
        super().__init__(endpoint="local://", **kwargs)
        self._tasks = tasks.copy()
        if len(self._tasks) == 0:
@@ -332,27 +295,24 @@ class DevTaskLoader(AgentLightningClient):
        if isinstance(resources, ResourcesUpdate):
            self._resources_update = resources
        else:
-            self._resources_update = ResourcesUpdate(
-                resources_id="local", resources=resources, create_time=time.time(), update_time=time.time(), version=1
-            )
+            self._resources_update = ResourcesUpdate(resources_id="local", resources=resources)

        # Store rollouts posted back to the loader for easy debugging of local runs
-        self._rollouts: List[RolloutLegacy] = []
+        self._rollouts: List[Rollout] = []

    @property
-    def rollouts(self) -> List[RolloutLegacy]:
-        """Return the rollouts posted back to the loader during development runs."""
+    def rollouts(self) -> List[Rollout]:
+        """Return rollouts that have been posted back to the loader."""
        return self._rollouts

    def poll_next_task(self) -> Optional[Task]:
-        """Return the next task from the local queue.
+        """Returns the next task from the local queue.

-        If [`TaskInput`][agentlightning.TaskInput] instances were provided,
-        they are converted into [`Task`][agentlightning.Task] objects on the
-        fly. Otherwise, the preconstructed tasks are returned in sequence.
+        If tasks are TaskInput objects, assembles them into Task objects.
+        If tasks are already Task objects, returns them directly.

        Returns:
-            Next task to execute.
+            The next Task object from the local task list.
        """
        if self._task_index >= len(self._tasks):
            self._task_index = 0
@@ -387,7 +347,7 @@ class DevTaskLoader(AgentLightningClient):
        logger.debug("DevTaskLoader returning latest resources.")
        return self._resources_update

-    def post_rollout(self, rollout: RolloutLegacy) -> Optional[Dict[str, Any]]:
+    def post_rollout(self, rollout: Rollout) -> Optional[Dict[str, Any]]:
        logger.debug(f"DevTaskLoader received rollout for task: {rollout.rollout_id}")
        self._rollouts.append(rollout)
        return {"status": "received", "rollout_id": rollout.rollout_id}
@@ -401,7 +361,7 @@ class DevTaskLoader(AgentLightningClient):
    async def get_latest_resources_async(self) -> Optional[ResourcesUpdate]:
        return self.get_latest_resources()

-    async def post_rollout_async(self, rollout: RolloutLegacy) -> Optional[Dict[str, Any]]:
+    async def post_rollout_async(self, rollout: Rollout) -> Optional[Dict[str, Any]]:
        return self.post_rollout(rollout)

    def __repr__(self):
@@ -31,8 +31,6 @@ CliConfigurable = Any

 logger = logging.getLogger(__name__)

-__all__ = ["lightning_cli"]
-
 # TypeVars for precise return type hinting with overloads
 _C = TypeVar("_C", bound=CliConfigurable)
 _C1 = TypeVar("_C1", bound=CliConfigurable)
@@ -83,17 +81,12 @@ def _str_to_bool(v: str) -> bool:


 def _get_param_type_details(param_annotation: Any) -> Tuple[Any, bool, bool]:
-    """Normalize an annotation into its core type, optionality, and list status.
-
-    Args:
-        param_annotation: The annotation to inspect.
-
-    Returns:
-        A tuple ``(core_type, is_optional, is_list)`` describing the normalized type.
-
-        - For ``Optional[T]`` → ``(T, True, is_list_status_of_T)``
-        - For ``List[T]`` → ``(List[T], is_optional_status_of_List, True)``
-        - For ``Optional[List[T]]`` → ``(List[T], True, True)``
+    """
+    Determines the core type, if it's Optional, and if it's a List.
+    Returns: (core_type, is_optional, is_list)
+    - For Optional[T]: (T, True, is_list_status_of_T)
+    - For List[T]: (List[T], is_optional_status_of_List, True)
+    - For Optional[List[T]]: (List[T], True, True)
    """
    is_optional = False
    is_list = False
@@ -314,9 +307,6 @@ def lightning_cli(cls1: Type[_C1], cls2: Type[_C2], cls3: Type[_C3], cls4: Type[
 def lightning_cli(*classes: Type[CliConfigurable]) -> Tuple[CliConfigurable, ...]: ...


-# FIXME: lightning_cli needs to be fixed to comply with the latest trainer implementation.
-
-
 def lightning_cli(*classes: Type[CliConfigurable]) -> CliConfigurable | Tuple[CliConfigurable, ...]:  # type: ignore
    """
    Parses command-line arguments to configure and instantiate provided CliConfigurable classes.
@@ -1,32 +1,27 @@
 # Copyright (c) Microsoft. All rights reserved.

-from .annotation import emit_annotation, operation
 from .exception import emit_exception
-from .message import emit_message, get_message_value
-from .object import emit_object, get_object_value
+from .message import emit_message
+from .object import emit_object
 from .reward import (
+    RewardSpanData,
    emit_reward,
    find_final_reward,
    find_reward_spans,
    get_reward_value,
-    get_rewards_from_span,
    is_reward_span,
    reward,
 )

 __all__ = [
    "reward",
-    "operation",
    "emit_reward",
    "get_reward_value",
-    "get_rewards_from_span",
    "is_reward_span",
    "find_reward_spans",
    "find_final_reward",
+    "RewardSpanData",
    "emit_message",
    "emit_object",
    "emit_exception",
-    "emit_annotation",
-    "get_message_value",
-    "get_object_value",
 ]
@@ -1,364 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Helpers for emitting annotation/operation spans."""
-
-import asyncio
-import functools
-import inspect
-import json
-import logging
-from types import TracebackType
-from typing import (
-    Any,
-    Callable,
-    ContextManager,
-    Dict,
-    Optional,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-    overload,
-)
-
-from opentelemetry import trace
-from opentelemetry.sdk.trace import ReadableSpan
-from opentelemetry.trace import Status, StatusCode
-
-from agentlightning.semconv import AGL_ANNOTATION, AGL_OPERATION, LightningSpanAttributes
-from agentlightning.utils.otel import flatten_attributes, get_tracer
-
-_FnType = TypeVar("_FnType", bound=Callable[..., Any])
-
-logger = logging.getLogger(__name__)
-
-
-def emit_annotation(annotation: Dict[str, Any], propagate: bool = True) -> ReadableSpan:
-    """Emit a new annotation span.
-
-    This is the underlying implementation of [`emit_reward`][agentlightning.emit_reward].
-
-    Annotation spans are used to annotate a specific event or a part of rollout.
-    See [semconv][agentlightning.semconv] for conventional annotation keys in Agent-lightning.
-
-    If annotations contain nested dicts, they will be flattened before emitting.
-    Complex objects will lead to emitting failures.
-
-    Args:
-        annotation: Dictionary containing annotation key-value pairs.
-            Representatives are rewards, tags, and metadata.
-        propagate: Whether to propagate the span to exporters automatically.
-    """
-    annotation_attributes = flatten_attributes(annotation)
-    if any(not isinstance(v, (str, int, float, bool, bytes)) for v in annotation_attributes.values()):
-        raise TypeError("All annotation attributes must be primitive types (str, int, float, bool, bytes)")
-
-    # TODO: this should use a tracer from current context rather than the singleton
-    tracer = get_tracer(use_active_span_processor=propagate)
-    span = tracer.start_span(
-        AGL_ANNOTATION,
-        attributes=annotation_attributes,
-    )
-    logger.debug("Emitting annotation span with keys %s", annotation_attributes)
-    with span:
-        pass
-    if not isinstance(span, ReadableSpan):
-        raise ValueError(f"Span is not a ReadableSpan: {span}")
-
-    return span
-
-
-def _safe_json_dump(obj: Any) -> str:
-    """Serialize an object to JSON, falling back to ``str(obj)`` if needed.
-
-    Args:
-        obj: Object to be serialized.
-
-    Returns:
-        The JSON-encoded string representation of the object, or its string
-        representation if JSON encoding fails.
-    """
-    try:
-        return json.dumps(obj, default=str, ensure_ascii=False)
-    except Exception:
-        return str(obj)
-
-
-class OperationContext:
-    """Context manager and decorator for tracing operations.
-
-    This class manages an OpenTelemetry span for a logical unit of work. It can
-    be used either:
-
-    * As a decorator, in which case inputs and outputs are inferred
-      automatically from the wrapped function's signature.
-    * As a context manager, in which case inputs and outputs can be recorded
-      explicitly via :meth:`set_input` and :meth:`set_output`.
-
-    Attributes:
-        name: Human-readable span name.
-        initial_attributes: Attributes applied when the span is created.
-        tracer: OpenTelemetry tracer used to create spans.
-        span: The currently active span, if any.
-    """
-
-    def __init__(self, name: str, attributes: Dict[str, Any], *, propagate: bool = True) -> None:
-        """Initialize a new operation context.
-
-        Args:
-            name: Human-readable name of the span.
-            attributes: Initial attributes attached to the span. Values are
-                JSON-serialized where necessary.
-            propagate: Whether the span should be sent to active exporters.
-        """
-        self.name: str = name
-        self.initial_attributes: Dict[str, Any] = attributes
-        self.propagate: bool = propagate
-        self.tracer: trace.Tracer = get_tracer(use_active_span_processor=propagate)
-        self.span: Optional[trace.Span] = None
-        self._ctx_token: Optional[ContextManager[Any]] = None
-
-    def __enter__(self) -> "OperationContext":
-        """Enter the context manager and start a new span.
-
-        Returns:
-            The current :class:`OperationContext` instance with an active span.
-        """
-        # 1. Start the span with initial attributes (JSON serialized)
-        sanitized_attrs = {
-            k: _safe_json_dump(v) if not isinstance(v, (str, int, float, bool)) else v
-            for k, v in self.initial_attributes.items()
-        }
-
-        self.span = self.tracer.start_span(self.name, attributes=sanitized_attrs)
-        self._ctx_token = trace.use_span(self.span, end_on_exit=True)
-        self._ctx_token.__enter__()
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        """Exit the context manager and finish the span.
-
-        Any exception raised inside the context is recorded on the span and the
-        span status is set to error.
-
-        Args:
-            exc_type: Exception type, if an exception occurred.
-            exc_val: Exception instance, if an exception occurred.
-            exc_tb: Traceback object, if an exception occurred.
-        """
-        # 1. Record Exception if present
-        if exc_val and self.span:
-            self.span.record_exception(exc_val)
-            self.span.set_status(Status(StatusCode.ERROR, str(exc_val)))
-
-        # 2. Close span
-        if self._ctx_token:
-            self._ctx_token.__exit__(exc_type, exc_val, exc_tb)
-
-    def set_input(self, *args: Any, **kwargs: Any) -> None:
-        """Record input arguments on the current span.
-
-        Positional arguments are stored under the ``input.args`` attribute,
-        and keyword arguments are stored under ``input.<name>`` attributes.
-
-        This is intended for use inside a ``with operation(...) as op`` block.
-
-        Args:
-            *args: Positional arguments to record.
-            **kwargs: Keyword arguments to record.
-        """
-        if not self.span:
-            return
-
-        if args:
-            self.span.set_attribute("input.args", _safe_json_dump(args))
-        if kwargs:
-            for k, v in kwargs.items():
-                self.span.set_attribute(f"input.{k}", _safe_json_dump(v))
-
-    def set_output(self, output: Any) -> None:
-        """Record the output value on the current span.
-
-        This is intended for use inside a ``with operation(...) as op`` block.
-
-        Args:
-            output: The output value to record.
-        """
-        if not self.span:
-            return
-        self.span.set_attribute("output", _safe_json_dump(output))
-
-    def __call__(self, fn: _FnType) -> _FnType:
-        """Wrap a callable so its execution is traced in a span.
-
-        When used as a decorator, a new span is created for each call to
-        the wrapped function. The bound arguments are recorded as input
-        attributes, the return value is recorded as an output attribute,
-        and any exception is recorded and marks the span as an error.
-
-        Args:
-            fn: The function or coroutine function to wrap.
-
-        Returns:
-            The wrapped callable.
-        """
-        function_name = fn.__name__
-
-        sig = inspect.signature(fn)
-
-        def _record_auto_inputs(span: trace.Span, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> None:
-            """Bind arguments to signature and log them on the span.
-
-            Args:
-                span: Span on which to record attributes.
-                args: Positional arguments passed to the wrapped callable.
-                kwargs: Keyword arguments passed to the wrapped callable.
-            """
-            try:
-                bound = sig.bind(*args, **kwargs)
-                bound.apply_defaults()
-                for k, v in bound.arguments.items():
-                    span.set_attribute(
-                        f"{LightningSpanAttributes.OPERATION_INPUT.value}.{k}",
-                        _safe_json_dump(v),
-                    )
-            except Exception:
-                span.set_attribute(
-                    f"{LightningSpanAttributes.OPERATION_INPUT.value}.args",
-                    _safe_json_dump(args),
-                )
-                span.set_attribute(
-                    f"{LightningSpanAttributes.OPERATION_INPUT.value}.kwargs",
-                    _safe_json_dump(kwargs),
-                )
-
-        if asyncio.iscoroutinefunction(fn) or inspect.iscoroutinefunction(fn):
-
-            @functools.wraps(fn)
-            async def async_wrapper(*args: Any, **kwargs: Any) -> Any:
-                """Async wrapper that traces the wrapped coroutine."""
-                # Reuse __enter__ logic via 'with self' would share state incorrectly
-                # across concurrent calls. We must create a new span per call.
-                # So we manually reimplement the span logic for the wrapper here.
-
-                sanitized_attrs = {
-                    k: _safe_json_dump(v) if not isinstance(v, (str, int, float, bool)) else v
-                    for k, v in self.initial_attributes.items()
-                }
-
-                with self.tracer.start_as_current_span(self.name, attributes=sanitized_attrs) as span:
-                    span.set_attribute(LightningSpanAttributes.OPERATION_NAME.value, function_name)
-                    _record_auto_inputs(span, args, kwargs)
-                    try:
-                        result = await fn(*args, **kwargs)
-                        span.set_attribute(
-                            LightningSpanAttributes.OPERATION_OUTPUT.value,
-                            _safe_json_dump(result),
-                        )
-                        return result
-                    except Exception as e:
-                        span.record_exception(e)
-                        span.set_status(Status(StatusCode.ERROR, str(e)))
-                        raise
-
-            return cast(_FnType, async_wrapper)
-
-        else:
-
-            @functools.wraps(fn)
-            def sync_wrapper(*args: Any, **kwargs: Any) -> Any:
-                """Sync wrapper that traces the wrapped callable."""
-                sanitized_attrs = {
-                    k: _safe_json_dump(v) if not isinstance(v, (str, int, float, bool)) else v
-                    for k, v in self.initial_attributes.items()
-                }
-
-                with self.tracer.start_as_current_span(self.name, attributes=sanitized_attrs) as span:
-                    span.set_attribute(LightningSpanAttributes.OPERATION_NAME.value, function_name)
-                    _record_auto_inputs(span, args, kwargs)
-                    try:
-                        result = fn(*args, **kwargs)
-                        span.set_attribute(
-                            LightningSpanAttributes.OPERATION_OUTPUT.value,
-                            _safe_json_dump(result),
-                        )
-                        return result
-                    except Exception as e:
-                        span.record_exception(e)
-                        span.set_status(Status(StatusCode.ERROR, str(e)))
-                        raise
-
-            return cast(_FnType, sync_wrapper)
-
-
-@overload
-def operation(fn: _FnType, *, propagate: bool = True, **additional_attributes: Any) -> _FnType: ...
-
-
-@overload
-def operation(*, propagate: bool = True, **additional_attributes: Any) -> OperationContext: ...
-
-
-def operation(
-    fn: Optional[_FnType] = None,
-    *,
-    propagate: bool = True,
-    **additional_attributes: Any,
-) -> Union[_FnType, OperationContext]:
-    """Entry point for tracking operations.
-
-    This helper can be used either as a decorator or as a context manager.
-    The span name is fixed to [`AGL_OPERATION`][agentlightning.semconv.AGL_OPERATION];
-    custom span names are not supported. Any keyword arguments are recorded as span attributes.
-
-    Usage as a decorator:
-
-    ```python
-    @operation
-    def func(...):
-        ...
-
-    @operation(category="compute")
-    def func(...):
-        ...
-    ```
-
-    Usage as a context manager:
-
-    ```python
-    with operation(user_id=123) as op:
-        op.set_input(data=data)
-        # ... do work ...
-        op.set_output(result)
-    ```
-
-    Args:
-        fn: When used as `@operation`, this is the wrapped function.
-            When used as `operation(**attrs)`, this should be omitted (or
-            left as `None`) and only keyword attributes are provided.
-        propagate: Whether spans should use the active span processor. When False,
-            spans will stay local and not be exported.
-        **additional_attributes: Additional span attributes to attach at
-            creation time.
-
-    Returns:
-        Either a wrapped callable (when used as a decorator) or an
-        [`OperationContext`][agentlightning.emitter.annotation.OperationContext]
-        (when used as a context manager factory).
-    """
-    # Case 1: Used as @operation (bare decorator or with attributes)
-    if callable(fn):
-        # Create context with fixed name, then immediately wrap the function
-        return OperationContext(AGL_OPERATION, additional_attributes, propagate=propagate)(fn)
-
-    # Case 2: Used as operation(...) / with operation(...)
-    # Custom span names are intentionally not supported; use AGL_OPERATION.
-    if fn is not None:
-        raise ValueError("Custom span names are intentionally not supported when used as a context manager.")
-    return OperationContext(AGL_OPERATION, additional_attributes, propagate=propagate)
@@ -2,53 +2,35 @@

 import logging
 import traceback
-from typing import Any, Dict, Optional

 from opentelemetry.semconv.attributes import exception_attributes

-from agentlightning.semconv import AGL_EXCEPTION
-from agentlightning.utils.otel import get_tracer
+from agentlightning.types import SpanNames
+
+from .utils import get_tracer

 logger = logging.getLogger(__name__)


-def emit_exception(
-    exception: BaseException, attributes: Optional[Dict[str, Any]] = None, propagate: bool = True
-) -> None:
-    """Record an exception with OpenTelemetry metadata.
-
-    Classic OpenTelemetry records exceptions in a dedicated logging service.
-    We simplify the model and use trace spans to record exceptions as well.
-
-    Args:
-        exception: Raised exception instance to serialize into telemetry attributes.
-        attributes: Additional attributes to attach to the exception span.
-        propagate: Whether to propagate the span to exporters automatically.
-
-    !!! note
-
-        The helper validates its input. If a non-exception value is provided,
-        a TypeError is raised to indicate a programming mistake.
-    """
+def emit_exception(exception: BaseException) -> None:
+    """Emit an exception as a span."""
    if not isinstance(exception, BaseException):  # type: ignore
-        raise TypeError(f"Expected a BaseException instance, got: {type(exception)}.")
+        logger.error(f"Expected an BaseException instance, got: {type(exception)}. Skip emit_exception.")
+        return

-    tracer = get_tracer(use_active_span_processor=propagate)
+    tracer = get_tracer()
    stacktrace = "".join(traceback.format_exception(type(exception), exception, exception.__traceback__))
-    span_attributes = {
+    attributes = {
        exception_attributes.EXCEPTION_TYPE: type(exception).__name__,
        exception_attributes.EXCEPTION_MESSAGE: str(exception),
        exception_attributes.EXCEPTION_ESCAPED: True,
    }
    if stacktrace.strip():
-        span_attributes[exception_attributes.EXCEPTION_STACKTRACE] = stacktrace
-
-    if attributes:
-        span_attributes.update(attributes)
+        attributes[exception_attributes.EXCEPTION_STACKTRACE] = stacktrace

    span = tracer.start_span(
-        AGL_EXCEPTION,
-        attributes=span_attributes,
+        SpanNames.EXCEPTION.value,
+        attributes=attributes,
    )
    logger.debug("Emitting exception span for %s", type(exception).__name__)
    with span:
@@ -1,55 +1,29 @@
 # Copyright (c) Microsoft. All rights reserved.

 import logging
-from typing import Any, Dict, Optional

-from agentlightning.semconv import AGL_MESSAGE, LightningSpanAttributes
-from agentlightning.types import SpanLike
-from agentlightning.utils.otel import get_tracer
+from agentlightning.types import SpanAttributeNames, SpanNames
+
+from .utils import get_tracer

 logger = logging.getLogger(__name__)


-def emit_message(message: str, attributes: Optional[Dict[str, Any]] = None, propagate: bool = True) -> None:
-    """Emit a textual message as an OpenTelemetry span.
+def emit_message(message: str) -> None:
+    """Emit a string message as a span.

-    Commonly used for sending debugging and logging messages.
-
-    Args:
-        message: Human readable message to attach as a span attribute.
-        attributes: Additional attributes to attach to the message span.
-        propagate: Whether to propagate the span to exporters automatically.
-
-    !!! note
-        OpenTelemetry distinguishes between logs and spans. Emitting the message as a
-        span keeps all Agent Lightning telemetry in a single data store for analysis.
+    OpenTelemetry has a dedicated design of logs by design, but we can also use spans to emit messages.
+    So that it can all be unified in the data store and analyzed together.
    """
    if not isinstance(message, str):  # type: ignore
-        raise TypeError(f"Message must be a string or list of strings, got: {type(message)}.")
+        logger.error(f"Message must be a string, got: {type(message)}. Skip emit_message.")
+        return

-    tracer = get_tracer(use_active_span_processor=propagate)
-    span_attributes = {LightningSpanAttributes.MESSAGE_BODY.value: message}
-    if attributes:
-        span_attributes.update(attributes)
+    tracer = get_tracer()
    span = tracer.start_span(
-        AGL_MESSAGE,
-        attributes=span_attributes,
+        SpanNames.MESSAGE.value,
+        attributes={SpanAttributeNames.MESSAGE.value: message},
    )
    logger.debug("Emitting message span with message: %s", message)
    with span:
        pass
-
-
-def get_message_value(span: SpanLike) -> Optional[str]:
-    """Extract the message string from a message span.
-
-    Args:
-        span: Span-like object to extract the message from.
-    """
-    span_attributes = span.attributes or {}
-    if LightningSpanAttributes.MESSAGE_BODY.value not in span_attributes:
-        return None
-    message = span_attributes[LightningSpanAttributes.MESSAGE_BODY.value]
-    if isinstance(message, str):
-        return message
-    raise TypeError(f"Message must be a string, got: {type(message)}.")
@@ -1,106 +1,29 @@
 # Copyright (c) Microsoft. All rights reserved.

-import base64
 import json
 import logging
-from typing import Any, Dict, Optional
+from typing import Any

-from agentlightning.semconv import AGL_OBJECT, LightningSpanAttributes
-from agentlightning.types import SpanLike
-from agentlightning.utils.otel import full_qualified_name, get_tracer
+from agentlightning.types import SpanAttributeNames, SpanNames
+
+from .utils import get_tracer

 logger = logging.getLogger(__name__)


-def emit_object(object: Any, attributes: Optional[Dict[str, Any]] = None, propagate: bool = True) -> None:
-    """Emit an object's serialized representation as an OpenTelemetry span.
+def emit_object(object: Any) -> None:
+    """Emit any object as a span. Make sure the object is JSON serializable."""
+    try:
+        serialized = json.dumps(object)
+    except (TypeError, ValueError):
+        logger.error(f"Object must be JSON serializable, got: {type(object)}. Skip emit_object.")
+        return

-    Args:
-        object: Data structure to encode as JSON and attach to the span payload.
-        attributes: Additional attributes to attach to the object span.
-        propagate: Whether to propagate the span to exporters automatically.
-
-    !!! note
-        The payload must be JSON serializable. Non-serializable objects will lead to a RuntimeError.
-    """
-    span_attributes = encode_object(object)
-    if attributes:
-        span_attributes.update(attributes)
-    tracer = get_tracer(use_active_span_processor=propagate)
+    tracer = get_tracer()
    span = tracer.start_span(
-        AGL_OBJECT,
-        attributes=span_attributes,
+        SpanNames.OBJECT.value,
+        attributes={SpanAttributeNames.OBJECT.value: serialized},
    )
-    attr_length = 0
-    if LightningSpanAttributes.OBJECT_JSON.value in span_attributes:
-        attr_length = len(span_attributes[LightningSpanAttributes.OBJECT_JSON.value])
-    elif LightningSpanAttributes.OBJECT_LITERAL.value in span_attributes:
-        attr_length = len(span_attributes[LightningSpanAttributes.OBJECT_LITERAL.value])
-    logger.debug("Emitting object span with payload size %d characters", attr_length)
+    logger.debug("Emitting object span with payload size %d characters", len(serialized))
    with span:
        pass
-
-
-def encode_object(object: Any) -> Dict[str, Any]:
-    """Encode an object as span attributes.
-
-    Args:
-        object: Data structure to encode as JSON.
-    """
-    span_attributes = {}
-    if isinstance(object, (str, int, float, bool)):
-        span_attributes = {
-            LightningSpanAttributes.OBJECT_TYPE.value: type(object).__name__,
-            LightningSpanAttributes.OBJECT_LITERAL.value: str(object),
-        }
-    elif isinstance(object, bytes):
-        b64_encoded = base64.b64encode(object).decode("utf-8")
-        span_attributes = {
-            LightningSpanAttributes.OBJECT_TYPE.value: "bytes",
-            LightningSpanAttributes.OBJECT_LITERAL.value: b64_encoded,
-        }
-    else:
-        try:
-            serialized = json.dumps(object)
-        except (TypeError, ValueError) as exc:
-            raise RuntimeError(f"Object must be JSON serializable, got: {type(object)}.") from exc
-
-        span_attributes = {
-            LightningSpanAttributes.OBJECT_TYPE.value: full_qualified_name(type(object)),  # type: ignore
-            LightningSpanAttributes.OBJECT_JSON.value: serialized,
-        }
-
-    return span_attributes
-
-
-def get_object_value(span: SpanLike) -> Any:
-    """Extract the object payload from an object span.
-
-    Args:
-        span: Span object produced by Agent Lightning emitters.
-    """
-    attributes = span.attributes or {}
-    if LightningSpanAttributes.OBJECT_JSON.value in attributes:
-        serialized = attributes[LightningSpanAttributes.OBJECT_JSON.value]
-        try:
-            return json.loads(serialized)  # type: ignore
-        except (TypeError, ValueError) as exc:
-            raise RuntimeError("Failed to deserialize object JSON from span.") from exc
-    elif LightningSpanAttributes.OBJECT_LITERAL.value in attributes:
-        literal = attributes[LightningSpanAttributes.OBJECT_LITERAL.value]
-        obj_type = attributes.get(LightningSpanAttributes.OBJECT_TYPE.value, "str")
-        if obj_type == "str":
-            return literal
-        elif obj_type == "int":
-            # Let it raise errors if there are any
-            return int(literal)  # type: ignore
-        elif obj_type == "float":
-            return float(literal)  # type: ignore
-        elif obj_type == "bool":
-            return literal.lower() == "true"  # type: ignore
-        elif obj_type == "bytes":
-            return base64.b64decode(literal.encode("utf-8"))  # type: ignore
-        else:
-            raise RuntimeError(f"Unsupported object type for literal deserialization: {obj_type}")
-    else:
-        return None
@@ -1,7 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Helpers for emitting reward spans and integrating with AgentOps telemetry."""
-
 import asyncio
 import inspect
 import json
@@ -23,13 +21,10 @@ from typing import (
 import agentops
 from agentops.sdk.decorators import operation
 from opentelemetry.sdk.trace import ReadableSpan
-from pydantic import TypeAdapter

-from agentlightning.semconv import AGL_ANNOTATION, LightningSpanAttributes, RewardPydanticModel
-from agentlightning.types import SpanLike
-from agentlightning.utils.otel import filter_and_unflatten_attributes
+from agentlightning.types import SpanLike, SpanNames

-from .annotation import emit_annotation
+from .utils import get_tracer

 logger = logging.getLogger(__name__)

@@ -37,52 +32,35 @@ __all__ = [
    "reward",
    "emit_reward",
    "get_reward_value",
-    "get_rewards_from_span",
    "is_reward_span",
    "find_reward_spans",
    "find_final_reward",
 ]


-class RewardDimension(TypedDict):
-    """Type representing a single dimension in a multi-dimensional reward."""
-
-    name: str
-    value: float
-
-
-class _RewardSpanData(TypedDict):
+class RewardSpanData(TypedDict):
    type: Literal["reward"]
    value: Optional[float]


-_FnType = TypeVar("_FnType", bound=Callable[..., Any])
+FnType = TypeVar("FnType", bound=Callable[..., Any])


 def _agentops_initialized() -> bool:
-    """Return `True` when the AgentOps client has been configured."""
+    """Check if AgentOps is initialized in the current context."""
    return agentops.get_client().initialized


-def reward(fn: _FnType) -> _FnType:
-    """Decorate a reward function so its outputs are tracked as spans.
-
-    The decorator integrates with AgentOps when it is available and falls back to
-    the built-in telemetry otherwise. Both synchronous and asynchronous functions
-    are supported transparently.
-
-    Deprecated:
-        This decorator is deprecated. Use [`emit_reward`][agentlightning.emit_reward] instead.
-
-    Args:
-        fn: Callable that produces a numeric reward.
-
-    Returns:
-        Wrapped callable that preserves the original signature.
+def reward(fn: FnType) -> FnType:
+    """
+    A decorator to wrap a function that computes rewards.
+    It will automatically handle the input and output of the function.
    """

-    def wrap_result(result: Optional[float]) -> _RewardSpanData:
-        """Normalize the reward value into the span payload format."""
+    def wrap_result(result: Optional[float]) -> RewardSpanData:
+        """
+        Wrap the result of the function in a dict.
+        """
        if result is None:
            return {"type": "reward", "value": None}
        if not isinstance(result, (float, int)):  # type: ignore
@@ -105,7 +83,7 @@ def reward(fn: _FnType) -> _FnType:
            result: Optional[float] = None

            @operation
-            async def agentops_reward_operation() -> _RewardSpanData:
+            async def agentops_reward_operation() -> RewardSpanData:
                # The reward function we are interested in tracing
                # It takes zero inputs and return a formatted dict
                nonlocal result
@@ -129,7 +107,7 @@ def reward(fn: _FnType) -> _FnType:
            result: Optional[float] = None

            @operation
-            def agentops_reward_operation() -> _RewardSpanData:
+            def agentops_reward_operation() -> RewardSpanData:
                nonlocal result
                result = fn(*args, **kwargs)
                return wrap_result(result)
@@ -140,90 +118,30 @@ def reward(fn: _FnType) -> _FnType:
        return wrapper  # type: ignore


-def emit_reward(
-    reward: float | Dict[str, Any],
-    *,
-    primary_key: str | None = None,
-    attributes: Dict[str, Any] | None = None,
-    propagate: bool = True,
-) -> ReadableSpan:
-    """Emit a reward value as an OpenTelemetry span.
-
-    Examples:
-        Emit a single-dimensional reward:
-        >>> emit_reward(1.0)
-
-        Emit multi-dimensional rewards:
-        >>> emit_reward({"task_completion": 1.0, "efficiency": 0.8}, primary_key="task_completion")
-
-        Emit a reward with additional attributes (for example linking to another response span):
-        >>> from agentlightning.utils.otel import make_link_attributes
-        >>> emit_reward(0.5, attributes=make_link_attributes({"gen_ai.response.id": "response-123"}))
-
-        Or adding tags onto the reward span:
-        >>> from agentlightning.utils.otel import make_tag_attributes
-        >>> emit_reward(0.7, attributes=make_tag_attributes(["fast", "reliable"]))
-
-    Args:
-        reward: Numeric reward to record. Integers and booleans are converted to
-            floating point numbers for consistency.
-            Use a dictionary to represent a multi-dimensional reward.
-        attributes: Other optional span attributes.
-        propagate: Whether to propagate the span to exporters automatically.
-
-    Returns:
-        Readable span capturing the recorded reward.
-
-    Raises:
-        ValueError: If the provided reward cannot be interpreted as a float or the
-            resulting span is not a [`ReadableSpan`](https://opentelemetry.io/docs/concepts/signals/traces/) instance.
+def emit_reward(reward: float) -> ReadableSpan:
+    """
+    Record a new reward as a new span.
    """
    logger.debug(f"Emitting reward: {reward}")
-    reward_dimensions: List[RewardDimension] = []
-    if isinstance(reward, dict):
-        reward_dict: Dict[str, float] = {}
-        for k, v in reward.items():
-            if isinstance(v, (int, bool)):
-                reward_dict[k] = float(v)
-            elif isinstance(v, float):
-                reward_dict[k] = v
-            else:
-                raise ValueError(f"Reward value must be a number, got: {type(v)} for key {k}")
-        if primary_key is None:
-            raise ValueError("When emitting a multi-dimensional reward as a dict, primary_key must be provided.")
-        if primary_key not in reward_dict:
-            raise ValueError(f"Primary key '{primary_key}' not found in reward dict keys: {list(reward_dict.keys())}")
-        reward_dimensions.append(RewardDimension(name=primary_key, value=reward_dict[primary_key]))
-        for k, v in reward_dict.items():
-            if k != primary_key:
-                reward_dimensions.append(RewardDimension(name=k, value=v))
-    else:
-        if isinstance(reward, (int, bool)):
-            reward = float(reward)
-        elif not isinstance(reward, float):  # pyright: ignore[reportUnnecessaryIsInstance]
-            raise TypeError(f"Reward must be a number, got: {type(reward)}")
-        reward_dimensions.append(RewardDimension(name="primary", value=reward))
+    if isinstance(reward, (int, bool)):
+        reward = float(reward)
+    if not isinstance(reward, float):
+        raise ValueError(f"Reward must be a number, got: {type(reward)}")

-    return emit_annotation(
-        {LightningSpanAttributes.REWARD.value: reward_dimensions, **(attributes or {})}, propagate=propagate
-    )
+    tracer = get_tracer()
+    span = tracer.start_span(SpanNames.REWARD.value, attributes={"reward": reward})
+    # Do nothing; it's just a number
+    with span:
+        pass
+    if not isinstance(span, ReadableSpan):
+        raise ValueError(f"Span is not a ReadableSpan: {span}")
+    return span


 def get_reward_value(span: SpanLike) -> Optional[float]:
-    """Extract the reward value from a span, if available.
-
-    Args:
-        span: Span object produced by AgentOps or Agent Lightning emitters.
-
-    Returns:
-        The primary reward encoded in the span or `None` when the span does not represent a reward.
    """
-    # v0.3+ emit reward format
-    reward_list = get_rewards_from_span(span)
-    if reward_list:
-        # Reward list is ordered and the first element is the primary reward
-        return reward_list[0].value
-
+    Get the reward value from a span.
+    """
    for key in [
        "agentops.task.output",  # newer versions of agentops
        "agentops.entity.output",
@@ -246,71 +164,49 @@ def get_reward_value(span: SpanLike) -> Optional[float]:
                return None
            if not isinstance(reward_value, float):
                logger.error(f"Reward is not a number, got: {type(reward_value)}. This may cause undefined behaviors.")
-            logger.warning(
-                f"Extracted reward {reward_value} from AgentOps. This format is deprecated, please migrate to using `emit_reward`."
-            )
            return cast(float, reward_value)

-    # v0.2 emit reward format
-    if span.name == AGL_ANNOTATION and span.attributes:
+    # Latest emit reward format
+    if span.name == SpanNames.REWARD.value and span.attributes:
        reward_value = span.attributes.get("reward", None)
        if reward_value is None:
            return None
        if not isinstance(reward_value, float):
            logger.error(f"Reward is not a number, got: {type(reward_value)}. This may cause undefined behaviors.")
-        logger.warning(
-            f"Extracted reward {reward_value} from a legacy version of reward span. You might have inconsistent agent-lightning versions."
-        )
        return cast(float, reward_value)
-
    return None


-def get_rewards_from_span(span: SpanLike) -> List[RewardPydanticModel]:
-    """Extract the reward as a list from a span, if available.
-
-    Args:
-        span: Span object produced by AgentOps or Agent Lightning emitters.
-
-    Returns:
-        A list of reward dimensions encoded in the span or an empty list when the span does not represent a reward.
-    """
-    if span.attributes and any(key.startswith(LightningSpanAttributes.REWARD.value) for key in span.attributes):
-        reward_attr = filter_and_unflatten_attributes(
-            cast(Any, span.attributes or {}), LightningSpanAttributes.REWARD.value
-        )
-        recovered_rewards = TypeAdapter(List[RewardPydanticModel]).validate_python(reward_attr)
-        return recovered_rewards
-    else:
-        return []
-
-
 def is_reward_span(span: SpanLike) -> bool:
-    """Return ``True`` when the provided span encodes a reward value."""
+    """
+    Check if a span is a reward span.
+    """
    maybe_reward = get_reward_value(span)
    return maybe_reward is not None


 def find_reward_spans(spans: Sequence[SpanLike]) -> List[SpanLike]:
-    """Return all reward spans in the provided sequence.
+    """
+    Find all reward spans in the given list of spans.

    Args:
-        spans: Sequence containing [`ReadableSpan`](https://opentelemetry.io/docs/concepts/signals/traces/) objects or mocked span-like values.
+        spans: A list of spans (either ReadableSpan or Span).

    Returns:
-        List of spans that could be parsed as rewards.
+        A list of spans whose name matches the reward span name.
    """
    return [span for span in spans if is_reward_span(span)]


 def find_final_reward(spans: Sequence[SpanLike]) -> Optional[float]:
-    """Return the last reward value present in the provided spans.
+    """
+    Get the last reward value from a list of spans.

    Args:
-        spans: Sequence containing [`ReadableSpan`](https://opentelemetry.io/docs/concepts/signals/traces/) objects or mocked span-like values.
+        spans: A list of spans (either ReadableSpan or Span).

    Returns:
-        Reward value from the latest reward span, or `None` when none are found.
+        The reward value from the last reward span, or None if not found.
    """
    for span in reversed(spans):
        reward = get_reward_value(span)
@@ -0,0 +1,22 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Common utilities for the emitter module."""
+
+import opentelemetry.trace as trace_api
+from opentelemetry.trace import get_tracer_provider
+
+
+def get_tracer() -> trace_api.Tracer:
+    """Return the tracer used for AgentLightning spans.
+
+    Raises:
+        RuntimeError: If the tracer is not initialized.
+
+    Returns:
+        The AgentLightning tracer instance.
+    """
+    if hasattr(trace_api, "_TRACER_PROVIDER") and trace_api._TRACER_PROVIDER is None:  # type: ignore[attr-defined]
+        raise RuntimeError("Tracer is not initialized. Cannot emit a meaningful span.")
+
+    tracer_provider = get_tracer_provider()
+    return tracer_provider.get_tracer("agentlightning")
@@ -1,156 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Environment variable managements."""
-
-from __future__ import annotations
-
-import os
-from enum import Enum
-from typing import overload
-
-__all__ = [
-    "LightningEnvVar",
-    "resolve_bool_env_var",
-    "resolve_int_env_var",
-    "resolve_str_env_var",
-]
-
-
-class LightningEnvVar(Enum):
-    """Environment variables for Agent Lightning."""
-
-    AGL_EMITTER_DEBUG = "AGL_EMITTER_DEBUG"
-    """Enable debug logging for the emitter."""
-
-    AGL_MANAGED_STORE = "AGL_MANAGED_STORE"
-    """If yes, the [`ExecutionStrategy`][agentlightning.ExecutionStrategy]
-    constructs LightningStore wrappers automatically. When `False` the provided
-    `store` is passed directly to the bundles, allowing callers to manage
-    store wrappers manually."""
-
-    AGL_CURRENT_ROLE = "AGL_CURRENT_ROLE"
-    """Which side(s) to run in this process. Used in
-    [`ClientServerExecutionStrategy`][agentlightning.ClientServerExecutionStrategy]."""
-
-    AGL_SERVER_HOST = "AGL_SERVER_HOST"
-    """Interface the [`LightningStoreServer`][agentlightning.LightningStoreServer]
-    binds to when running the algorithm bundle locally."""
-
-    AGL_SERVER_PORT = "AGL_SERVER_PORT"
-    """Port the [`LightningStoreServer`][agentlightning.LightningStoreServer] listens to."""
-
-
-_TRUTHY_VALUES = {"1", "true", "yes", "on"}
-_FALSY_VALUES = {"0", "false", "no", "off"}
-
-
-@overload
-def resolve_bool_env_var(env_var: LightningEnvVar, override: bool, fallback: bool) -> bool: ...
-
-
-@overload
-def resolve_bool_env_var(env_var: LightningEnvVar, *, fallback: bool) -> bool: ...
-
-
-@overload
-def resolve_bool_env_var(
-    env_var: LightningEnvVar, override: bool | None = None, fallback: bool | None = None
-) -> bool | None: ...
-
-
-def resolve_bool_env_var(
-    env_var: LightningEnvVar, override: bool | None = None, fallback: bool | None = None
-) -> bool | None:
-    """Resolve a boolean environment variable.
-
-    Args:
-        env_var: The environment variable to resolve.
-        override: Optional override supplied by the caller.
-        fallback: Default value if the environment variable is not set.
-    """
-
-    if override is not None:
-        return override
-
-    env_value = os.getenv(env_var.value)
-    if env_value is None:
-        return fallback
-
-    normalized = env_value.strip().lower()
-    if normalized in _TRUTHY_VALUES:
-        return True
-    if normalized in _FALSY_VALUES:
-        return False
-
-    raise ValueError(f"{env_var.value} must be one of {_TRUTHY_VALUES} or {_FALSY_VALUES}")
-
-
-@overload
-def resolve_int_env_var(env_var: LightningEnvVar, override: int, fallback: int) -> int: ...
-
-
-@overload
-def resolve_int_env_var(env_var: LightningEnvVar, *, fallback: int) -> int: ...
-
-
-@overload
-def resolve_int_env_var(
-    env_var: LightningEnvVar, override: int | None = None, fallback: int | None = None
-) -> int | None: ...
-
-
-def resolve_int_env_var(
-    env_var: LightningEnvVar, override: int | None = None, fallback: int | None = None
-) -> int | None:
-    """Resolve an integer environment variable.
-
-    Args:
-        env_var: The environment variable to resolve.
-        override: Optional override supplied by the caller.
-        fallback: Default value if the environment variable is not set.
-    """
-    if override is not None:
-        return override
-
-    env_value = os.getenv(env_var.value)
-    if env_value is None:
-        return fallback
-
-    try:
-        return int(env_value)
-    except ValueError:
-        raise ValueError(f"{env_var.value} must be an integer")
-
-
-@overload
-def resolve_str_env_var(env_var: LightningEnvVar, override: str, fallback: str) -> str: ...
-
-
-@overload
-def resolve_str_env_var(env_var: LightningEnvVar, *, fallback: str) -> str: ...
-
-
-@overload
-def resolve_str_env_var(
-    env_var: LightningEnvVar, override: str | None = None, fallback: str | None = None
-) -> str | None: ...
-
-
-def resolve_str_env_var(
-    env_var: LightningEnvVar, override: str | None = None, fallback: str | None = None
-) -> str | None:
-    """Resolve a string environment variable.
-
-    Args:
-        env_var: The environment variable to resolve.
-        override: Optional override supplied by the caller.
-        fallback: Default value if the environment variable is not set.
-    """
-    if override is not None:
-        return override
-
-    env_value = os.getenv(env_var.value)
-    if env_value is None:
-        return fallback
-
-    return env_value
@@ -1,15 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from .base import ExecutionStrategy
-from .client_server import ClientServerExecutionStrategy
-from .events import ExecutionEvent, MultiprocessingEvent, ThreadingEvent
-from .shared_memory import SharedMemoryExecutionStrategy
-
-__all__ = [
-    "ExecutionStrategy",
-    "ClientServerExecutionStrategy",
-    "ExecutionEvent",
-    "ThreadingEvent",
-    "MultiprocessingEvent",
-    "SharedMemoryExecutionStrategy",
-]
@@ -1,64 +1,37 @@
 # Copyright (c) Microsoft. All rights reserved.

-from __future__ import annotations
-
 import logging
 from typing import Protocol

 from agentlightning.store.base import LightningStore

-from .events import ExecutionEvent
+from .events import Event

 logger = logging.getLogger(__name__)


 class AlgorithmBundle(Protocol):
-    """Callable bundle produced by [`Trainer`][agentlightning.Trainer].
-
-    Execution strategies treat the returned coroutine as opaque, only providing
-    the shared store instance and cooperative stop event. Bundles typically
-    encapsulate algorithm setup plus adapter and LLM proxy, etc.
-    """
-
-    async def __call__(self, store: LightningStore, event: ExecutionEvent) -> None:
-        """Execute algorithm logic using ``store`` until completion or stop."""
+    async def __call__(self, store: LightningStore, event: Event) -> None:
+        """Initalization and execution logic."""


 class RunnerBundle(Protocol):
-    """Callable bundle wrapping runner setup and the worker loop, as opposed to the
-    [`AlgorithmBundle`][agentlightning.AlgorithmBundle]."""
-
-    async def __call__(self, store: LightningStore, worker_id: int, event: ExecutionEvent) -> None:
-        """Execute runner logic for ``worker_id`` using ``store`` and ``event``."""
+    async def __call__(self, store: LightningStore, worker_id: int, event: Event) -> None:
+        """Initalization and execution logic."""


 class ExecutionStrategy:
-    """Coordinate algorithm and runner bundles within a single process abstraction.
+    """When trainer has created the executable of algorithm and runner in two bundles,
+    the execution strategy defines how to run them together, and how many parallel runners to run.

-    Strategies decide how many worker bundles to launch, whether to communicate
-    through shared memory or an HTTP boundary, and how to react to shutdown
-    signals. They intentionally avoid inspecting the bundle internals; instead,
-    each bundle remains responsible for its own scheduling semantics.
+    The store is the centric place for the two bundles to communicate.

-    !!! note
-        Implementations must honor the [execute()][agentlightning.ExecutionStrategy.execute]
-        contract by propagating `KeyboardInterrupt` and ensuring resources are
-        released when an error occurs on either side of the algorithm/runner
-        pair.
+    The algorithm and runner's behavior (whether runner should perform one step or run forever,
+    whether the algo would send out the tasks or not) are defined inside the bundle,
+    and does not belong to the execution strategy.
+
+    The execute should support Ctrl+C to exit gracefully.
    """

    def execute(self, algorithm: AlgorithmBundle, runner: RunnerBundle, store: LightningStore) -> None:
-        """Run the provided bundles using the configured orchestration model.
-
-        Args:
-            algorithm: Callable bundle responsible for algorithm execution.
-            runner: Callable bundle for runner workers.
-            store: Concrete [`LightningStore`][agentlightning.LightningStore]
-                shared across bundles.
-
-        Raises:
-            NotImplementedError: Subclasses must provide the orchestration
-                implementation.
-        """
-
        raise NotImplementedError()
@@ -9,55 +9,55 @@ import time
 from multiprocessing.context import BaseContext
 from typing import Callable, Iterable, Literal, cast

-from agentlightning.env_var import LightningEnvVar, resolve_bool_env_var, resolve_int_env_var, resolve_str_env_var
 from agentlightning.store.base import LightningStore
 from agentlightning.store.client_server import LightningStoreClient, LightningStoreServer

 from .base import AlgorithmBundle, ExecutionStrategy, RunnerBundle
-from .events import ExecutionEvent, MultiprocessingEvent
+from .events import Event, MultiprocessingEvent

 logger = logging.getLogger(__name__)


 class ClientServerExecutionStrategy(ExecutionStrategy):
-    """Run algorithm and runner bundles as separate processes over HTTP.
+    """Run algorithm (server) and runners (clients) as separate processes over HTTP.

-    Execution Roles:
+    **Execution Roles:**

-    - `"algorithm"`: Start [`LightningStoreServer`][agentlightning.LightningStoreServer]
-      in-process and execute the algorithm bundle against it.
-    - `"runner"`: Connect to an existing server with
-      [`LightningStoreClient`][agentlightning.LightningStoreClient] and run the
-      runner bundle locally (spawning multiple processes when requested).
-    - `"both"`: Spawn runner processes first, then execute the algorithm and
-      server on the same machine. This mode orchestrates the full loop locally.
+    - "algorithm": Start the HTTP server (`LightningStoreServer`) in-process and run the
+      algorithm bundle against it.
+    - "runner": Connect to an already running server via `LightningStoreClient` and
+      execute runner bundles (optionally in multiple processes).
+    - "both": Spawn the runner processes first, then launch the algorithm/server
+      bundle on the main process. This mode orchestrates the full loop locally.

-    When `role == "both"` you may choose which side runs on the main process
-    via `main_process`. The runner-on-main option is limited to
-    `n_runners == 1` because each additional runner requires its own event
-    loop and process.
+    When role == "both", you may choose which side runs on the main process via
+    `main_process` (debug helper). Running the runner bundle on the main process
+    is only supported with `n_runners == 1`.

-    !!! warning
-        When `main_process == "runner"` the algorithm and HTTP server execute
-        in a child process. Store mutations remain isolated inside that process,
-        so the original store instance passed to
-        [execute()][agentlightning.ExecutionStrategy.execute] is not updated.
+    Important: When `main_process == "runner"`, the algorithm runs in a subprocess
+    with the LightningStore server. This means any state modifications made during
+    execution remain in that subprocess and are NOT reflected in the original store
+    object passed to `execute()`. The main process runner accesses the store only
+    through the HTTP client interface.

-    Abort Model (four-step escalation):
+    **Abort / Stop Model (four-step escalation):**

-    1. Cooperative stop. Every bundle receives a shared
-       [`MultiprocessingEvent`][agentlightning.MultiprocessingEvent] (`stop_evt`).
-       Any failure flips the event so peers can exit cleanly. Ctrl+C on the main
-       process also sets the flag.
-    2. KeyboardInterrupt synthesis. Remaining subprocesses receive ``SIGINT`` to
-       trigger `KeyboardInterrupt` handlers.
-    3. Termination. Stubborn processes are asked to ``terminate()``
-       (`SIGTERM` on POSIX).
-    4. Kill. As a last resort `kill()` is invoked (`SIGKILL` on POSIX).
+    1. Cooperative stop:
+       A shared :class:`~agentlightning.execution.events.MultiprocessingEvent`
+       (`stop_evt`) is passed to *all* bundles. Bundles should check it to exit.
+       Any crash (algorithm or runner) sets `stop_evt` so the other side can
+       stop cooperatively. Ctrl+C on the main process also flips the event.
+    2. KeyboardInterrupt synth:
+       Remaining subprocesses receive `SIGINT` to trigger `KeyboardInterrupt`
+       handlers.
+    3. Termination:
+       Stubborn subprocesses get `terminate()` (SIGTERM on POSIX).
+    4. Kill:
+        As a last resort we call `kill()` (SIGKILL on POSIX).

-    This mirrors the semantics implemented in
-    [`SharedMemoryExecutionStrategy`][agentlightning.SharedMemoryExecutionStrategy]
-    but adapts them to multiple processes and the HTTP client/server boundary.
+    Notes:
+        This mirrors the semantics implemented in :mod:`shared_memory`, but adapted
+        to multiple processes and the HTTP client/server boundary.
    """

    alias: str = "cs"
@@ -68,22 +68,20 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
        server_host: str | None = None,
        server_port: int | None = None,
        n_runners: int = 1,
-        graceful_timeout: float = 10.0,
-        terminate_timeout: float = 10.0,
+        graceful_timeout: float = 5.0,
+        terminate_timeout: float = 5.0,
        main_process: Literal["algorithm", "runner"] = "algorithm",
-        managed_store: bool | None = None,
-        allowed_exit_codes: Iterable[int] = (0, -15),
    ) -> None:
        """Configure the strategy.

        Args:
            role: Which side(s) to run in this process. When omitted, the
-                `AGL_CURRENT_ROLE` environment variable is used.
+                :envvar:`AGL_CURRENT_ROLE` environment variable is used.
            server_host: Interface the HTTP server binds to when running the
-                algorithm bundle locally. Defaults to `AGL_SERVER_HOST`
-                or `"localhost"` if unset.
+                algorithm bundle locally. Defaults to :envvar:`AGL_SERVER_HOST`
+                or ``"localhost"`` if unset.
            server_port: Port for the HTTP server in "algorithm"/"both" modes.
-                Defaults to `AGL_SERVER_PORT` or `4747` if unset.
+                Defaults to :envvar:`AGL_SERVER_PORT` or ``4747`` if unset.
            n_runners: Number of runner processes to spawn in "runner"/"both".
            graceful_timeout: How long to wait (seconds) after setting the stop
                event before escalating to signals.
@@ -92,56 +90,53 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
            main_process: Which bundle runs on the main process when
                `role == "both"`. `"runner"` requires `n_runners == 1` and is
                primarily intended for debugging.
-            managed_store: When `True` (default) the strategy constructs
-                LightningStore client/server wrappers automatically. When
-                `False` the provided `store` is passed directly to the
-                bundles, allowing callers to manage store wrappers manually.
-            allowed_exit_codes: Allowed exit codes for subprocesses.
-                By default, runner can exit gracefully with code 0 or terminated
-                by SIGTERM (-15).
        """
-        resolved_role = resolve_str_env_var(LightningEnvVar.AGL_CURRENT_ROLE, override=role, fallback="both")
-        if resolved_role not in ("algorithm", "runner", "both"):
-            raise ValueError("role must be one of 'algorithm', 'runner', or 'both'")
-        self.role: Literal["algorithm", "runner", "both"] = resolved_role
+        if role is None:
+            role_env = os.getenv("AGL_CURRENT_ROLE")
+            if role_env is None:
+                raise ValueError("role must be provided via argument or AGL_CURRENT_ROLE env var")
+            if role_env not in ("algorithm", "runner", "both"):
+                raise ValueError("role must be one of 'algorithm', 'runner', or 'both'")
+            role = role_env
+
+        if server_host is None:
+            server_host = os.getenv("AGL_SERVER_HOST", "localhost")
+
+        if server_port is None:
+            server_port_env = os.getenv("AGL_SERVER_PORT")
+            if server_port_env is None:
+                server_port = 4747
+            else:
+                try:
+                    server_port = int(server_port_env)
+                except ValueError as exc:
+                    raise ValueError("AGL_SERVER_PORT must be an integer") from exc
+
+        self.role = role
        self.n_runners = n_runners
-        self.server_host = resolve_str_env_var(
-            LightningEnvVar.AGL_SERVER_HOST, override=server_host, fallback="localhost"
-        )
-        self.server_port = resolve_int_env_var(LightningEnvVar.AGL_SERVER_PORT, override=server_port, fallback=4747)
+        self.server_host = server_host
+        self.server_port = server_port
        self.graceful_timeout = graceful_timeout
        self.terminate_timeout = terminate_timeout
        if main_process not in ("algorithm", "runner"):
            raise ValueError("main_process must be 'algorithm' or 'runner'")
        if main_process == "runner":
-            if self.role != "both":
+            if role != "both":
                raise ValueError("main_process='runner' is only supported when role='both'")
            if n_runners != 1:
                raise ValueError("main_process='runner' requires n_runners to be 1")
        self.main_process = main_process
-        self.managed_store = resolve_bool_env_var(
-            LightningEnvVar.AGL_MANAGED_STORE, override=managed_store, fallback=True
-        )
-        self.allowed_exit_codes = tuple(allowed_exit_codes)

-    async def _execute_algorithm(
-        self, algorithm: AlgorithmBundle, store: LightningStore, stop_evt: ExecutionEvent
-    ) -> None:
-        wrapper_store: LightningStore | None = None
-        if self.managed_store:
-            logger.info("Starting LightningStore server on %s:%s", self.server_host, self.server_port)
-            wrapper_store = LightningStoreServer(store, host=self.server_host, port=self.server_port)
-            server_started = False
-        else:
-            wrapper_store = store
-            server_started = False
+    async def _execute_algorithm(self, algorithm: AlgorithmBundle, store: LightningStore, stop_evt: Event) -> None:
+        logger.info("Starting LightningStore server on %s:%s", self.server_host, self.server_port)
+        server_store = LightningStoreServer(store, host=self.server_host, port=self.server_port)
+        server_started = False

        try:
-            if self.managed_store and isinstance(wrapper_store, LightningStoreServer):
-                await wrapper_store.start()
-                server_started = True
-                logger.debug("Algorithm bundle starting against endpoint %s", wrapper_store.endpoint)
-            await algorithm(wrapper_store, stop_evt)
+            await server_store.start()
+            server_started = True
+            logger.debug("Algorithm bundle starting against endpoint %s", server_store.endpoint)
+            await algorithm(server_store, stop_evt)
            logger.debug("Algorithm bundle completed successfully")
        except KeyboardInterrupt:
            logger.warning("Algorithm received KeyboardInterrupt; signaling stop event")
@@ -152,31 +147,18 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
            stop_evt.set()
            raise
        finally:
-            if self.managed_store and isinstance(wrapper_store, LightningStoreServer) and server_started:
+            if server_started:
                try:
-                    await wrapper_store.stop()
+                    await server_store.stop()
                except Exception:
                    logger.exception("Error stopping LightningStore server")
                else:
                    logger.debug("LightningStore server shutdown completed")

-    async def _execute_runner(
-        self,
-        runner: RunnerBundle,
-        worker_id: int,
-        store: LightningStore,
-        stop_evt: ExecutionEvent,
-    ) -> None:
-        if self.managed_store:
-            # If managed, we actually do not use the provided store
-            client_store = LightningStoreClient(f"http://{self.server_host}:{self.server_port}")
-        else:
-            client_store = store
+    async def _execute_runner(self, runner: RunnerBundle, worker_id: int, stop_evt: Event) -> None:
+        client_store = LightningStoreClient(f"http://{self.server_host}:{self.server_port}")
        try:
-            if self.managed_store:
-                logger.debug("Runner %s connecting to server at %s:%s", worker_id, self.server_host, self.server_port)
-            else:
-                logger.debug("Runner %s executing with provided store", worker_id)
+            logger.debug("Runner %s connecting to server at %s:%s", worker_id, self.server_host, self.server_port)
            await runner(client_store, worker_id, stop_evt)
            logger.debug("Runner %s completed successfully", worker_id)
        except KeyboardInterrupt:
@@ -188,34 +170,32 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
            stop_evt.set()
            raise
        finally:
-            if self.managed_store and isinstance(client_store, LightningStoreClient):
-                try:
-                    await client_store.close()
-                except Exception:
-                    logger.exception("Error closing LightningStore client for runner %s", worker_id)
-                else:
-                    logger.debug("Runner %s closed LightningStore client", worker_id)
+            try:
+                await client_store.close()
+            except Exception:
+                logger.exception("Error closing LightningStore client for runner %s", worker_id)
+            else:
+                logger.debug("Runner %s closed LightningStore client", worker_id)

    def _spawn_runners(
        self,
        runner: RunnerBundle,
-        store: LightningStore,
-        stop_evt: ExecutionEvent,
+        stop_evt: Event,
        *,
        ctx: BaseContext,
    ) -> list[multiprocessing.Process]:
        """Used when `role == "runner"` or `role == "both"` and `n_runners > 1`."""
        processes: list[multiprocessing.Process] = []

-        def _runner_sync(runner: RunnerBundle, worker_id: int, store: LightningStore, stop_evt: ExecutionEvent) -> None:
+        def _runner_sync(runner: RunnerBundle, worker_id: int, stop_evt: Event) -> None:
            # Runners are executed in child processes; each process owns its own
            # event loop to keep the asyncio scheduler isolated.
-            asyncio.run(self._execute_runner(runner, worker_id, store, stop_evt))
+            asyncio.run(self._execute_runner(runner, worker_id, stop_evt))

        for i in range(self.n_runners):
            process = cast(
                multiprocessing.Process,
-                ctx.Process(target=_runner_sync, args=(runner, i, store, stop_evt), name=f"runner-{i}"),  # type: ignore
+                ctx.Process(target=_runner_sync, args=(runner, i, stop_evt), name=f"runner-{i}"),  # type: ignore
            )
            process.start()
            logger.debug("Spawned runner process %s (pid=%s)", process.name, process.pid)
@@ -227,13 +207,13 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
        self,
        algorithm: AlgorithmBundle,
        store: LightningStore,
-        stop_evt: ExecutionEvent,
+        stop_evt: Event,
        *,
        ctx: BaseContext,
    ) -> multiprocessing.Process:
        """Used when `main_process == "runner"`."""

-        def _algorithm_sync(algorithm: AlgorithmBundle, store: LightningStore, stop_evt: ExecutionEvent) -> None:
+        def _algorithm_sync(algorithm: AlgorithmBundle, store: LightningStore, stop_evt: Event) -> None:
            asyncio.run(self._execute_algorithm(algorithm, store, stop_evt))

        process = cast(
@@ -277,7 +257,7 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
    def _shutdown_processes(
        self,
        processes: list[multiprocessing.Process],
-        stop_evt: ExecutionEvent,
+        stop_evt: Event,
    ) -> None:
        """4-step escalation shutdown of ``processes``."""
        if not processes:
@@ -328,10 +308,10 @@ class ClientServerExecutionStrategy(ExecutionStrategy):

    def _check_process_exitcodes(self, processes: Iterable[multiprocessing.Process]) -> None:
        """Raise an error if any managed process exited with a non-zero status."""
-        failed = [p for p in processes if p.exitcode not in self.allowed_exit_codes + (None,)]
+        failed = [p for p in processes if p.exitcode not in (0, None)]
        if failed:
            formatted = ", ".join(f"{p.name or p.pid} (exitcode={p.exitcode})" for p in failed)
-            raise RuntimeError(f"Subprocesses failed with unexpected exit codes: {formatted}")
+            raise RuntimeError(f"Subprocesses failed: {formatted}")

    def execute(self, algorithm: AlgorithmBundle, runner: RunnerBundle, store: LightningStore) -> None:
        logger.info(
@@ -359,10 +339,10 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
            elif self.role == "runner":
                if self.n_runners == 1:
                    logger.info("Running runner solely...")
-                    asyncio.run(self._execute_runner(runner, 0, store, stop_evt))
+                    asyncio.run(self._execute_runner(runner, 0, stop_evt))
                else:
                    logger.info("Spawning runner processes...")
-                    processes = self._spawn_runners(runner, store, stop_evt, ctx=ctx)
+                    processes = self._spawn_runners(runner, stop_evt, ctx=ctx)
                    # Wait for the processes to finish naturally.
                    for process in processes:
                        process.join()
@@ -370,7 +350,7 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
            elif self.role == "both":
                if self.main_process == "algorithm":
                    logger.info("Spawning runner processes...")
-                    processes = self._spawn_runners(runner, store, stop_evt, ctx=ctx)
+                    processes = self._spawn_runners(runner, stop_evt, ctx=ctx)
                    try:
                        logger.info("Running algorithm...")
                        asyncio.run(self._execute_algorithm(algorithm, store, stop_evt))
@@ -391,7 +371,7 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
                    # the background process spawned above (the provided
                    # store must therefore be picklable when using spawn).
                    logger.info("Running runner...")
-                    asyncio.run(self._execute_runner(runner, 0, store, stop_evt))
+                    asyncio.run(self._execute_runner(runner, 0, stop_evt))

                    # Wait for the algorithm process to finish.
                    algorithm_process.join()
@@ -6,19 +6,16 @@ from multiprocessing.context import BaseContext
 from typing import Optional, Protocol


-class ExecutionEvent(Protocol):
-    """Protocol capturing the cooperative stop contract shared by strategies.
-
-    Implementations mirror the API of ``threading.Event`` and
-    ``multiprocessing.Event`` so the rest of the execution layer can remain
-    agnostic to the underlying concurrency primitive.
+class Event(Protocol):
+    """
+    A minimal protocol similar to threading.Event.

    Methods:
-
-        set: Signal cancellation. The call must be idempotent.
-        clear: Reset the event to the unsignaled state.
-        is_set: Return ``True`` when cancellation has been requested.
-        wait: Block until the event is signaled or an optional timeout elapses.
+        set(): Signal event like a cancellation (idempotent).
+        clear(): Reset to the non-set state.
+        is_set() -> bool: True if event has been signaled.
+        wait(timeout: Optional[float] = None) -> bool:
+            Block until event is set or timeout. Returns True if event has signaled.
    """

    def set(self) -> None: ...
@@ -28,7 +25,11 @@ class ExecutionEvent(Protocol):


 class ThreadingEvent:
-    """Thread-safe implementation of [`ExecutionEvent`][agentlightning.ExecutionEvent]."""
+    """
+    An Event implementation using threading.Event.
+
+    Provides a thread-safe event object for signaling between threads.
+    """

    __slots__ = ("_evt",)

@@ -49,7 +50,12 @@ class ThreadingEvent:


 class MultiprocessingEvent:
-    """Process-safe implementation of [`ExecutionEvent`][agentlightning.ExecutionEvent]."""
+    """
+    An Event implementation using multiprocessing.Event.
+
+    Provides a process-safe event object for signaling between processes.
+    Optionally accepts a multiprocessing context for custom process start methods.
+    """

    __slots__ = ("_evt",)

@@ -4,12 +4,6 @@ from .base import ExecutionStrategy


 class InterProcessExecutionStrategy(ExecutionStrategy):
-    """Placeholder strategy for future inter-process primitives.
-
-    The class exists to reserve the `ipc` alias and make the planned
-    implementation discoverable. Attempting to use it today will raise
-    `NotImplementedError` once the execution contract is finalized.
-    """

    alias: str = "ipc"

@@ -7,37 +7,31 @@ from contextlib import suppress
 from queue import SimpleQueue
 from typing import Any, Awaitable, Callable, List, Literal, Optional, Tuple

-from agentlightning.env_var import LightningEnvVar, resolve_bool_env_var
 from agentlightning.store.base import LightningStore
 from agentlightning.store.threading import LightningStoreThreaded

 from .base import AlgorithmBundle, ExecutionStrategy, RunnerBundle
-from .events import ExecutionEvent, ThreadingEvent
+from .events import Event, ThreadingEvent

 logger = logging.getLogger(__name__)


 class SharedMemoryExecutionStrategy(ExecutionStrategy):
-    """Execute bundles in a single process with cooperative worker threads.
+    """Run algorithm and runners in a single process with threads sharing memory.

-    Stop Model:
+    Termination & abort model:

-    - All bundles share one [`ThreadingEvent`][agentlightning.ThreadingEvent]
-      named `stop_evt`.
-    - Only the main thread receives `KeyboardInterrupt`. When Ctrl+C occurs we
-      set `stop_evt`.
-    - Any exception raised inside a bundle sets `stop_evt` so other threads can
-      unwind cooperatively.
-    - Once the bundle running on the main thread exits successfully the
-      treatment depends on `main_thread`:
-        - `"algorithm"`: the runners are asked to stop by setting `stop_evt`.
-        - `"runner"`: the algorithm keeps running until it exits naturally.
-    - Background threads are marked as daemons. We join them briefly and log any
-      stragglers before shutting down.
+    - One shared ThreadingEvent (`stop_evt`) is passed to *all* bundles.
+    - The main thread (only) receives KeyboardInterrupt on Ctrl+C; we set `stop_evt` there.
+    - If any bundle raises, we set `stop_evt` from that thread to stop the rest.
+    - After the main-thread bundle finishes normally:
+      - If main_thread is "algorithm", we also set `stop_evt` to stop the runners.
+      - If main_thread is "runner", we do not set `stop_evt` to stop the algorithm.
+        We instead wait for the algorithm to finish naturally.
+    - Background threads are daemons; we join briefly and log any stragglers.

-    !!! note
-        Signals other than `SIGINT` (such as `SIGTERM`) are not intercepted;
-        Python's default behavior for those signals is preserved.
+    Notes: Signals other than SIGINT (e.g., SIGTERM) are not intercepted; we respect
+    Python's default behavior for them.
    """

    alias: str = "shm"
@@ -49,41 +43,32 @@ class SharedMemoryExecutionStrategy(ExecutionStrategy):
        join_timeout: float = 15.0,
        graceful_delay: float = 5.0,
        poll_interval: float = 0.05,
-        managed_store: bool | None = None,
    ) -> None:
        if main_thread not in ("algorithm", "runner"):
            raise ValueError("main_thread must be 'algorithm' or 'runner'")
        if main_thread == "runner" and n_runners != 1:
-            raise ValueError(
-                "When main_thread is 'runner', n_runners must be 1. "
-                "Either use 'algorithm' on the main thread or set n_runners to 1."
-            )
+            raise ValueError("When main_thread is 'runner', n_runners must be 1")
        self.n_runners = n_runners
        self.main_thread = main_thread
        self.join_timeout = join_timeout
        self.graceful_delay = graceful_delay
        self.poll_interval = poll_interval
-        self.managed_store = resolve_bool_env_var(
-            LightningEnvVar.AGL_MANAGED_STORE, override=managed_store, fallback=True
-        )

-    async def _run_until_completed_or_canceled(self, coro: Awaitable[Any], stop_evt: ExecutionEvent) -> Any:
+    async def _run_until_completed_or_canceled(self, coro: Awaitable[Any], stop_evt: Event) -> Any:
        """Run `coro` until it finishes or a cooperative stop is requested.

        Control flow:
-
-        1. Start the bundle coroutine as `task`.
-        2. Launch a watcher that polls `stop_evt` without blocking the loop.
-        3. When the stop event flips:
-            a. Give the bundle `graceful_delay` seconds to finish on its own,
-               because well-behaved bundles will check the event and return.
-            b. Cancel the bundle task if it is still running after the grace
-               period.
-        4. Await both tasks and swallow `CancelledError` where appropriate.
+          1) Start the bundle coroutine as `task`.
+          2) Start a watcher task that waits for `stop_evt` *without blocking* the loop
+             by periodically polling the threading event.
+          3) When the stop event flips:
+               a) Give the bundle *graceful_delay* seconds to finish on its own,
+                  because well-behaved bundles will check the event and return.
+               b) If still running after the grace period, cancel the bundle task.
+          4) Ensure both tasks are awaited; swallow `CancelledError` where appropriate.

        This is a *backup* mechanism for bundles that might not poll the event
-        frequently; cooperative shutdown (checking `stop_evt` inside the
-        bundle) remains the preferred approach.
+        frequently; cooperative shutdown (checking `stop_evt` yourself) is still preferred.
        """
        task: asyncio.Task[Any] = asyncio.create_task(coro)  # type: ignore
        task_exception: Optional[BaseException] = None
@@ -164,7 +149,7 @@ class SharedMemoryExecutionStrategy(ExecutionStrategy):
        self,
        algorithm: AlgorithmBundle,
        store: LightningStore,
-        stop_evt: ExecutionEvent,
+        stop_evt: Event,
        thread_exceptions: Optional[SimpleQueue[BaseException]],
    ) -> None:
        try:
@@ -183,7 +168,7 @@ class SharedMemoryExecutionStrategy(ExecutionStrategy):
        runner: RunnerBundle,
        store: LightningStore,
        worker_id: int,
-        stop_evt: ExecutionEvent,
+        stop_evt: Event,
        thread_exceptions: Optional[SimpleQueue[BaseException]],
    ) -> None:
        try:
@@ -206,10 +191,7 @@ class SharedMemoryExecutionStrategy(ExecutionStrategy):

        # Create stop event and thread-safe store.
        stop_evt = ThreadingEvent()
-        if self.managed_store:
-            thread_safe_store = LightningStoreThreaded(store)
-        else:
-            thread_safe_store = store
+        thread_safe_store = LightningStoreThreaded(store)

        thread_exceptions: SimpleQueue[BaseException] = SimpleQueue()
        raised_from_thread: Optional[BaseException] = None
@@ -40,7 +40,6 @@ except ImportError:


 def instrument_all():
-    """Instrument all the instrumentation libraries."""
    if AGENTOPS_INSTALLED:
        from .agentops import instrument_agentops

@@ -71,7 +70,6 @@ def instrument_all():


 def uninstrument_all():
-    """Uninstrument all the instrumentation libraries."""
    if AGENTOPS_INSTALLED:
        try:
            from .agentops import uninstrument_agentops
@@ -2,78 +2,21 @@

 from __future__ import annotations

-import json
 import logging
-from typing import Any, Callable, no_type_check
+import multiprocessing
+import signal
+import socket
+import time
+from typing import Any, Callable

-import requests
-from agentops.client.api import V3Client, V4Client
-from agentops.client.api.types import AuthTokenResponse
-from agentops.sdk.exporters import AuthenticatedOTLPExporter
-from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-from opentelemetry.sdk.metrics.export import MetricExportResult
-
-from agentlightning.utils.otlp import LightningStoreOTLPExporter
+import flask
+import setproctitle

 logger = logging.getLogger(__name__)

-__all__ = [
-    "instrument_agentops",
-    "uninstrument_agentops",
-]
-
 # Module-level storage for originals
 _original_handle_chat_attributes: Callable[..., Any] | None = None
 _original_handle_response: Callable[..., Any] | None = None
-_agentops_service_enabled = False
-
-
-def enable_agentops_service(enabled: bool = True) -> None:
-    """
-    Enable or disable communication with the AgentOps service.
-
-    By default, AgentOps exporters and clients will run in local mode
-    and will NOT attempt to communicate with the remote AgentOps service.
-
-    Args:
-        enabled: If True, enable all AgentOps exporters and clients.
-            All exporters and clients will operate in normal mode and send data
-            to the [AgentOps service](https://www.agentops.ai).
-    """
-    global _agentops_service_enabled
-    _agentops_service_enabled = enabled
-    logger.info(f"AgentOps service enabled is set to {enabled}.")
-
-
-def _patch_exporters():
-    import agentops.client.api
-    import agentops.sdk.core
-
-    agentops.sdk.core.AuthenticatedOTLPExporter = BypassableAuthenticatedOTLPExporter  # type: ignore
-    agentops.sdk.core.OTLPMetricExporter = BypassableOTLPMetricExporter
-    if hasattr(agentops.sdk.core, "OTLPSpanExporter"):
-        agentops.sdk.core.OTLPSpanExporter = BypassableOTLPSpanExporter  # type: ignore
-    agentops.client.api.V3Client = BypassableV3Client
-    agentops.client.api.V4Client = BypassableV4Client
-
-
-def _unpatch_exporters():
-    import agentops.client.api
-    import agentops.sdk.core
-
-    agentops.sdk.core.AuthenticatedOTLPExporter = AuthenticatedOTLPExporter  # type: ignore
-    agentops.sdk.core.OTLPMetricExporter = OTLPMetricExporter
-    if hasattr(agentops.sdk.core, "OTLPSpanExporter"):
-        agentops.sdk.core.OTLPSpanExporter = OTLPSpanExporter  # type: ignore
-    agentops.client.api.V3Client = V3Client
-    agentops.client.api.V4Client = V4Client
-
-
-def _unwrap_legacy_response(response: Any) -> Any:
-    if hasattr(response, "parse") and callable(response.parse):
-        return response.parse()
-    return response


 def _patch_new_agentops():
@@ -89,58 +32,41 @@ def _patch_new_agentops():

    _original_handle_chat_attributes = handle_chat_attributes  # type: ignore

-    @no_type_check
    def _handle_chat_attributes_with_tokens(args=None, kwargs=None, return_value=None, **kws):  # type: ignore
-        attributes = _original_handle_chat_attributes(args=args, kwargs=kwargs, return_value=return_value, **kws)
-
-        # In some cases, response is a openai._legacy_response.LegacyAPIResponse (e.g., LiteLLM, or LangChain),
-        # This is created by client.with_raw_response.create()
-        return_value = _unwrap_legacy_response(return_value)
-
-        if (
-            return_value is not None
-            and hasattr(return_value, "prompt_token_ids")
-            and return_value.prompt_token_ids is not None
-        ):
-            attributes["prompt_token_ids"] = list(return_value.prompt_token_ids)
-        if (
-            return_value is not None
-            and hasattr(return_value, "response_token_ids")
-            and return_value.response_token_ids is not None
-        ):
-            attributes["response_token_ids"] = list(return_value.response_token_ids[0])
+        attributes = _original_handle_chat_attributes(args=args, kwargs=kwargs, return_value=return_value, **kws)  # type: ignore
+        if return_value is not None and hasattr(return_value, "prompt_token_ids"):  # type: ignore
+            attributes["prompt_token_ids"] = list(return_value.prompt_token_ids)  # type: ignore
+        if return_value is not None and hasattr(return_value, "response_token_ids"):  # type: ignore
+            attributes["response_token_ids"] = list(return_value.response_token_ids[0])  # type: ignore

        # For LiteLLM Proxy (v0.2) with vLLM return_token_ids, response_token_ids now lives in choices
        if (
-            return_value is not None
-            and hasattr(return_value, "choices")
-            and return_value.choices
-            and isinstance(return_value.choices, list)
-            and len(return_value.choices) > 0
+            not attributes.get("response_token_ids")
+            and return_value is not None
+            and hasattr(return_value, "choices")  # type: ignore
+            and return_value.choices  # type: ignore
+            and isinstance(return_value.choices, list)  # type: ignore
        ):
-            first_choice = return_value.choices[0]
-            # Token IDs from "choices[0].token_ids"
-            if "response_token_ids" not in attributes:
-                if hasattr(first_choice, "token_ids") and first_choice.token_ids is not None:
-                    attributes["response_token_ids"] = list(first_choice.token_ids)
-                # newer versions of OpenAI client SDK
-                elif (
-                    hasattr(first_choice, "provider_specific_fields")
-                    and first_choice.provider_specific_fields.get("token_ids") is not None
-                ):
-                    attributes["response_token_ids"] = list(first_choice.provider_specific_fields["token_ids"])
+            first_choice = return_value.choices[0]  # type: ignore
+            if hasattr(first_choice, "token_ids"):  # type: ignore
+                attributes["response_token_ids"] = list(first_choice.token_ids)  # type: ignore
+            # newer versions of OpenAI client SDK
+            elif hasattr(first_choice, "provider_specific_fields") and "token_ids" in first_choice.provider_specific_fields:  # type: ignore
+                attributes["response_token_ids"] = list(first_choice.provider_specific_fields["token_ids"])  # type: ignore

-            # log probability
-            # This is temporary. We need a unified convention for classifying and naming logprobs.
-            if hasattr(first_choice, "logprobs") and first_choice.logprobs is not None:
-                if hasattr(first_choice.logprobs, "content") and first_choice.logprobs.content is not None:
-                    attributes["logprobs.content"] = json.dumps(
-                        [logprob.model_dump() for logprob in first_choice.logprobs.content]
-                    )
-                if hasattr(first_choice.logprobs, "refusal") and first_choice.logprobs.refusal is not None:
-                    attributes["logprobs.refusal"] = json.dumps(
-                        [logprob.model_dump() for logprob in first_choice.logprobs.refusal]
-                    )
+        # For LiteLLM, response is a openai._legacy_response.LegacyAPIResponse
+        if (
+            return_value is not None
+            and hasattr(return_value, "http_response")  # type: ignore
+            and return_value.http_response is not None  # type: ignore
+            and hasattr(return_value.http_response, "json")  # type: ignore
+        ):
+            json_data = return_value.http_response.json()  # type: ignore
+            if isinstance(json_data, dict):
+                if "prompt_token_ids" in json_data:
+                    attributes["prompt_token_ids"] = list(json_data["prompt_token_ids"])  # type: ignore
+                if "response_token_ids" in json_data:
+                    attributes["response_token_ids"] = list(json_data["response_token_ids"][0])  # type: ignore

        return attributes

@@ -212,8 +138,6 @@ def instrument_agentops():
    Instrument agentops to capture token IDs.
    Automatically detects and uses the appropriate patching method based on the installed agentops version.
    """
-    _patch_exporters()
-
    # Try newest version first (tested for 0.4.16)
    try:
        return _patch_new_agentops()
@@ -232,9 +156,6 @@ def instrument_agentops():


 def uninstrument_agentops():
-    """Uninstrument agentops to stop capturing token IDs."""
-    _unpatch_exporters()
-
    try:
        _unpatch_new_agentops()
    except Exception:
@@ -245,70 +166,100 @@ def uninstrument_agentops():
        pass


-class BypassableAuthenticatedOTLPExporter(LightningStoreOTLPExporter, AuthenticatedOTLPExporter):
+def agentops_local_server():
    """
-    AuthenticatedOTLPExporter with switchable service control.
-
-    When `_agentops_service_enabled` is False, skip export and return success.
+    Returns a Flask app that can be used to test agentops integration.
+    This server provides endpoints for token fetching and a catch-all endpoint.
    """
+    app = flask.Flask(__name__)

-    def should_bypass(self) -> bool:
-        return not _agentops_service_enabled
+    @app.route("/v3/auth/token", methods=["POST"])
+    def fetch_token():  # type: ignore
+        return {"token": "dummy", "project_id": "dummy"}
+
+    @app.route("/", defaults={"path": ""}, methods=["GET", "POST"])
+    @app.route("/<path:path>", methods=["GET", "POST"])
+    def catch_all(path: str):  # type: ignore
+        return {"path": path}
+
+    return app


-class BypassableOTLPMetricExporter(OTLPMetricExporter):
+def _run_server(**kwargs: Any):  # type: ignore
    """
-    OTLPMetricExporter with switchable service control.
-    When `_agentops_service_enabled` is False, skip export and return success.
+    Internal function to run the Flask server.
+    This is used to avoid issues with multiprocessing and Flask's reloader.
    """
+    signal.signal(signal.SIGINT, signal.SIG_IGN)  # Ignore SIGINT in worker processes
+    setproctitle.setproctitle(multiprocessing.current_process().name)
+    app = agentops_local_server()
+    app.run(**kwargs)

-    def export(self, *args: Any, **kwargs: Any) -> MetricExportResult:
-        if _agentops_service_enabled:
-            return super().export(*args, **kwargs)  # type: ignore[reportUnknownMemberType]
+
+class AgentOpsServerManager:
+    def __init__(self, daemon: bool = True, port: int | None = None):
+        self.server_process: multiprocessing.Process | None = None
+        self.server_port = port
+        self.daemon = daemon
+        logger.info("AgentOpsServerManager initialized.")
+
+    def _find_available_port(self) -> int:
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.bind(("", 0))
+            return s.getsockname()[1]
+
+    def start(self):
+        if self.server_process and self.server_process.is_alive():
+            logger.warning("AgentOps server process appears to be already running.")
+            return
+
+        if self.server_port is None:
+            self.server_port = self._find_available_port()
+
+        logger.info(f"Starting AgentOps local server on port {self.server_port}...")
+
+        self.server_process = multiprocessing.Process(
+            target=_run_server,
+            kwargs={"host": "127.0.0.1", "port": self.server_port, "use_reloader": False, "debug": False},
+            daemon=self.daemon,
+            name="AgentLightning-AgentOpsServer",
+        )
+        self.server_process.start()
+        logger.info(
+            f"AgentOps local server process (PID: {self.server_process.pid}) started, targeting port {self.server_port}."
+        )
+        time.sleep(0.5)  # Brief wait for server to start up
+        if not self.server_process.is_alive():
+            logger.error(f"AgentOps local server failed to start or exited prematurely.")
+
+    def is_alive(self) -> bool:
+        if self.server_process and self.server_process.is_alive():
+            return True
+        return False
+
+    def stop(self):
+        if self.server_process is not None and self.server_process.is_alive():
+            logger.info(f"Stopping AgentOps local server (PID: {self.server_process.pid})...")
+            self.server_process.terminate()  # Send SIGTERM
+            self.server_process.join(timeout=5)  # Wait for clean exit
+            if self.server_process.is_alive():
+                logger.warning(
+                    f"AgentOps server (PID: {self.server_process.pid}) did not terminate gracefully, killing..."
+                )
+                self.server_process.kill()  # Force kill
+                self.server_process.join(timeout=10)  # Wait for kill
+            self.server_process = None
+            logger.info(f"AgentOps local server stopped.")
        else:
-            logger.debug("SwitchableOTLPMetricExporter is switched off, skipping export.")
-            return MetricExportResult.SUCCESS
+            logger.info("AgentOps local server was not running or already stopped.")

-
-class BypassableOTLPSpanExporter(LightningStoreOTLPExporter):
-    """
-    OTLPSpanExporter with switchable service control.
-    When `_agentops_service_enabled` is False, skip export and return success.
-
-    This is used instead of BypassableAuthenticatedOTLPExporter on legacy AgentOps versions.
-    """
-
-    def should_bypass(self) -> bool:
-        return not _agentops_service_enabled
-
-
-class BypassableV3Client(V3Client):
-    """
-    V3Client with toggleable authentication calls.
-    Returns dummy auth response when `_agentops_service_enabled` is False.
-    """
-
-    # Temporary synchronous override of fetch_auth_token for mock purposes.
-    def fetch_auth_token(self, *args: Any, **kwargs: Any) -> AuthTokenResponse:  # type: ignore[override]
-        if _agentops_service_enabled:
-            return super().fetch_auth_token(*args, **kwargs)  # type: ignore[override]
-        else:
-            logger.debug("SwitchableV3Client is switched off, skipping fetch_auth_token request.")
-            return AuthTokenResponse(token="dummy", project_id="dummy")
-
-
-class BypassableV4Client(V4Client):
-    """
-    V4Client with toggleable post requests.
-    Returns dummy response when `_agentops_service_enabled` is False.
-    """
-
-    def post(self, *args: Any, **kwargs: Any) -> requests.Response:
-        if _agentops_service_enabled:
-            return super().post(*args, **kwargs)
-        else:
-            logger.debug("SwitchableV4Client is switched off, skipping post request.")
-            response = requests.Response()
-            response.status_code = 200
-            response._content = b"{}"
-            return response
+    def get_port(self) -> int | None:
+        # Check liveness again in case it died since start()
+        if self.is_alive() and self.server_port is not None:
+            return self.server_port
+        # If called after server stopped or failed, port might be stale or None
+        if self.server_port is not None and (self.server_process is None or not self.server_process.is_alive()):
+            logger.warning(
+                f"AgentOps server port {self.server_port} is stored, but server process is not alive. Returning stored port."
+            )
+        return self.server_port
@@ -8,11 +8,6 @@ from agentops.integration.callbacks.langchain import LangchainCallbackHandler
 original_on_chain_start = LangchainCallbackHandler.on_chain_start
 langgraph_entry = None

-__all__ = [
-    "instrument_agentops_langchain",
-    "uninstrument_agentops_langchain",
-]
-

 def on_chain_start(self: Any, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) -> None:
    if "name" in kwargs:
@@ -30,14 +25,12 @@ def on_chain_start(self: Any, serialized: Dict[str, Any], inputs: Dict[str, Any]


 def instrument_agentops_langchain():
-    """Bypass AgentOp's native support for Langchain."""
    global langgraph_entry
    langgraph_entry = instrumentation.AGENTIC_LIBRARIES.pop("langgraph", None)
    LangchainCallbackHandler.on_chain_start = on_chain_start


 def uninstrument_agentops_langchain():
-    """Restore AgentOp's native support for Langchain."""
    global langgraph_entry
    if langgraph_entry is not None:
        instrumentation.AGENTIC_LIBRARIES["langgraph"] = langgraph_entry
@@ -1,21 +1,12 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""LiteLLM instrumentations.
-
-It's unclear whether or not this file is useful.
-It seems that LiteLLM owns its own telemetry from their own entrance
-
-[Related documentation](https://docs.litellm.ai/docs/observability/agentops_integration).
-"""
-
 from typing import Any, Optional

 from litellm.integrations.opentelemetry import OpenTelemetry

-__all__ = [
-    "instrument_litellm",
-    "uninstrument_litellm",
-]
+# It's unclear whether or not this file is useful
+# It seems that LiteLLM owns its own telemetry from their own entrance
+# https://docs.litellm.ai/docs/observability/agentops_integration

 original_set_attributes = OpenTelemetry.set_attributes  # type: ignore

@@ -30,10 +21,8 @@ def patched_set_attributes(self: Any, span: Any, kwargs: Any, response_obj: Opti


 def instrument_litellm():
-    """Instrument litellm to capture token IDs."""
    OpenTelemetry.set_attributes = patched_set_attributes


 def uninstrument_litellm():
-    """Uninstrument litellm to stop capturing token IDs."""
    OpenTelemetry.set_attributes = original_set_attributes
@@ -9,11 +9,6 @@ import vllm.entrypoints.openai.protocol
 from vllm.entrypoints.openai.protocol import ChatCompletionResponse
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat

-__all__ = [
-    "instrument_vllm",
-    "uninstrument_vllm",
-]
-

 class ChatCompletionResponsePatched(ChatCompletionResponse):
    prompt_token_ids: List[int] | None = None
@@ -64,10 +59,6 @@ async def chat_completion_full_generator(


 def instrument_vllm():
-    """Instrument vLLM to capture token IDs generated by engine.
-
-    This instrumentation has been merged to upstream vLLM since v0.10.2.
-    """
    if vllm.entrypoints.openai.protocol.ChatCompletionResponse is ChatCompletionResponsePatched:
        warnings.warn("vllm is already instrumented. Skip the instrumentation.")
        return
@@ -77,5 +68,4 @@ def instrument_vllm():


 def uninstrument_vllm():
-    """Uninstrument vLLM to stop capturing token IDs generated by engine."""
    OpenAIServingChat.chat_completion_full_generator = original_chat_completion_full_generator
@@ -5,6 +5,7 @@ from .litagent import *

 __all__ = [
    "LitAgent",
+    "is_v0_1_rollout_api",
    "llm_rollout",
    "prompt_rollout",
    "rollout",
@@ -1,7 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Convenience decorators for building lightweight `LitAgent` implementations."""
-
 from __future__ import annotations

 import functools
@@ -15,8 +13,8 @@ from agentlightning.types import (
    NamedResources,
    PromptTemplate,
    ProxyLLM,
-    Rollout,
-    RolloutRawResult,
+    RolloutRawResultV2,
+    RolloutV2,
 )

 from .litagent import LitAgent
@@ -36,19 +34,19 @@ T_contra = TypeVar("T_contra", contravariant=True)


 class LlmRolloutFuncSync2(Protocol[T_contra]):
-    def __call__(self, task: T_contra, llm: LLM) -> RolloutRawResult: ...
+    def __call__(self, task: T_contra, llm: LLM) -> RolloutRawResultV2: ...


 class LlmRolloutFuncSync3(Protocol[T_contra]):
-    def __call__(self, task: T_contra, llm: LLM, rollout: Rollout) -> RolloutRawResult: ...
+    def __call__(self, task: T_contra, llm: LLM, rollout: RolloutV2) -> RolloutRawResultV2: ...


 class LlmRolloutFuncAsync2(Protocol[T_contra]):
-    def __call__(self, task: T_contra, llm: LLM) -> Awaitable[RolloutRawResult]: ...
+    def __call__(self, task: T_contra, llm: LLM) -> Awaitable[RolloutRawResultV2]: ...


 class LlmRolloutFuncAsync3(Protocol[T_contra]):
-    def __call__(self, task: T_contra, llm: LLM, rollout: Rollout) -> Awaitable[RolloutRawResult]: ...
+    def __call__(self, task: T_contra, llm: LLM, rollout: RolloutV2) -> Awaitable[RolloutRawResultV2]: ...


 LlmRolloutFunc = Union[
@@ -60,21 +58,21 @@ LlmRolloutFunc = Union[


 class PromptRolloutFuncSync2(Protocol[T_contra]):
-    def __call__(self, task: T_contra, prompt_template: PromptTemplate) -> RolloutRawResult: ...
+    def __call__(self, task: T_contra, prompt_template: PromptTemplate) -> RolloutRawResultV2: ...


 class PromptRolloutFuncAsync2(Protocol[T_contra]):
-    def __call__(self, task: T_contra, prompt_template: PromptTemplate) -> Awaitable[RolloutRawResult]: ...
+    def __call__(self, task: T_contra, prompt_template: PromptTemplate) -> Awaitable[RolloutRawResultV2]: ...


 class PromptRolloutFuncSync3(Protocol[T_contra]):
-    def __call__(self, task: T_contra, prompt_template: PromptTemplate, rollout: Rollout) -> RolloutRawResult: ...
+    def __call__(self, task: T_contra, prompt_template: PromptTemplate, rollout: RolloutV2) -> RolloutRawResultV2: ...


 class PromptRolloutFuncAsync3(Protocol[T_contra]):
    def __call__(
-        self, task: T_contra, prompt_template: PromptTemplate, rollout: Rollout
-    ) -> Awaitable[RolloutRawResult]: ...
+        self, task: T_contra, prompt_template: PromptTemplate, rollout: RolloutV2
+    ) -> Awaitable[RolloutRawResultV2]: ...


 PromptRolloutFunc = Union[
@@ -88,29 +86,28 @@ PromptRolloutFunc = Union[
 class FunctionalLitAgentFunc(Protocol[T_contra]):
    def __call__(
        self, task: T_contra, *args: Any, **kwargs: Any
-    ) -> Union[RolloutRawResult, Awaitable[RolloutRawResult]]: ...
+    ) -> Union[RolloutRawResultV2, Awaitable[RolloutRawResultV2]]: ...


 class FunctionalLitAgent(LitAgent[T]):
-    """Adapter that turns plain rollout functions into [`LitAgent`][agentlightning.LitAgent] instances.
+    """A specialized LitAgent that wraps a function-based rollout that accepts
+    dynamically a task input and a configured resource (LLM / prompt template / ...).

-    The helper inspects the wrapped function to determine which resources to
-    inject, allowing both synchronous and asynchronous callables to participate
-    in the training loop without writing a dedicated subclass.
+    This class allows users to define agent behavior using a simple function
+    that takes task input and a resource, rather than implementing a full
+    LitAgent subclass.
    """

    def __init__(self, rollout_func: FunctionalLitAgentFunc[T], *, strip_proxy: bool = True) -> None:
-        """Initialize the wrapper around a rollout function.
+        """
+        Initialize the FunctionalLitAgent with a functional rollout function.

        Args:
-            rollout_func: Callable that implements the rollout. It may be synchronous
-                or asynchronous and can optionally receive a
-                [`Rollout`][agentlightning.Rollout] alongside resources such as
-                `llm` or `prompt_template`.
-            strip_proxy: When ``True``, convert
-                [`ProxyLLM`][agentlightning.ProxyLLM] inputs into
-                [`LLM`][agentlightning.LLM] instances before calling the
-                rollout function. Defaults to `True`.
+            rollout_func: A function that defines the agent's behavior.
+                          Can be sync or async, and can optionally accept a Rollout parameter.
+                          The function signature determines which resources are injected (llm, prompt_template, etc.).
+            strip_proxy: Whether to strip the ProxyLLM resource into a LLM resource when the function accepts an llm parameter.
+                         Defaults to True.
        """
        super().__init__()
        self._rollout_func = rollout_func
@@ -137,19 +134,16 @@ class FunctionalLitAgent(LitAgent[T]):
    def is_async(self) -> bool:
        return self._is_async

-    def rollout(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
+    def rollout(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
        """Execute a synchronous rollout using the wrapped function.

        Args:
-            task: Task input data.
-            resources: Mapping of named resources available to the agent.
-            rollout: Rollout metadata provided by the runtime.
+            task: The task input data.
+            resources: Dictionary of named resources including LLMs.
+            rollout: The rollout object with metadata.

        Returns:
-            Result produced by the wrapped rollout function.
-
-        Raises:
-            RuntimeError: If the wrapped function is asynchronous.
+            The result from the wrapped rollout function.
        """
        if self._is_async:
            raise RuntimeError(f"{self._rollout_func} is asynchronous. Use rollout_async instead.")
@@ -157,19 +151,16 @@ class FunctionalLitAgent(LitAgent[T]):
        kwargs = self._get_kwargs(resources, rollout)
        return self._rollout_func(task, **kwargs)  # type: ignore

-    async def rollout_async(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
+    async def rollout_async(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
        """Execute an asynchronous rollout using the wrapped function.

        Args:
-            task: Task input data.
-            resources: Mapping of named resources available to the agent.
-            rollout: Rollout metadata provided by the runtime.
+            task: The task input data.
+            resources: Dictionary of named resources including LLMs.
+            rollout: The rollout object with metadata.

        Returns:
-            Result produced by the wrapped rollout coroutine.
-
-        Raises:
-            RuntimeError: If the wrapped function is synchronous.
+            The result from the wrapped rollout function.
        """
        if not self._is_async:
            raise RuntimeError(f"{self._rollout_func} is synchronous. Use rollout instead.")
@@ -177,20 +168,19 @@ class FunctionalLitAgent(LitAgent[T]):
        kwargs = self._get_kwargs(resources, rollout)
        return await self._rollout_func(task, **kwargs)  # type: ignore

-    def _get_kwargs(self, resources: NamedResources, rollout: Rollout) -> Dict[str, Any]:
-        """Prepare keyword arguments expected by the wrapped rollout function.
+    def _get_kwargs(self, resources: NamedResources, rollout: RolloutV2) -> Dict[str, Any]:
+        """Extract the kwargs needed for the rollout function based on its signature.

-
-        It dynamically builds the `kwargs` dictionary by inspecting the function signature and
+        Dynamically builds the kwargs dictionary by inspecting the function signature and
        including only the parameters the function accepts. This allows flexible function
        signatures that can request any combination of: rollout, llm, and/or prompt_template.

        Args:
-            resources: Mapping of named resources available for the rollout.
-            rollout: Rollout metadata provided by the runtime.
+            resources: Dictionary of named resources available for the rollout.
+            rollout: The rollout object with metadata.

        Returns:
-            Dictionary of keyword arguments to forward to the rollout function.
+            A dictionary of kwargs to pass to the rollout function.
        """

        kwargs: Dict[str, Any] = {}
@@ -203,20 +193,20 @@ class FunctionalLitAgent(LitAgent[T]):

        return kwargs

-    def _get_llm_resource(self, resources: NamedResources, rollout: Rollout) -> LLM:
-        """Retrieve the first LLM resource from the available resources.
+    def _get_llm_resource(self, resources: NamedResources, rollout: RolloutV2) -> LLM:
+        """Extract the first LLM resource from the resources dictionary.

        Strip the ProxyLLM resource into a LLM resource if needed.

        Args:
-            resources: Mapping of named resources.
-            rollout: Rollout metadata used when stripping proxy endpoints.
+            resources: Dictionary of named resources.
+            rollout: The rollout object with metadata.

        Returns:
-            First [`LLM`][agentlightning.LLM] resource encountered.
+            The first LLM resource found.

        Raises:
-            ValueError: If no LLM resource is present.
+            ValueError: If no LLM resource is found.
        """
        resource_found: LLM | None = None
        for name, resource in resources.items():
@@ -234,18 +224,18 @@ class FunctionalLitAgent(LitAgent[T]):

        return resource_found

-    def _get_prompt_template_resource(self, resources: NamedResources, rollout: Rollout) -> PromptTemplate:
-        """Retrieve the first prompt template resource from the available resources.
+    def _get_prompt_template_resource(self, resources: NamedResources, rollout: RolloutV2) -> PromptTemplate:
+        """Extract the first PromptTemplate resource from the resources dictionary.

        Args:
-            resources: Mapping of named resources.
-            rollout: Rollout metadata (unused).
+            resources: Dictionary of named resources.
+            rollout: The rollout object with metadata. Not used in this method.

        Returns:
-            First [`PromptTemplate`][agentlightning.PromptTemplate] resource encountered.
+            The first PromptTemplate resource found.

        Raises:
-            ValueError: If no prompt template resource is present.
+            ValueError: If no PromptTemplate resource is found.
        """
        resource_found: PromptTemplate | None = None
        for name, resource in resources.items():
@@ -262,30 +252,29 @@ class FunctionalLitAgent(LitAgent[T]):

        return resource_found

-    def _strip_proxy_helper(self, proxy_llm: LLM, rollout: Rollout) -> LLM:
-        """Convert [`ProxyLLM`][agentlightning.ProxyLLM] instances into concrete LLMs.
+    def _strip_proxy_helper(self, proxy_llm: LLM, rollout: RolloutV2) -> LLM:
+        """Strip the ProxyLLM resource into a concrete LLM resource.

-        It resolves ProxyLLM instances to their concrete LLM implementation
+        This method resolves ProxyLLM instances to their concrete LLM implementation
        by attaching the attempted rollout context. This is only used when the function
-        signature accepts an `llm` parameter and strip_proxy is True.
+        signature accepts an 'llm' parameter and strip_proxy is True.

        Args:
-            proxy_llm: Candidate LLM resource.
-            rollout: Rollout metadata that provides rollout and attempt identifiers.
+            proxy_llm: The LLM resource, which may be a ProxyLLM.
+            rollout: The rollout object with metadata.

        Returns:
-            [`LLM`][agentlightning.LLM] with rollout context baked into the endpoint.
+            The concrete LLM resource.

        Raises:
-            ValueError: If the rollout is not an
-                [`AttemptedRollout`][agentlightning.AttemptedRollout].
+            ValueError: If the rollout is not an AttemptedRollout (required for stripping ProxyLLM).
        """

        if not isinstance(proxy_llm, ProxyLLM):
            # Not a ProxyLLM, nothing to strip here.
            return proxy_llm

-        # Rollout is still a Rollout here because API is not stabilized yet.
+        # Rollout is still a RolloutV2 here because API is not stabilized yet.
        # In practice, it must be an AttemptedRollout.
        if not isinstance(rollout, AttemptedRollout):
            raise ValueError("Rollout is not an AttemptedRollout.")
@@ -304,37 +293,41 @@ def llm_rollout(*, strip_proxy: bool = True) -> Callable[[LlmRolloutFunc[T]], Fu
 def llm_rollout(
    func: LlmRolloutFunc[T] | None = None, *, strip_proxy: bool = True
 ) -> FunctionalLitAgent[T] | Callable[[LlmRolloutFunc[T]], FunctionalLitAgent[T]]:
-    """Create a [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] for LLM-based rollouts.
+    """Create a FunctionalLitAgent from a function that takes (task, llm[, rollout]).
+
+    This decorator allows you to define an agent using a simple function
+    instead of creating a full LitAgent subclass. The returned FunctionalLitAgent
+    instance is callable, preserving the original function's behavior.

    Args:
-        func: Callable defining the agent's behaviour. Supported signatures include:
-
-            * `(task, llm) -> result`
-            * `(task, llm, rollout) -> result`
-            * `async (task, llm) -> result`
-            * `async (task, llm, rollout) -> result`
-
-        strip_proxy: When `True`, convert proxy resources into concrete
-            [`LLM`][agentlightning.LLM] instances before calling the
-            function. Defaults to `True`.
+        func: A function that defines the agent's behavior. Can be:
+              - sync: (task, llm) -> result
+              - sync with rollout: (task, llm, rollout) -> result
+              - async: async (task, llm) -> result
+              - async with rollout: async (task, llm, rollout) -> result
+        strip_proxy: Whether to strip the ProxyLLM resource into a LLM resource.
+                     Defaults to True.

    Returns:
-        [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] that
-        wraps the supplied function.
+        A callable FunctionalLitAgent instance that preserves the original function's
+        type hints and behavior while providing all agent functionality.

-    Examples:
-        ```python
+    Example:
        @llm_rollout
        def my_agent(task, llm):
-            return llm.endpoint
+            # Agent logic here
+            return response

        @llm_rollout(strip_proxy=False)
        def my_agent_no_strip(task, llm):
-            return llm.model
+            # Agent logic here
+            return response

+        # Function is still callable with original behavior
        result = my_agent(task, llm)
+
+        # Agent methods are also available
        result = my_agent.rollout(task, resources, rollout)
-        ```
    """

    def decorator(f: LlmRolloutFunc[T]) -> FunctionalLitAgent[T]:
@@ -350,20 +343,19 @@ def llm_rollout(


 def _validate_llm_rollout_func(func: Any) -> TypeGuard[LlmRolloutFunc[Any]]:
-    """Validate the function signature of an LLM rollout function.
+    """Validate the function signature of a LLM rollout function.

    Ensures the function follows the expected pattern for LLM-based rollouts:
-
    - Must have at least 2 parameters
    - First parameter must be named 'task'
    - Must have a parameter named 'llm'
    - Optionally can have a 'rollout' parameter

    Args:
-        func: Function to inspect.
+        func: The function to validate.

    Returns:
-        `True` when the signature matches the supported patterns.
+        True if the function signature is valid.

    Raises:
        ValueError: If the function signature does not match the expected pattern.
@@ -391,34 +383,36 @@ def prompt_rollout() -> Callable[[PromptRolloutFunc[T]], FunctionalLitAgent[T]]:
 def prompt_rollout(
    func: PromptRolloutFunc[T] | None = None,
 ) -> FunctionalLitAgent[T] | Callable[[PromptRolloutFunc[T]], FunctionalLitAgent[T]]:
-    """Create a [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] for prompt-based rollouts.
+    """Create a FunctionalLitAgent from a function that takes (task, prompt_template[, rollout]).

    This decorator is designed for agents that work with tunable prompt templates. It enables
    a workflow where algorithms manage and optimize the prompt template, while agents consume
    the template to perform rollouts. This is particularly useful for prompt optimization scenarios.

    Args:
-        func: Callable defining the agent's behavior. Supported signatures include:
-
-            * `(task, prompt_template) -> result`
-            * `(task, prompt_template, rollout) -> result`
-            * `async (task, prompt_template) -> result`
-            * `async (task, prompt_template, rollout) -> result`
+        func: A function that defines the agent's behavior. Can be:
+              - sync: (task, prompt_template) -> result
+              - sync with rollout: (task, prompt_template, rollout) -> result
+              - async: async (task, prompt_template) -> result
+              - async with rollout: async (task, prompt_template, rollout) -> result

    Returns:
-        [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] that
-        wraps the supplied function.
+        A callable FunctionalLitAgent instance that preserves the original function's
+        type hints and behavior while providing all agent functionality.

-    Examples:
-        ```python
+    Example:
        @prompt_rollout
        def my_agent(task, prompt_template):
+            # Use the prompt template to generate a response
            messages = prompt_template.format(task=task.input)
-            return messages
+            # ... perform rollout with the formatted prompt
+            return response

+        # Function is still callable with original behavior
        result = my_agent(task, prompt_template)
+
+        # Agent methods are also available
        result = my_agent.rollout(task, resources, rollout)
-        ```
    """

    def decorator(f: PromptRolloutFunc[T]) -> FunctionalLitAgent[T]:
@@ -435,17 +429,16 @@ def _validate_prompt_rollout_func(func: Any) -> TypeGuard[PromptRolloutFunc[Any]
    """Validate the function signature of a prompt rollout function.

    Ensures the function follows the expected pattern for prompt-template-based rollouts:
-
    - Must have at least 2 parameters
    - First parameter must be named 'task'
    - Must have a parameter named 'prompt_template'
    - Optionally can have a 'rollout' parameter

    Args:
-        func: Function to inspect.
+        func: The function to validate.

    Returns:
-        `True` when the signature matches the supported patterns.
+        True if the function signature is valid.

    Raises:
        ValueError: If the function signature does not match the expected pattern.
@@ -463,30 +456,23 @@ def _validate_prompt_rollout_func(func: Any) -> TypeGuard[PromptRolloutFunc[Any]


 def rollout(func: Union[LlmRolloutFunc[T], PromptRolloutFunc[T], Callable[..., Any]]) -> FunctionalLitAgent[T]:
-    """Create a [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] from an arbitrary rollout function.
+    """Create a LitAgent from a function, automatically detecting the appropriate type.

    This function inspects the provided callable and creates the appropriate
    agent type based on its signature. It supports both LLM-based and prompt-template-based
    agents. The returned agent instance is callable, preserving the original function's
    behavior and type hints.

-    See [`llm_rollout`][agentlightning.litagent.decorator.llm_rollout] and
-    [`prompt_rollout`][agentlightning.litagent.decorator.prompt_rollout] for more details.
-
    Args:
-        func: Callable that implements the rollout. Supported signatures:
-
-            - `[async ](task, llm[, rollout])` for LLM-based agents
-            - `[async ](task, prompt_template[, rollout])` for prompt-template-based agents
-
-            The supported output types of `func` is same as the return type of [`rollout`][agentlightning.LitAgent.rollout].
+        func: A function that defines the agent's behavior. Supported signatures:
+              - (task, llm[, rollout]) for LLM-based agents
+              - (task, prompt_template[, rollout]) for prompt-template-based agents

    Returns:
-        [`FunctionalLitAgent`][agentlightning.litagent.decorator.FunctionalLitAgent] that
-        wraps the supplied function.
+        A callable FunctionalLitAgent instance that preserves the original function's
+        type hints and behavior while providing all agent functionality.

-    Examples:
-        ```python
+    Example:
        # LLM-based agent
        @rollout
        def my_llm_agent(task, llm):
@@ -509,7 +495,6 @@ def rollout(func: Union[LlmRolloutFunc[T], PromptRolloutFunc[T], Callable[..., A

        # Agent methods are also available
        result = my_llm_agent.rollout(task, resources, rollout)
-        ```

    Raises:
        NotImplementedError: If the function signature doesn't match any known patterns.
@@ -1,7 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Base abstractions for building agents that plug into Agent Lightning."""
-
 from __future__ import annotations

 import inspect
@@ -10,11 +8,11 @@ import warnings
 import weakref
 from typing import TYPE_CHECKING, Any, Callable, Generic, Optional, TypeVar

-from agentlightning.types import NamedResources, Rollout, RolloutRawResult, Task
+from agentlightning.types import NamedResources, RolloutRawResultV2, RolloutV2, Task

 if TYPE_CHECKING:
-    from agentlightning.runner import Runner
-    from agentlightning.tracer import Tracer
+    from agentlightning.runner import BaseRunner
+    from agentlightning.tracer import BaseTracer
    from agentlightning.trainer import Trainer


@@ -24,42 +22,37 @@ T = TypeVar("T")

 __all__ = [
    "LitAgent",
+    "is_v0_1_rollout_api",
 ]


 def is_v0_1_rollout_api(func: Callable[..., Any]) -> bool:
-    """Return `True` when the rollout function uses the deprecated v0.1 signature.
-
-    The helper inspects the callable's signature to detect whether a `rollout_id`
-    parameter is present, which indicates the legacy API.
+    """Check if the rollout API is v0.1.
+    Inspect the function signature to see if it has a rollout_id parameter.

    Args:
-        func: Function to analyze.
-
-    Returns:
-        `True` if the callable exposes a `rollout_id` parameter.
+        func: The function to check.
    """
    return "rollout_id" in inspect.signature(func).parameters


 class LitAgent(Generic[T]):
-    """Base class for implementing agent rollouts.
+    """Base class for the training and validation logic of an agent.

-    Subclasses override the rollout methods to process tasks while the trainer and
-    runner infrastructure manages orchestration, tracing, and persistence.
+    Developers should subclass this class and implement the rollout methods
+    to define the agent's behavior for a single task. The agent's logic
+    is completely decoupled from the server communication and training
+    infrastructure.
    """

    def __init__(self, *, trained_agents: Optional[str] = None) -> None:  # FIXME: str | None won't work for cli
-        """Initialize the agent instance.
+        """
+        Initialize the LitAgent.

        Args:
-            trained_agents: Optional identifier used by legacy tooling to mark trained
-                agents.
-
-        !!! warning "Deprecated"
-            The `trained_agents` flag is deprecated. Configure `agent_match` in the adapter
-            layer instead. See [`TracerTraceToTriplet`][agentlightning.TracerTraceToTriplet]
-            for more details.
+            trained_agents: Optional string representing the trained agents.
+                            This can be used to track which agents have been trained by this instance.
+                            Deprecated. Configure `agent_match` in adapter instead.
        """
        if trained_agents is not None:
            warnings.warn(
@@ -70,12 +63,15 @@ class LitAgent(Generic[T]):
        self.trained_agents = trained_agents

        self._trainer_ref: weakref.ReferenceType[Trainer] | None = None
-        self._runner_ref: weakref.ReferenceType[Runner[T]] | None = None
+        self._runner_ref: weakref.ReferenceType[BaseRunner[T]] | None = None

    def is_async(self) -> bool:
-        """Return `True` when the agent overrides any asynchronous rollout methods.
+        """
+        Check if the agent implements asynchronous rollout methods.
+        Override this property for customized async detection logic.

-        Override this method for customized async detection logic.
+        Returns:
+            True if the agent has custom async rollout methods, False otherwise.
        """
        return (
            (
@@ -90,15 +86,21 @@ class LitAgent(Generic[T]):
        )

    def set_trainer(self, trainer: Trainer) -> None:
-        """Attach the trainer responsible for orchestration.
+        """
+        Set the trainer for this agent.

        Args:
-            trainer: [`Trainer`][agentlightning.Trainer] that manages the agent.
+            trainer: The Trainer instance that will handle training and validation.
        """
        self._trainer_ref = weakref.ref(trainer)

    def get_trainer(self) -> Trainer:
-        """Return the trainer associated with this agent."""
+        """
+        Get the trainer for this agent.
+
+        Returns:
+            The Trainer instance associated with this agent.
+        """
        if self._trainer_ref is None:
            raise ValueError("Trainer has not been set for this agent.")
        trainer = self._trainer_ref()
@@ -108,31 +110,39 @@ class LitAgent(Generic[T]):

    @property
    def trainer(self) -> Trainer:
-        """Return the trainer associated with this agent."""
+        """Convenient shortcut of self.get_trainer()."""
        return self.get_trainer()

-    def get_tracer(self) -> Tracer:
-        """Return the tracer configured for this agent."""
-        if hasattr(self.runner, "tracer"):
-            return self.runner.tracer  # type: ignore
-        else:
-            return self.trainer.tracer
+    def get_tracer(self) -> BaseTracer:
+        """
+        Get the tracer for this agent.
+
+        Returns:
+            The BaseTracer instance associated with this agent.
+        """
+        return self.trainer.tracer

    @property
-    def tracer(self) -> Tracer:
-        """Return the tracer configured for this agent."""
+    def tracer(self) -> BaseTracer:
+        """Convenient shortcut of self.get_tracer()."""
        return self.get_tracer()

-    def set_runner(self, runner: Runner[T]) -> None:
-        """Attach the runner responsible for executing rollouts.
+    def set_runner(self, runner: BaseRunner[T]) -> None:
+        """
+        Set the runner for this agent.

        Args:
-            runner: [`Runner`][agentlightning.Runner] coordinating execution.
+            runner: The runner instance that will handle the execution of rollouts.
        """
        self._runner_ref = weakref.ref(runner)

-    def get_runner(self) -> Runner[T]:
-        """Return the runner responsible for executing rollouts."""
+    def get_runner(self) -> BaseRunner[T]:
+        """
+        Get the runner for this agent.
+
+        Returns:
+            The runner instance associated with this agent.
+        """
        if self._runner_ref is None:
            raise ValueError("Runner has not been set for this agent.")
        runner = self._runner_ref()
@@ -141,111 +151,163 @@ class LitAgent(Generic[T]):
        return runner

    @property
-    def runner(self) -> Runner[T]:
-        """Return the runner responsible for executing rollouts."""
+    def runner(self) -> BaseRunner[T]:
+        """Convenient shortcut of self.get_runner()."""
        return self.get_runner()

-    def on_rollout_start(self, task: Task, runner: Runner[T], tracer: Tracer) -> None:
-        """Hook invoked immediately before a rollout begins.
+    def on_rollout_start(self, task: Task, runner: BaseRunner[T], tracer: BaseTracer) -> None:
+        """Hook called immediately before a rollout begins.

-        Subclasses can override this method to implement custom logic such as logging,
-        metric collection, or resource setup. The default implementation is a no-op.
+        Deprecated in favor of `on_rollout_start` in the `Hook` interface.

        Args:
-            task: [`Task`][agentlightning.Task] that will be processed.
-            runner: [`Runner`][agentlightning.Runner] managing the rollout.
-            tracer: [`Tracer`][agentlightning.Tracer] associated with the runner.
+            task: The :class:`Task` object that will be processed.
+            runner: The :class:`BaseRunner` managing the rollout.
+            tracer: The tracer instance associated with the runner.

-        !!! warning "Deprecated"
-            Override [`Hook.on_rollout_start`][agentlightning.Hook.on_rollout_start]
-            instead of this method when extending agents.
+        Subclasses can override this method to implement custom logic such as
+        logging, metric collection, or resource setup. By default, this is a
+        no-op.
        """

-    def on_rollout_end(self, task: Task, rollout: Rollout, runner: Runner[T], tracer: Tracer) -> None:
-        """Hook invoked after a rollout completes.
+    def on_rollout_end(self, task: Task, rollout: RolloutV2, runner: BaseRunner[T], tracer: BaseTracer) -> None:
+        """Hook called after a rollout completes.

-        Subclasses can override this method for cleanup or additional logging. The default
-        implementation is a no-op.
+        Deprecated in favor of `on_rollout_end` in the `Hook` interface.

        Args:
-            task: [`Task`][agentlightning.Task] that was processed.
-            rollout: Resulting [`Rollout`][agentlightning.Rollout].
-            runner: [`Runner`][agentlightning.Runner] managing the rollout.
-            tracer: [`Tracer`][agentlightning.Tracer] associated with the runner.
+            task: The :class:`Task` object that was processed.
+            rollout: The resulting :class:`Rollout` object.
+            runner: The :class:`BaseRunner` managing the rollout.
+            tracer: The tracer instance associated with the runner.

-        !!! warning "Deprecated"
-            Override [`Hook.on_rollout_end`][agentlightning.Hook.on_rollout_end]
-            instead of this method when extending agents.
+        Subclasses can override this method for cleanup or additional
+        logging. By default, this is a no-op.
        """

-    def rollout(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Execute a rollout synchronously.
+    def rollout(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
+        """Main entry point for executing a rollout.

+        This method determines whether to call the synchronous or
+        asynchronous rollout method based on the agent's implementation.

        If you don't wish to implement both training rollout and validation
        rollout separately, you can just implement `rollout` which will work for both.

        Args:
-            task: Task payload provided by the scheduler.
-            resources: Mapping of named resources (for example LLMs or prompt templates).
-            rollout: Rollout metadata. Avoid mutating this object directly unless a
-                subclass needs to override defaults.
+            task: The task object received from the server, containing the
+                  input data and metadata.
+            resources: A dictionary of named resources (e.g., LLMs, prompt
+                       templates) for the agent to use.
+            rollout: The full rollout object, please avoid from directly modifying it.
+                     Most agents should only use `task` and `resources`. Use `rollout`
+                     only if you need to access metadata like `rollout_id`.

        Returns:
-            One of the following values:
-
-            * `None` when tracing is handled by the runner.
-            * `float` representing the final reward.
-            * `List[ReadableSpan]` with OpenTelemetry spans.
-            * `List[Span]` with Agent Lightning spans.
+            The result of the rollout, which can be one of:
+            - None. The tracing should be handled by the agent runner.
+            - A float representing the final reward.
+            - A list of `Triplet` objects for detailed, step-by-step feedback.
+            - A list of `ReadableSpan` objects for OpenTelemetry tracing.
+            - A list of dictionaries for any trace spans.
+            - A complete `Rollout` object for full control over reporting.
        """
        raise NotImplementedError("Agents must implement the `rollout` method.")

-    async def rollout_async(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Execute a rollout asynchronously.
+    async def rollout_async(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
+        """Asynchronous version of the main rollout method.
+
+        This method determines whether to call the synchronous or
+        asynchronous rollout method based on the agent's implementation.

        Args:
-            task: Task payload provided by the scheduler.
-            resources: Mapping of named resources (for example LLMs or prompt templates).
-            rollout: Rollout metadata. Avoid mutating this object directly unless a
-                subclass needs to override defaults.
+            task: The task object received from the server, containing the
+                  input data and metadata.
+            resources: A dictionary of named resources (e.g., LLMs, prompt
+                       templates) for the agent to use.
+            rollout: The full rollout object, please avoid from directly modifying it.
+                     Most agents should only use `task` and `resources`. Use `rollout`
+                     only if you need to access metadata like `rollout_id`.

        Returns:
-            Same possible return values as
-            [`rollout`][agentlightning.LitAgent.rollout].
+            The result of the rollout, which can be one of:
+            - None. The tracing should be handled by the agent runner.
+            - A float representing the final reward.
+            - A list of `Triplet` objects for detailed, step-by-step feedback.
+            - A list of `ReadableSpan` objects for OpenTelemetry tracing.
+            - A list of dictionaries for any trace spans.
+            - A complete `Rollout` object for full control over reporting.
        """
        raise NotImplementedError("Agents must implement the `rollout_async` method for async operations.")

-    def training_rollout(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Process a single training task synchronously.
+    def training_rollout(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
+        """Defines the agent's behavior for a single training task.

-        By default, this method delegates to
-        [`rollout`][agentlightning.LitAgent.rollout].
+        This method should contain the logic for how the agent processes an
+        input, uses the provided resources (like LLMs or prompts), and
+        produces a result.
+
+        Args:
+            task: The task object received from the server, containing the
+                  input data and metadata.
+            resources: A dictionary of named resources (e.g., LLMs, prompt
+                       templates) for the agent to use.
+            rollout: The full rollout object, please avoid from directly modifying it.
        """
        return self.rollout(task, resources, rollout)

-    def validation_rollout(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Process a single validation task synchronously.
+    def validation_rollout(self, task: T, resources: NamedResources, rollout: RolloutV2) -> RolloutRawResultV2:
+        """Defines the agent's behavior for a single validation task.

-        Override this method when validation should differ from training. The default
-        implementation delegates to
-        [`training_rollout`][agentlightning.LitAgent.training_rollout].
+        By default, this method redirects to `training_rollout`. Override it
+        if the agent should behave differently during validation.
+
+        Args:
+            task: The task object received from the server, containing the
+                  input data and metadata.
+            resources: A dictionary of named resources for the agent to use.
+            rollout: The full rollout object, avoid from modifying it.
+
+        Returns:
+            The result of the validation rollout. See `rollout` for
+            possible return types.
        """
        return self.rollout(task, resources, rollout)

-    async def training_rollout_async(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Process a single training task asynchronously.
+    async def training_rollout_async(
+        self, task: T, resources: NamedResources, rollout: RolloutV2
+    ) -> RolloutRawResultV2:
+        """Asynchronous version of `training_rollout`.

-        By default, this method delegates to
-        [`rollout_async`][agentlightning.LitAgent.rollout_async].
+        This method should be implemented by agents that perform asynchronous
+        operations (e.g., non-blocking I/O, concurrent API calls).
+
+        Args:
+            task: The task object received from the server.
+            resources: A dictionary of named resources for the agent to use.
+            rollout: The full rollout object, avoid from modifying it.
+
+        Returns:
+            The result of the asynchronous training rollout. See `rollout` for
+            possible return types.
        """
        return await self.rollout_async(task, resources, rollout)

-    async def validation_rollout_async(self, task: T, resources: NamedResources, rollout: Rollout) -> RolloutRawResult:
-        """Process a single validation task asynchronously.
+    async def validation_rollout_async(
+        self, task: T, resources: NamedResources, rollout: RolloutV2
+    ) -> RolloutRawResultV2:
+        """Asynchronous version of `validation_rollout`.

-        Override this method when validation should differ from training. The default
-        implementation delegates to
-        [`training_rollout_async`][agentlightning.LitAgent.training_rollout_async].
+        By default, this method redirects to `training_rollout_async`.
+        Override it for different asynchronous validation behavior.
+
+        Args:
+            task: The task object received from the server.
+            resources: A dictionary of named resources for the agent to use.
+            rollout: The full rollout object, avoid from modifying it.
+
+        Returns:
+            The result of the asynchronous validation rollout. See `rollout` for
+            possible return types.
        """
        return await self.rollout_async(task, resources, rollout)
@@ -1,370 +1,18 @@
 # Copyright (c) Microsoft. All rights reserved.

-from __future__ import annotations
-
 import logging
-import os
-import platform
-import sys
-import warnings
-from logging.config import dictConfig
-from typing import Any, Dict, Optional
-
-from rich.console import Console
-
-__all__ = ["setup", "configure_logger", "setup_module"]


 def configure_logger(level: int = logging.INFO, name: str = "agentlightning") -> logging.Logger:
-    """Create or reset a namespaced logger with a consistent console format.
+    logger = logging.getLogger(name)
+    logger.handlers.clear()  # clear existing handlers

-    This helper clears any previously attached handlers before binding a single
-    `StreamHandler` that writes to standard output. The resulting logger does
-    not propagate to the root logger, preventing duplicate log emission when
-    applications compose multiple logging configurations.
-
-    !!! danger
-
-        This function is deprecated in favor of [`setup_logging`][agentlightning.setup_logging].
-
-    Args:
-        level: Logging level applied both to the logger and the installed
-            handler. Defaults to `logging.INFO`.
-        name: Dotted path for the logger instance. Defaults to
-            `"agentlightning"`.
-
-    Returns:
-        Configured logger instance ready for immediate use.
-
-    Examples:
-        ```python
-        from agentlightning import configure_logger
-
-        logger = configure_logger(level=logging.INFO)
-        logger.info("agent-lightning is ready!")
-        ```
-    """
-    warnings.warn("This function is deprecated in favor of `setup_logging`.", DeprecationWarning, stacklevel=2)
-
-    return setup_module(level=level, name=name, console=True, color=True, propagate=False)
-
-
-DEFAULT_FORMAT = "%(asctime)s [%(levelname)s] (Process-%(process)d %(name)s)   %(message)s"
-DATE_FORMAT = "%H:%M:%S"
-
-
-def _to_level_value(lvl: int | str) -> int:
-    if isinstance(lvl, int):
-        return lvl
-    val = getattr(logging, str(lvl).upper(), None)
-    if val is None:
-        raise ValueError(f"Invalid log level: {lvl}")
-    return val
-
-
-def _ensure_file_handler(
-    logger: logging.Logger,
-    filename: str,
-    *,
-    level: int,
-    formatter: Optional[logging.Formatter],
-) -> None:
-    """Attach a FileHandler to `logger` for `filename` if it doesn't already exist."""
-    abspath = os.path.abspath(filename)
-
-    # Avoid duplicates
-    for h in logger.handlers:
-        if isinstance(h, logging.FileHandler) and getattr(h, "baseFilename", None) == abspath:
-            return
-
-    # Ensure directory exists
-    dirname = os.path.dirname(abspath)
-    if dirname:
-        os.makedirs(dirname, exist_ok=True)
-
-    fh = logging.FileHandler(abspath, encoding="utf-8")
-    fh.setLevel(level)
-    if formatter is not None:
-        fh.setFormatter(formatter)
-    else:
-        fh.setFormatter(logging.Formatter(DEFAULT_FORMAT, DATE_FORMAT))
-
-    logger.addHandler(fh)
-
-
-def setup(
-    level: int | str = "INFO",
-    *,
-    console: bool = True,
-    color: bool | Dict[str, Any] = True,
-    propagate: bool = False,
-    disable_existing_loggers: bool = False,
-    capture_warnings: bool = False,
-    submodule_levels: Optional[dict[str, int | str]] = None,
-    extra_handlers: Optional[list[logging.Handler]] = None,
-    formatter: Optional[logging.Formatter] = None,
-    apply_to: Optional[list[str]] = None,
-    files: Optional[str | dict[str, str]] = None,
-) -> None:
-    """Configures logging for the `agentlightning` logger hierarchy.
-
-    This function provides a one-stop setup utility for configuring the
-    `agentlightning` root logger and optionally its submodules or external
-    loggers. It supports console logging, colored rich output, per-submodule
-    log levels, and optional handler/formatter injection.
-
-    The setup is intentionally isolated: it does not modify the global root
-    logger or loggers belonging to other libraries unless explicitly directed
-    via `apply_to`.
-
-    Args:
-        level:
-            Logging level for the base `agentlightning` logger. Accepts either
-            an integer (e.g., `logging.DEBUG`) or a string level name
-            (e.g., `"INFO"`). Defaults to `"INFO"`.
-        console:
-            Whether to attach a console handler to the logger. Defaults to
-            `True`.
-        color:
-            Enables rich-formatted output using `RichHandler` when `True`
-            or a configuration dict. If `False`, a plain text formatter is
-            used instead. Defaults to `True`.
-        propagate:
-            Whether `agentlightning` logs should propagate to ancestor
-            loggers. Defaults to `False`.
-        disable_existing_loggers:
-            Passed to `logging.config.dictConfig`. If `True`, disables all
-            existing configured loggers before applying this configuration.
-            Defaults to `False`.
-        capture_warnings:
-            If `True`, redirects Python `warnings` emitted via the `warnings`
-            module into the logging system. Defaults to `False`.
-        submodule_levels:
-            Mapping of submodule logger names to logging levels. If a specified
-            submodule level is more verbose than the base level, a warning is emitted.
-        extra_handlers:
-            A list of user-provided handlers to attach to the `agentlightning` logger.
-            Handlers are added idempotently; duplicates are not reattached.
-        formatter:
-            A formatter to apply to any handler under `agentlightning` that does not
-            already have one assigned. Useful for customizing output without overwriting
-            formatters on custom handlers.
-        apply_to:
-            A list of additional logger names to configure identically to
-            `agentlightning` base logger. Their handlers are replaced with copies of the base
-            handlers, and propagation is disabled to avoid duplicate log emission.
-        files:
-            If a string, attach a FileHandler to the base `agentlightning` logger.
-            If a dict, for each `(logger_name, filename)` pair, attach a FileHandler
-            directly to that logger.
-            Each file handler should use the logger's effective level at creation.
-
-    Notes:
-        * On Windows, this function forces UTF-8 mode in the console to prevent
-          issues with rich output or special characters.
-        * Submodule loggers can generate records below the handler's emission
-          threshold. Whether such records appear depends on both the logger's
-          level and the handler's level.
-        * `apply_to` loggers inherit the same handlers but do not propagate
-          upward, yielding isolated, consistent behavior.
-
-    Examples:
-        Basic setup:
-
-        >>> setup()
-
-        Enabling debug mode with no color:
-
-        >>> setup(level="DEBUG", color=False)
-
-        Overriding specific submodule levels:
-
-        >>> setup(submodule_levels={"agentlightning.io": "DEBUG"})
-
-        Attaching an additional file handler:
-
-        >>> fh = logging.FileHandler("app.log")
-        >>> setup(extra_handlers=[fh])
-    """
-    # Ensure UTF-8 encoding on Windows consoles
-    # Note: This change does not fully represent support for execution under the windows system.
-    # It only fixes console printing issues caused by special characters.
-    # TODO: More comprehensive Windows support may be needed in the future.
-    if platform.system() == "Windows":
-        os.environ["PYTHONUTF8"] = "1"
-
-    base_logger = setup_module(
-        level,
-        name="agentlightning",
-        console=console,
-        color=color,
-        propagate=propagate,
-        disable_existing_loggers=disable_existing_loggers,
-    )
-
-    base_level_value = base_logger.level
-
-    # Apply user-provided formatter (only to handlers without one,
-    # so we don't clobber custom extra_handlers)
-    if formatter is not None:
-        for h in base_logger.handlers:
-            if h.formatter is None:
-                h.setFormatter(formatter)
-
-    # Attach user-provided handler(s) if any, idempotently
-    if extra_handlers:
-        for h in extra_handlers:
-            if h not in base_logger.handlers:
-                base_logger.addHandler(h)
-
-    # Per-submodule levels
-    if submodule_levels:
-        for name, lvl in submodule_levels.items():
-            sub_level = _to_level_value(lvl)
-
-            # Emit a warning if submodule level is lower (more verbose) than the global/base level
-            if sub_level < base_level_value:
-                base_logger.warning(
-                    "Submodule logger '%s' level %s (%s) is more verbose than base "
-                    "logger level %s (%s). Records below the base level may still be "
-                    "filtered out by handlers depending on their own levels.",
-                    name,
-                    lvl,
-                    sub_level,
-                    logging.getLevelName(base_level_value),
-                    base_level_value,
-                )
-
-            # The logger will *create* records down to the logger's level, but a handler
-            # with a higher level will still drop anything below its own threshold.
-            # Effective emission is gated by both: record.level >= logger.level AND handler.level.
-            logging.getLogger(name).setLevel(lvl)
-
-    # Attach file handlers if requested
-    if files is not None:
-        if isinstance(files, str):
-            # Single file for the entire `agentlightning` hierarchy.
-            _ensure_file_handler(
-                logger=base_logger,
-                filename=files,
-                level=base_level_value,
-                formatter=formatter,
-            )
-        else:
-            # Per-logger files
-            for logger_name, filename in files.items():
-                lg = logging.getLogger(logger_name)
-                # Use the logger's *effective* level at creation time
-                effective_level = lg.getEffectiveLevel()
-                _ensure_file_handler(
-                    logger=lg,
-                    filename=filename,
-                    level=effective_level,
-                    formatter=formatter,
-                )
-
-    # Optionally apply the same handler setup to other loggers outside this module
-    if apply_to:
-        for name in apply_to:
-            lg = logging.getLogger(name)
-            # This removes any existing handlers so we don't duplicate output
-            # and ensures these loggers share exactly the same handlers as base_logger.
-            lg.handlers.clear()
-            for h in base_logger.handlers:
-                lg.addHandler(h)
-            lg.setLevel(base_logger.level)
-            # We've attached handlers directly to these loggers; if propagate
-            # stayed True, records would bubble up to ancestor loggers and could be
-            # emitted twice (here and on the parent/root). Setting False isolates them.
-            lg.propagate = False
-
-    # Optionally capture warnings
-    if capture_warnings:
-        logging.captureWarnings(True)
-
-
-def setup_module(
-    level: int | str = "INFO",
-    *,
-    name: str = "agentlightning",
-    console: bool = True,
-    color: bool | Dict[str, Any] = True,
-    propagate: bool = False,
-    disable_existing_loggers: bool = False,
-) -> logging.Logger:
-    """Initializes and returns the base logger for `agentlightning`.
-
-    This function constructs and applies a `dictConfig` configuration for the
-    logger hierarchy rooted at `name`. It supports either rich console
-    formatting (via `RichHandler`) or plain text formatting, based on the
-    `color` argument.
-
-    Unlike [`setup_logging`][agentlightning.setup_logging], this function configures only a single logger namespace
-    and does not attach extra handlers or submodule levels. It is primarily used
-    internally by [`setup_logging`][agentlightning.setup_logging] but is also suitable for direct integration in
-    custom logging workflows.
-    """
-    root_cfg: Dict[str, Any] = {
-        "version": 1,
-        "disable_existing_loggers": disable_existing_loggers,
-        "loggers": {
-            name: {
-                "handlers": [],
-                "level": level,
-                "propagate": propagate,
-            }
-        },
-        "handlers": {},
-        "formatters": {},
-    }
-
-    # Choose formatter / handler definition
-    if color is not False and console:
-        # Console must be true to display colored outputs
-        if isinstance(color, dict):
-            rich_handler_config = color
-        else:
-            rich_handler_config: Dict[str, Any] = {
-                "rich_tracebacks": False,
-                "markup": False,
-                "show_time": True,
-                "show_path": True,
-            }
-
-            if not _has_width():
-                # e.g., in a CI environment.
-                rich_handler_config["console"] = Console(width=200)
-
-        root_cfg["handlers"]["console"] = {
-            "class": "rich.logging.RichHandler",
-            "level": level,
-            **rich_handler_config,
-        }
-        # RichHandler manages its own style; keep formatter None
-    else:
-        fmt_name = "plain"
-        root_cfg["formatters"][fmt_name] = {
-            "format": DEFAULT_FORMAT,
-            "datefmt": DATE_FORMAT,
-        }
-
-        if console:
-            root_cfg["handlers"]["console"] = {
-                "class": "logging.StreamHandler",
-                "level": level,
-                "formatter": fmt_name,
-            }
-
-    # Attach selected handlers to agentlightning
-    handler_names = list(root_cfg["handlers"].keys())
-    root_cfg["loggers"][name]["handlers"] = handler_names
-
-    # Apply dictConfig (this resets the logger handlers)
-    dictConfig(root_cfg)
-
-    return logging.getLogger(name)
-
-
-def _has_width() -> bool:
-    """Automatically determine whether the terminal has a width."""
-    return sys.stdout.isatty()
+    # log to stdout
+    handler = logging.StreamHandler()
+    handler.setLevel(level)
+    formatter = logging.Formatter("%(asctime)s [%(levelname)s] (Process-%(process)d %(name)s)   %(message)s")
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    logger.setLevel(level)
+    logger.propagate = False  # prevent double logging
+    return logger
@@ -1,11 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.

-from .agent import LitAgentRunner
-from .base import Runner
-from .legacy import LegacyAgentRunner
+from .agent import AgentRunnerV2
+from .base import BaseRunner
+from .legacy import AgentRunner

 __all__ = [
-    "Runner",
-    "LegacyAgentRunner",
-    "LitAgentRunner",
+    "BaseRunner",
+    "AgentRunner",
+    "AgentRunnerV2",
 ]
@@ -11,93 +11,61 @@ from __future__ import annotations

 import asyncio
 import logging
-import random
-import threading
 import time
-from contextlib import suppress
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Awaitable,
-    Callable,
-    List,
-    Literal,
-    Optional,
-    Sequence,
-    TypeVar,
-    cast,
-)
+from typing import TYPE_CHECKING, Any, List, Literal, Optional, Sequence, TypeVar, cast

 from opentelemetry.sdk.trace import ReadableSpan

 from agentlightning.litagent import LitAgent
 from agentlightning.reward import emit_reward, find_final_reward
 from agentlightning.store.base import LightningStore
-from agentlightning.tracer.base import Tracer
-from agentlightning.tracer.otel import OtelTracer
+from agentlightning.tracer.agentops import AgentOpsTracer
+from agentlightning.tracer.base import BaseTracer
 from agentlightning.types import (
    AttemptedRollout,
    Hook,
    NamedResources,
-    Rollout,
    RolloutMode,
-    RolloutRawResult,
+    RolloutRawResultV2,
+    RolloutV2,
    Span,
 )
-from agentlightning.utils.system_snapshot import system_snapshot

 if TYPE_CHECKING:
-    from agentlightning.execution.events import ExecutionEvent
+    from agentlightning.execution.events import Event

-from .base import Runner
+from .base import BaseRunner

 T_task = TypeVar("T_task")

 logger = logging.getLogger(__name__)


-class LitAgentRunner(Runner[T_task]):
-    """Execute [`LitAgent`][agentlightning.LitAgent] tasks with tracing support.
+class AgentRunnerV2(BaseRunner[T_task]):
+    """Runner implementation for executing agent tasks with distributed support.

    This runner manages the complete lifecycle of agent rollout execution,
    including task polling, resource management, tracing, and hooks. It supports
    both continuous iteration over tasks from the store and single-step execution.

    Attributes:
-        worker_id: Identifier for the active worker process, if any.
+        worker_id: The unique identifier for this worker process.
    """

-    def __init__(
-        self,
-        tracer: Tracer,
-        max_rollouts: Optional[int] = None,
-        poll_interval: float = 5.0,
-        heartbeat_interval: float = 10.0,
-        interval_jitter: float = 0.5,
-        heartbeat_launch_mode: Literal["asyncio", "thread"] = "asyncio",
-    ) -> None:
+    def __init__(self, tracer: BaseTracer, max_rollouts: Optional[int] = None, poll_interval: float = 5.0) -> None:
        """Initialize the agent runner.

        Args:
-            tracer: [`Tracer`][agentlightning.Tracer] used for rollout spans.
-            max_rollouts: Optional cap on iterations processed by
-                [`iter`][agentlightning.LitAgentRunner.iter].
-            poll_interval: Seconds to wait between store polls when no work is available.
-            heartbeat_interval: Seconds to wait between sending heartbeats to the store.
-            interval_jitter: Jitter factor for the poll interval. The actual interval will be between
-                poll_interval - interval_jitter and poll_interval + interval_jitter.
-                This is to avoid the overload caused by the synchronization of the runners.
-            heartbeat_launch_mode: Launch mode for the heartbeat loop. Can be "asyncio" or "thread".
-                "asyncio" is the default and recommended mode. Use "thread" if you are experiencing blocking coroutines.
+            tracer: The tracer instance for recording execution traces and spans.
+            max_rollouts: Maximum number of tasks to process in iter() mode. If None,
+                the runner will continue indefinitely until interrupted.
+            poll_interval: Time in seconds to wait between polling attempts when
+                no tasks are available in the store.
        """
        super().__init__()
        self._tracer = tracer
        self._max_rollouts = max_rollouts
        self._poll_interval = poll_interval
-        self._heartbeat_interval = heartbeat_interval
-        self._interval_jitter = interval_jitter
-        self._heartbeat_launch_mode = heartbeat_launch_mode
-        self._random_state = random.Random()

        # Set later
        self._agent: Optional[LitAgent[T_task]] = None
@@ -112,9 +80,10 @@ class LitAgentRunner(Runner[T_task]):
        initializes the tracer.

        Args:
-            agent: [`LitAgent`][agentlightning.LitAgent] instance executed by the runner.
-            hooks: Optional sequence of [`Hook`][agentlightning.Hook]
-                callbacks invoked around tracing and rollout boundaries.
+            agent: The LitAgent instance to be managed by this runner.
+            hooks: Optional sequence of Hook objects to be called at various
+                lifecycle stages (on_trace_start, on_trace_end, on_rollout_start,
+                on_rollout_end).
            **kwargs: Additional initialization arguments (currently unused).
        """
        self._agent = agent
@@ -131,14 +100,13 @@ class LitAgentRunner(Runner[T_task]):

        Args:
            worker_id: Unique identifier for this worker process.
-            store: [`LightningStore`][agentlightning.LightningStore]
-                used for task coordination and persistence.
+            store: The LightningStore instance for task coordination and data persistence.
            **kwargs: Additional worker-specific initialization arguments (currently unused).
        """
        self._store = store
        self.worker_id = worker_id

-        self._tracer.init_worker(worker_id, store)
+        self._tracer.init_worker(worker_id)

    def teardown(self, *args: Any, **kwargs: Any) -> None:
        """Teardown the runner and clean up all resources.
@@ -163,7 +131,7 @@ class LitAgentRunner(Runner[T_task]):
        This method cleans up worker-specific resources and resets the worker ID.

        Args:
-            worker_id: Unique identifier of the worker being torn down.
+            worker_id: The unique identifier of the worker being torn down.
            *args: Additional teardown arguments (currently unused).
            **kwargs: Additional teardown keyword arguments (currently unused).
        """
@@ -171,15 +139,6 @@ class LitAgentRunner(Runner[T_task]):

        self._tracer.teardown_worker(worker_id)

-    @property
-    def tracer(self) -> Tracer:
-        """Get the tracer instance.
-
-        Returns:
-            The Tracer instance used by this runner.
-        """
-        return self._tracer
-
    def get_agent(self) -> LitAgent[T_task]:
        """Get the agent instance.

@@ -187,7 +146,7 @@ class LitAgentRunner(Runner[T_task]):
            The LitAgent instance managed by this runner.

        Raises:
-            ValueError: If the agent has not been initialized via [`init`][agentlightning.LitAgentRunner.init].
+            ValueError: If the agent has not been initialized via init().
        """
        if self._agent is None:
            raise ValueError("Agent not initialized. Call init() first.")
@@ -200,7 +159,7 @@ class LitAgentRunner(Runner[T_task]):
            The LightningStore instance for this worker.

        Raises:
-            ValueError: If the store has not been initialized via [`init_worker`][agentlightning.LitAgentRunner.init_worker].
+            ValueError: If the store has not been initialized via init_worker().
        """
        if self._store is None:
            raise ValueError("Store not initialized. Call init_worker() first.")
@@ -262,7 +221,7 @@ class LitAgentRunner(Runner[T_task]):
                logger.exception(f"{self._log_prefix()} Exception during {hook_type} hook {hook}.")

    async def _post_process_rollout_result(
-        self, rollout: AttemptedRollout, raw_result: RolloutRawResult
+        self, rollout: AttemptedRollout, raw_result: RolloutRawResultV2
    ) -> List[ReadableSpan] | List[Span]:
        """Standardizes the agent's return value and report what's needed to report to the store.

@@ -277,31 +236,20 @@ class LitAgentRunner(Runner[T_task]):
        store = self.get_store()

        trace_spans: list[ReadableSpan] | list[Span] = []
-        result_recognized: bool = False

        # Case 0: result is None
        if raw_result is None:
            trace_spans = self._tracer.get_last_trace()
-            result_recognized = True

        # Case 1: result is a float (final reward)
-        if isinstance(raw_result, (bool, int, float)):
-            if isinstance(raw_result, (bool, int)):
-                logger.warning(
-                    f"{self._log_prefix(rollout.rollout_id)} Reward is not a number, got: {type(raw_result)}. "
-                    "Auto converting to float."
-                )
-                raw_result = float(raw_result)
+        if isinstance(raw_result, float):
            # Preserve the existing spans before another span is emitted
            trace_spans = list(self._tracer.get_last_trace())
-            # This will NOT emit another span to the tracer
-            reward_span = emit_reward(raw_result, propagate=False)
-            # We add it to the store manually
+            # This will emit another span to the tracer
+            reward_span = emit_reward(raw_result)
            await store.add_otel_span(rollout.rollout_id, rollout.attempt.attempt_id, reward_span)
            trace_spans.append(reward_span)
-            result_recognized = True

-        # Case 2-3: result is a list
        if isinstance(raw_result, list):
            # For rollout methods that return a list, we assume that the returned spans
            # are the complete span set from the whole rollout
@@ -309,7 +257,10 @@ class LitAgentRunner(Runner[T_task]):

            # Case 2: result is a list of ReadableSpan (OpenTelemetry spans)
            if len(raw_result) > 0 and all(isinstance(t, ReadableSpan) for t in raw_result):
-                if not isinstance(self._tracer, OtelTracer):
+
+                if not isinstance(
+                    self._tracer, AgentOpsTracer
+                ):  # TODO: this should be replaced with general OpenTelemetry tracer in next version
                    for span in raw_result:
                        await store.add_otel_span(
                            rollout.rollout_id, rollout.attempt.attempt_id, cast(ReadableSpan, span)
@@ -320,7 +271,6 @@ class LitAgentRunner(Runner[T_task]):
                        "The traces should have already been added to the store. "
                        "No need to return anything from rollout."
                    )
-                result_recognized = True

            # Case 3: result is a list of Span (agentlightning spans)
            elif len(raw_result) > 0 and all(isinstance(t, Span) for t in raw_result):
@@ -328,7 +278,6 @@ class LitAgentRunner(Runner[T_task]):
                for span in raw_result:
                    await store.add_span(cast(Span, span))
                trace_spans = raw_result
-                result_recognized = True

            # Left over cases for list
            elif len(raw_result) == 0:
@@ -337,7 +286,6 @@ class LitAgentRunner(Runner[T_task]):
                    "Please check your rollout implementation."
                )
                trace_spans = raw_result
-                result_recognized = True

            else:
                types = [type(t).__name__ for t in raw_result][:10]
@@ -346,106 +294,29 @@ class LitAgentRunner(Runner[T_task]):
                    f"but got: {', '.join(types)}..."
                )

-        if not result_recognized:
-            raise TypeError(
-                f"Invalid raw result type. It's expected to be none, float, or a list of ReadableSpan or Span, "
-                f"but got: {type(raw_result).__name__}..."
-            )
-
        return trace_spans

-    async def _emit_heartbeat(self, store: LightningStore) -> None:
-        """Send a heartbeat tick to the store."""
-        worker_id = self.get_worker_id()
-
-        try:
-            await store.update_worker(worker_id, system_snapshot())
-        except asyncio.CancelledError:
-            # bypass the exception
-            raise
-        except Exception:
-            logger.exception("%s Unable to update worker heartbeat.", self._log_prefix())
-
-    def _start_heartbeat_loop(self, store: LightningStore) -> Optional[Callable[[], Awaitable[None]]]:
-        """Start a background heartbeat loop and return an async stopper."""
-
-        if self._heartbeat_interval <= 0:
-            return None
-
-        if self.worker_id is None:
-            logger.warning("%s Cannot start heartbeat loop without worker_id.", self._log_prefix())
-            return None
-
-        if self._heartbeat_launch_mode == "asyncio":
-            stop_event = asyncio.Event()
-
-            async def heartbeat_loop() -> None:
-                while not stop_event.is_set():
-                    await self._emit_heartbeat(store)
-                    with suppress(asyncio.TimeoutError):
-                        interval = self._heartbeat_interval + self._random_state.uniform(
-                            -self._interval_jitter, self._interval_jitter
-                        )
-                        interval = max(interval, 0.01)
-                        await asyncio.wait_for(stop_event.wait(), timeout=interval)
-
-            task = asyncio.create_task(heartbeat_loop(), name=f"{self.get_worker_id()}-heartbeat")
-
-            async def stop() -> None:
-                stop_event.set()
-                with suppress(asyncio.CancelledError):
-                    await task
-
-            return stop
-
-        if self._heartbeat_launch_mode == "thread":
-            stop_evt = threading.Event()
-
-            def thread_worker() -> None:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-                while not stop_evt.is_set():
-                    loop.run_until_complete(self._emit_heartbeat(store))
-                    interval = self._heartbeat_interval + self._random_state.uniform(
-                        -self._interval_jitter, self._interval_jitter
-                    )
-                    interval = max(interval, 0.01)
-                    stop_evt.wait(interval)
-
-            thread = threading.Thread(target=thread_worker, name=f"{self.get_worker_id()}-heartbeat", daemon=True)
-            thread.start()
-
-            async def stop() -> None:
-                stop_evt.set()
-                await asyncio.to_thread(thread.join)
-
-            return stop
-
-        raise ValueError(f"Unsupported heartbeat launch mode: {self._heartbeat_launch_mode}")
-
-    async def _sleep_until_next_poll(self, event: Optional[ExecutionEvent] = None) -> None:
+    async def _sleep_until_next_poll(self, event: Optional[Event] = None) -> None:
        """Sleep until the next poll interval, with optional event-based interruption.

        If an event is provided, the method will check it periodically (every 0.1s)
        and return early if the event is set.

        Args:
-            event: Optional [`ExecutionEvent`][agentlightning.ExecutionEvent] object that can be used to interrupt the sleep.
+            event: Optional Event object that can be used to interrupt the sleep.
                If set during the sleep period, the method returns immediately.
        """
-        interval = self._poll_interval + self._random_state.uniform(-self._interval_jitter, self._interval_jitter)
-        interval = max(interval, 0.01)
        if event is None:
-            await asyncio.sleep(interval)
+            await asyncio.sleep(self._poll_interval)
            return
        current_time = time.time()
-        next_time = current_time + interval
+        next_time = current_time + self._poll_interval
        while time.time() < next_time:
            await asyncio.sleep(0.1)
            if event.is_set():
                return

-    async def _step_impl(self, next_rollout: AttemptedRollout, raise_on_exception: bool = False) -> str:
+    async def _step_impl(self, next_rollout: AttemptedRollout, raise_on_exception: bool = False) -> None:
        """Execute a single rollout implementation.

        This is the core method that handles the execution of a single rollout,
@@ -475,7 +346,7 @@ class LitAgentRunner(Runner[T_task]):
                raise RuntimeError(f"{self._log_prefix(rollout_id)} Failed to fetch resources")
            else:
                logger.error(f"{self._log_prefix(rollout_id)} Failed to fetch resources. Skipping.")
-                return rollout_id
+                return

        trace_spans: List[ReadableSpan] | List[Span] = []
        has_exception: bool = False
@@ -484,8 +355,8 @@ class LitAgentRunner(Runner[T_task]):
            await self._trigger_hooks(hook_type="on_rollout_start", agent=agent, runner=self, rollout=next_rollout)

            start_time = time.time()
-            async with self._tracer.trace_context(
-                name=rollout_id, rollout_id=rollout_id, attempt_id=next_rollout.attempt.attempt_id
+            with self._tracer.trace_context(
+                name=rollout_id, store=store, rollout_id=rollout_id, attempt_id=next_rollout.attempt.attempt_id
            ):
                await self._trigger_hooks(
                    hook_type="on_trace_start", agent=agent, runner=self, tracer=self._tracer, rollout=next_rollout
@@ -549,13 +420,10 @@ class LitAgentRunner(Runner[T_task]):
                    f"{self._log_prefix(rollout_id)} Exception during update_attempt. Giving up the update."
                )

-        return rollout_id
-
-    async def iter(self, *, event: Optional[ExecutionEvent] = None) -> None:
+    async def iter(self, *, event: Optional[Event] = None) -> None:
        """Run the runner, continuously iterating over tasks in the store.

        This method polls the store for new rollouts and executes them until:
-
        - The event is set (if provided)
        - The max_rollouts limit is reached (if configured)
        - No more tasks are available
@@ -564,46 +432,46 @@ class LitAgentRunner(Runner[T_task]):
        propagated, allowing the runner to continue processing subsequent tasks.

        Args:
-            event: Optional ExecutionEvent object to signal the runner to stop. The runner
+            event: Optional Event object to signal the runner to stop. The runner
                will check this event periodically and stop gracefully when set.
        """
        num_tasks_processed = 0
        logger.info(f"{self._log_prefix()} Started async rollouts (max: {self._max_rollouts or 'unlimited'}).")
        store = self.get_store()

-        stop_heartbeat = self._start_heartbeat_loop(store)
-
-        try:
-            while not (event is not None and event.is_set()) and (
-                self._max_rollouts is None or num_tasks_processed < self._max_rollouts
-            ):
-                # Retrieve the next rollout
-                next_rollout: Optional[Rollout] = None
-                while not (event is not None and event.is_set()):
-                    logger.debug(f"{self._log_prefix()} Try to poll for next rollout.")
-                    next_rollout = await store.dequeue_rollout(worker_id=self.get_worker_id())
-                    if next_rollout is None:
-                        logger.debug(
-                            f"{self._log_prefix()} No rollout to poll. Waiting for {self._poll_interval} seconds."
-                        )
-                        await self._sleep_until_next_poll(event)
-                    else:
-                        break
-
+        while not (event is not None and event.is_set()) and (
+            self._max_rollouts is None or num_tasks_processed < self._max_rollouts
+        ):
+            # Retrieve the next rollout
+            next_rollout: Optional[RolloutV2] = None
+            while not (event is not None and event.is_set()):
+                logger.debug(f"{self._log_prefix()} Try to poll for next rollout.")
+                next_rollout = await store.dequeue_rollout()
                if next_rollout is None:
-                    return
+                    logger.debug(f"{self._log_prefix()} No rollout to poll. Waiting for {self._poll_interval} seconds.")
+                    await self._sleep_until_next_poll(event)
+                else:
+                    break

-                # Execute the step
-                await self._step_impl(next_rollout)
+            if next_rollout is None:
+                return

-                num_tasks_processed += 1
-                if num_tasks_processed % 10 == 0 or num_tasks_processed == 1:
-                    logger.info(
-                        f"{self._log_prefix()} Progress: {num_tasks_processed}/{self._max_rollouts or 'unlimited'}"
-                    )
-        finally:
-            if stop_heartbeat is not None:
-                await stop_heartbeat()
+            try:
+                # Claim the rollout but updating the current worker id
+                await store.update_attempt(
+                    next_rollout.rollout_id, next_rollout.attempt.attempt_id, worker_id=self.get_worker_id()
+                )
+            except Exception:
+                # This exception could happen if the rollout is dequeued and the other end died for some reason
+                logger.exception(f"{self._log_prefix()} Exception during update_attempt, giving up the rollout.")
+                continue
+
+            # Execute the step
+            await self._step_impl(next_rollout)
+
+            num_tasks_processed += 1
+            if num_tasks_processed % 10 == 0 or num_tasks_processed == 1:
+                logger.info(f"{self._log_prefix()} Progress: {num_tasks_processed}/{self._max_rollouts or 'unlimited'}")

        logger.info(f"{self._log_prefix()} Finished async rollouts. Processed {num_tasks_processed} tasks.")

@@ -613,13 +481,12 @@ class LitAgentRunner(Runner[T_task]):
        *,
        resources: Optional[NamedResources] = None,
        mode: Optional[RolloutMode] = None,
-        event: Optional[ExecutionEvent] = None,
-    ) -> Rollout:
+        event: Optional[Event] = None,
+    ) -> None:
        """Execute a single task directly, bypassing the task queue.

        This method creates a new rollout for the given input and executes it
-        immediately. Unlike [`iter()`][agentlightning.LitAgentRunner.iter],
-        exceptions are propagated to the caller.
+        immediately. Unlike iter(), exceptions are propagated to the caller.

        Args:
            input: The task input to be processed by the agent.
@@ -628,12 +495,9 @@ class LitAgentRunner(Runner[T_task]):
                If not provided, the latest resources from the store will be used.
            mode: Optional rollout mode ("train" or "validation"). If not provided,
                the agent's default mode will be used.
-            event: Optional ExecutionEvent object to signal interruption (currently unused
+            event: Optional Event object to signal interruption (currently unused
                but included for interface consistency).

-        Returns:
-            The completed rollout.
-
        Raises:
            Exception: Any exception that occurs during rollout execution will be
                re-raised to the caller.
@@ -646,12 +510,5 @@ class LitAgentRunner(Runner[T_task]):
        else:
            resources_id = None

-        attempted_rollout = await self.get_store().start_rollout(
-            input=input, mode=mode, resources_id=resources_id, worker_id=self.get_worker_id()
-        )
-        rollout_id = await self._step_impl(attempted_rollout, raise_on_exception=True)
-
-        completed_rollout = await store.get_rollout_by_id(rollout_id)
-        if completed_rollout is None:
-            raise RuntimeError(f"{self._log_prefix()} Failed to fetch completed rollout by id after step: {rollout_id}")
-        return completed_rollout
+        attempted_rollout = await self.get_store().start_rollout(input=input, mode=mode, resources_id=resources_id)
+        await self._step_impl(attempted_rollout, raise_on_exception=True)
@@ -1,6 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Abstract runner interface for executing agent tasks."""
+"""Base runner interface for executing agent tasks.
+
+This module defines the abstract base class for all runner implementations
+in the agent-lightning framework. Runners are responsible for managing the
+execution lifecycle of agents and coordinating with the store.
+"""

 from __future__ import annotations

@@ -8,13 +13,12 @@ import logging
 from contextlib import contextmanager
 from typing import TYPE_CHECKING, Any, Generic, Iterator, Optional, Sequence, TypeVar

-from agentlightning.execution.events import ExecutionEvent
 from agentlightning.litagent import LitAgent
 from agentlightning.store.base import LightningStore
-from agentlightning.types import Hook, NamedResources, ParallelWorkerBase, Rollout, RolloutMode
+from agentlightning.types import Hook, NamedResources, ParallelWorkerBase, RolloutMode

 if TYPE_CHECKING:
-    from agentlightning.execution.events import ExecutionEvent
+    from agentlightning.execution.events import Event


 T_task = TypeVar("T_task")
@@ -22,75 +26,90 @@ T_task = TypeVar("T_task")
 logger = logging.getLogger(__name__)


-class Runner(ParallelWorkerBase, Generic[T_task]):
-    """Abstract base class for long-running agent executors.
+class BaseRunner(ParallelWorkerBase, Generic[T_task]):
+    """Base class for all runners.

-    Runner implementations coordinate [`LitAgent`][agentlightning.LitAgent]
-    instances, acquire work from a [`LightningStore`][agentlightning.LightningStore],
-    and emit [`Rollout`][agentlightning.Rollout] objects. Subclasses decide how
-    to schedule work (polling, streaming, etc.) while this base class provides a
-    minimal lifecycle contract.
+    This abstract base class defines the interface that all runner implementations
+    must follow. Runners are responsible for executing agent tasks, managing the
+    execution lifecycle, and coordinating with the store.
    """

    def init(self, agent: LitAgent[T_task], **kwargs: Any) -> None:
-        """Prepare the runner to execute tasks for `agent`.
+        """Initialize the runner with the agent.

-        This method is called only once during the setup for all workers, not for each worker.
+        This method is called once during setup to configure the runner with
+        the agent it will execute.

        Args:
-            agent: Agent instance providing task-specific logic.
-            **kwargs: Optional runner-specific configuration.
+            agent: The LitAgent instance to be managed by this runner.
+            **kwargs: Additional initialization arguments specific to the runner implementation.

        Raises:
-            NotImplementedError: Subclasses must supply the initialization
-                routine.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()

    def init_worker(self, worker_id: int, store: LightningStore, **kwargs: Any) -> None:
-        """Configure worker-local state before processing tasks.
+        """Initialize the runner for each worker with worker_id and store.

-        This method is called for **each** worker during the setup.
+        This method is called once per worker process in a distributed setup.
+        It provides the worker with its unique ID and the store instance for
+        task coordination.

        Args:
-            worker_id: Unique identifier for this worker process or thread.
-            store: Shared [`LightningStore`][agentlightning.LightningStore]
-                backing task coordination.
-            **kwargs: Optional worker-specific configuration.
+            worker_id: Unique identifier for this worker process.
+            store: The LightningStore instance for task coordination and data persistence.
+            **kwargs: Additional worker-specific initialization arguments.

        Raises:
-            NotImplementedError: Subclasses must prepare per-worker resources.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()

    def run(self, *args: Any, **kwargs: Any) -> None:
-        """Deprecated synchronous entry point.
+        """Undefined method - use iter() or step() instead.

-        Use [`iter()`][agentlightning.Runner.iter] or [`step()`][agentlightning.Runner.step] instead.
+        This method is intentionally not implemented as the execution behavior
+        should be defined through iter() for continuous execution or step()
+        for single-task execution.
+
+        Args:
+            *args: Unused positional arguments.
+            **kwargs: Unused keyword arguments.

        Raises:
-            RuntimeError: Always raised to direct callers to
-                [iter()][agentlightning.Runner.iter] or
-                [step()][agentlightning.Runner.step].
+            RuntimeError: Always raised to indicate this method should not be used.
        """
        raise RuntimeError("The behavior of run() of Runner is undefined. Use iter() or step() instead.")

    def teardown(self, *args: Any, **kwargs: Any) -> None:
-        """Release resources acquired during [`init()`][agentlightning.Runner.init].
+        """Clean up runner resources and reset state.
+
+        This method is called once during shutdown to clean up any resources
+        allocated during initialization and reset the runner state.
+
+        Args:
+            *args: Additional teardown arguments.
+            **kwargs: Additional teardown keyword arguments.

        Raises:
-            NotImplementedError: Subclasses must implement the shutdown routine.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()

    def teardown_worker(self, worker_id: int, *args: Any, **kwargs: Any) -> None:
-        """Release per-worker resources allocated by [`init_worker()`][agentlightning.Runner.init_worker].
+        """Clean up worker-specific resources.
+
+        This method is called once per worker during shutdown to clean up
+        any resources specific to that worker.

        Args:
-            worker_id: Identifier of the worker being torn down.
+            worker_id: The unique identifier of the worker being torn down.
+            *args: Additional teardown arguments.
+            **kwargs: Additional teardown keyword arguments.

        Raises:
-            NotImplementedError: Subclasses must implement the shutdown routine.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()

@@ -102,21 +121,18 @@ class Runner(ParallelWorkerBase, Generic[T_task]):
        store: LightningStore,
        hooks: Optional[Sequence[Hook]] = None,
        worker_id: Optional[int] = None,
-    ) -> Iterator[Runner[T_task]]:
-        """Initialize and tear down a runner within a simple context manager.
-
-        The helper is primarily intended for debugging runner implementations
-        outside of a full [`Trainer`][agentlightning.Trainer] stack.
+    ) -> Iterator[BaseRunner[T_task]]:
+        """Context manager for quickly init and teardown the runner,
+        so that you can debug the runner without a trainer environment.

        Args:
-            agent: Agent executed by this runner.
-            store: Backing [`LightningStore`][agentlightning.LightningStore].
-                If you don't have one, you can easily create one with
-                [`InMemoryLightningStore`][agentlightning.InMemoryLightningStore].
-            hooks: Optional sequence of hooks recognised by the runner.
-                Not all runners support hooks.
-            worker_id: Override the worker identifier used during setup. Defaults
-                to `0`.
+            agent: The LitAgent instance to be managed by this runner.
+                   It should be the same agent that is to be run within the context.
+            store: The LightningStore instance for task coordination and data persistence.
+                   If you don't have one, you can easily create one with `InMemoryLightningStore()`.
+            hooks: Optional sequence of Hook instances to be used by the runner.
+                   Only some runners support hooks.
+            worker_id: Optional worker ID to be used by the runner.
        """
        _initialized: bool = False
        _worker_initialized: bool = False
@@ -139,18 +155,19 @@ class Runner(ParallelWorkerBase, Generic[T_task]):
            except Exception:
                logger.error("Error during runner teardown", exc_info=True)

-    async def iter(self, *, event: Optional[ExecutionEvent] = None) -> None:
+    async def iter(self, *, event: Optional[Event] = None) -> None:
        """Run the runner, continuously iterating over tasks in the store.

        This method runs in a loop, polling the store for new tasks and executing
        them until interrupted by the event or when no more tasks are available.

        Args:
-            event: Cooperative stop signal. When set, the runner should complete
-                the current unit of work and exit the loop.
+            event: Optional Event object that can be used to signal the runner
+                to stop gracefully. When set, the runner should finish its current
+                task and exit the iteration loop.

        Raises:
-            NotImplementedError: Subclasses provide the iteration behavior.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()

@@ -160,23 +177,23 @@ class Runner(ParallelWorkerBase, Generic[T_task]):
        *,
        resources: Optional[NamedResources] = None,
        mode: Optional[RolloutMode] = None,
-        event: Optional[ExecutionEvent] = None,
-    ) -> Rollout:
+        event: Optional[Event] = None,
+    ) -> None:
        """Execute a single task with the given input.

        This method provides fine-grained control for executing individual tasks
        directly, bypassing the store's task queue.

        Args:
-            input: Task payload consumed by the agent.
-            resources: Optional named resources scoped to this invocation.
-            mode: Optional rollout mode such as `"train"` or `"eval"`.
-            event: Cooperative stop signal for long-running tasks.
-
-        Returns:
-            Completed rollout produced by the agent.
+            input: The task input to be processed by the agent.
+            resources: Optional named resources to be used for this specific task.
+                If not provided, the latest resources from the store will be used.
+            mode: Optional rollout mode (e.g., "train", "test"). If not provided,
+                the default mode will be used.
+            event: Optional Event object to signal interruption. When set, the
+                runner may abort the current execution.

        Raises:
-            NotImplementedError: Subclasses provide the execution behavior.
+            NotImplementedError: Must be implemented by subclasses.
        """
        raise NotImplementedError()
@@ -1,5 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

+# type: ignore
+
 import json
 import logging
 import time
@@ -7,23 +9,22 @@ from typing import Any, Dict, List, Optional, cast

 from opentelemetry.sdk.trace import ReadableSpan

-from agentlightning.adapter import TracerTraceToTriplet
+from agentlightning.adapter import TraceTripletAdapter
 from agentlightning.client import AgentLightningClient
-from agentlightning.litagent import LitAgent
-from agentlightning.litagent.litagent import is_v0_1_rollout_api
-from agentlightning.tracer.base import Tracer
-from agentlightning.types import RolloutLegacy, RolloutRawResultLegacy, Triplet
+from agentlightning.litagent import LitAgent, is_v0_1_rollout_api
+from agentlightning.tracer.base import BaseTracer
+from agentlightning.types import Rollout, RolloutRawResult, Triplet

-from .base import Runner
+from .base import BaseRunner

 logger = logging.getLogger(__name__)

 __all__ = [
-    "LegacyAgentRunner",
+    "AgentRunner",
 ]


-class LegacyAgentRunner(Runner[Any]):
+class AgentRunner(BaseRunner[Any]):
    """Manages the agent's execution loop and integrates with AgentOps.

    This class orchestrates the interaction between the agent (`LitAgent`) and
@@ -43,8 +44,8 @@ class LegacyAgentRunner(Runner[Any]):
        self,
        agent: LitAgent[Any],
        client: AgentLightningClient,
-        tracer: Tracer,
-        triplet_exporter: TracerTraceToTriplet,
+        tracer: BaseTracer,
+        triplet_exporter: TraceTripletAdapter,
        worker_id: Optional[int] = None,
        max_tasks: Optional[int] = None,
    ):
@@ -58,7 +59,7 @@ class LegacyAgentRunner(Runner[Any]):
        self.worker_id = worker_id
        self.max_tasks = max_tasks

-    # These methods are overridden by Runner, getting them back to old behavior.
+    # These methods are overridden by BaseRunner, getting them back to old behavior.
    def init(self, *args: Any, **kwargs: Any) -> None:
        pass

@@ -75,26 +76,26 @@ class LegacyAgentRunner(Runner[Any]):
        """Generates a standardized log prefix for the current worker."""
        if self.worker_id is not None:
            if rollout_id:
-                return f"[Worker {self.worker_id} | RolloutLegacy {rollout_id}]"
+                return f"[Worker {self.worker_id} | Rollout {rollout_id}]"
            else:
                return f"[Worker {self.worker_id}]"
        if rollout_id:
-            return f"[RolloutLegacy {rollout_id}]"
+            return f"[Rollout {rollout_id}]"
        return "[Default Worker]"

    def _to_rollout_object(
        self,
-        result: RolloutRawResultLegacy,
+        result: RolloutRawResult,
        rollout_id: str,
-    ) -> RolloutLegacy:
-        """Standardizes the agent's return value into a RolloutLegacy object.
+    ) -> Rollout:
+        """Standardizes the agent's return value into a Rollout object.

        Args:
            result: The output from the agent's rollout method.
            rollout_id: The unique identifier for the current task.

        Returns:
-            A standardized `RolloutLegacy` object for reporting to the server.
+            A standardized `Rollout` object for reporting to the server.
        """
        trace: Any = None
        final_reward: Optional[float] = None
@@ -115,8 +116,8 @@ class LegacyAgentRunner(Runner[Any]):
        # Case 4: result is a list of dict (trace JSON)
        if isinstance(result, list) and all(isinstance(t, dict) for t in result):
            trace = result
-        # Case 5: result is a RolloutLegacy object
-        if isinstance(result, RolloutLegacy):
+        # Case 5: result is a Rollout object
+        if isinstance(result, Rollout):
            final_reward = result.final_reward
            triplets = result.triplets
            trace = result.trace
@@ -128,15 +129,15 @@ class LegacyAgentRunner(Runner[Any]):
                trace = [json.loads(readable_span.to_json()) for readable_span in spans]
                trace_spans = spans

-        # Always extract triplets from the trace using TracerTraceToTriplet
+        # Always extract triplets from the trace using TraceTripletAdapter
        if trace_spans:
-            triplets = self.triplet_exporter(trace_spans)  # type: ignore
+            triplets = self.triplet_exporter(trace_spans)

        # If the agent has triplets, use the last one for final reward if not set
        if triplets and triplets[-1].reward is not None and final_reward is None:
            final_reward = triplets[-1].reward

-        # Create the RolloutLegacy object with standardized fields
+        # Create the Rollout object with standardized fields
        result_dict: Dict[str, Any] = {
            "rollout_id": rollout_id,
        }
@@ -147,9 +148,9 @@ class LegacyAgentRunner(Runner[Any]):
        if trace is not None:
            result_dict["trace"] = trace

-        if isinstance(result, RolloutLegacy):
+        if isinstance(result, Rollout):
            return result.model_copy(update=result_dict)
-        return RolloutLegacy(**result_dict)
+        return Rollout(**result_dict)

    def run(self) -> bool:  # type: ignore
        """Poll the task and rollout once synchronously."""
@@ -172,7 +173,7 @@ class LegacyAgentRunner(Runner[Any]):
            logger.error(f"{self._log_prefix(rollout_id)} Failed to fetch resources. Skipping.")
            return False

-        rollout_obj = RolloutLegacy(rollout_id=task.rollout_id, task=task)  # Default empty rollout
+        rollout_obj = Rollout(rollout_id=task.rollout_id, task=task)  # Default empty rollout

        try:
            try:
@@ -180,20 +181,20 @@ class LegacyAgentRunner(Runner[Any]):
            except Exception:
                logger.exception(f"{self._log_prefix(rollout_id)} Exception during on_rollout_start hook.")

-            with self.tracer._trace_context_sync(name=f"rollout_{rollout_id}"):  # pyright: ignore[reportPrivateUsage]
+            with self.tracer.trace_context(name=f"rollout_{rollout_id}"):
                start_time = time.time()
                rollout_method = self.agent.training_rollout if task.mode == "train" else self.agent.validation_rollout
                # Pass the task input, not the whole task object
                if is_v0_1_rollout_api(rollout_method):
                    result = cast(
-                        RolloutRawResultLegacy,
+                        RolloutRawResult,
                        rollout_method(
                            task.input, rollout_id=rollout_obj.rollout_id, resources=resources_update.resources  # type: ignore
                        ),
                    )  # type: ignore
                else:
-                    result = rollout_method(task.input, resources=resources_update.resources, rollout=rollout_obj)  # type: ignore
-                rollout_obj = self._to_rollout_object(result, task.rollout_id)  # type: ignore
+                    result = rollout_method(task.input, resources=resources_update.resources, rollout=rollout_obj)
+                rollout_obj = self._to_rollout_object(result, task.rollout_id)
                end_time = time.time()
                logger.info(
                    f"{self._log_prefix(rollout_id)} Completed in "
@@ -206,7 +207,7 @@ class LegacyAgentRunner(Runner[Any]):
            logger.exception(f"{self._log_prefix(rollout_id)} Exception during rollout.")
        finally:
            try:
-                self.agent.on_rollout_end(task, rollout_obj, self, self.tracer)  # type: ignore
+                self.agent.on_rollout_end(task, rollout_obj, self, self.tracer)
            except Exception:
                logger.exception(f"{self._log_prefix(rollout_id)} Exception during on_rollout_end hook.")
            self.client.post_rollout(rollout_obj)
@@ -249,7 +250,7 @@ class LegacyAgentRunner(Runner[Any]):
            logger.error(f"{self._log_prefix(rollout_id)} Failed to fetch resources. Skipping.")
            return False

-        rollout_obj = RolloutLegacy(rollout_id=task.rollout_id, task=task)  # Default empty rollout
+        rollout_obj = Rollout(rollout_id=task.rollout_id, task=task)  # Default empty rollout

        try:
            try:
@@ -257,7 +258,7 @@ class LegacyAgentRunner(Runner[Any]):
            except Exception:
                logger.exception(f"{self._log_prefix(rollout_id)} Exception during on_rollout_start hook.")

-            async with self.tracer.trace_context(name=f"rollout_{rollout_id}"):
+            with self.tracer.trace_context(name=f"rollout_{rollout_id}"):
                start_time = time.time()
                rollout_method = (
                    self.agent.training_rollout_async if task.mode == "train" else self.agent.validation_rollout_async
@@ -265,14 +266,14 @@ class LegacyAgentRunner(Runner[Any]):
                # Pass the task input, not the whole task object
                if is_v0_1_rollout_api(rollout_method):
                    result = cast(
-                        RolloutRawResultLegacy,
+                        RolloutRawResult,
                        await rollout_method(
                            task.input, rollout_id=rollout_obj.rollout_id, resources=resources_update.resources  # type: ignore
                        ),
                    )  # type: ignore
                else:
-                    result = await rollout_method(task.input, resources=resources_update.resources, rollout=rollout_obj)  # type: ignore
-                rollout_obj = self._to_rollout_object(result, task.rollout_id)  # type: ignore
+                    result = await rollout_method(task.input, resources=resources_update.resources, rollout=rollout_obj)
+                rollout_obj = self._to_rollout_object(result, task.rollout_id)
                end_time = time.time()
                logger.info(
                    f"{self._log_prefix(rollout_id)} Completed in "
@@ -284,7 +285,7 @@ class LegacyAgentRunner(Runner[Any]):
            logger.exception(f"{self._log_prefix(rollout_id)} Exception during rollout.")
        finally:
            try:
-                self.agent.on_rollout_end(task, rollout_obj, self, self.tracer)  # type: ignore
+                self.agent.on_rollout_end(task, rollout_obj, self, self.tracer)
            except Exception:
                logger.exception(f"{self._log_prefix(rollout_id)} Exception during on_rollout_end hook.")
            await self.client.post_rollout_async(rollout_obj)
@@ -1,158 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""Semantic conventions for Agent-lightning spans.
-
-Conventions in this file are added on demand. We generally DO NOT add
-new semantic conventions unless it's absolutely needed for certain algorithms or scenarios.
-"""
-
-from enum import Enum
-
-from pydantic import BaseModel
-
-AGL_ANNOTATION = "agentlightning.annotation"
-"""Agent-lightning's standard span name for annotations.
-
-Annotations are minimal span units for rewards, tags, and metadatas.
-They are used to "annotate" a specific event or a part of rollout.
-"""
-
-AGL_MESSAGE = "agentlightning.message"
-"""Agent-lightning's standard span name for messages and logs."""
-
-AGL_OBJECT = "agentlightning.object"
-"""Agent-lightning's standard span name for customized objects."""
-
-AGL_EXCEPTION = "agentlightning.exception"
-"""Agent-lightning's standard span name for exceptions.
-
-Used by the exception emitter to record exception details.
-"""
-
-AGL_OPERATION = "agentlightning.operation"
-"""Agent-lightning's standard span name for functions.
-Wrap function or code-blocks as operations.
-"""
-
-AGL_VIRTUAL = "agentlightning.virtual"
-"""Agent-lightning's standard span name for virtual operations.
-
-Mostly used in adapter when needing to represent the root or intermediate operations.
-"""
-
-
-class LightningResourceAttributes(Enum):
-    """Resource attribute names used in Agent-lightning spans."""
-
-    ROLLOUT_ID = "agentlightning.rollout_id"
-    """Resource name for rollout ID in Agent-lightning spans."""
-
-    ATTEMPT_ID = "agentlightning.attempt_id"
-    """Resource name for attempt ID in Agent-lightning spans."""
-
-    SPAN_SEQUENCE_ID = "agentlightning.span_sequence_id"
-    """Resource name for span sequence ID in Agent-lightning spans."""
-
-
-class LightningSpanAttributes(Enum):
-    """Attribute names that commonly appear in Agent-lightning spans.
-
-    Exception types can't be found here because they are defined in OpenTelemetry's official semantic conventions.
-    """
-
-    REWARD = "agentlightning.reward"
-    """Attribute prefix for rewards-related data in reward spans.
-
-    It should be used as a prefix. For example, "agentlightning.reward.0.value" can
-    be used to track a specific metric. See [RewardAttributes][agentlightning.semconv.RewardAttributes].
-    """
-
-    LINK = "agentlightning.link"
-    """Attribute name for linking the current span to another span or other objects like requests/responses."""
-
-    TAG = "agentlightning.tag"
-    """Attribute name for tagging spans with customized strings."""
-
-    MESSAGE_BODY = "agentlightning.message.body"
-    """Attribute name for message text in message spans."""
-
-    OBJECT_TYPE = "agentlightning.object.type"
-    """Attribute name for object type (full qualified name) in object spans.
-
-    I think builtin types like str, int, bool, list, dict are self-explanatory and
-    should also be qualified to use here.
-    """
-
-    OBJECT_LITERAL = "agentlightning.object.literal"
-    """Attribute name for object literal value in object spans (for str, int, bool, ...)."""
-
-    OBJECT_JSON = "agentlightning.object.json"
-    """Attribute name for object serialized value (JSON) in object spans."""
-
-    OPERATION_NAME = "agentlightning.operation.name"
-    """Attribute name for operation name in operation spans, normally the function name."""
-
-    OPERATION_INPUT = "agentlightning.operation.input"
-    """Attribute name for operation input in operation spans."""
-
-    OPERATION_OUTPUT = "agentlightning.operation.output"
-    """Attribute name for operation output in operation spans."""
-
-
-class RewardAttributes(Enum):
-    """Multi-dimensional reward attributes will look like:
-
-    ```json
-    {"agentlightning.reward.0.name": "efficiency", "agentlightning.reward.0.value": 0.75}
-    ```
-
-    The first reward in the reward list will automatically be the primary reward.
-    If the reward list has greater than 1, it shall be a multi-dimensional case.
-    """
-
-    REWARD_NAME = "name"
-    """Key for each dimension in multi-dimensional reward spans."""
-
-    REWARD_VALUE = "value"
-    """Value for each dimension in multi-dimensional reward spans."""
-
-
-class RewardPydanticModel(BaseModel):
-    """A stricter implementation of RewardAttributes used in otel helpers."""
-
-    name: str
-    """Name of the reward dimension."""
-
-    value: float
-    """Value of the reward dimension."""
-
-
-class LinkAttributes(Enum):
-    """Standard link types used in Agent-lightning spans.
-
-    The link is more powerful than [OpenTelemetry link](https://opentelemetry.io/docs/specs/otel/trace/api/#link)
-    in that it supports linking to a queryset of spans.
-    It can even link to span object that hasn't been emitted yet.
-    """
-
-    KEY_MATCH = "key_match"
-    """Linking to spans with matching attribute keys.
-
-    `trace_id` and `span_id` are reserved and will be used to link to specific spans directly.
-
-    For example, it can be `gen_ai.response.id` if intended to be link to a chat completion response span.
-    Or it can be `span_id` to link to a specific span by its ID.
-    """
-
-    VALUE_MATCH = "value_match"
-    """Linking to spans with corresponding attribute values on those keys."""
-
-
-class LinkPydanticModel(BaseModel):
-    """A stricter implementation of LinkAttributes used in otel helpers."""
-
-    key_match: str
-    """The attribute key to match on the target spans."""
-
-    value_match: str
-    """The attribute value to match on the target spans."""
@@ -1,11 +1,6 @@
 # Copyright (c) Microsoft. All rights reserved.

-"""Legacy HTTP server compatible with the original Agent Lightning protocol.
-
-The implementation in this module predates the modern store-powered runtime and
-is kept for backwards compatibility with older deployments. New applications
-should migrate to the store architecture where possible.
-"""
+"""Legacy server for the Agent Lightning framework. Deprecated in favor of agentlightning.store."""

 from __future__ import annotations

@@ -14,7 +9,6 @@ import logging
 import threading
 import time
 import uuid
-import warnings
 from contextlib import asynccontextmanager
 from typing import Any, Dict, List, Literal, Optional

@@ -25,7 +19,7 @@ from .types import (
    GenericResponse,
    NamedResources,
    ResourcesUpdate,
-    RolloutLegacy,
+    Rollout,
    Task,
    TaskIfAny,
 )
@@ -34,21 +28,15 @@ logger = logging.getLogger(__name__)


 class ServerDataStore:
-    """Async-safe container for in-memory server state.
-
-    The store tracks queued tasks, claimed tasks, uploaded rollouts, and the
-    currently published resources. All interactions are guarded by asyncio locks
-    so that the FastAPI handlers can safely run in parallel.
-
-    !!! warning "Deprecated"
-        [`ServerDataStore`][agentlightning.server.ServerDataStore] is part of
-        the legacy client/server stack. Use [`LightningStore`][agentlightning.LightningStore] instead.
+    """
+    A centralized, thread-safe, async, in-memory data store for the server's state.
+    This holds the task queue, versioned resources, and completed rollouts.
    """

    def __init__(self):
        self._task_queue: asyncio.Queue[Task] = asyncio.Queue()
        self._processing_tasks: Dict[str, Task] = {}  # Currently processing tasks
-        self._completed_rollouts: Dict[str, RolloutLegacy] = {}
+        self._completed_rollouts: Dict[str, Rollout] = {}

        # Store for versioned resources
        self._resource_versions: Dict[str, NamedResources] = {}
@@ -65,18 +53,8 @@ class ServerDataStore:
        resources_id: str | None = None,
        metadata: Dict[str, Any] | None = None,
    ) -> str:
-        """Enqueue a new task and return the generated rollout identifier.
-
-        Args:
-            sample: Payload that describes the task input.
-            mode: Phase in which the sample should be executed (`"train"`, `"val"`, or
-                `"test"`).
-            resources_id: Identifier of a resource bundle that the executor should
-                load before running the task.
-            metadata: Optional metadata forwarded to the executor.
-
-        Returns:
-            Unique rollout identifier assigned to the task.
+        """
+        Adds a new task to the queue with specific metadata and returns its unique ID.
        """
        rollout_id = f"rollout-{uuid.uuid4()}"
        task = Task(
@@ -93,11 +71,9 @@ class ServerDataStore:
        return rollout_id

    async def get_next_task(self) -> Optional[Task]:
-        """Retrieve the next task from the queue without blocking.
-
-        Returns:
-            Next [`Task`][agentlightning.Task] ready to execute, or ``None``
-            when the queue is empty.
+        """
+        Retrieves the next task from the queue without blocking.
+        Returns None if the queue is empty.
        """
        try:
            async with self._results_lock:
@@ -118,10 +94,8 @@ class ServerDataStore:
            return None

    async def update_resources(self, update: ResourcesUpdate):
-        """Persist a new resource bundle and mark it as the latest version.
-
-        Args:
-            update: Resource payload received from a client.
+        """
+        Safely stores a new version of named resources and sets it as the latest.
        """
        # TODO: evict old resources if necessary.
        async with self._resources_lock:
@@ -130,70 +104,54 @@ class ServerDataStore:
            logger.info(f"Resources updated. New version '{update.resources_id}' is now latest.")

    async def get_resources_by_id(self, resources_id: str) -> Optional[ResourcesUpdate]:
-        """Retrieve a specific resource bundle by identifier.
-
-        Args:
-            resources_id: Identifier that was previously published to the store.
-
-        Returns:
-            Matching [`ResourcesUpdate`][agentlightning.ResourcesUpdate]
-            instance, or ``None`` when the identifier is unknown.
+        """
+        Safely retrieves a specific version of named resources by its ID.
        """
        async with self._resources_lock:
            resources = self._resource_versions.get(resources_id)
            if resources:
-                return ResourcesUpdate(
-                    resources_id=resources_id,
-                    resources=resources,
-                    create_time=time.time(),
-                    update_time=time.time(),
-                    version=1,
-                )
+                return ResourcesUpdate(resources_id=resources_id, resources=resources)
            return None

    async def get_latest_resources(self) -> Optional[ResourcesUpdate]:
-        """Return the most recent resource bundle, if one exists."""
+        """
+        Safely retrieves the latest version of named resources.
+        """
        if self._latest_resources_id:
            return await self.get_resources_by_id(self._latest_resources_id)
        return None

-    async def store_rollout(self, rollout: RolloutLegacy):
-        """Persist a completed rollout for later inspection.
-
-        Args:
-            rollout: Rollout returned by a client.
+    async def store_rollout(self, rollout: Rollout):
+        """
+        Safely stores a completed rollout from a client.
        """
        async with self._results_lock:
            self._processing_tasks.pop(rollout.rollout_id, None)
            self._completed_rollouts[rollout.rollout_id] = rollout
            logger.info(f"Rollout received and stored: {rollout.rollout_id}")

-    async def retrieve_rollout(self, rollout_id: str) -> Optional[RolloutLegacy]:
-        """Retrieve and remove a stored rollout by identifier.
-
-        Args:
-            rollout_id: Identifier of the rollout to fetch.
-
-        Returns:
-            Stored [`RolloutLegacy`][agentlightning.RolloutLegacy], or ``None``
-            when the identifier is unknown.
+    async def retrieve_rollout(self, rollout_id: str) -> Optional[Rollout]:
+        """
+        Safely retrieves a single rollout by its ID, removing it from the store.
        """
        async with self._results_lock:
            return self._completed_rollouts.pop(rollout_id, None)

-    async def retrieve_completed_rollouts(self) -> List[RolloutLegacy]:
-        """Return all completed rollouts and clear the internal buffer."""
+    async def retrieve_completed_rollouts(self) -> List[Rollout]:
+        """
+        Retrieves all completed rollouts and clears the store.
+        """
        async with self._results_lock:
            rollouts = list(self._completed_rollouts.values())
            self._completed_rollouts.clear()
            return rollouts

    def get_processing_tasks(self) -> Dict[str, Task]:
-        """Return a copy of currently processing tasks for timeout checking."""
+        """Returns a copy of currently processing tasks for timeout checking."""
        return self._processing_tasks.copy()

    async def requeue_task(self, task: Task):
-        """Requeue a task that timed out while being processed."""
+        """Requeues a task that has timed out and removes it from processing."""
        logger.warning(f"Requeuing task {task.rollout_id} after timeout (attempt {task.num_claims})")
        async with self._results_lock:
            # Remove from processing tasks
@@ -202,30 +160,22 @@ class ServerDataStore:


 class AgentLightningServer:
-    """High-level controller for the legacy Agent Lightning FastAPI server.
+    """
+    The main SDK class for developers to control the Agent Lightning Server.

-    The controller orchestrates server start-up, task queueing, resource updates,
-    and retrieval of client rollouts. It is primarily used by existing systems that
-    still rely on the HTTP-based workflow.
-
-    !!! warning "Deprecated"
-        [`AgentLightningServer`][agentlightning.server.AgentLightningServer] is part of
-        the legacy client/server stack. Prefer the store-based runtime for new
-        integrations.
+    This class manages the server lifecycle, task queueing, resources updates,
+    and retrieval of results, providing a simple interface for the optimization logic.
    """

    def __init__(self, host: str = "127.0.0.1", port: int = 8000, task_timeout_seconds: float = 300.0):
-        """Initialize the controller.
+        """
+        Initializes the server controller.

        Args:
-            host: Hostname or IP address to bind the HTTP server to.
-            port: TCP port exposed by the server.
-            task_timeout_seconds: Seconds before a claimed task is considered stale and
-                re-queued.
+            host: The host to bind the server to.
+            port: The port to bind the server to.
+            task_timeout_seconds: Time in seconds after which a claimed task is considered stale and requeued.
        """
-        warnings.warn(
-            "AgentLightningServer is deprecated. Please use LightningStoreServer instead.", DeprecationWarning
-        )
        self.host = host
        self.port = port
        self.endpoint = f"http://{host}:{port}"
@@ -246,7 +196,9 @@ class AgentLightningServer:
    # --- ADDED: Lifespan context manager ---
    @asynccontextmanager
    async def _lifespan(self, app: FastAPI):
-        """Manage server start-up and shutdown within the event loop."""
+        """
+        Manages server startup and shutdown. This runs inside the server's event loop.
+        """
        logger.info("Server is starting up...")
        self.loop = asyncio.get_running_loop()
        self._store = ServerDataStore()  # Initialize data store here
@@ -260,7 +212,9 @@ class AgentLightningServer:
        self.loop = None

    async def _check_and_requeue_stale_tasks(self):
-        """Check for stale tasks and requeue them when they exceed the timeout."""
+        """
+        Check for stale tasks and requeue them. Called reactively during get_next_task.
+        """
        current_time = time.time()
        # Ensure store is initialized before checking
        if not self._store:
@@ -275,11 +229,11 @@ class AgentLightningServer:
                )

    def _setup_routes(self):
-        """Configure the FastAPI routes that make up the legacy HTTP API."""
+        """Setup FastAPI routes."""

        @self._app.get("/task", response_model=TaskIfAny)
        async def next_task() -> TaskIfAny:  # type: ignore
-            """Provide the next available task to a client."""
+            """Endpoint for clients to poll for the next available task."""
            await self._check_and_requeue_stale_tasks()

            if not self._store:
@@ -295,7 +249,7 @@ class AgentLightningServer:

        @self._app.get("/resources/latest", response_model=ResourcesUpdate)
        async def fetch_latest_resources() -> ResourcesUpdate:  # type: ignore
-            """Return the most recent resource bundle published to the server."""
+            """Endpoint for clients to poll for the latest available resources."""
            if not self._store:
                raise HTTPException(status_code=503, detail="Server not fully initialized.")
            resources_update = await self._store.get_latest_resources()
@@ -308,7 +262,7 @@ class AgentLightningServer:
        async def fetch_resources_by_id(  # type: ignore
            resource_id: str = Path(..., description="The unique identifier for the resource version.")
        ) -> ResourcesUpdate:
-            """Return a specific version of resources by identifier."""
+            """Endpoint for clients to fetch a specific version of resources."""
            if not self._store:
                raise HTTPException(status_code=503, detail="Server not fully initialized.")
            resources_update = await self._store.get_resources_by_id(resource_id)
@@ -318,8 +272,8 @@ class AgentLightningServer:
            return resources_update

        @self._app.post("/rollout", response_model=GenericResponse)
-        async def post_rollout(payload: RolloutLegacy) -> GenericResponse:  # type: ignore
-            """Persist the rollout reported by a client."""
+        async def post_rollout(payload: Rollout) -> GenericResponse:  # type: ignore
+            """Endpoint for clients to report a completed rollout."""
            if not self._store:
                raise HTTPException(status_code=503, detail="Server not fully initialized.")
            await self._store.store_rollout(payload)
@@ -329,13 +283,13 @@ class AgentLightningServer:
            )

    async def start(self):
-        """Start the FastAPI server in the background."""
+        """Starts the FastAPI server in the background."""
        logger.info(f"Starting server at {self.endpoint}")
        asyncio.create_task(self._uvicorn_server.serve())
        await asyncio.sleep(1)  # Allow time for server to start up.

    async def stop(self):
-        """Stop the FastAPI server and wait for a graceful shutdown."""
+        """Gracefully stops the running FastAPI server."""
        if self._uvicorn_server.started:
            logger.info("Stopping server...")
            self._uvicorn_server.should_exit = True
@@ -343,7 +297,10 @@ class AgentLightningServer:
            logger.info("Server stopped.")

    async def run_forever(self):
-        """Run the server indefinitely until `stop()` is invoked."""
+        """
+        Runs the server indefinitely until stopped.
+        This is useful when async start and stop methods do not work.
+        """
        await self._uvicorn_server.serve()

    async def queue_task(
@@ -353,37 +310,35 @@ class AgentLightningServer:
        resources_id: str | None = None,
        metadata: Dict[str, Any] | None = None,
    ) -> str:
-        """Add a task to the queue for a client to process."""
+        """
+        Adds a task to the queue for a client to process.
+        """
        if not self._store:
            raise RuntimeError("Store not initialized. The server may not be running.")
        return await self._store.add_task(sample, mode=mode, resources_id=resources_id, metadata=metadata)

    async def update_resources(self, resources: NamedResources) -> str:
-        """Publish a new resource bundle and return its generated identifier."""
+        """
+        Updates the resources, creating a new version and setting it as the latest.
+        """
        if not self._store:
            raise RuntimeError("Store not initialized. The server may not be running.")
        resources_id = f"res-{uuid.uuid4()}"
-        update = ResourcesUpdate(
-            resources_id=resources_id, resources=resources, create_time=time.time(), update_time=time.time(), version=1
-        )
+        update = ResourcesUpdate(resources_id=resources_id, resources=resources)
        await self._store.update_resources(update)
        return resources_id

-    async def get_completed_rollout(self, rollout_id: str) -> Optional[RolloutLegacy]:
-        """Retrieve a specific completed rollout by identifier."""
+    async def get_completed_rollout(self, rollout_id: str) -> Optional[Rollout]:
+        """
+        Retrieves a specific completed rollout by its ID.
+        """
        if not self._store:
            raise RuntimeError("Store not initialized. The server may not be running.")
        return await self._store.retrieve_rollout(rollout_id)

-    async def poll_completed_rollout(self, rollout_id: str, timeout: Optional[float] = None) -> Optional[RolloutLegacy]:
-        """Poll for a completed rollout until it becomes available or a timeout expires.
-
-        Args:
-            rollout_id: Identifier of the rollout to wait for.
-            timeout: Maximum number of seconds to wait. ``None`` waits indefinitely.
-
-        Returns:
-            Retrieved rollout, or ``None`` when the timeout is reached without success.
+    async def poll_completed_rollout(self, rollout_id: str, timeout: Optional[float] = None) -> Optional[Rollout]:
+        """
+        Polls for a completed rollout by its ID, waiting up to `timeout` seconds.
        """
        start_time = time.time()
        while True:
@@ -394,8 +349,10 @@ class AgentLightningServer:
                return None
            await asyncio.sleep(1)

-    async def retrieve_completed_rollouts(self) -> List[RolloutLegacy]:
-        """Return every completed rollout and clear the internal buffer."""
+    async def retrieve_completed_rollouts(self) -> List[Rollout]:
+        """
+        Retrieves all available completed trajectories and clears the internal store.
+        """
        if not self._store:
            raise RuntimeError("Store not initialized. The server may not be running.")
        return await self._store.retrieve_completed_rollouts()
@@ -1,18 +1 @@
 # Copyright (c) Microsoft. All rights reserved.
-
-from .base import LightningStore, LightningStoreCapabilities, LightningStoreStatistics
-from .client_server import LightningStoreClient, LightningStoreServer
-from .collection_based import CollectionBasedLightningStore
-from .memory import InMemoryLightningStore
-from .threading import LightningStoreThreaded
-
-__all__ = [
-    "LightningStore",
-    "LightningStoreCapabilities",
-    "LightningStoreStatistics",
-    "LightningStoreClient",
-    "LightningStoreServer",
-    "InMemoryLightningStore",
-    "CollectionBasedLightningStore",
-    "LightningStoreThreaded",
-]
@@ -2,7 +2,7 @@

 from __future__ import annotations

-from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, TypedDict
+from typing import Any, Dict, List, Literal, Optional, Sequence

 from opentelemetry.sdk.trace import ReadableSpan

@@ -10,29 +10,25 @@ from agentlightning.types import (
    Attempt,
    AttemptedRollout,
    AttemptStatus,
-    EnqueueRolloutRequest,
    NamedResources,
    ResourcesUpdate,
-    Rollout,
    RolloutConfig,
-    RolloutMode,
    RolloutStatus,
+    RolloutV2,
    Span,
    TaskInput,
-    Worker,
-    WorkerStatus,
 )


-def is_queuing(rollout: Rollout) -> bool:
+def is_queuing(rollout: RolloutV2) -> bool:
    return rollout.status == "queuing" or rollout.status == "requeuing"


-def is_running(rollout: Rollout) -> bool:
+def is_running(rollout: RolloutV2) -> bool:
    return rollout.status == "preparing" or rollout.status == "running"


-def is_finished(rollout: Rollout) -> bool:
+def is_finished(rollout: RolloutV2) -> bool:
    return rollout.status == "failed" or rollout.status == "succeeded" or rollout.status == "cancelled"


@@ -56,144 +52,33 @@ UNSET = _UnsetType()
 Unset = _UnsetType  # Alias for convenience


-class LightningStoreCapabilities(TypedDict, total=False):
-    """Capability of a LightningStore implementation.
-
-    All keys are optional and false by default.
-    """
-
-    thread_safe: bool
-    """Whether the store is thread-safe."""
-    async_safe: bool
-    """Whether the store is async-safe."""
-    zero_copy: bool
-    """Whether the store has only one copy across all threads/processes."""
-    otlp_traces: bool
-    """Whether the store supports OTLP/HTTP traces."""
-
-
-class LightningStoreStatistics(TypedDict, total=False):
-    """Statistics of a LightningStore implementation."""
-
-    name: str
-    """Name of the store implementation."""
-    total_rollouts: int
-    """Total number of rollouts in the store."""
-    total_attempts: int
-    """Total number of attempts in the store."""
-    total_spans: int
-    """Total number of spans in the store."""
-    total_resources: int
-    """Total number of resources in the store."""
-    total_workers: int
-    """Total number of workers in the store."""
-    uptime: float
-    """Uptime of since the store has been started."""
-
-    # Memory-related statistics
-    total_span_bytes: int
-    """Total number of bytes of spans in the store."""
-    eviction_threshold_bytes: int
-    """Eviction threshold for spans in bytes."""
-    safe_threshold_bytes: int
-    """Safe threshold for spans in bytes."""
-    memory_capacity_bytes: int
-    """Memory capacity of the store in bytes."""
-
-
 class LightningStore:
-    """Contract for the persistent control-plane that coordinates training rollouts.
-
-    A `LightningStore` mediates every interaction between algorithms and runners:
-
-    - **Rollout lifecycle:** accept new rollouts, queue them for execution, create attempts,
-      and drive the rollout status machine (`"queuing"` → `"preparing"` → `"running"` →
-      `{"succeeded","failed","cancelled"}` or `"requeuing"` when a retry is justified).
-    - **Attempt tracking:** record each execution attempt, including progress heartbeats,
-      retry sequencing, and terminal states such as `"timeout"` or `"unresponsive"`.
-    - **Span ingest:** capture structured telemetry emitted by runners (either as native
-      [`Span`][agentlightning.Span] objects or as `opentelemetry.sdk.trace.ReadableSpan`
-      instances) so that algorithms can reconstruct trajectories and rewards.
-    - **Resource versioning:** manage immutable snapshots of named resources
-      (prompt templates, model checkpoints, proxy endpoints, …) and expose a single
-      "latest" snapshot that runners can fetch just after claiming work.
-
-    Implementations must provide thread-safe/async-safe semantics: each coroutine should
-    appear atomic to callers even when multiple algorithms or runners call the API concurrently.
-    Unless stated otherwise, missing identifiers should result in a `ValueError`.
    """
+    A centralized, thread-safe, async, data store for the lightning's state.
+    This holds the task queue, versioned resources, and completed rollouts.

-    @property
-    def capabilities(self) -> LightningStoreCapabilities:
-        """Return the capabilities of the store."""
-        return LightningStoreCapabilities(
-            thread_safe=False,
-            async_safe=False,
-            zero_copy=False,
-            otlp_traces=False,
-        )
-
-    async def statistics(self) -> LightningStoreStatistics:
-        """Return the statistics of the store."""
-        return {
-            "name": self.__class__.__name__,
-        }
-
-    def otlp_traces_endpoint(self) -> str:
-        """Return the OTLP/HTTP traces endpoint of the store.
-
-        The traces can have rollout ID and attempt ID (and optionally sequence ID)
-        saved in the "resource" of the spans.
-        The store, if it supports OTLP, should be able to receive the traces and save them
-        via [`add_span`][agentlightning.LightningStore.add_span] or
-        [`add_otel_span`][agentlightning.LightningStore.add_otel_span].
-
-        The endpoint should be compatible with [OTLP HTTP protocol](https://opentelemetry.io/docs/specs/otlp/).
-        It's not necessarily compatible with OTLP gRPC protocol.
-
-        The returned endpoint will usually ends with `/v1/traces`.
-        """
-        raise NotImplementedError()
+    The store has a built-in clock and it should be responsible for tracking the times.
+    All the time-based operations like retry, timeout, etc. should be handled by the store.
+    """

    async def start_rollout(
        self,
        input: TaskInput,
-        mode: RolloutMode | None = None,
+        mode: Literal["train", "val", "test"] | None = None,
        resources_id: str | None = None,
-        config: RolloutConfig | None = None,
        metadata: Dict[str, Any] | None = None,
-        worker_id: str | None = None,
    ) -> AttemptedRollout:
-        """Register a rollout and immediately create its first attempt.
+        """
+        Add one incomplete rollout to the store, and get an attempt created for it.
+        This will immediately sets the rollout to a preparing state, and should be
+        used by whoever is going to execute the rollout.

-        !!! note
-            Use [`enqueue_rollout()`][agentlightning.LightningStore.enqueue_rollout] when the
-            caller only wants to submit work for later scheduling.
+        Return a special rollout with attempt object. Do not update it directly.

-        The rollout must be persisted with `status="preparing"` and an initial attempt
-        with `sequence_id == 1` so the caller can begin execution without visiting the
-        public queue. Implementations are expected to:
+        But if the rollout fails or timeouts, it's still possible that the watchdog
+        sends it back to the queue for retry.

-        1. Generate a unique `rollout_id` and `attempt_id`.
-        2. Record `start_time` for both rollout and attempt based on the current clock.
-        3. Copy `config` and `metadata` so later mutations do not leak shared references.
-        4. Resolve `resources_id` to the latest resource snapshot when `None` is supplied.
-
-        Args:
-            input: Arbitrary task payload supplied by an algorithm.
-            mode: Optional semantic mode for downstream analytics (`"train"`, `"val"`, `"test"`).
-            resources_id: Concrete resource snapshot to execute against; defaults to the latest stored snapshot.
-            config: Rollout retry/timeout policy. Should default to a fresh [`RolloutConfig`][agentlightning.RolloutConfig].
-            metadata: Free-form metadata persisted verbatim with the rollout.
-            worker_id: Optional worker identifier to associate the new attempt with.
-
-        Returns:
-            The fully-populated [`AttemptedRollout`][agentlightning.AttemptedRollout] including
-            the just-created attempt.
-
-        Raises:
-            NotImplementedError: Subclasses must provide durable storage for the rollout.
-            ValueError: Implementations should raise when `resources_id` does not exist.
+        To enqueue a rollout to the task queue, use `enqueue_rollout` instead.
        """
        raise NotImplementedError()

@@ -202,153 +87,34 @@ class LightningStore:
        input: TaskInput,
        mode: Literal["train", "val", "test"] | None = None,
        resources_id: str | None = None,
-        config: RolloutConfig | None = None,
        metadata: Dict[str, Any] | None = None,
-    ) -> Rollout:
-        """Persist a rollout in `queuing` state so runners can claim it later.
-
-        !!! note
-            Different from [`start_rollout()`][agentlightning.LightningStore.start_rollout],
-            this method is called when the caller only wants to submit work for later scheduling.
-
-        Implementations must generate a unique `rollout_id`, stamp `start_time` with
-        the current time, default `config` to a fresh [`RolloutConfig`][agentlightning.RolloutConfig],
-        and insert the rollout at the tail of the scheduling queue. No attempt is created yet.
-
-        Args:
-            input: Arbitrary task payload supplied by an algorithm.
-            mode: Optional semantic mode indicator (`"train"`, `"val"`, `"test"`).
-            resources_id: Resource snapshot used when a runner eventually executes the rollout.
-            config: Fine-grained retry/timeout parameters to persist with the rollout.
-            metadata: Free-form metadata stored verbatim with the rollout record.
-
-        Returns:
-            The stored [`Rollout`][agentlightning.Rollout] in `queuing` status.
-
-        Raises:
-            NotImplementedError: Subclasses must persist the rollout.
-            ValueError: Implementations should raise when `resources_id` does not exist.
+    ) -> RolloutV2:
+        """
+        Adds a new task to the queue with specific metadata and
+        returns the rollout object with its unique ID.
        """
        raise NotImplementedError()

-    async def enqueue_many_rollouts(self, rollouts: Sequence[EnqueueRolloutRequest]) -> Sequence[Rollout]:
-        """Persist multiple rollouts in `queuing` state.
+    async def dequeue_rollout(self) -> Optional[AttemptedRollout]:
+        """
+        Retrieves the next task from the queue without blocking.
+        Returns None if the queue is empty.

-        The implementation can delegate to [`enqueue_rollout()`][agentlightning.LightningStore.enqueue_rollout]
-        per request and preserves the input ordering. Subclasses can override to provide
-        more efficient bulk enqueue semantics.
-
-        Args:
-            rollouts: Rollout submission payloads mirroring [`enqueue_rollout()`][agentlightning.LightningStore.enqueue_rollout]'s
-                parameters. Each entry requires `input` and can optionally include other fields.
-
-        Returns:
-            Rollouts enqueued in the same order as `rollouts`.
+        Will set the rollout status to preparing.
        """
        raise NotImplementedError()

-    async def dequeue_rollout(self, worker_id: Optional[str] = None) -> Optional[AttemptedRollout]:
-        """Claim the oldest queued rollout and transition it to `preparing`.
-
-        This function do not block.
-
-        Retrieval must be FIFO across rollouts that remain in `queuing` or `requeuing`
-        state. When a rollout is claimed, implementations must:
-
-        * Transition its status to `"preparing"`.
-        * Create a new attempt with `status="preparing"` and `sequence_id` equal to
-          the number of attempts already registered for the rollout plus one.
-        * Return an [`AttemptedRollout`][agentlightning.AttemptedRollout] snapshot so the
-          runner knows both rollout metadata and the attempt identifier.
-        * Optionally refresh the caller's [`Worker`][agentlightning.Worker] telemetry
-          (e.g., `last_dequeue_time`) when `worker_id` is provided.
-
-        Args:
-            worker_id: Optional worker identifier to associate the claimed attempt with.
-
-        Returns:
-            The next attempt to execute, or `None` when no eligible rollouts are queued.
-
-        Raises:
-            NotImplementedError: Subclasses must implement queue retrieval.
+    async def start_attempt(self, rollout_id: str) -> AttemptedRollout:
+        """
+        Create a new attempt for a given rollout ID and return the attempt details.
        """
        raise NotImplementedError()

-    async def dequeue_many_rollouts(
-        self,
-        *,
-        limit: int = 1,
-        worker_id: Optional[str] = None,
-    ) -> Sequence[AttemptedRollout]:
-        """Claim up to `limit` queued rollouts without blocking.
-
-        The implementation can repeatedly invokes
-        [`dequeue_rollout()`][agentlightning.LightningStore.dequeue_rollout] until reaching
-        the requested limit or the queue is empty. Subclasses can override it to fetch
-        multiple rollouts atomically.
-
-        Args:
-            limit: Maximum number of rollouts to claim. Non-positive values return an empty list.
-            worker_id: Optional worker identifier passed through to each dequeue call.
-
-        Returns:
-            Attempted rollouts claimed in FIFO order. May contain fewer than `limit` entries
-            when the queue is exhausted.
+    async def add_span(self, span: Span) -> Span:
        """
-        raise NotImplementedError()
+        Add a span to the store.

-    async def start_attempt(self, rollout_id: str, worker_id: Optional[str] = None) -> AttemptedRollout:
-        """Create a manual retry attempt for an existing rollout.
-
-        This is typically invoked by runners that wish to retry outside of the
-        normal queue flow (for example in an online RL setup).
-        Implementations must validate that the rollout exists, allocate a fresh `attempt_id`,
-        increment the `sequence_id` monotonically, stamp the new attempt with `status="preparing"`,
-        and return an up-to-date [`AttemptedRollout`][agentlightning.AttemptedRollout].
-
-        Args:
-            rollout_id: Unique identifier of the rollout receiving a new attempt.
-            worker_id: Optional worker identifier to associate the new attempt with.
-
-        Returns:
-            The rollout paired with its newly-created attempt.
-
-        Raises:
-            NotImplementedError: Subclasses must implement attempt creation.
-            ValueError: Implementations must raise when `rollout_id` is unknown.
-        """
-        raise NotImplementedError()
-
-    async def add_many_spans(self, spans: Sequence[Span]) -> Sequence[Span]:
-        """Persist a sequence of pre-constructed spans emitted during rollout execution.
-
-        Implementations can simply delegate to [`add_span()`][agentlightning.LightningStore.add_span] for each span.
-        However, if the store supports bulk insertion, it can implement this method to improve performance.
-        """
-        raise NotImplementedError()
-
-    async def add_span(self, span: Span) -> Optional[Span]:
-        """Persist a pre-constructed span emitted during rollout execution.
-
-        The provided [`Span`][agentlightning.Span] must already contain the `rollout_id`,
-        `attempt_id`, and `sequence_id`. Implementations must:
-
-        * Verify that both rollout and attempt exist.
-        * Ensure span ordering remains strictly increasing per attempt (rejecting or keeping duplicates).
-        * Treat the span arrival as a heartbeat: update the attempt's `last_heartbeat_time`
-          and transition both attempt and rollout to `"running"` if they were still
-          `"preparing"` or `"requeuing"`.
-
-        Args:
-            span: Fully populated span to persist.
-
-        Returns:
-            The stored span record (implementations may return a copy).
-            Return `None` if the span was not added due to a duplicate.
-
-        Raises:
-            NotImplementedError: Subclasses must implement span persistence.
-            ValueError: Implementations must raise when the referenced rollout or attempt is missing.
+        This method is responsible for updating the rollout/attempt status to "running" if needed.
        """
        raise NotImplementedError()

@@ -358,360 +124,89 @@ class LightningStore:
        attempt_id: str,
        readable_span: ReadableSpan,
        sequence_id: int | None = None,
-    ) -> Optional[Span]:
-        """Convert and persist an OpenTelemetry span for a particular attempt.
+    ) -> Span:
+        """
+        Add an opentelemetry span to the store.

-        Implementations must transform the `readable_span` into a [`Span`][agentlightning.Span]
-        (typically via [`Span.from_opentelemetry()`][agentlightning.Span.from_opentelemetry]),
-        assign a strictly increasing `sequence_id` when one is not provided, and persist it
-        using the same semantics as [`add_span()`][agentlightning.LightningStore.add_span].
-
-        Args:
-            rollout_id: Identifier of the rollout that produced the span.
-            attempt_id: Attempt identifier the span belongs to.
-            readable_span: OpenTelemetry span in SDK form.
-            sequence_id: Optional explicit ordering hint. When omitted, call
-                [`get_next_span_sequence_id()`][agentlightning.LightningStore.get_next_span_sequence_id]
-                automatically.
-
-        Returns:
-            The stored span record. Return `None` if the span was not added due to a duplicate.
-
-        Raises:
-            NotImplementedError: Subclasses must implement span persistence.
-            ValueError: Implementations must raise when the rollout or attempt is unknown.
+        If sequence_id is not provided, it will be fetched from `get_next_span_sequence_id` and assigned automatically.
        """
        raise NotImplementedError()

    async def query_rollouts(
-        self,
-        *,
-        status_in: Optional[Sequence[RolloutStatus]] = None,
-        rollout_id_in: Optional[Sequence[str]] = None,
-        rollout_id_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-        # Deprecated fields
-        status: Optional[Sequence[RolloutStatus]] = None,
-        rollout_ids: Optional[Sequence[str]] = None,
-    ) -> Sequence[Rollout]:
-        """Retrieve rollouts filtered by status and/or explicit identifiers.
-
-        This interface supports structured filtering, sorting, and pagination so
-        callers can build simple dashboards without copying data out of the
-        store. The legacy parameters `status` and `rollout_ids` remain valid and
-        are treated as aliases for `status_in` and `rollout_id_in`
-        respectively—when both the new and deprecated parameters are supplied
-        the new parameters take precedence.
-
-        Args:
-            status_in: Optional whitelist of [`RolloutStatus`][agentlightning.RolloutStatus] values.
-            rollout_id_in: Optional whitelist of rollout identifiers to include.
-            rollout_id_contains: Optional substring match for rollout identifiers.
-            filter_logic: Logical operator to combine filters.
-            sort_by: Optional field to sort by. Must reference a numeric or string
-                field on [`Rollout`][agentlightning.Rollout].
-            sort_order: Direction to sort when `sort_by` is provided.
-            limit: Maximum number of rows to return. Use `-1` for "no limit".
-            offset: Number of rows to skip before returning results.
-            status: Deprecated field. Use `status_in` instead.
-            rollout_ids: Deprecated field. Use `rollout_id_in` instead.
-
-        Returns:
-            A sequence of matching rollouts (or [`AttemptedRollout`][agentlightning.AttemptedRollout]
-            when attempts exist). Ordering is deterministic when `sort_by` is set.
-            The return value is not guaranteed to be a list.
-
-        Raises:
-            NotImplementedError: Subclasses must implement the query.
+        self, *, status: Optional[Sequence[RolloutStatus]] = None, rollout_ids: Optional[Sequence[str]] = None
+    ) -> List[RolloutV2]:
+        """
+        Query and retrieve rollouts filtered by their status.
+        If no status is provided, returns all rollouts.
        """
        raise NotImplementedError()

-    async def query_attempts(
-        self,
-        rollout_id: str,
-        *,
-        sort_by: Optional[str] = "sequence_id",
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[Attempt]:
-        """Return every attempt ever created for `rollout_id` in ascending sequence order.
-
-        The parameters allow callers to re-order or paginate the attempts so that
-        large retry histories can be streamed lazily.
-
-        Args:
-            rollout_id: Identifier of the rollout being inspected.
-            sort_by: Field to sort by. Must be a numeric or string field of
-                [`Attempt`][agentlightning.Attempt]. Defaults to `sequence_id` (oldest first).
-            sort_order: Order to sort by.
-            limit: Limit on the number of results. `-1` for unlimited.
-            offset: Offset into the results.
-
-        Returns:
-            Sequence of Attempts. Returns an empty sequence when none exist.
-            The return value is not guaranteed to be a list.
-
-        Raises:
-            NotImplementedError: Subclasses must implement the query.
-            ValueError: Implementations must raise when the rollout does not exist.
+    async def query_attempts(self, rollout_id: str) -> List[Attempt]:
+        """
+        Query and retrieve all attempts associated with a specific rollout ID.
+        Returns an empty list if no attempts are found.
        """
        raise NotImplementedError()

-    async def get_rollout_by_id(self, rollout_id: str) -> Optional[Rollout]:
-        """Fetch a rollout by identifier without mutating its state.
-
-        Args:
-            rollout_id: Identifier to retrieve.
-
-        Returns:
-            The rollout when found, otherwise `None`.
-
-        Raises:
-            NotImplementedError: Subclasses must implement retrieval.
+    async def get_rollout_by_id(self, rollout_id: str) -> Optional[RolloutV2]:
+        """
+        Safely retrieves a specific rollout by its ID.
        """
        raise NotImplementedError()

    async def get_latest_attempt(self, rollout_id: str) -> Optional[Attempt]:
-        """Fetch the attempt with the highest `sequence_id` for `rollout_id`.
-
-        Args:
-            rollout_id: Identifier to inspect.
-
-        Returns:
-            The most recent attempt or `None` when no attempts exist yet.
-
-        Raises:
-            NotImplementedError: Subclasses must implement retrieval.
-            ValueError: Implementations must raise when the rollout does not exist.
        """
-        raise NotImplementedError()
-
-    async def query_resources(
-        self,
-        *,
-        resources_id: Optional[str] = None,
-        resources_id_contains: Optional[str] = None,
-        # Filter logic is not supported here because I can't see why it's needed.
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[ResourcesUpdate]:
-        """List every stored resource snapshot in insertion order.
-
-        Supports lightweight filtering, sorting, and pagination for embedding in
-        dashboards.
-
-        Args:
-            resources_id: Optional identifier of the resources to include.
-            resources_id_contains: Optional substring match for resources identifiers.
-            sort_by: Optional field to sort by (must be numeric or string on
-                [`ResourcesUpdate`][agentlightning.ResourcesUpdate]).
-            sort_order: Order to sort by.
-            limit: Limit on the number of results. `-1` for unlimited.
-            offset: Offset into the results.
-
-        Returns:
-            [`ResourcesUpdate`][agentlightning.ResourcesUpdate] objects.
-            By default, resources are sorted in a deterministic but undefined order.
-            The return value is not guaranteed to be a list.
-
-        Raises:
-            NotImplementedError: Subclasses must implement retrieval.
+        Safely retrieves the latest attempt for a given rollout ID.
        """
        raise NotImplementedError()

    async def get_resources_by_id(self, resources_id: str) -> Optional[ResourcesUpdate]:
-        """Return a specific named resource snapshot by identifier.
-
-        Args:
-            resources_id: Identifier of the snapshot.
-
-        Returns:
-            The stored [`ResourcesUpdate`][agentlightning.ResourcesUpdate], or `None` when missing.
-
-        Raises:
-            NotImplementedError: Subclasses must implement retrieval.
+        """
+        Safely retrieves a specific version of named resources by its ID.
        """
        raise NotImplementedError()

    async def get_latest_resources(self) -> Optional[ResourcesUpdate]:
-        """Fetch the latest resource snapshot marked as the global default.
-
-        Returns:
-            The current latest [`ResourcesUpdate`][agentlightning.ResourcesUpdate], or `None` when
-            no resources have been registered yet.
-
-        Raises:
-            NotImplementedError: Subclasses must implement retrieval.
+        """
+        Safely retrieves the latest version of named resources.
        """
        raise NotImplementedError()

    async def get_next_span_sequence_id(self, rollout_id: str, attempt_id: str) -> int:
-        """Allocate the next strictly increasing sequence number used to order spans.
+        """
+        Get the next span sequence ID for a given rollout and attempt.
+        This should be used to assign a unique sequence ID to each span within an attempt.

-        Implementations must retain counters so repeated calls return `1, 2, ...` without
-        gaps unless spans were explicitly inserted with a custom `sequence_id`. The
-        counter may be scoped per rollout or per attempt, but the sequence must be
-        strictly increasing for spans emitted by the specified attempt so traces remain
-        totally ordered.
-
-        See [Distributed Tracing][distributed-tracing] for detailed motivations.
-
-        Args:
-            rollout_id: Identifier of the rollout emitting spans.
-            attempt_id: Attempt identifier for the upcoming span.
-
-        Returns:
-            The next integer sequence identifier, unique within the attempt.
-
-        Raises:
-            NotImplementedError: Subclasses must provide the allocator.
-            ValueError: Implementations must raise when the rollout or attempt does not exist.
+        Recommend getting the ID before the operation even begins to avoid racing conditions.
        """
        raise NotImplementedError()

-    async def get_many_span_sequence_ids(self, rollout_attempt_ids: Sequence[Tuple[str, str]]) -> Sequence[int]:
-        """Bulk allocate the next strictly increasing sequence number used to order spans.
+    async def wait_for_rollouts(self, *, rollout_ids: List[str], timeout: Optional[float] = None) -> List[RolloutV2]:
+        """
+        Wait for specified rollouts to complete with a timeout.
+        Returns the completed rollouts, potentially incomplete if timeout is reached.

-        Implementations may delegate to [`get_next_span_sequence_id()`][agentlightning.LightningStore.get_next_span_sequence_id]
-        for each rollout and attempt.
-
-        Args:
-            rollout_attempt_ids: List of tuples of rollout and attempt identifiers.
-
-        Returns:
-            List of sequence numbers.
+        TODO: Add support for waiting for 20 new rollouts, or wait until 80% of the pending ids are completed.
        """
        raise NotImplementedError()

-    async def wait_for_rollouts(self, *, rollout_ids: List[str], timeout: Optional[float] = None) -> List[Rollout]:
-        """Block until the targeted rollouts reach a terminal status or the timeout expires.
-
-        Terminal statuses are `"succeeded"`, `"failed"`, and `"cancelled"`. When the timeout
-        elapses, implementations should return the subset of rollouts that are already terminal
-        and omit the rest.
-
-        !!! warning
-            It's dangerous and might be event-loop blocking to call this function
-            with a long timeout. It's a good idea to poll for the method to check
-            if new completed rollouts can coming. Be careful in implementing the sleep logic
-            to avoid busy-waiting.
-
-        Args:
-            rollout_ids: Identifiers of rollouts to watch.
-            timeout: Maximum time in seconds to wait. `None` waits indefinitely.
-
-        Returns:
-            Rollouts that finished before the deadline, in arbitrary order.
-
-        Raises:
-            NotImplementedError: Subclasses must implement waiting semantics.
-            ValueError: Implementations must raise when a rollout identifier is unknown.
+    async def query_spans(self, rollout_id: str, attempt_id: str | Literal["latest"] | None = None) -> List[Span]:
        """
-        raise NotImplementedError()
-
-    async def query_spans(
-        self,
-        rollout_id: str,
-        attempt_id: str | Literal["latest"] | None = None,
-        *,
-        # Filtering
-        trace_id: Optional[str] = None,
-        trace_id_contains: Optional[str] = None,
-        span_id: Optional[str] = None,
-        span_id_contains: Optional[str] = None,
-        parent_id: Optional[str] = None,
-        parent_id_contains: Optional[str] = None,
-        name: Optional[str] = None,
-        name_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        # Pagination
-        limit: int = -1,
-        offset: int = 0,
-        # Sorting
-        sort_by: Optional[str] = "sequence_id",
-        sort_order: Literal["asc", "desc"] = "asc",
-    ) -> Sequence[Span]:
-        """Return the stored spans for a rollout, optionally scoped to one attempt.
-
-        Supports a handful of filters that cover the most common debugging
-        scenarios (matching `trace_id`/`span_id`/`parent_id` or substring
-        matches on the span name). `attempt_id="latest"` acts as a convenience
-        that resolves the most recent attempt before evaluating filters. When
-        `attempt_id=None`, spans across every attempt are eligible. By default
-        results are sorted by `sequence_id` (oldest first). Implementations may
-        raise a `RuntimeError` when spans were evicted or expired.
-
-        Args:
-            rollout_id: Identifier of the rollout being inspected.
-            attempt_id: Attempt identifier to filter by. Pass `"latest"` to retrieve only the
-                most recent attempt, or `None` to return all spans across attempts.
-            trace_id: Optional trace ID to filter by.
-            trace_id_contains: Optional substring match for trace IDs.
-            span_id: Optional span ID to filter by.
-            span_id_contains: Optional substring match for span IDs.
-            parent_id: Optional parent span ID to filter by.
-            parent_id_contains: Optional substring match for parent span IDs.
-            name: Optional span name to filter by.
-            name_contains: Optional substring match for span names.
-            filter_logic: Logical operator to combine the optional filters above.
-                The `rollout_id` argument is always applied with AND semantics.
-            limit: Limit on the number of results. `-1` for unlimited.
-            offset: Offset into the results.
-            sort_by: Field to sort by. Must be a numeric or string field of
-                [`Span`][agentlightning.Span].
-            sort_order: Order to sort by.
-
-        Returns:
-            An ordered list of spans (possibly empty).
-            The return value is not guaranteed to be a list.
-
-        Raises:
-            NotImplementedError: Subclasses must implement the query.
-            ValueError: Implementations must raise when the rollout or attempt is unknown.
+        Query and retrieve all spans associated with a specific rollout ID.
+        Returns an empty list if no spans are found.
        """
        raise NotImplementedError()

    async def add_resources(self, resources: NamedResources) -> ResourcesUpdate:
-        """Persist a new immutable snapshot of named resources and mark it as latest.
-
-        Implementations must assign a fresh `resources_id` and ensure subsequent calls to
-        [`get_latest_resources()`][agentlightning.LightningStore.get_latest_resources] return the
-        snapshot produced here.
-
-        Args:
-            resources: Mapping of resource names to their serialized payloads.
-
-        Returns:
-            The stored [`ResourcesUpdate`][agentlightning.ResourcesUpdate] including its generated id.
-
-        Raises:
-            NotImplementedError: Subclasses must implement resource persistence.
+        """
+        Safely stores a new version of named resources and sets it as the latest.
+        Not implemented by many stores yet.
        """
        raise NotImplementedError()

    async def update_resources(self, resources_id: str, resources: NamedResources) -> ResourcesUpdate:
-        """Overwrite or extend an existing resource snapshot and mark it as latest.
-
-        This API is typically used by algorithms that maintain mutable resources (e.g., model
-        checkpoints) under a stable identifier.
-
-        Args:
-            resources_id: Identifier of the snapshot to replace.
-            resources: Updated mapping of resource names to payloads.
-
-        Returns:
-            The persisted [`ResourcesUpdate`][agentlightning.ResourcesUpdate].
-
-        Raises:
-            NotImplementedError: Subclasses must implement resource persistence.
-            ValueError: Implementations must raise when `resources_id` does not exist.
+        """
+        Safely stores a new version or updates an existing version of named resources and sets it as the latest.
        """
        raise NotImplementedError()

@@ -724,32 +219,23 @@ class LightningStore:
        status: RolloutStatus | Unset = UNSET,
        config: RolloutConfig | Unset = UNSET,
        metadata: Optional[Dict[str, Any]] | Unset = UNSET,
-    ) -> Rollout:
-        """Update rollout metadata and, when provided, drive status transitions.
+    ) -> RolloutV2:
+        """
+        Update the rollout status and related metadata.

-        Parameters default to the sentinel [`UNSET`][agentlightning.store.base.UNSET] to
-        distinguish omitted fields from explicit `None` assignments. Implementations must:
+        Not-listed fields here either cannot be updated, or should be auto-updated (e.g., end_time).

-        * Validate the rollout exists before mutating it.
-        * Replace each property when a concrete value (including `None`) is supplied.
-        * When the status switches into a terminal state, set `end_time` and signal any waiters.
-        * When the status re-enters a queueing state, ensure the rollout is enqueued exactly once.
+        When status is updated to a finished / problematic state, other states like task
+        queues will be updated accordingly.

        Args:
-            rollout_id: Identifier of the rollout to update.
-            input: Replacement task payload; pass `None` to explicitly clear the input.
-            mode: Replacement rollout mode.
-            resources_id: Replacement resources snapshot reference.
-            status: Target rollout status.
-            config: Replacement retry/timeout configuration.
-            metadata: Replacement metadata dictionary.
-
-        Returns:
-            The updated rollout record.
-
-        Raises:
-            NotImplementedError: Subclasses must implement mutation logic.
-            ValueError: Implementations must raise when the rollout is unknown or the update is invalid.
+            rollout_id: Unique identifier for the rollout to update
+            input: New input data for the rollout. If set, will be updated. Can be updated to None
+            mode: New mode for the rollout. If set, will be updated. Can be updated to None
+            resources_id: New resources ID for the rollout. If set, will be updated. Can be updated to None
+            status: New status for the rollout. If set, will be updated
+            config: New config for the rollout. If set, will be updated
+            metadata: Dictionary of additional metadata to update. If set, will replace the existing metadata
        """
        raise NotImplementedError()

@@ -762,97 +248,18 @@ class LightningStore:
        last_heartbeat_time: float | Unset = UNSET,
        metadata: Optional[Dict[str, Any]] | Unset = UNSET,
    ) -> Attempt:
-        """Update attempt bookkeeping such as status, worker ownership, and heartbeats.
+        """
+        Update a specific or latest attempt for a given rollout.

-        When `attempt_id` is `"latest"` the update must target the attempt with the highest
-        `sequence_id`; otherwise it must target the specific attempt. Implementations should
-        propagate status changes to the rollout (for example
-        via [`rollout_status_from_attempt()`][agentlightning.store.utils.rollout_status_from_attempt])
-        once the latest attempt transitions to a terminal state.
+        Update the latest attempt will NOT affect the corresponding rollout status.

-        Similar to [`update_rollout()`][agentlightning.LightningStore.update_rollout],
-        parameters also default to the sentinel [`UNSET`][agentlightning.store.base.UNSET].
-
-        If `worker_id` is present, the worker status will be updated following the rules:
-
-        1. If attempt status is "succeeded" or "failed", the corresponding worker status will be set to "idle".
-        2. If attempt status is "unresponsive" or "timeout", the corresponding worker status will be set to "unknown".
-        3. Otherwise, the worker status will be set to "busy".

        Args:
-            rollout_id: Identifier of the rollout whose attempt will be updated.
-            attempt_id: Attempt identifier or `"latest"` as a convenience.
-            status: Replacement attempt status. Terminal statuses must set `end_time`.
-            worker_id: Identifier for the worker currently processing the attempt.
-            last_heartbeat_time: Wall-clock timestamp (seconds) of the latest heartbeat/span.
-            metadata: Replacement metadata dictionary.
-
-        Returns:
-            The updated attempt record.
-
-        Raises:
-            NotImplementedError: Subclasses must implement mutation logic.
-            ValueError: Implementations must raise when the rollout or attempt is unknown.
-        """
-        raise NotImplementedError()
-
-    async def query_workers(
-        self,
-        *,
-        status_in: Optional[Sequence[WorkerStatus]] = None,
-        worker_id_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[Worker]:
-        """Query all workers in the system.
-
-        Args:
-            status_in: Optional whitelist of [`WorkerStatus`][agentlightning.WorkerStatus] values.
-            worker_id_contains: Optional substring match for worker identifiers.
-            filter_logic: Logical operator to combine the optional filters above.
-            sort_by: Field to sort by. Must be a numeric or string field of [`Worker`][agentlightning.Worker].
-            sort_order: Order to sort by.
-            limit: Limit on the number of results. `-1` for unlimited.
-            offset: Offset into the results.
-
-        Returns:
-            Sequence of Workers. Returns an empty sequence when none exist.
-            The return value is not guaranteed to be a list.
-        """
-        raise NotImplementedError()
-
-    async def get_worker_by_id(self, worker_id: str) -> Optional[Worker]:
-        """Retrieve a single worker by identifier.
-
-        Args:
-            worker_id: Identifier of the worker.
-
-        Returns:
-            The worker record if it exists, otherwise `None`.
-
-        Raises:
-            NotImplementedError: Subclasses must implement lookup semantics.
-        """
-        raise NotImplementedError()
-
-    async def update_worker(
-        self,
-        worker_id: str,
-        heartbeat_stats: Dict[str, Any] | Unset = UNSET,
-    ) -> Worker:
-        """Record a heartbeat for `worker_id` and refresh telemetry.
-
-        Implementations must treat this API as heartbeat-only: it should snapshot
-        the latest stats when provided, stamp `last_heartbeat_time` with the
-        current wall clock, and rely on other store mutations (`dequeue_rollout`,
-        `update_attempt`, etc.) to drive the worker's busy/idle status,
-        assignment, and activity timestamps.
-
-        Args:
-            worker_id: Identifier of the worker to update.
-            heartbeat_stats: Replacement worker heartbeat statistics (non-null when provided).
+            rollout_id: Unique identifier for the rollout
+            attempt_id: Unique identifier for the attempt
+            status: Status to set for the attempt, update if provided
+            worker_id: Worker identifier, update if provided
+            last_heartbeat_time: Timestamp of the last heartbeat from the worker
+            metadata: Dictionary of additional metadata to update, will replace the existing metadata
        """
        raise NotImplementedError()
@@ -1,30 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from .base import (
-    AtomicLabels,
-    AtomicMode,
-    Collection,
-    FilterOptions,
-    KeyValue,
-    LightningCollections,
-    PaginatedResult,
-    Queue,
-    SortOptions,
-)
-from .memory import DequeBasedQueue, DictBasedKeyValue, InMemoryLightningCollections, ListBasedCollection
-
-__all__ = [
-    "AtomicLabels",
-    "AtomicMode",
-    "Collection",
-    "Queue",
-    "KeyValue",
-    "FilterOptions",
-    "SortOptions",
-    "PaginatedResult",
-    "LightningCollections",
-    "ListBasedCollection",
-    "DequeBasedQueue",
-    "DictBasedKeyValue",
-    "InMemoryLightningCollections",
-]
@@ -1,414 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    AsyncContextManager,
-    Awaitable,
-    Callable,
-    Dict,
-    Generic,
-    List,
-    Literal,
-    Mapping,
-    MutableMapping,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    TypeVar,
-    cast,
-)
-
-if TYPE_CHECKING:
-    from typing import Self
-
-from agentlightning.types import (
-    Attempt,
-    FilterField,
-    FilterOptions,
-    PaginatedResult,
-    ResourcesUpdate,
-    Rollout,
-    SortOptions,
-    Span,
-    Worker,
-)
-
-T = TypeVar("T")  # Recommended to be a BaseModel
-K = TypeVar("K")
-V = TypeVar("V")
-
-AtomicMode = Literal["r", "w", "rw"]
-"""What is expected within the atomic context. Can be "read", "write", or "read-write"."""
-
-AtomicLabels = Literal["rollouts", "attempts", "spans", "resources", "workers", "rollout_queue", "span_sequence_ids"]
-"""Labels for atomic operations.
-
-These labels are used to identify the collections that are affected by the atomic operation.
-"""
-
-
-class Collection(Generic[T]):
-    """Behaves like a list of items. Supporting addition, updating, and deletion of items."""
-
-    def primary_keys(self) -> Sequence[str]:
-        """Get the primary keys of the collection."""
-        raise NotImplementedError()
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}[{self.item_type().__name__}]>"
-
-    def item_type(self) -> Type[T]:
-        """Get the type of the items in the collection."""
-        raise NotImplementedError()
-
-    async def size(self) -> int:
-        """Get the number of items in the collection."""
-        raise NotImplementedError()
-
-    async def query(
-        self,
-        filter: Optional[FilterOptions] = None,
-        sort: Optional[SortOptions] = None,
-        limit: int = -1,
-        offset: int = 0,
-    ) -> PaginatedResult[T]:
-        """Query the collection with the given filters, sort order, and pagination.
-
-        Args:
-            filter:
-                The filters to apply to the collection. See [`FilterOptions`][agentlightning.FilterOptions].
-
-            sort:
-                The options for sorting the collection. See [`SortOptions`][agentlightning.SortOptions].
-                The field must exist in the model. If field might contain null values, in which case the behavior is undefined
-                (i.e., depending on the implementation).
-
-            limit:
-                Max number of items to return. Use -1 for "no limit".
-
-            offset:
-                Number of items to skip from the start of the *matching* items.
-
-        Returns:
-            PaginatedResult with items, limit, offset, and total matched items.
-        """
-        raise NotImplementedError()
-
-    async def get(
-        self,
-        filter: Optional[FilterOptions] = None,
-        sort: Optional[SortOptions] = None,
-    ) -> Optional[T]:
-        """Get the first item that matches the given filters.
-
-        Args:
-            filter: The filters to apply to the collection.
-                See [`FilterOptions`][agentlightning.store.collection.FilterOptions].
-            sort: Sort options. See [`SortOptions`][agentlightning.store.collection.SortOptions].
-
-        Returns:
-            The first item that matches the given filters, or None if no item matches.
-        """
-        raise NotImplementedError()
-
-    async def insert(self, items: Sequence[T]) -> None:
-        """Add the given items to the collection.
-
-        Raises:
-            ValueError: If an item with the same primary key already exists.
-        """
-        raise NotImplementedError()
-
-    async def update(self, items: Sequence[T], update_fields: Sequence[str] | None = None) -> Sequence[T]:
-        """Update the given items in the collection.
-
-        Args:
-            items: The items to update in the collection.
-            update_fields: The fields to update. If not provided, all fields in the type will be updated.
-                Only applicable if the item type is a Pydantic BaseModel.
-
-        Raises:
-            ValueError: If an item with the primary keys does not exist.
-
-        Returns:
-            The items that were updated.
-        """
-        raise NotImplementedError()
-
-    async def upsert(self, items: Sequence[T], update_fields: Sequence[str] | None = None) -> Sequence[T]:
-        """Upsert the given items into the collection.
-
-        If the items with the same primary keys already exist, they will be updated.
-        Otherwise, they will be inserted.
-
-        The operation has three semantics configurable via `update_fields`:
-
-        - `update_or_insert` via `collection.upsert(items, update_fields=["status", "updated_at"])`.
-          If the item with the same primary keys already exists, only the specified fields will be updated.
-          Otherwise, the item will be inserted.
-        - `get_or_insert` via `collection.upsert(items, update_fields=[])`.
-          If the item with the same primary keys already exists, the item will be left unchanged.
-          Otherwise, the item will be inserted.
-        - `replace_ish` via `collection.upsert(items)`.
-          If the item with the same primary keys already exists, all fields from the item will be set.
-          Otherwise, the item will be inserted.
-
-        Returns:
-            The items that were upserted.
-        """
-        raise NotImplementedError()
-
-    async def delete(self, items: Sequence[T]) -> None:
-        """Delete the given items from the collection.
-
-        Args:
-            items: The items to delete from the collection.
-
-        Raises:
-            ValueError: If the items with the primary keys to be deleted do not exist.
-        """
-        raise NotImplementedError()
-
-
-class Queue(Generic[T]):
-    """Behaves like a deque. Supporting appending items to the end and popping items from the front."""
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}[{self.item_type().__name__}]>"
-
-    def item_type(self) -> Type[T]:
-        """Get the type of the items in the queue."""
-        raise NotImplementedError()
-
-    async def has(self, item: T) -> bool:
-        """Check if the given item is in the queue."""
-        raise NotImplementedError()
-
-    async def enqueue(self, items: Sequence[T]) -> Sequence[T]:
-        """Append the given items to the end of the queue.
-
-        Args:
-            items: The items to append to the end of the queue.
-
-        Returns:
-            The items that were appended to the end of the queue.
-        """
-        raise NotImplementedError()
-
-    async def dequeue(self, limit: int = 1) -> Sequence[T]:
-        """Pop the given number of items from the front of the queue.
-
-        Args:
-            limit: The number of items to pop from the front of the queue.
-
-        Returns:
-            The items that were popped from the front of the queue.
-            If there are less than `limit` items in the queue, the remaining items will be returned.
-        """
-        raise NotImplementedError()
-
-    async def peek(self, limit: int = 1) -> Sequence[T]:
-        """Peek the given number of items from the front of the queue.
-
-        Args:
-            limit: The number of items to peek from the front of the queue.
-
-        Returns:
-            The items that were peeked from the front of the queue.
-            If there are less than `limit` items in the queue, the remaining items will be returned.
-        """
-        raise NotImplementedError()
-
-    async def size(self) -> int:
-        """Get the number of items in the queue."""
-        raise NotImplementedError()
-
-
-class KeyValue(Generic[K, V]):
-    """Behaves like a dictionary. Supporting addition, updating, and deletion of items."""
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}>"
-
-    async def has(self, key: K) -> bool:
-        """Check if the given key is in the dictionary."""
-        raise NotImplementedError()
-
-    async def get(self, key: K, default: V | None = None) -> V | None:
-        """Get the value for the given key, or the default value if the key is not found."""
-        raise NotImplementedError()
-
-    async def set(self, key: K, value: V) -> None:
-        """Set the value for the given key."""
-        raise NotImplementedError()
-
-    async def pop(self, key: K, default: V | None = None) -> V | None:
-        """Pop the value for the given key, or the default value if the key is not found."""
-        raise NotImplementedError()
-
-    async def size(self) -> int:
-        """Get the number of items in the dictionary."""
-        raise NotImplementedError()
-
-
-class LightningCollections:
-    """Collections of rollouts, attempts, spans, resources, and workers.
-
-    [LightningStore][agentlightning.LightningStore] implementations can use this as a storage base
-    to implement the store API.
-    """
-
-    @property
-    def rollouts(self) -> Collection[Rollout]:
-        """Collections of rollouts."""
-        raise NotImplementedError()
-
-    @property
-    def attempts(self) -> Collection[Attempt]:
-        """Collections of attempts."""
-        raise NotImplementedError()
-
-    @property
-    def spans(self) -> Collection[Span]:
-        """Collections of spans."""
-        raise NotImplementedError()
-
-    @property
-    def resources(self) -> Collection[ResourcesUpdate]:
-        """Collections of resources."""
-        raise NotImplementedError()
-
-    @property
-    def workers(self) -> Collection[Worker]:
-        """Collections of workers."""
-        raise NotImplementedError()
-
-    @property
-    def rollout_queue(self) -> Queue[str]:
-        """Queue of rollouts (tasks)."""
-        raise NotImplementedError()
-
-    @property
-    def span_sequence_ids(self) -> KeyValue[str, int]:
-        """Dictionary (counter) of span sequence IDs."""
-        raise NotImplementedError()
-
-    def atomic(
-        self,
-        *,
-        mode: AtomicMode = "rw",
-        snapshot: bool = False,
-        commit: bool = False,
-        labels: Optional[Sequence[AtomicLabels]] = None,
-        **kwargs: Any,
-    ) -> AsyncContextManager[Self]:
-        """Perform a atomic operation on the collections.
-
-        Subclass may use args and kwargs to support multiple levels of atomicity.
-        The arguments can be seen as tags. They only imply the behavior of the operation, not the implementation.
-
-        Args:
-            mode: The mode of atomicity. See [`AtomicMode`][agentlightning.store.collection.AtomicMode].
-            snapshot: Enable read snapshot for repeatable reads. Data consistency is guaranteed. The real behavior is implementation-dependent.
-            commit: Enable commitment for write operations. Unsuccessful operations will be rolled back depending on the implementation.
-                Recommend to use [`execute()`][agentlightning.store.collection.LightningCollections.execute] for this level to enable automatic retries.
-                Remember that the real behavior is implementation-dependent.
-            labels: Labels to add to the atomic operation (commonly used as lock names or collection names).
-            **kwargs: Keyword arguments to pass to the operation.
-        """
-        raise NotImplementedError()
-
-    async def execute(
-        self,
-        callback: Callable[[Self], Awaitable[T]],
-        *,
-        mode: AtomicMode = "rw",
-        snapshot: bool = False,
-        commit: bool = False,
-        labels: Optional[Sequence[AtomicLabels]] = None,
-        **kwargs: Any,
-    ) -> T:
-        """Execute the given callback within an atomic operation. Retry on transient errors is implied.
-
-        See [`atomic()`][agentlightning.store.collection.LightningCollections.atomic] for more details.
-        """
-        async with self.atomic(mode=mode, snapshot=snapshot, commit=commit, labels=labels, **kwargs) as collections:
-            return await callback(collections)
-
-
-FilterMap = Mapping[str, FilterField]
-
-
-def merge_must_filters(target: MutableMapping[str, FilterField], definition: Any) -> None:
-    """Normalize a `_must` filter group into the provided mapping.
-
-    Mainly for validation purposes.
-    """
-    if definition is None:
-        return
-
-    entries: List[Mapping[str, FilterField]] = []
-    if isinstance(definition, Mapping):
-        entries.append(cast(Mapping[str, FilterField], definition))
-    elif isinstance(definition, Sequence) and not isinstance(definition, (str, bytes)):
-        for entry in definition:  # type: ignore
-            if not isinstance(entry, Mapping):
-                raise TypeError("Each `_must` entry must be a mapping of field names to operators")
-            entries.append(cast(Mapping[str, FilterField], entry))
-    else:
-        raise TypeError("`_must` filters must be provided as a mapping or sequence of mappings")
-
-    for entry in entries:
-        for field_name, ops in entry.items():
-            existing = target.get(field_name, {})
-            merged_ops: Dict[str, Any] = dict(existing)
-            for op_name, expected in ops.items():
-                if op_name in merged_ops:
-                    raise ValueError(f"Duplicate operator '{op_name}' for field '{field_name}' in must filters")
-                merged_ops[op_name] = expected
-            target[field_name] = cast(FilterField, merged_ops)
-
-
-def normalize_filter_options(
-    filter_options: Optional[FilterOptions],
-) -> Tuple[Optional[FilterMap], Optional[FilterMap], Literal["and", "or"]]:
-    """Convert FilterOptions to the internal structure and resolve aggregate logic."""
-    if not filter_options:
-        return None, None, "and"
-
-    aggregate = cast(Literal["and", "or"], filter_options.get("_aggregate", "and"))
-    if aggregate not in ("and", "or"):
-        raise ValueError(f"Unsupported filter aggregate '{aggregate}'")
-
-    # Extract normalized filters and must filters from the filter options.
-    normalized: Dict[str, FilterField] = {}
-    must_filters: Dict[str, FilterField] = {}
-    for field_name, ops in filter_options.items():
-        if field_name == "_aggregate":
-            continue
-        if field_name == "_must":
-            merge_must_filters(must_filters, ops)
-            continue
-        normalized[field_name] = cast(FilterField, dict(ops))  # type: ignore
-
-    return (normalized or None, must_filters or None, aggregate)
-
-
-def resolve_sort_options(sort: Optional[SortOptions]) -> Tuple[Optional[str], Literal["asc", "desc"]]:
-    """Extract sort field/order from the caller-provided SortOptions."""
-    if not sort:
-        return None, "asc"
-
-    sort_name = sort.get("name")
-    if not sort_name:
-        raise ValueError("Sort options must include a 'name' field")
-
-    sort_order = sort.get("order", "asc")
-    if sort_order not in ("asc", "desc"):
-        raise ValueError(f"Unsupported sort order '{sort_order}'")
-
-    return sort_name, sort_order
@@ -1,884 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import time
-import weakref
-from collections import deque
-from contextlib import AsyncExitStack, asynccontextmanager
-from typing import (
-    Any,
-    Deque,
-    Dict,
-    Iterable,
-    List,
-    Literal,
-    Mapping,
-    MutableMapping,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    TypeVar,
-    Union,
-)
-
-import aiologic
-from pydantic import BaseModel
-
-from agentlightning.store.utils import LATENCY_BUCKETS
-from agentlightning.types import (
-    Attempt,
-    FilterField,
-    FilterOptions,
-    PaginatedResult,
-    ResourcesUpdate,
-    Rollout,
-    SortOptions,
-    Span,
-    Worker,
-)
-
-from .base import (
-    AtomicMode,
-    Collection,
-    FilterMap,
-    KeyValue,
-    LightningCollections,
-    Queue,
-    normalize_filter_options,
-    resolve_sort_options,
-)
-
-T = TypeVar("T")  # Recommended to be a BaseModel, not a dict
-K = TypeVar("K")
-V = TypeVar("V")
-
-logger = logging.getLogger(__name__)
-
-# Nested structure type:
-# dict[pk1] -> dict[pk2] -> ... -> item
-ListBasedCollectionItemType = Union[
-    Dict[Any, "ListBasedCollectionItemType[T]"],  # intermediate node
-    Dict[Any, T],  # leaf node dictionary
-]
-
-MutationMode = Literal["insert", "update", "upsert", "delete"]
-
-
-def _item_matches_filters(
-    item: object,
-    filters: Optional[FilterMap],
-    filter_logic: Literal["and", "or"],
-    must_filters: Optional[FilterMap] = None,
-) -> bool:
-    """Check whether an item matches the provided filter definition.
-
-    Filter format:
-
-    ```json
-    {
-        "_aggregate": "or",
-        "field_name": {
-            "exact": <value>,
-            "within": <iterable_of_allowed_values>,
-            "contains": <substring_or_element>,
-        },
-        ...
-    }
-    ```
-
-    Operators within the same field are stored in a unified pool and combined using
-    a universal logical operator.
-    """
-    if must_filters and not _item_matches_filters(item, must_filters, "and"):
-        return False
-
-    if not filters:
-        return True
-
-    all_conditions_match: List[bool] = []
-
-    for field_name, ops in filters.items():
-        item_value = getattr(item, field_name, None)
-
-        for op_name, expected in ops.items():
-            # Ignore no-op filters
-            if expected is None:
-                continue
-
-            if op_name == "exact":
-                all_conditions_match.append(item_value == expected)
-
-            elif op_name == "within":
-                try:
-                    all_conditions_match.append(item_value in expected)  # type: ignore[arg-type]
-                except TypeError:
-                    all_conditions_match.append(False)
-
-            elif op_name == "contains":
-                if item_value is None:
-                    all_conditions_match.append(False)
-                elif isinstance(item_value, str) and isinstance(expected, str):
-                    all_conditions_match.append(expected in item_value)
-                else:
-                    # Fallback: treat as generic iterable containment.
-                    try:
-                        all_conditions_match.append(expected in item_value)  # type: ignore[arg-type]
-                    except TypeError:
-                        all_conditions_match.append(False)
-            else:
-                raise ValueError(f"Unsupported filter operator '{op_name}' for field '{field_name}'")
-
-    return all(all_conditions_match) if filter_logic == "and" else any(all_conditions_match)
-
-
-def _get_sort_value(item: object, sort_by: str) -> Any:
-    """Get a sort key for the given item/field.
-
-    - If the field name ends with '_time', values are treated as comparable timestamps.
-    - For other fields we try to infer a safe default from the Pydantic model annotation.
-    """
-    value = getattr(item, sort_by, None)
-
-    if sort_by.endswith("_time"):
-        # For *_time fields, push missing values to the end.
-        return float("inf") if value is None else value
-
-    if value is None:
-        # Introspect model field type to choose a reasonable default for None.
-        model_fields = getattr(item.__class__, "model_fields", {})
-        if sort_by not in model_fields:
-            raise ValueError(
-                f"Failed to sort items by '{sort_by}': field does not exist " f"on {item.__class__.__name__}"
-            )
-
-        field_type_str = str(model_fields[sort_by].annotation)
-        if "str" in field_type_str or "Literal" in field_type_str:
-            return ""
-        if "int" in field_type_str:
-            return 0
-        if "float" in field_type_str:
-            return 0.0
-        raise ValueError(f"Failed to sort items by '{sort_by}': unsupported field type {field_type_str!r}")
-
-    return value
-
-
-class ListBasedCollection(Collection[T]):
-    """In-memory implementation of Collection using a nested dict for O(1) primary-key lookup.
-
-    The internal structure is:
-
-        {
-            pk1_value: {
-                pk2_value: {
-                    ...
-                        pkN_value: item
-                }
-            }
-        }
-
-    where the nesting depth equals the number of primary keys.
-
-    Sorting behavior:
-
-    1. If no sort_by is provided, the items are returned in the order of insertion.
-    2. If sort_by is provided, the items are sorted by the value of the sort_by field.
-    3. If the sort_by field is a timestamp, the null values are treated as infinity.
-    4. If the sort_by field is not a timestamp, the null values are treated as empty string
-       if the field is str-like, 0 if the field is int-like, 0.0 if the field is float-like.
-    """
-
-    def __init__(self, items: List[T], item_type: Type[T], primary_keys: Sequence[str]):
-        if not primary_keys:
-            raise ValueError("primary_keys must be non-empty")
-
-        self._items: Dict[Any, Any] = {}
-        self._size: int = 0
-        if issubclass(item_type, dict):
-            raise TypeError(f"Expect item to be not a dict, got {item_type.__name__}")
-        self._item_type: Type[T] = item_type
-        self._primary_keys: Tuple[str, ...] = tuple(primary_keys)
-
-        # Pre-populate the collection with the given items.
-        for item in items or []:
-            self._mutate_single(item, mode="insert")
-
-    def primary_keys(self) -> Sequence[str]:
-        """Return the primary key field names for this collection."""
-        return self._primary_keys
-
-    def item_type(self) -> Type[T]:
-        """Return the Pydantic model type of items stored in this collection."""
-        return self._item_type
-
-    async def size(self) -> int:
-        """Return the number of items stored in the collection."""
-        return self._size
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}[{self.item_type().__name__}] ({self._size})>"
-
-    # -------------------------------------------------------------------------
-    # Internal helpers
-    # -------------------------------------------------------------------------
-
-    def _ensure_item_type(self, item: T) -> None:
-        """Validate that the item matches the declared item_type."""
-        if not isinstance(item, self._item_type):
-            raise TypeError(f"Expected item of type {self._item_type.__name__}, " f"got {type(item).__name__}")
-
-    def _extract_primary_key_values(self, item: T) -> Tuple[Any, ...]:
-        """Extract the primary key values from an item.
-
-        Raises:
-            ValueError: If any primary key is missing on the item.
-        """
-        values: List[Any] = []
-        for key in self._primary_keys:
-            if not hasattr(item, key):
-                raise ValueError(f"Item {item} does not have primary key field '{key}'")
-            values.append(getattr(item, key))
-        return tuple(values)
-
-    def _render_key_values(self, key_values: Sequence[Any]) -> str:
-        return ", ".join(f"{name}={value!r}" for name, value in zip(self._primary_keys, key_values))
-
-    def _locate_node(
-        self,
-        key_values: Sequence[Any],
-        create_missing: bool,
-    ) -> Tuple[MutableMapping[Any, Any], Any]:
-        """Locate the parent mapping and final key for an item path.
-
-        Args:
-            key_values: The sequence of primary key values.
-            create_missing: Whether to create intermediate dictionaries as needed.
-
-        Returns:
-            (parent_mapping, final_key)
-
-        Raises:
-            KeyError: If the path does not exist and create_missing is False.
-            ValueError: If the internal structure is corrupted (non-dict where dict is expected).
-        """
-        if not key_values:
-            raise ValueError("key_values must be non-empty")
-
-        current: MutableMapping[Any, Any] = self._items
-        for idx, value in enumerate(key_values):
-            is_last = idx == len(key_values) - 1
-            if is_last:
-                # At the final level, current[value] is the item (or will be).
-                return current, value  # type: ignore
-
-            # Intermediate level: current[value] must be a dict.
-            if value not in current:
-                if not create_missing:
-                    raise KeyError(f"Path does not exist for given primary keys: {self._render_key_values(key_values)}")
-                current[value] = {}
-            next_node = current[value]  # type: ignore
-            if not isinstance(next_node, dict):
-                raise ValueError(f"Internal structure corrupted: expected dict, got {type(next_node)!r}")  # type: ignore
-            current = next_node  # type: ignore
-
-        # We should always return inside the loop.
-        raise RuntimeError("Unreachable")
-
-    def _mutate_single(self, item: T, mode: MutationMode, update_fields: Sequence[str] | None = None) -> Optional[T]:
-        """Core mutation logic shared by insert, update, upsert, and delete."""
-        self._ensure_item_type(item)
-        key_values = self._extract_primary_key_values(item)
-
-        if mode in ("insert", "upsert"):
-            parent, final_key = self._locate_node(key_values, create_missing=True)
-            exists = final_key in parent
-
-            if mode == "insert":
-                if exists:
-                    raise ValueError(f"Item already exists with primary key(s): {self._render_key_values(key_values)}")
-                parent[final_key] = item
-                self._size += 1
-            else:  # upsert
-                if not exists:
-                    self._size += 1
-                    parent[final_key] = item
-
-                elif update_fields is None:
-                    # update_or_insert: update all fields
-                    parent[final_key] = item
-
-                else:
-                    if not issubclass(self._item_type, BaseModel):
-                        raise TypeError(
-                            f"When using update_fields, the item type must be a Pydantic BaseModel, got {self._item_type.__name__}"
-                        )
-
-                    # Try to fetch the existing item
-                    existing = parent[final_key]
-                    if not isinstance(existing, self._item_type):
-                        raise ValueError(
-                            f"Internal structure corrupted: expected {self._item_type.__name__}, got {type(existing)!r}"
-                        )
-
-                    if not isinstance(item, self._item_type):
-                        raise TypeError(
-                            f"When using update_fields, the item type must be a Pydantic BaseModel, got {type(item).__name__}"
-                        )
-
-                    parent[final_key] = parent[final_key].model_copy(
-                        update={field: getattr(item, field) for field in update_fields}
-                    )
-
-            return parent[final_key]
-
-        elif mode in ("update", "delete"):
-            # For update/delete we must not create missing paths.
-            try:
-                parent, final_key = self._locate_node(key_values, create_missing=False)
-            except KeyError:
-                raise ValueError(
-                    f"Item does not exist with primary key(s): {self._render_key_values(key_values)}"
-                ) from None
-
-            if final_key not in parent:
-                raise ValueError(f"Item does not exist with primary key(s): {self._render_key_values(key_values)}")
-
-            if mode == "update":
-                if update_fields is None:
-                    # replace the entire item
-                    parent[final_key] = item
-                else:
-                    if not issubclass(self._item_type, BaseModel):
-                        raise TypeError(
-                            f"When using update_fields, the item type must be a Pydantic BaseModel, got {self._item_type.__name__}"
-                        )
-                    if not isinstance(item, self._item_type):
-                        raise TypeError(
-                            f"When using update_fields, the item type must be a Pydantic BaseModel, got {type(item).__name__}"
-                        )
-                    parent[final_key] = parent[final_key].model_copy(
-                        update={field: getattr(item, field) for field in update_fields}
-                    )
-                return parent[final_key]
-            else:  # delete
-                del parent[final_key]
-                self._size -= 1
-        else:
-            raise ValueError(f"Unknown mutation mode: {mode}")
-
-    def _iter_items(
-        self,
-        root: Optional[Mapping[Any, Any]] = None,
-        filters: Optional[FilterMap] = None,
-        must_filters: Optional[FilterMap] = None,
-        filter_logic: Literal["and", "or"] = "and",
-    ) -> Iterable[T]:
-        """Iterate over all items in the nested dictionary structure, optionally applying filters."""
-        if root is None:
-            root = self._items
-        if not root:
-            return
-        stack: List[Mapping[Any, Any]] = [root]
-        while stack:
-            node = stack.pop()
-            for value in node.values():
-                # Leaf nodes contain items; intermediate nodes are dicts.
-                if isinstance(value, self._item_type):
-                    if _item_matches_filters(value, filters, filter_logic, must_filters):
-                        yield value
-                elif isinstance(value, dict):
-                    stack.append(value)  # type: ignore
-                else:
-                    raise ValueError(
-                        f"Internal structure corrupted: expected dict or {self._item_type.__name__}, "
-                        f"got {type(value)!r}"
-                    )
-
-    def _iter_matching_items(
-        self,
-        filters: Optional[FilterMap],
-        must_filters: Optional[FilterMap],
-        filter_logic: Literal["and", "or"],
-    ) -> Iterable[T]:
-        """Efficiently iterate over items matching filters, using primary-key prefix when possible."""
-        # Fast path: when optional filters can't form a prefix, fall back to scanning.
-        if filter_logic != "and" and must_filters is None:
-            return self._iter_items(filters=filters, must_filters=must_filters, filter_logic=filter_logic)
-
-        # Try to derive a primary-key prefix from exact filters.
-        pk_values_prefix: List[Any] = []
-        prefix_sources: List[FilterMap] = []
-        if must_filters:
-            prefix_sources.append(must_filters)
-        if filter_logic == "and" and filters:
-            prefix_sources.append(filters)
-
-        for pk in self._primary_keys:
-            # combined_ops are: [{"exact": value}, {"within": [...]}, ...]
-            combined_ops: List[FilterField] = []
-            for source in prefix_sources:
-                field_ops = source.get(pk)  # type: ignore[union-attr]
-                if field_ops:
-                    combined_ops.append(field_ops)
-            if not combined_ops:
-                break
-            # Only allow a pure {"exact": value} constraint.
-            exact_value: Any | None = None
-            allow_prefix = True
-            for ops in combined_ops:
-                if set(ops.keys()) != {"exact"}:
-                    allow_prefix = False
-                    break
-                candidate = ops.get("exact")
-                if candidate is None:
-                    allow_prefix = False
-                    break
-                if exact_value is not None and candidate != exact_value:
-                    # Contradictory exact filters mean no items can match.
-                    logger.warning(f"Contradictory exact filters for field '{pk}': {exact_value} != {candidate}")
-                    return ()
-                exact_value = candidate
-
-            if not allow_prefix:
-                break
-
-            value = exact_value
-            if value is None:
-                break
-            pk_values_prefix.append(value)
-
-        if not pk_values_prefix:
-            return self._iter_items(filters=filters, must_filters=must_filters, filter_logic=filter_logic)
-
-        try:
-            if len(pk_values_prefix) == len(self._primary_keys):
-                # All primary keys specified -> at most a single item.
-                parent, final_key = self._locate_node(pk_values_prefix, create_missing=False)
-                single_item = parent.get(final_key)
-                if isinstance(single_item, self._item_type) and _item_matches_filters(
-                    single_item,
-                    filters,
-                    filter_logic,
-                    must_filters,
-                ):
-                    return (single_item,)
-                return ()
-            else:
-                # Prefix of primary keys specified -> iterate only the subtree below that prefix.
-                parent, final_key = self._locate_node(pk_values_prefix, create_missing=False)
-                subtree = parent.get(final_key)
-                if isinstance(subtree, dict):
-                    return self._iter_items(
-                        subtree,  # type: ignore
-                        filters=filters,
-                        must_filters=must_filters,
-                        filter_logic=filter_logic,
-                    )
-                return ()
-        except KeyError:
-            # No items exist for this primary-key prefix.
-            return ()
-
-    async def query(
-        self,
-        filter: Optional[FilterOptions] = None,
-        sort: Optional[SortOptions] = None,
-        limit: int = -1,
-        offset: int = 0,
-    ) -> PaginatedResult[T]:
-        """Query the collection with filters, sort order, and pagination.
-
-        Args:
-            filter: Mapping of field name to operator dict along with the optional `_aggregate` logic.
-            sort: Options describing which field to sort by and in which order.
-            limit: Max number of items to return. Use -1 for "no limit".
-            offset: Number of items to skip from the start of the *matching* items.
-        """
-        filters, must_filters, filter_logic = normalize_filter_options(filter)
-        sort_by, sort_order = resolve_sort_options(sort)
-        items_iter: Iterable[T] = self._iter_matching_items(filters, must_filters, filter_logic)
-
-        # No sorting: stream through items and apply pagination on the fly.
-        if not sort_by:
-            matched_items: List[T] = []
-            total_matched = 0
-
-            for item in items_iter:
-                # Count every match for 'total'
-                total_matched += 1
-
-                # Apply offset/limit window
-                if total_matched <= offset:
-                    continue
-                if limit != -1 and len(matched_items) >= limit:
-                    # Still need to finish iteration to get accurate total_matched.
-                    continue
-
-                matched_items.append(item)
-
-            return PaginatedResult(
-                items=matched_items,
-                limit=limit,
-                offset=offset,
-                total=total_matched,
-            )
-
-        # With sorting: we must materialize all matching items to sort them.
-        all_matches: List[T] = list(items_iter)
-
-        total_matched = len(all_matches)
-        reverse = sort_order == "desc"
-        all_matches.sort(key=lambda x: _get_sort_value(x, sort_by), reverse=reverse)
-
-        if limit == -1:
-            paginated_items = all_matches[offset:]
-        else:
-            paginated_items = all_matches[offset : offset + limit]
-
-        return PaginatedResult(
-            items=paginated_items,
-            limit=limit,
-            offset=offset,
-            total=total_matched,
-        )
-
-    async def get(
-        self,
-        filter: Optional[FilterOptions] = None,
-        sort: Optional[SortOptions] = None,
-    ) -> Optional[T]:
-        """Return the first (or best-sorted) item that matches the given filters, or None."""
-        filters, must_filters, filter_logic = normalize_filter_options(filter)
-        sort_by, sort_order = resolve_sort_options(sort)
-        items_iter: Iterable[T] = self._iter_matching_items(filters, must_filters, filter_logic)
-
-        if not sort_by:
-            # Just return the first matching item, if any.
-            for item in items_iter:
-                return item
-            return None
-
-        # Single-pass min/max according to sort_order.
-        best_item: Optional[T] = None
-        best_key: Any = None
-
-        for item in items_iter:
-            key = _get_sort_value(item, sort_by)
-            if best_item is None:
-                best_item = item
-                best_key = key
-                continue
-
-            if sort_order == "asc":
-                if key < best_key:
-                    best_item, best_key = item, key
-            else:
-                if key > best_key:
-                    best_item, best_key = item, key
-
-        return best_item
-
-    async def insert(self, items: Sequence[T]) -> None:
-        """Insert the given items.
-
-        Raises:
-            ValueError: If any item with the same primary keys already exists.
-        """
-        seen_keys: set[Tuple[Any, ...]] = set()
-        prepared: List[T] = []
-        for item in items:
-            self._ensure_item_type(item)
-            key_values = self._extract_primary_key_values(item)
-            if key_values in seen_keys:
-                raise ValueError(
-                    f"Insert payload contains duplicate primary key(s): {self._render_key_values(key_values)}"
-                )
-            seen_keys.add(key_values)
-            prepared.append(item)
-
-        for item in prepared:
-            self._mutate_single(item, mode="insert")
-
-    async def update(self, items: Sequence[T], update_fields: Sequence[str] | None = None) -> Sequence[T]:
-        """Update the given items.
-
-        Raises:
-            ValueError: If any item with the given primary keys does not exist.
-        """
-        updated_items: List[T] = []
-        for item in items:
-            updated = self._mutate_single(item, mode="update", update_fields=update_fields)
-            if updated is None:
-                raise RuntimeError(f"_mutate_single returned None for item {item}. This should never happen.")
-            updated_items.append(updated)
-        return updated_items
-
-    async def upsert(self, items: Sequence[T], update_fields: Sequence[str] | None = None) -> Sequence[T]:
-        """Upsert the given items (insert if missing, otherwise update)."""
-        upserted_items: List[T] = []
-        for item in items:
-            upserted = self._mutate_single(item, mode="upsert", update_fields=update_fields)
-            if upserted is None:
-                raise RuntimeError(f"_mutate_single returned None for item {item}. This should never happen.")
-            upserted_items.append(upserted)
-        return upserted_items
-
-    async def delete(self, items: Sequence[T]) -> None:
-        """Delete the given items.
-
-        Raises:
-            ValueError: If any item with the given primary keys does not exist.
-        """
-        # We use a two-phase approach to avoid partial deletion if one fails:
-        # first compute key_values to validate, then perform deletions.
-        for item in items:
-            # _mutate_single will validate existence and update size.
-            self._mutate_single(item, mode="delete")
-
-
-class DequeBasedQueue(Queue[T]):
-    """Queue implementation backed by collections.deque.
-
-    Provides O(1) amortized enqueue (append) and dequeue (popleft).
-    """
-
-    def __init__(self, item_type: Type[T], items: Optional[Sequence[T]] = None):
-        self._items: Deque[T] = deque()
-        self._item_type: Type[T] = item_type
-        if items:
-            self._items.extend(items)
-
-    def item_type(self) -> Type[T]:
-        return self._item_type
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__}[{self.item_type().__name__}] ({len(self._items)})>"
-
-    async def has(self, item: T) -> bool:
-        if not isinstance(item, self._item_type):
-            raise TypeError(f"Expected item of type {self._item_type.__name__}, got {type(item).__name__}")
-        return item in self._items
-
-    async def enqueue(self, items: Sequence[T]) -> Sequence[T]:
-        for item in items:
-            if not isinstance(item, self._item_type):
-                raise TypeError(f"Expected item of type {self._item_type.__name__}, got {type(item).__name__}")
-            self._items.append(item)
-        return items
-
-    async def dequeue(self, limit: int = 1) -> Sequence[T]:
-        if limit <= 0:
-            return []
-        out: List[T] = []
-        for _ in range(min(limit, len(self._items))):
-            out.append(self._items.popleft())
-        return out
-
-    async def peek(self, limit: int = 1) -> Sequence[T]:
-        if limit <= 0:
-            return []
-        result: List[T] = []
-        count = min(limit, len(self._items))
-        for idx, item in enumerate(self._items):
-            if idx >= count:
-                break
-            result.append(item)
-        return result
-
-    async def size(self) -> int:
-        return len(self._items)
-
-
-class DictBasedKeyValue(KeyValue[K, V]):
-    """KeyValue implementation backed by a plain dictionary."""
-
-    def __init__(self, data: Optional[Mapping[K, V]] = None):
-        self._values: Dict[K, V] = dict(data) if data else {}
-
-    async def has(self, key: K) -> bool:
-        return key in self._values
-
-    async def get(self, key: K, default: V | None = None) -> V | None:
-        return self._values.get(key, default)
-
-    async def set(self, key: K, value: V) -> None:
-        self._values[key] = value
-
-    async def pop(self, key: K, default: V | None = None) -> V | None:
-        return self._values.pop(key, default)
-
-    async def size(self) -> int:
-        return len(self._values)
-
-
-class InMemoryLightningCollections(LightningCollections):
-    """In-memory implementation of LightningCollections using Python data structures.
-
-    Serves as the storage base for [`InMemoryLightningStore`][agentlightning.InMemoryLightningStore].
-    """
-
-    def __init__(self, lock_type: Literal["thread", "asyncio"], prometheus: bool = False):
-        self._lock = {
-            "rollouts": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "attempts": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "spans": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "resources": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "workers": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "rollout_queue": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-            "span_sequence_ids": _LoopAwareAsyncLock() if lock_type == "asyncio" else _ThreadSafeAsyncLock(),
-        }
-        self._rollouts = ListBasedCollection(items=[], item_type=Rollout, primary_keys=["rollout_id"])
-        self._attempts = ListBasedCollection(items=[], item_type=Attempt, primary_keys=["rollout_id", "attempt_id"])
-        self._spans = ListBasedCollection(
-            items=[], item_type=Span, primary_keys=["rollout_id", "attempt_id", "span_id"]
-        )
-        self._resources = ListBasedCollection(items=[], item_type=ResourcesUpdate, primary_keys=["resources_id"])
-        self._workers = ListBasedCollection(items=[], item_type=Worker, primary_keys=["worker_id"])
-        self._rollout_queue = DequeBasedQueue(items=[], item_type=str)
-        self._span_sequence_ids = DictBasedKeyValue[str, int](data={})  # rollout_id -> sequence_id
-
-        self._prometheus = prometheus
-        if self._prometheus:
-            from prometheus_client import Counter, Histogram
-
-            self._rate_metric = Counter(
-                "memory_collection_lock_rate",
-                "Rate of memory collection locks",
-                ["collection"],
-            )
-            self._latency_metric = Histogram(
-                "memory_collection_lock_latency_seconds",
-                "Latency of memory collection locks",
-                ["collection"],
-                buckets=LATENCY_BUCKETS,
-            )
-
-    @property
-    def rollouts(self) -> ListBasedCollection[Rollout]:
-        return self._rollouts
-
-    @property
-    def attempts(self) -> ListBasedCollection[Attempt]:
-        return self._attempts
-
-    @property
-    def spans(self) -> ListBasedCollection[Span]:
-        return self._spans
-
-    @property
-    def resources(self) -> ListBasedCollection[ResourcesUpdate]:
-        return self._resources
-
-    @property
-    def workers(self) -> ListBasedCollection[Worker]:
-        return self._workers
-
-    @property
-    def rollout_queue(self) -> DequeBasedQueue[str]:
-        return self._rollout_queue
-
-    @property
-    def span_sequence_ids(self) -> DictBasedKeyValue[str, int]:
-        return self._span_sequence_ids
-
-    @asynccontextmanager
-    async def atomic(
-        self, *, mode: AtomicMode = "rw", snapshot: bool = False, labels: Optional[Sequence[str]] = None, **kwargs: Any
-    ):
-        """In-memory collections apply a lock outside. It doesn't need to manipulate the collections inside.
-
-        Skip the locking if mode is "r" and snapshot is False.
-
-        This collection implementation does NOT support rollback / commit.
-        """
-        if mode == "r" and not snapshot:
-            yield self
-            return
-        if not labels:
-            # If no labels are provided, use all locks.
-            labels = list(self._lock.keys())
-
-        # IMPORTANT: Sort the labels to ensure consistent locking order.
-        # This is necessary to avoid deadlocks when multiple threads/coroutines
-        # are trying to acquire the same locks in different orders.
-        labels = sorted(labels)
-
-        managers = [(label, self._lock[label]) for label in labels]
-        async with AsyncExitStack() as stack:
-            for label, manager in managers:
-                start_time = time.perf_counter()
-                await stack.enter_async_context(manager)
-                elapsed = time.perf_counter() - start_time
-                if self._prometheus:
-                    self._rate_metric.labels(collection=label).inc()
-                    self._latency_metric.labels(collection=label).observe(elapsed)
-            yield self
-
-    async def evict_spans_for_rollout(self, rollout_id: str) -> None:
-        """Evict all spans for a given rollout ID.
-
-        Uses private API for efficiency.
-        """
-        self._spans._items.pop(rollout_id, [])  # pyright: ignore[reportPrivateUsage]
-
-
-class _LoopAwareAsyncLock:
-    """Async lock that transparently rebinds to the current event loop.
-
-    The lock intentionally remains *thread-unsafe*: callers must only use it from
-    one thread at a time. If multiple threads interact with the store, each
-    thread gets its own event loop specific lock.
-    """
-
-    def __init__(self) -> None:
-        self._locks: weakref.WeakKeyDictionary[asyncio.AbstractEventLoop, asyncio.Lock] = weakref.WeakKeyDictionary()
-
-    # When serializing and deserializing, we don't need to serialize the locks.
-    # Because another process will have its own set of event loops and its own lock.
-    def __getstate__(self) -> dict[str, Any]:
-        return {}
-
-    def __setstate__(self, state: dict[str, Any]) -> None:
-        self._locks = weakref.WeakKeyDictionary()
-
-    def _get_lock_for_current_loop(self) -> asyncio.Lock:
-        loop = asyncio.get_running_loop()
-        lock = self._locks.get(loop)
-        if lock is None:
-            lock = asyncio.Lock()
-            self._locks[loop] = lock
-        return lock
-
-    async def __aenter__(self) -> asyncio.Lock:
-        lock = self._get_lock_for_current_loop()
-        await lock.acquire()
-        return lock
-
-    async def __aexit__(self, exc_type: type[BaseException] | None, exc: BaseException | None, tb: Any) -> None:
-        loop = asyncio.get_running_loop()
-        lock = self._locks.get(loop)
-        if lock is None or not lock.locked():
-            raise RuntimeError("Lock released without being acquired")
-        lock.release()
-
-
-class _ThreadSafeAsyncLock:
-    """A thread lock powered by aiologic that can be used in both async and sync contexts.
-
-    aiologic claims itself to be a thread-safe asyncio lock.
-    """
-
-    def __init__(self):
-        self._lock = aiologic.Lock()
-
-    async def __aenter__(self):
-        await self._lock.async_acquire()
-        return self
-
-    async def __aexit__(self, *args: Any, **kwargs: Any):
-        # .release() is non-blocking, so we can call it directly
-        self._lock.async_release()
@@ -1,165 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-from __future__ import annotations
-
-import asyncio
-import hashlib
-import logging
-import time
-import uuid
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    List,
-    Mapping,
-    Optional,
-    Sequence,
-    TypeVar,
-    Union,
-)
-
-from pymongo import AsyncMongoClient
-
-from agentlightning.types import Attempt, AttemptedRollout, Rollout
-
-from .base import LightningStoreCapabilities, is_finished
-from .collection.mongo import MongoClientPool, MongoLightningCollections, MongoOperationPrometheusTracker
-from .collection_based import CollectionBasedLightningStore, healthcheck_before, tracked
-
-T_callable = TypeVar("T_callable", bound=Callable[..., Any])
-
-logger = logging.getLogger(__name__)
-
-
-def _generate_partition_id() -> str:
-    return "pt-" + hashlib.sha1(uuid.uuid4().bytes).hexdigest()[:12]
-
-
-class MongoLightningStore(CollectionBasedLightningStore[MongoLightningCollections]):
-    """
-    MongoDB implementation of LightningStore using MongoDB collections.
-    Data is persistent and can be shared between multiple processes.
-
-    Args:
-        client: The MongoDB client. Could be a string URI or an instance of AsyncMongoClient.
-        database: The MongoDB database. Could be a string name or an instance of AsyncDatabase.
-            You must provide at least one of client or database.
-        partition_id: The partition id. Useful when sharing the database among multiple Agent-lightning trainers.
-    """
-
-    def __init__(
-        self,
-        *,
-        client: AsyncMongoClient[Mapping[str, Any]] | str,
-        database_name: str | None = None,
-        partition_id: str | None = None,
-        prometheus: bool = False,
-    ) -> None:
-        self._enable_prometheus = prometheus
-        self._auto_created_client = False
-        if isinstance(client, str):
-            self._client = AsyncMongoClient[Mapping[str, Any]](client)
-            self._auto_created_client = True
-        else:
-            self._client = client
-        if database_name is None:
-            database_name = "agentlightning"
-            logger.info("No database name provided, using default 'agentlightning'")
-
-        if partition_id is None:
-            partition_id = _generate_partition_id()
-            logger.info("No partition id provided, generated a new one: %s", partition_id)
-
-        self._client_pool = MongoClientPool(self._client)
-
-        super().__init__(
-            collections=MongoLightningCollections(
-                self._client_pool,
-                database_name,
-                partition_id,
-                prometheus_tracker=MongoOperationPrometheusTracker(enabled=self._enable_prometheus),
-            ),
-            prometheus=self._enable_prometheus,
-        )
-
-    @property
-    def capabilities(self) -> LightningStoreCapabilities:
-        """Return the capabilities of the store."""
-        return LightningStoreCapabilities(
-            thread_safe=True,
-            async_safe=True,
-            zero_copy=True,
-            otlp_traces=False,
-        )
-
-    async def close(self) -> None:
-        """Close the store by closing the client pool."""
-        await self._client_pool.close()
-        # If I created the client, I should close it too.
-        if self._auto_created_client:
-            await self._client.close()
-
-    @tracked("wait_for_rollouts")
-    @healthcheck_before
-    async def wait_for_rollouts(self, *, rollout_ids: List[str], timeout: Optional[float] = None) -> List[Rollout]:
-        """Wait for specified rollouts to complete with a timeout.
-
-        Concurrently wait for all rollouts to complete with a timeout.
-        """
-        start_time = time.time()
-        current_time = start_time
-        deadline = start_time + timeout if timeout is not None else None
-
-        finished_rollouts: Dict[str, Rollout] = {}
-        unfinished_rollout_ids = set(rollout_ids)
-
-        while deadline is None or current_time <= deadline:
-            async with self.collections.atomic(
-                mode="r", snapshot=self._read_snapshot, labels=["rollouts"]
-            ) as collections:
-                # Query the rollouts that are not finished in a single query
-                rollouts = await collections.rollouts.query(
-                    filter={"rollout_id": {"within": list(unfinished_rollout_ids)}}
-                )
-            for rollout in rollouts.items:
-                if is_finished(rollout):
-                    finished_rollouts[rollout.rollout_id] = rollout
-                    unfinished_rollout_ids.remove(rollout.rollout_id)
-
-            if not unfinished_rollout_ids:
-                break
-
-            # Poll every 10 seconds by default
-            # Minus 0.1 to make sure the time is still sufficient for another call
-            rest_time = max(0.01, min(deadline - time.time() - 0.1, 10.0)) if deadline is not None else 10.0
-            await asyncio.sleep(rest_time)
-            current_time = time.time()
-
-        # Reorder the rollouts to match the input order
-        return [finished_rollouts[rollout_id] for rollout_id in rollout_ids if rollout_id in finished_rollouts]
-
-    @tracked("_unlocked_many_rollouts_to_attempted_rollouts")
-    async def _unlocked_many_rollouts_to_attempted_rollouts(
-        self, collections: MongoLightningCollections, rollouts: Sequence[Rollout]
-    ) -> List[Union[Rollout, AttemptedRollout]]:
-        """Query the latest attempts for the rollouts, and attach them to the rollout objects."""
-        async with collections.atomic(mode="r", snapshot=self._read_snapshot, labels=["attempts"]) as collections:
-            attempts = await collections.attempts.query(
-                filter={"rollout_id": {"within": [rollout.rollout_id for rollout in rollouts]}},
-                sort={"name": "sequence_id", "order": "desc"},
-            )
-        latest_attempts: Dict[str, Attempt] = {}
-        for attempt in attempts:
-            if attempt.rollout_id not in latest_attempts:
-                latest_attempts[attempt.rollout_id] = attempt
-            # Otherwise we ignore the attempt because there's already a newer attempt
-
-        return [
-            (
-                AttemptedRollout(**rollout.model_dump(), attempt=latest_attempts[rollout.rollout_id])
-                if rollout.rollout_id in latest_attempts
-                else rollout
-            )
-            for rollout in rollouts
-        ]
@@ -1,3 +1 @@
 # Copyright (c) Microsoft. All rights reserved.
-
-# TODO: Implement this
@@ -3,7 +3,7 @@
 from __future__ import annotations

 import threading
-from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple
+from typing import Any, Dict, List, Literal, Optional, Sequence

 from opentelemetry.sdk.trace import ReadableSpan

@@ -11,19 +11,16 @@ from agentlightning.types import (
    Attempt,
    AttemptedRollout,
    AttemptStatus,
-    EnqueueRolloutRequest,
    NamedResources,
    ResourcesUpdate,
-    Rollout,
    RolloutConfig,
    RolloutStatus,
+    RolloutV2,
    Span,
    TaskInput,
-    Worker,
-    WorkerStatus,
 )

-from .base import UNSET, LightningStore, LightningStoreCapabilities, LightningStoreStatistics, Unset
+from .base import UNSET, LightningStore, Unset


 class LightningStoreThreaded(LightningStore):
@@ -38,119 +35,48 @@ class LightningStoreThreaded(LightningStore):
        self.store = store
        self._lock = threading.Lock()

-    @property
-    def capabilities(self) -> LightningStoreCapabilities:
-        """Return the capabilities of the store."""
-        capabilities = self.store.capabilities
-        return {
-            **capabilities,
-            "async_safe": True,
-            "thread_safe": True,
-        }
-
-    async def statistics(self) -> LightningStoreStatistics:
-        """Return the statistics of the store."""
-        with self._lock:
-            return await self.store.statistics()
-
    async def start_rollout(
        self,
        input: TaskInput,
        mode: Literal["train", "val", "test"] | None = None,
        resources_id: str | None = None,
-        config: RolloutConfig | None = None,
        metadata: Dict[str, Any] | None = None,
-        worker_id: Optional[str] = None,
    ) -> AttemptedRollout:
        with self._lock:
-            return await self.store.start_rollout(
-                input,
-                mode,
-                resources_id,
-                config,
-                metadata,
-                worker_id,
-            )
+            return await self.store.start_rollout(input, mode, resources_id, metadata)

    async def enqueue_rollout(
        self,
        input: TaskInput,
        mode: Literal["train", "val", "test"] | None = None,
        resources_id: str | None = None,
-        config: RolloutConfig | None = None,
        metadata: Dict[str, Any] | None = None,
-    ) -> Rollout:
+    ) -> RolloutV2:
        with self._lock:
-            return await self.store.enqueue_rollout(input, mode, resources_id, config, metadata)
+            return await self.store.enqueue_rollout(input, mode, resources_id, metadata)

-    async def enqueue_many_rollouts(self, rollouts: Sequence[EnqueueRolloutRequest]) -> Sequence[Rollout]:
+    async def dequeue_rollout(self) -> Optional[AttemptedRollout]:
        with self._lock:
-            return await self.store.enqueue_many_rollouts(rollouts)
+            return await self.store.dequeue_rollout()

-    async def dequeue_rollout(self, worker_id: Optional[str] = None) -> Optional[AttemptedRollout]:
+    async def start_attempt(self, rollout_id: str) -> AttemptedRollout:
        with self._lock:
-            return await self.store.dequeue_rollout(worker_id=worker_id)
-
-    async def dequeue_many_rollouts(
-        self,
-        *,
-        limit: int = 1,
-        worker_id: Optional[str] = None,
-    ) -> Sequence[AttemptedRollout]:
-        with self._lock:
-            return await self.store.dequeue_many_rollouts(limit=limit, worker_id=worker_id)
-
-    async def start_attempt(self, rollout_id: str, worker_id: Optional[str] = None) -> AttemptedRollout:
-        with self._lock:
-            return await self.store.start_attempt(rollout_id, worker_id)
+            return await self.store.start_attempt(rollout_id)

    async def query_rollouts(
        self,
        *,
-        status_in: Optional[Sequence[RolloutStatus]] = None,
-        rollout_id_in: Optional[Sequence[str]] = None,
-        rollout_id_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
        status: Optional[Sequence[RolloutStatus]] = None,
        rollout_ids: Optional[Sequence[str]] = None,
-    ) -> Sequence[Rollout]:
+    ) -> List[RolloutV2]:
        with self._lock:
-            return await self.store.query_rollouts(
-                status_in=status_in,
-                rollout_id_in=rollout_id_in,
-                rollout_id_contains=rollout_id_contains,
-                filter_logic=filter_logic,
-                sort_by=sort_by,
-                sort_order=sort_order,
-                limit=limit,
-                offset=offset,
-                status=status,
-                rollout_ids=rollout_ids,
-            )
+            return await self.store.query_rollouts(status=status, rollout_ids=rollout_ids)

-    async def query_attempts(
-        self,
-        rollout_id: str,
-        *,
-        sort_by: Optional[str] = "sequence_id",
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[Attempt]:
+    async def query_attempts(self, rollout_id: str) -> List[Attempt]:
        with self._lock:
-            return await self.store.query_attempts(
-                rollout_id,
-                sort_by=sort_by,
-                sort_order=sort_order,
-                limit=limit,
-                offset=offset,
-            )
+            return await self.store.query_attempts(rollout_id)

-    async def get_rollout_by_id(self, rollout_id: str) -> Optional[Rollout]:
+    async def get_rollout_by_id(self, rollout_id: str) -> Optional[RolloutV2]:
        with self._lock:
            return await self.store.get_rollout_by_id(rollout_id)

@@ -158,26 +84,6 @@ class LightningStoreThreaded(LightningStore):
        with self._lock:
            return await self.store.get_latest_attempt(rollout_id)

-    async def query_resources(
-        self,
-        *,
-        resources_id: Optional[str] = None,
-        resources_id_contains: Optional[str] = None,
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[ResourcesUpdate]:
-        with self._lock:
-            return await self.store.query_resources(
-                resources_id=resources_id,
-                resources_id_contains=resources_id_contains,
-                sort_by=sort_by,
-                sort_order=sort_order,
-                limit=limit,
-                offset=offset,
-            )
-
    async def add_resources(self, resources: NamedResources) -> ResourcesUpdate:
        with self._lock:
            return await self.store.add_resources(resources)
@@ -194,11 +100,7 @@ class LightningStoreThreaded(LightningStore):
        with self._lock:
            return await self.store.get_latest_resources()

-    async def add_many_spans(self, spans: Sequence[Span]) -> Sequence[Span]:
-        with self._lock:
-            return await self.store.add_many_spans(spans)
-
-    async def add_span(self, span: Span) -> Optional[Span]:
+    async def add_span(self, span: Span) -> Span:
        with self._lock:
            return await self.store.add_span(span)

@@ -208,11 +110,11 @@ class LightningStoreThreaded(LightningStore):
        attempt_id: str,
        readable_span: ReadableSpan,
        sequence_id: int | None = None,
-    ) -> Optional[Span]:
+    ) -> Span:
        with self._lock:
            return await self.store.add_otel_span(rollout_id, attempt_id, readable_span, sequence_id)

-    async def wait_for_rollouts(self, *, rollout_ids: List[str], timeout: Optional[float] = None) -> List[Rollout]:
+    async def wait_for_rollouts(self, *, rollout_ids: List[str], timeout: Optional[float] = None) -> List[RolloutV2]:
        # This method does not change the state of the store, and it's not thread-safe.
        return await self.store.wait_for_rollouts(rollout_ids=rollout_ids, timeout=timeout)

@@ -220,47 +122,13 @@ class LightningStoreThreaded(LightningStore):
        with self._lock:
            return await self.store.get_next_span_sequence_id(rollout_id, attempt_id)

-    async def get_many_span_sequence_ids(self, rollout_attempt_ids: Sequence[Tuple[str, str]]) -> Sequence[int]:
-        with self._lock:
-            return await self.store.get_many_span_sequence_ids(rollout_attempt_ids)
-
    async def query_spans(
        self,
        rollout_id: str,
        attempt_id: str | Literal["latest"] | None = None,
-        *,
-        trace_id: Optional[str] = None,
-        trace_id_contains: Optional[str] = None,
-        span_id: Optional[str] = None,
-        span_id_contains: Optional[str] = None,
-        parent_id: Optional[str] = None,
-        parent_id_contains: Optional[str] = None,
-        name: Optional[str] = None,
-        name_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        limit: int = -1,
-        offset: int = 0,
-        sort_by: Optional[str] = "sequence_id",
-        sort_order: Literal["asc", "desc"] = "asc",
-    ) -> Sequence[Span]:
+    ) -> List[Span]:
        with self._lock:
-            return await self.store.query_spans(
-                rollout_id,
-                attempt_id,
-                trace_id=trace_id,
-                trace_id_contains=trace_id_contains,
-                span_id=span_id,
-                span_id_contains=span_id_contains,
-                parent_id=parent_id,
-                parent_id_contains=parent_id_contains,
-                name=name,
-                name_contains=name_contains,
-                filter_logic=filter_logic,
-                limit=limit,
-                offset=offset,
-                sort_by=sort_by,
-                sort_order=sort_order,
-            )
+            return await self.store.query_spans(rollout_id, attempt_id)

    async def update_rollout(
        self,
@@ -271,7 +139,7 @@ class LightningStoreThreaded(LightningStore):
        status: RolloutStatus | Unset = UNSET,
        config: RolloutConfig | Unset = UNSET,
        metadata: Optional[Dict[str, Any]] | Unset = UNSET,
-    ) -> Rollout:
+    ) -> RolloutV2:
        with self._lock:
            return await self.store.update_rollout(
                rollout_id=rollout_id,
@@ -301,39 +169,3 @@ class LightningStoreThreaded(LightningStore):
                last_heartbeat_time=last_heartbeat_time,
                metadata=metadata,
            )
-
-    async def query_workers(
-        self,
-        *,
-        status_in: Optional[Sequence[WorkerStatus]] = None,
-        worker_id_contains: Optional[str] = None,
-        filter_logic: Literal["and", "or"] = "and",
-        sort_by: Optional[str] = None,
-        sort_order: Literal["asc", "desc"] = "asc",
-        limit: int = -1,
-        offset: int = 0,
-    ) -> Sequence[Worker]:
-        with self._lock:
-            return await self.store.query_workers(
-                status_in=status_in,
-                worker_id_contains=worker_id_contains,
-                sort_by=sort_by,
-                sort_order=sort_order,
-                limit=limit,
-                offset=offset,
-            )
-
-    async def get_worker_by_id(self, worker_id: str) -> Optional[Worker]:
-        with self._lock:
-            return await self.store.get_worker_by_id(worker_id)
-
-    async def update_worker(
-        self,
-        worker_id: str,
-        heartbeat_stats: Dict[str, Any] | Unset = UNSET,
-    ) -> Worker:
-        with self._lock:
-            return await self.store.update_worker(
-                worker_id=worker_id,
-                heartbeat_stats=heartbeat_stats,
-            )
@@ -1,110 +1,73 @@
 # Copyright (c) Microsoft. All rights reserved.

 import time
-from typing import Awaitable, Callable, Dict, List, Tuple
+from typing import Awaitable, Callable, List, cast

-from agentlightning.types import Attempt, AttemptedRollout, AttemptStatus, Rollout, RolloutConfig, RolloutStatus
+from agentlightning.types import Attempt, AttemptedRollout, AttemptStatus, RolloutConfig, RolloutStatus, RolloutV2

-UpdateRolloutStatus = Callable[[str, RolloutStatus], Awaitable[Rollout]]
+UpdateRolloutStatus = Callable[[str, RolloutStatus], Awaitable[RolloutV2]]
 UpdateAttemptStatus = Callable[[str, str, AttemptStatus], Awaitable[Attempt]]


-LATENCY_BUCKETS = [
-    0.000001,
-    0.000002,
-    0.000005,
-    0.00001,
-    0.00002,
-    0.00005,
-    0.0001,
-    0.0002,
-    0.0005,
-    0.001,
-    0.002,
-    0.003,
-    0.005,
-    0.007,
-    0.01,
-    0.015,
-    0.02,
-    0.03,
-    0.05,
-    0.07,
-    0.1,
-    0.2,
-    0.3,
-    0.5,
-    0.7,
-    1.0,
-    2.0,
-    3.0,
-    5.0,
-    7.0,
-    10.0,
-    12.0,
-    15.0,
-    20.0,
-    25.0,
-    30.0,
-    40.0,
-    50.0,
-    60.0,
-    90.0,
-    120.0,
-    180.0,
-    240.0,
-    300.0,
-]
-
-
-async def rollout_status_from_attempt(
+async def propagate_status(
+    update_rollout_status: UpdateRolloutStatus,  # this should be unlocked
    attempt: Attempt,
    config: RolloutConfig,
-) -> RolloutStatus:
+) -> RolloutV2:
    """
    Propagate the status of an attempt to the rollout.

-    Returns:
-        The status of the rollout from the perspective of the attempt.
+    The rollout should be made sure in a state to be outdated.
+    Requeue the rollout if it should be retried.
+
+    This operation is completely unlocked. The caller is responsible for locking the store.
    """
    # Propagate the status directly to the rollout
    if attempt.status == "preparing" or attempt.status == "running" or attempt.status == "succeeded":
-        return attempt.status
+        return await update_rollout_status(
+            attempt.rollout_id,
+            attempt.status,
+        )

    if attempt.status == "failed" or attempt.status == "timeout" or attempt.status == "unresponsive":
        # Check if this status should trigger a retry
        if attempt.status in config.retry_condition:
            # If we haven't exceeded max attempts, retry
            if attempt.sequence_id < config.max_attempts:
-                return "requeuing"
+                return await update_rollout_status(
+                    attempt.rollout_id,
+                    "requeuing",
+                )

        # If we can't retry or shouldn't retry, mark as failed
-        return "failed"
+        return await update_rollout_status(
+            attempt.rollout_id,
+            "failed",
+        )

    raise ValueError(f"Invalid attempt status: {attempt.status}")


-async def scan_unhealthy_rollouts(
+async def healthcheck(
    rollouts: List[AttemptedRollout],
-) -> Dict[Tuple[str, str], AttemptStatus]:
+    update_rollout_status: UpdateRolloutStatus,
+    update_attempt_status: UpdateAttemptStatus,
+) -> None:
    """
    Perform health check on all running rollouts in the store.

    This method should be called periodically to:
-
-    1. Check for unresponsive attempts (no heartbeat or spans for a while)
-    2. Check for timed-out rollouts (running too long since start_time)
+    1. Update rollout status to failed to succeeded when the attempt is done
+    2. Check for unresponsive attempts (no heartbeat or spans for a while)
+    3. Check for timed-out rollouts (running too long since start_time)
+    4. Update attempt/rollout status accordingly

    This operation is completely unlocked. The caller is responsible for locking the store.

    Args:
-        rollouts: The list of running rollouts to check.
-
-    Returns:
-        A dictionary of updates to the rollouts.
+        store: The LightningStore instance to check rollouts from
    """
    current_time = time.time()
-    updates: Dict[Tuple[str, str], AttemptStatus] = {}

    for rollout in rollouts:
        config = rollout.config  # policy for retry and timeout
@@ -112,31 +75,52 @@ async def scan_unhealthy_rollouts(
        # Get the latest attempt for this rollout
        latest_attempt = rollout.attempt
        if not latest_attempt:
-            # This should not happen
+            continue
+
+        # Check if the attempt has already failed or succeeded
+        if latest_attempt.status == "failed" or latest_attempt.status == "succeeded":
+            await propagate_status(update_rollout_status, latest_attempt, config)
            continue

        # Check for timeout condition (based on attempt start_time, instead of rollout start_time)
        if config.timeout_seconds is not None and current_time - latest_attempt.start_time > config.timeout_seconds:
-            updates[(latest_attempt.rollout_id, latest_attempt.attempt_id)] = "timeout"
+            await update_attempt_status(
+                latest_attempt.rollout_id,
+                latest_attempt.attempt_id,
+                "timeout",
+            )
            continue

        # Check for unresponsive condition (based on last heartbeat)
-        # (1) Haven't received heartbeat for a while
-        if (
-            latest_attempt.last_heartbeat_time
-            and config.unresponsive_seconds is not None
-            and current_time - latest_attempt.last_heartbeat_time > config.unresponsive_seconds
-        ):
-            updates[(latest_attempt.rollout_id, latest_attempt.attempt_id)] = "unresponsive"
-            continue
+        if latest_attempt.last_heartbeat_time:
+            if latest_attempt.status == "preparing":
+                # If still preparing, mark it as running
+                latest_attempt = await update_attempt_status(
+                    latest_attempt.rollout_id,
+                    latest_attempt.attempt_id,
+                    "running",
+                )

-        # (2) Check if there's no last heartbeat (no spans) at all
+            # Haven't received heartbeat for a while
+            if (
+                config.unresponsive_seconds is not None
+                and current_time - cast(float, latest_attempt.last_heartbeat_time) > config.unresponsive_seconds
+            ):
+                await update_attempt_status(
+                    latest_attempt.rollout_id,
+                    latest_attempt.attempt_id,
+                    "unresponsive",
+                )
+                continue
+
+        # Check if there's no last heartbeat (no spans) at all
        if (
            latest_attempt.last_heartbeat_time is None
            and config.unresponsive_seconds is not None
            and current_time - latest_attempt.start_time > config.unresponsive_seconds
        ):
-            updates[(latest_attempt.rollout_id, latest_attempt.attempt_id)] = "unresponsive"
-            continue
-
-    return updates
+            await update_attempt_status(
+                latest_attempt.rollout_id,
+                latest_attempt.attempt_id,
+                "unresponsive",
+            )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Yuge Zhang	4e87832970	Merge branch 'main' of github.com:microsoft/agent-lightning into apo-algorithm	2025-10-14 09:03:39 +08:00
Yuge Zhang	47fdf4416a	add room tasks	2025-10-14 09:03:20 +08:00
Yuge Zhang	3e12d9e774	add variant03	2025-10-14 09:01:29 +08:00
Yuge Zhang	badd4de08e	.	2025-10-14 01:04:22 +08:00
Yuge Zhang	c0c65d5cad	fix dataset wrong labels	2025-10-14 01:01:35 +08:00
Yuge Zhang	a445b49e9d	update data	2025-10-14 00:39:35 +08:00
Yuge Zhang	91b148a966	update all data samples	2025-10-14 00:03:26 +08:00
Yuge Zhang	d6c53cbc52	.	2025-10-13 23:45:18 +08:00
Yuge Zhang	31a8097e7d	update tests	2025-10-13 21:14:31 +08:00
Yuge Zhang	6c1c53f1ba	.	2025-10-13 20:08:14 +08:00
Yuge Zhang	c149e66026	.	2025-10-13 19:57:57 +08:00
Yuge Zhang	b407430419	apo implementation	2025-10-13 18:51:20 +08:00