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37 Commits

Author SHA1 Message Date
Yuge Zhang 73133cdb2d update catalog 2025-12-02 00:33:23 +08:00
Yuge Zhang abb23bdeef update readme 2025-12-02 00:32:09 +08:00
Yuge Zhang 8c219175f5 Add CI for Claude Code (#346) 2025-12-01 23:48:51 +08:00
Yuge Zhang 931ddcfdcc Store Benchmark - Part 2 (#342) 2025-11-29 07:32:09 +08:00
Yuge Zhang ce80b09a4a Patch LiteLLM root span (#341) 2025-11-28 11:34:03 +08:00
Yuge Zhang f0546ca6c5 Semantic Convention (#340) 2025-11-28 01:22:42 +08:00
Ni Hao 3a3bfeef31 add test code to agentops's tracer (#324) 2025-11-27 21:25:47 +08:00
Geng Zhang a733950b74 Support Claude Code as LitAgent (#332) 2025-11-27 18:39:26 +08:00
Yuge Zhang 662fd90784 Upgrade transformers and CrewAI versions (#336) 2025-11-26 09:25:31 +08:00
Yuge Zhang 475c2adb91 Add Examples Catalog and Refine Contribution Guide (#331) 2025-11-23 16:17:16 +00:00
Yuge Zhang bffc7013f9 Store Benchmark - Part 1 (#328) 2025-11-22 23:35:29 +08:00
Yuge Zhang 4cf8fb94e7 Github Actions Workflow for Tinker and Azure (#327) 2025-11-22 01:47:53 +08:00
Yuge Zhang ab185a5c5a MongoDB-based Lightning Store (#323) 2025-11-21 11:49:54 +08:00
Yuge Zhang d581cbcd63 Upgrade VM image (#325) 2025-11-20 17:49:16 +08:00
Yuge Zhang 3459caa1de Fix OpenAI Agents 0.6 compatibility and pin vLLM < 0.11.1 (#322) 2025-11-20 07:13:15 +08:00
Yuge Zhang f3fd58e72a Put store init in the right place of tracer (#321) 2025-11-19 20:35:27 +08:00
Yuge Zhang b3cb5e1337 Minor improvements to make RL workflow more robust (#319) 2025-11-18 15:40:51 +08:00
Yuge Zhang 3761c0f54c Support native advanced queries in LightningStore (#318) 2025-11-18 10:54:55 +08:00
Yuge Zhang d4334182be Adding check traces with reward for VERL (#317) 2025-11-17 21:18:15 +08:00
Yuge Zhang 57c3c0525e Collection-based Lightning Store (#315) 2025-11-17 18:51:51 +08:00
Yuge Zhang e356593f73 Bump to 0.3.0 (#316) 2025-11-17 17:32:42 +08:00
Yuge Zhang 0e033831d5 Support OTLP in LightningStore (#313) 2025-11-15 16:34:09 +08:00
xiaochulaoban 0d721228d5 Added the README and script files for training sql_agent on NPU (#272)
Co-authored-by: Yuge Zhang <scottyugochang@gmail.com>
2025-11-15 01:27:07 +08:00
Yuge Zhang e49b75b7d8 Check all matching jobs per variant (#310) 2025-11-13 17:10:50 +00:00
Yuge Zhang eab691b1a1 Refactor logging (#306) 2025-11-13 22:48:52 +08:00
Yuge Zhang fd6494873d Make health timeout configurable (#305) 2025-11-13 19:46:02 +08:00
Yuge Zhang 6cbfc1fee0 Fix CI Badge and make Calc-X pipeline faster (#304) 2025-11-13 18:06:18 +08:00
Yuge Zhang b986ae132a Use PythonServerLauncher in LightningStoreServer (#303) 2025-11-13 14:22:54 +08:00
Yuge Zhang f24a47969e Increase graceful timeout on CI (#302) 2025-11-13 10:15:14 +08:00
Yuge Zhang a0bc1827d9 [Release] v0.2.2 (#298) 2025-11-12 23:54:35 +08:00
Yuge Zhang f2869cea30 Fix local model support in VERL (#299) 2025-11-12 22:56:10 +08:00
Geng Zhang 77cf447717 fix stream response for anthropic and openai api (#293)
Co-authored-by: Yuge Zhang <scottyugochang@gmail.com>
2025-11-12 21:29:02 +08:00
Yuge Zhang 790ed3efb3 View worker status on Dashboard (#296) 2025-11-12 21:27:31 +08:00
Yuge Zhang 5ae7933d41 Use unified server launcher for LiteLLM Proxy (#292) 2025-11-12 02:45:29 +08:00
Yuge Zhang 2ab977ed18 Dashboard - build into Python package (#291) 2025-11-11 16:29:02 +08:00
Yuge Zhang 1eae9a34f0 Fix dashboard pipeline (#289) 2025-11-11 00:27:02 +08:00
Yuge Zhang 4e7748b059 Dashboard - tests and infrastructure (#288) 2025-11-10 22:30:00 +08:00
293 changed files with 71002 additions and 4846 deletions
+14
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@@ -0,0 +1,14 @@
.venv
**/.venv
__pycache__
.git
.gitignore
**/node_modules
dist
build
.env
docker
.pytest_cache
.vscode
**/*.log
examples/**/data
+29
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@@ -0,0 +1,29 @@
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 });
+29
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@@ -0,0 +1,29 @@
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 });
+29
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@@ -0,0 +1,29 @@
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 });
+6
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@@ -7,6 +7,9 @@ on:
- Examples - Spider
- Examples - APO
- Examples - Unsloth
- Examples - Tinker
- Examples - Azure
- Examples - Claude Code
types: [completed]
workflow_dispatch:
@@ -31,5 +34,8 @@ jobs:
{ 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 });
+29
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@@ -0,0 +1,29 @@
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 });
+31
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@@ -0,0 +1,31 @@
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: ${{ secrets.GITHUB_TOKEN }}
script: |
const badgeAggregation = require('./scripts/badge_aggregation.js');
const dependencies = [
{ workflow: 'tests-full.yml', label: 'tests-full', variants: ['legacy', 'stable'] },
{ workflow: 'tests.yml', label: 'tests', variants: ['legacy', 'stable', 'Lint', 'documentation', 'JavaScript'] },
];
await badgeAggregation({ github, context, core, dependencies });
+187
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@@ -0,0 +1,187 @@
name: Benchmark
permissions:
contents: read
on:
workflow_dispatch:
jobs:
benchmark:
name: Benchmark (${{ 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
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
- 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
+33
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@@ -0,0 +1,33 @@
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
+1 -1
View File
@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'APO - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
+98
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@@ -0,0 +1,98 @@
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
+146 -11
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@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'Calc-X - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
@@ -22,12 +22,12 @@ run-name: >-
|| format('Calc-X - {0}', github.event_name) }}
jobs:
calc-x:
calc-x-perf:
if: >
github.event_name != 'repository_dispatch' ||
github.event.action == 'ci-calc-x' ||
github.event.action == 'ci-all'
name: Calc-X (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
name: Calc-X Performance (Python ${{ matrix.python-version }}, ${{ matrix.setup-script }})
runs-on: [self-hosted, 1ES.Pool=agl-runner-gpu]
timeout-minutes: 90
strategy:
@@ -74,7 +74,7 @@ jobs:
- name: Upload dependencies artifact
uses: actions/upload-artifact@v4
with:
name: dependencies-calc-x-${{ matrix.python-version }}-${{ matrix.setup-script }}
name: dependencies-calc-x-performance-${{ matrix.python-version }}-${{ matrix.setup-script }}
path: requirements-freeze.txt
compression-level: 0
@@ -116,13 +116,11 @@ jobs:
# Don't ask why. Don't touch this.
- name: Calc-X training
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
sleep 10
python train_calc_agent.py --val-file data/test_mini.parquet --ci
shell: bash
env:
WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
@@ -137,14 +135,126 @@ jobs:
WANDB_BASE_URL: ${{ secrets.MSR_WANDB_BASE_URL }}
WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
- name: Calc-X training LLM Proxy
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
PYTHONUNBUFFERED=1 python train_calc_agent.py --val-file data/test_mini.parquet --ci --llm-proxy
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:
@@ -152,7 +262,15 @@ jobs:
WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
id: calc_x_train_llm_proxy
- name: Calc-X training with external store
- 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
@@ -182,7 +300,15 @@ jobs:
WANDB_API_KEY: ${{ secrets.MSR_WANDB_API_KEY }}
id: calc_x_train_external_store
- name: Calc-X training with role-based environment variables
- 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
@@ -203,3 +329,12 @@ jobs:
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 }}
+151
View File
@@ -0,0 +1,151 @@
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
+2 -2
View File
@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'Backward Compatibility - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
@@ -56,7 +56,7 @@ jobs:
--group dev --group experiment --group agents --group torch-gpu-${{ matrix.setup-script }}
- name: Override VERL (stable)
run: |
uv pip install verl==0.5.0
uv pip install verl==0.5.0 vllm==0.10.2
if: matrix.setup-script == 'stable'
- name: Freeze dependencies
run: |
+2 -2
View File
@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'Spider - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
@@ -121,7 +121,7 @@ jobs:
- 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 }}
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 }}
+160
View File
@@ -0,0 +1,160 @@
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 -1
View File
@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'Unsloth - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
+8
View File
@@ -26,6 +26,14 @@ jobs:
- 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: Get current version
id: get_version
run: |
+8
View File
@@ -60,6 +60,14 @@ jobs:
- 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: Build package
run: |
uv build
+238 -5
View File
@@ -14,7 +14,7 @@ on:
run-name: >-
${{ github.event_name == 'repository_dispatch'
&& format(
'PR #{0} - Label {1} - {2}',
'GPU Test - PR #{0} - {1} - {2}',
github.event.client_payload.pull_number,
github.event.client_payload.ci_label,
github.event.client_payload.correlation_id
@@ -29,6 +29,129 @@ jobs:
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'
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: ${{ matrix.python-version }}
- name: Upgrade dependencies (latest)
run: uv lock --upgrade
if: matrix.setup-script == 'latest'
- name: Sync dependencies (latest)
run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group torch-gpu-stable
if: matrix.setup-script == 'latest'
- name: Sync dependencies (stable & legacy)
run: uv sync --frozen --no-default-groups --extra apo --extra mongo --group dev --group agents --group torch-gpu-${{ 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" >> $GITHUB_ENV
- name: Upload dependencies artifact
uses: actions/upload-artifact@v4
with:
name: dependencies-tests-full-${{ matrix.python-version }}-${{ matrix.setup-script }}
path: requirements-freeze.txt
compression-level: 0
- uses: actions/setup-node@v6
with:
node-version: '22'
- name: Install JavaScript dependencies
run: cd dashboard && npm ci
- name: Build dashboard
run: cd dashboard && npm run build
- name: Setup Docker environments
run: |
set -euo pipefail
cd docker
# Setup data directories
./setup.sh
# Start Dockers
docker compose -f compose.mongo.yml up -d
SERVICE_NAME=mongo
TIMEOUT=60 # seconds
SLEEP=2
cid="$(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
env:
AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
- name: Run tests
run: |
uv run 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:
@@ -69,7 +192,7 @@ jobs:
- name: Upload dependencies artifact
uses: actions/upload-artifact@v4
with:
name: dependencies-${{ matrix.python-version }}-${{ matrix.setup-script }}
name: dependencies-minimal-examples-${{ matrix.python-version }}-${{ matrix.setup-script }}
path: requirements-freeze.txt
compression-level: 0
@@ -80,10 +203,120 @@ jobs:
AZURE_API_BASE: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_BASE }}
AZURE_API_KEY: ${{ secrets.AZURE_GROUP_SUBSCRIPTION_API_KEY }}
- name: Run tests
- name: Write Traces via Otel Tracer
run: |
uv run pytest -v --durations=0 tests
set -euo pipefail
source .venv/bin/activate
cd examples/minimal
python write_traces.py otel
sleep 5
- name: Write Traces via AgentOps Tracer
env:
PYTEST_ADDOPTS: "--color=yes"
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
+57 -2
View File
@@ -37,6 +37,7 @@ jobs:
uv sync --frozen \
--extra apo \
--extra verl \
--extra mongo \
--group dev \
--group torch-cpu \
--group torch-stable \
@@ -45,10 +46,11 @@ jobs:
--group agents \
--no-default-groups
if: matrix.setup == 'slow'
# This pre-commit skips JavaScript on purpose.
- name: Run pre-commit
uses: pre-commit/action@v3.0.1
- name: Check Python headers
run: uv run --locked --no-sync scripts/check_python_headers.py
run: uv run --locked --no-sync scripts/check_headers.py
- name: Run Black
run: uv run --locked --no-sync black --check .
- name: Run isort
@@ -60,6 +62,28 @@ jobs:
run: uv run --locked --no-sync pyright -p pyrightconfig.json
if: matrix.setup == 'slow'
lint-js:
name: Lint - JavaScript
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v6
with:
node-version: '22'
- 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
docs:
name: Build documentation
runs-on: ubuntu-latest
@@ -132,8 +156,39 @@ jobs:
name: dependencies-${{ matrix.python-version }}-${{ matrix.setup-script }}
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: Run tests
run: |
uv run pytest -v --durations=0 tests
uv run pytest -v --durations=0 tests -m "not mongo"
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
+9
View File
@@ -207,3 +207,12 @@ 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/
+50
View File
@@ -24,3 +24,53 @@ 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
'
+11 -4
View File
@@ -4,7 +4,7 @@
# Agent Lightning⚡
[![Test](https://github.com/microsoft/agent-lightning/actions/workflows/tests-full.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/tests-full.yml)
[![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/)
[![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)
@@ -34,6 +34,12 @@ Read more on our [documentation website](https://microsoft.github.io/agent-light
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).
@@ -69,10 +75,11 @@ No rewrites, no lock-in, just a clear path from first rollout to steady improvem
| 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) |
| GPU Tests | [![tests-full workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/tests-full.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/tests-full.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 | [![examples compatibility workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/examples-compat.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-compat.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) |
## ⚡ Citation
@@ -92,7 +99,7 @@ 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 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. 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.
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.
+5 -2
View File
@@ -1,16 +1,19 @@
# Copyright (c) Microsoft. All rights reserved.
__version__ = "0.2.2"
__version__ = "0.3.0"
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 .litagent import *
from .llm_proxy import *
from .logging 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 *
+3 -3
View File
@@ -1,6 +1,6 @@
# Copyright (c) Microsoft. All rights reserved.
from typing import Generic, List, TypeVar
from typing import Generic, Sequence, TypeVar
from opentelemetry.sdk.trace import ReadableSpan
@@ -66,7 +66,7 @@ class Adapter(Generic[T_from, T_to]):
raise NotImplementedError("Adapter.adapt() is not implemented")
class OtelTraceAdapter(Adapter[List[ReadableSpan], T_to], Generic[T_to]):
class OtelTraceAdapter(Adapter[Sequence[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
@@ -84,7 +84,7 @@ class OtelTraceAdapter(Adapter[List[ReadableSpan], T_to], Generic[T_to]):
"""
class TraceAdapter(Adapter[List[Span], T_to], Generic[T_to]):
class TraceAdapter(Adapter[Sequence[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
+3 -3
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import json
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, Generator, Iterable, List, Optional, TypedDict, Union, cast
from typing import TYPE_CHECKING, Any, Dict, Generator, Iterable, List, Optional, Sequence, TypedDict, Union, cast
from pydantic import TypeAdapter
@@ -208,7 +208,7 @@ class TraceToMessages(TraceAdapter[List[OpenAIMessages]]):
children of the associated completion span.
"""
def get_tool_calls(self, completion: Span, all_spans: List[Span], /) -> Iterable[Dict[str, Any]]:
def get_tool_calls(self, completion: Span, all_spans: Sequence[Span], /) -> Iterable[Dict[str, Any]]:
"""Yield tool call payloads for a completion span.
Args:
@@ -231,7 +231,7 @@ class TraceToMessages(TraceAdapter[List[OpenAIMessages]]):
if tool_call:
yield tool_call
def adapt(self, source: List[Span], /) -> List[OpenAIMessages]:
def adapt(self, source: Sequence[Span], /) -> List[OpenAIMessages]:
"""Transform trace spans into OpenAI chat payloads.
Args:
+11 -40
View File
@@ -6,12 +6,13 @@ import json
import logging
import re
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union, cast
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
from opentelemetry.sdk.trace import ReadableSpan
from pydantic import BaseModel
from agentlightning.types import Span, SpanNames, Triplet
from agentlightning.emitter.reward import get_reward_value
from agentlightning.types import Span, Triplet
from .base import TraceAdapter
@@ -313,24 +314,11 @@ class TraceTree:
Returns:
Dictionary containing reward metadata, or an empty dictionary when no reward is found.
"""
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 {}
# 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 {}
reward_value = get_reward_value(self.span)
if reward_value is not None:
return {"type": "reward", "value": reward_value}
else:
return {}
def is_reward_span(self) -> bool:
"""Return whether the span explicitly encodes a reward.
@@ -670,7 +658,7 @@ class TracerTraceToTriplet(TraceToTripletBase):
trace_tree.visualize(filename, interested_span_match=interested_span_match)
return trace_tree
def adapt(self, source: Union[List[Span], List[ReadableSpan]], /) -> List[Triplet]: # type: ignore
def adapt(self, source: Union[Sequence[Span], Sequence[ReadableSpan]], /) -> List[Triplet]: # type: ignore
"""Convert tracer spans into [`Triplet`][agentlightning.Triplet] trajectories.
Args:
@@ -776,31 +764,14 @@ class LlmProxyTraceToTriplet(TraceToTripletBase):
def _maybe_reward_value(self, span: Span) -> Optional[float]:
"""Parse reward from typical AgentOps payloads or explicit reward spans."""
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
return get_reward_value(span)
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: List[Span], /) -> List[Triplet]: # type: ignore
def adapt(self, source: Sequence[Span], /) -> List[Triplet]: # type: ignore
"""Convert LLM Proxy spans into [`Triplet`][agentlightning.Triplet] trajectories.
Args:
+2 -2
View File
@@ -143,7 +143,7 @@ class Baseline(FastAlgorithm):
store = self.get_store()
for index in train_indices + val_indices:
queuing_rollouts = await store.query_rollouts(status=["queuing", "requeuing"])
queuing_rollouts = await store.query_rollouts(status_in=["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]
@@ -222,7 +222,7 @@ class Baseline(FastAlgorithm):
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=["queuing", "requeuing"])
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]
+55 -4
View File
@@ -9,7 +9,7 @@ import asyncio
import logging
from typing import Iterable
from agentlightning.logging import configure_logger
from agentlightning import setup_logging
from agentlightning.store.client_server import LightningStoreServer
from agentlightning.store.memory import InMemoryLightningStore
@@ -18,6 +18,7 @@ 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",
@@ -25,16 +26,66 @@ def main(argv: Iterable[str] | None = None) -> int:
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)
configure_logger()
setup_logging(args.log_level)
store = InMemoryLightningStore()
if args.backend == "memory":
store = InMemoryLightningStore(prometheus=args.prometheus)
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="0.0.0.0",
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())
+8 -2
View File
@@ -1,13 +1,15 @@
# Copyright (c) Microsoft. All rights reserved.
from .annotation import emit_annotation
from .exception import emit_exception
from .message import emit_message
from .object import emit_object
from .message import emit_message, get_message_value
from .object import emit_object, get_object_value
from .reward import (
emit_reward,
find_final_reward,
find_reward_spans,
get_reward_value,
get_rewards_from_span,
is_reward_span,
reward,
)
@@ -16,10 +18,14 @@ __all__ = [
"reward",
"emit_reward",
"get_reward_value",
"get_rewards_from_span",
"is_reward_span",
"find_reward_spans",
"find_final_reward",
"emit_message",
"emit_object",
"emit_exception",
"emit_annotation",
"get_message_value",
"get_object_value",
]
+48
View File
@@ -0,0 +1,48 @@
# Copyright (c) Microsoft. All rights reserved.
"""Helpers for emitting annotation spans."""
import logging
from typing import Any, Dict
from opentelemetry.sdk.trace import ReadableSpan
from agentlightning.semconv import AGL_ANNOTATION
from agentlightning.utils.otel import flatten_attributes, get_tracer
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
+23 -13
View File
@@ -2,43 +2,53 @@
import logging
import traceback
from typing import Any, Dict, Optional
from opentelemetry.semconv.attributes import exception_attributes
from agentlightning.types import SpanNames
from .utils import get_tracer
from agentlightning.semconv import AGL_EXCEPTION
from agentlightning.utils.otel import get_tracer
logger = logging.getLogger(__name__)
def emit_exception(exception: BaseException) -> None:
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. Non-exception values are ignored to prevent
noisy telemetry and indicate programming mistakes via the logger.
The helper validates its input. If a non-exception value is provided,
a TypeError is raised to indicate a programming mistake.
"""
if not isinstance(exception, BaseException): # type: ignore
logger.error(f"Expected an BaseException instance, got: {type(exception)}. Skip emit_exception.")
return
raise TypeError(f"Expected a BaseException instance, got: {type(exception)}.")
tracer = get_tracer()
tracer = get_tracer(use_active_span_processor=propagate)
stacktrace = "".join(traceback.format_exception(type(exception), exception, exception.__traceback__))
attributes = {
span_attributes = {
exception_attributes.EXCEPTION_TYPE: type(exception).__name__,
exception_attributes.EXCEPTION_MESSAGE: str(exception),
exception_attributes.EXCEPTION_ESCAPED: True,
}
if stacktrace.strip():
attributes[exception_attributes.EXCEPTION_STACKTRACE] = stacktrace
span_attributes[exception_attributes.EXCEPTION_STACKTRACE] = stacktrace
if attributes:
span_attributes.update(attributes)
span = tracer.start_span(
SpanNames.EXCEPTION.value,
attributes=attributes,
AGL_EXCEPTION,
attributes=span_attributes,
)
logger.debug("Emitting exception span for %s", type(exception).__name__)
with span:
+31 -9
View File
@@ -1,33 +1,55 @@
# Copyright (c) Microsoft. All rights reserved.
import logging
from typing import Any, Dict, Optional
from agentlightning.types import SpanAttributeNames, SpanNames
from .utils import get_tracer
from agentlightning.semconv import AGL_MESSAGE, LightningSpanAttributes
from agentlightning.types import SpanLike
from agentlightning.utils.otel import get_tracer
logger = logging.getLogger(__name__)
def emit_message(message: str) -> None:
def emit_message(message: str, attributes: Optional[Dict[str, Any]] = None, propagate: bool = True) -> None:
"""Emit a textual message as an OpenTelemetry 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.
"""
if not isinstance(message, str): # type: ignore
logger.error(f"Message must be a string, got: {type(message)}. Skip emit_message.")
return
raise TypeError(f"Message must be a string or list of strings, got: {type(message)}.")
tracer = get_tracer()
tracer = get_tracer(use_active_span_processor=propagate)
span_attributes = {LightningSpanAttributes.MESSAGE_BODY.value: message}
if attributes:
span_attributes.update(attributes)
span = tracer.start_span(
SpanNames.MESSAGE.value,
attributes={SpanAttributeNames.MESSAGE.value: message},
AGL_MESSAGE,
attributes=span_attributes,
)
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)}.")
+86 -17
View File
@@ -1,37 +1,106 @@
# Copyright (c) Microsoft. All rights reserved.
import base64
import json
import logging
from typing import Any
from typing import Any, Dict, Optional
from agentlightning.types import SpanAttributeNames, SpanNames
from .utils import get_tracer
from agentlightning.semconv import AGL_OBJECT, LightningSpanAttributes
from agentlightning.types import SpanLike
from agentlightning.utils.otel import full_qualified_name, get_tracer
logger = logging.getLogger(__name__)
def emit_object(object: Any) -> None:
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.
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 are ignored and
an error is logged to aid debugging.
The payload must be JSON serializable. Non-serializable objects will lead to a RuntimeError.
"""
try:
serialized = json.dumps(object)
except (TypeError, ValueError):
logger.error(f"Object must be JSON serializable, got: {type(object)}. Skip emit_object.")
return
tracer = get_tracer()
span_attributes = encode_object(object)
if attributes:
span_attributes.update(attributes)
tracer = get_tracer(use_active_span_processor=propagate)
span = tracer.start_span(
SpanNames.OBJECT.value,
attributes={SpanAttributeNames.OBJECT.value: serialized},
AGL_OBJECT,
attributes=span_attributes,
)
logger.debug("Emitting object span with payload size %d characters", len(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)
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
+106 -25
View File
@@ -23,10 +23,13 @@ from typing import (
import agentops
from agentops.sdk.decorators import operation
from opentelemetry.sdk.trace import ReadableSpan
from pydantic import TypeAdapter
from agentlightning.types import SpanLike, SpanNames
from agentlightning.semconv import AGL_ANNOTATION, LightningSpanAttributes, RewardPydanticModel
from agentlightning.types import SpanLike
from agentlightning.utils.otel import filter_and_unflatten_attributes
from .utils import get_tracer
from .annotation import emit_annotation
logger = logging.getLogger(__name__)
@@ -34,18 +37,26 @@ __all__ = [
"reward",
"emit_reward",
"get_reward_value",
"get_rewards_from_span",
"is_reward_span",
"find_reward_spans",
"find_final_reward",
]
class RewardSpanData(TypedDict):
class RewardDimension(TypedDict):
"""Type representing a single dimension in a multi-dimensional reward."""
name: str
value: float
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:
@@ -53,7 +64,7 @@ def _agentops_initialized() -> bool:
return agentops.get_client().initialized
def reward(fn: FnType) -> FnType:
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
@@ -70,7 +81,7 @@ def reward(fn: FnType) -> FnType:
Wrapped callable that preserves the original signature.
"""
def wrap_result(result: Optional[float]) -> RewardSpanData:
def wrap_result(result: Optional[float]) -> _RewardSpanData:
"""Normalize the reward value into the span payload format."""
if result is None:
return {"type": "reward", "value": None}
@@ -94,7 +105,7 @@ 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
@@ -118,7 +129,7 @@ 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)
@@ -129,12 +140,36 @@ def reward(fn: FnType) -> FnType:
return wrapper # type: ignore
def emit_reward(reward: float) -> ReadableSpan:
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.
@@ -144,20 +179,34 @@ def emit_reward(reward: float) -> ReadableSpan:
resulting span is not a [`ReadableSpan`](https://opentelemetry.io/docs/concepts/signals/traces/) instance.
"""
logger.debug(f"Emitting reward: {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)}")
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))
# TODO: This should use the tracer from current context by tracer
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
return emit_annotation(
{LightningSpanAttributes.REWARD.value: reward_dimensions, **(attributes or {})}, propagate=propagate
)
def get_reward_value(span: SpanLike) -> Optional[float]:
@@ -167,8 +216,14 @@ def get_reward_value(span: SpanLike) -> Optional[float]:
span: Span object produced by AgentOps or Agent Lightning emitters.
Returns:
The reward encoded in the span or `None` when the span does not represent a reward.
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
for key in [
"agentops.task.output", # newer versions of agentops
"agentops.entity.output",
@@ -191,19 +246,45 @@ 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)
# Latest emit reward format
if span.name == SpanNames.REWARD.value and span.attributes:
# v0.2 emit reward format
if span.name == AGL_ANNOTATION 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."""
maybe_reward = get_reward_value(span)
-22
View File
@@ -1,22 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
"""Utilities shared across emitter implementations."""
import opentelemetry.trace as trace_api
from opentelemetry.trace import get_tracer_provider
def get_tracer() -> trace_api.Tracer:
"""Resolve the OpenTelemetry tracer configured for Agent Lightning.
Returns:
OpenTelemetry tracer tagged with the `agentlightning` instrumentation name.
Raises:
RuntimeError: If OpenTelemetry was not initialized before calling this helper.
"""
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")
+156
View File
@@ -0,0 +1,156 @@
# 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
-42
View File
@@ -3,7 +3,6 @@
from __future__ import annotations
import logging
import os
from typing import Protocol
from agentlightning.store.base import LightningStore
@@ -13,47 +12,6 @@ from .events import ExecutionEvent
logger = logging.getLogger(__name__)
_TRUTHY_VALUES = {"1", "true", "yes", "on"}
_FALSY_VALUES = {"0", "false", "no", "off"}
def resolve_managed_store_flag(value: bool | None) -> bool:
"""Determine whether execution helpers should wrap the provided store.
The helper first honours an explicit `value`. When `None` it falls back
to the `AGL_MANAGED_STORE` environment variable, accepting a variety
of truthy and falsy spellings. Missing environment configuration defaults to
`True` so that higher-level strategies create the appropriate client or
server wrappers automatically.
Args:
value: Optional override supplied by the caller.
Returns:
`True` when a managed store should be created around the provided
instance, otherwise `False`.
Raises:
ValueError: If `AGL_MANAGED_STORE` is set to an unsupported
value.
"""
if value is not None:
return value
env_value = os.getenv("AGL_MANAGED_STORE")
if env_value is None:
return True
normalized = env_value.strip().lower()
if normalized in _TRUTHY_VALUES:
return True
if normalized in _FALSY_VALUES:
return False
raise ValueError("AGL_MANAGED_STORE must be one of 1, 0, true, false, yes, no, on, or off")
class AlgorithmBundle(Protocol):
"""Callable bundle produced by [`Trainer`][agentlightning.Trainer].
+23 -33
View File
@@ -9,10 +9,11 @@ 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, resolve_managed_store_flag
from .base import AlgorithmBundle, ExecutionStrategy, RunnerBundle
from .events import ExecutionEvent, MultiprocessingEvent
logger = logging.getLogger(__name__)
@@ -67,10 +68,11 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
server_host: str | None = None,
server_port: int | None = None,
n_runners: int = 1,
graceful_timeout: float = 5.0,
terminate_timeout: float = 5.0,
graceful_timeout: float = 10.0,
terminate_timeout: float = 10.0,
main_process: Literal["algorithm", "runner"] = "algorithm",
managed_store: bool | None = None,
allowed_exit_codes: Iterable[int] = (0, -15),
) -> None:
"""Configure the strategy.
@@ -94,45 +96,33 @@ class ClientServerExecutionStrategy(ExecutionStrategy):
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).
"""
if role is None:
role_env = os.getenv("AGL_CURRENT_ROLE")
if role_env is None:
# Use both if not specified via env var or argument
role = "both"
elif role_env not in ("algorithm", "runner", "both"):
raise ValueError("role must be one of 'algorithm', 'runner', or 'both'")
else:
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
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
self.n_runners = n_runners
self.server_host = server_host
self.server_port = server_port
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.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 role != "both":
if self.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_managed_store_flag(managed_store)
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
@@ -338,10 +328,10 @@ 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 (0, None)]
failed = [p for p in processes if p.exitcode not in self.allowed_exit_codes + (None,)]
if failed:
formatted = ", ".join(f"{p.name or p.pid} (exitcode={p.exitcode})" for p in failed)
raise RuntimeError(f"Subprocesses failed: {formatted}")
raise RuntimeError(f"Subprocesses failed with unexpected exit codes: {formatted}")
def execute(self, algorithm: AlgorithmBundle, runner: RunnerBundle, store: LightningStore) -> None:
logger.info(
+5 -2
View File
@@ -7,10 +7,11 @@ 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, resolve_managed_store_flag
from .base import AlgorithmBundle, ExecutionStrategy, RunnerBundle
from .events import ExecutionEvent, ThreadingEvent
logger = logging.getLogger(__name__)
@@ -62,7 +63,9 @@ class SharedMemoryExecutionStrategy(ExecutionStrategy):
self.join_timeout = join_timeout
self.graceful_delay = graceful_delay
self.poll_interval = poll_interval
self.managed_store = resolve_managed_store_flag(managed_store)
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:
"""Run `coro` until it finishes or a cooperative stop is requested.
+25 -28
View File
@@ -13,7 +13,8 @@ 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 opentelemetry.sdk.trace.export import SpanExportResult
from agentlightning.utils.otlp import LightningStoreOTLPExporter
logger = logging.getLogger(__name__)
@@ -32,25 +33,27 @@ def enable_agentops_service(enabled: bool = True) -> None:
"""
Enable or disable communication with the AgentOps service.
False (default): AgentOps exporters and clients will run in local mode
and will not attempt to communicate with the remote AgentOps service.
True: all exporters and clients will operate in normal mode and send data
to the AgentOps service as expected.
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"Switch set to {enabled} for exporters and clients.")
logger.info(f"AgentOps service enabled is set to {enabled}.")
def _patch_exporters():
import agentops.client.api
import agentops.sdk.core
import opentelemetry.exporter.otlp.proto.http.metric_exporter
import opentelemetry.exporter.otlp.proto.http.trace_exporter
agentops.sdk.core.AuthenticatedOTLPExporter = BypassableAuthenticatedOTLPExporter # type: ignore
opentelemetry.exporter.otlp.proto.http.metric_exporter.OTLPMetricExporter = BypassableOTLPMetricExporter
opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter = BypassableOTLPSpanExporter
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
@@ -58,12 +61,11 @@ def _patch_exporters():
def _unpatch_exporters():
import agentops.client.api
import agentops.sdk.core
import opentelemetry.exporter.otlp.proto.http.metric_exporter
import opentelemetry.exporter.otlp.proto.http.trace_exporter
agentops.sdk.core.AuthenticatedOTLPExporter = AuthenticatedOTLPExporter # type: ignore
opentelemetry.exporter.otlp.proto.http.metric_exporter.OTLPMetricExporter = OTLPMetricExporter
opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter = OTLPSpanExporter
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
@@ -243,18 +245,15 @@ def uninstrument_agentops():
pass
class BypassableAuthenticatedOTLPExporter(AuthenticatedOTLPExporter):
class BypassableAuthenticatedOTLPExporter(LightningStoreOTLPExporter, AuthenticatedOTLPExporter):
"""
AuthenticatedOTLPExporter with switchable service control.
When `_agentops_service_enabled` is False, skip export and return success.
"""
def export(self, *args: Any, **kwargs: Any) -> SpanExportResult:
if _agentops_service_enabled:
return super().export(*args, **kwargs)
else:
logger.debug("SwitchableAuthenticatedOTLPExporter is switched off, skipping export.")
return SpanExportResult.SUCCESS
def should_bypass(self) -> bool:
return not _agentops_service_enabled
class BypassableOTLPMetricExporter(OTLPMetricExporter):
@@ -271,18 +270,16 @@ class BypassableOTLPMetricExporter(OTLPMetricExporter):
return MetricExportResult.SUCCESS
class BypassableOTLPSpanExporter(OTLPSpanExporter):
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 export(self, *args: Any, **kwargs: Any) -> SpanExportResult:
if _agentops_service_enabled:
return super().export(*args, **kwargs)
else:
logger.debug("SwitchableOTLPSpanExporter is switched off, skipping export.")
return SpanExportResult.SUCCESS
def should_bypass(self) -> bool:
return not _agentops_service_enabled
class BypassableV3Client(V3Client):
File diff suppressed because it is too large Load Diff
+329 -13
View File
@@ -1,10 +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
__all__ = ["configure_logger"]
from rich.console import Console
__all__ = ["setup", "configure_logger", "setup_module"]
def configure_logger(level: int = logging.INFO, name: str = "agentlightning") -> logging.Logger:
@@ -15,6 +23,10 @@ def configure_logger(level: int = logging.INFO, name: str = "agentlightning") ->
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`.
@@ -32,23 +44,327 @@ def configure_logger(level: int = logging.INFO, name: str = "agentlightning") ->
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 windown system.
# 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"
logger = logging.getLogger(name)
logger.handlers.clear() # clear existing handlers
base_logger = setup_module(
level,
name="agentlightning",
console=console,
color=color,
propagate=propagate,
disable_existing_loggers=disable_existing_loggers,
)
# 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
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()
+158 -37
View File
@@ -11,8 +11,22 @@ from __future__ import annotations
import asyncio
import logging
import random
import threading
import time
from typing import TYPE_CHECKING, Any, List, Literal, Optional, Sequence, TypeVar, cast
from contextlib import suppress
from typing import (
TYPE_CHECKING,
Any,
Awaitable,
Callable,
List,
Literal,
Optional,
Sequence,
TypeVar,
cast,
)
from opentelemetry.sdk.trace import ReadableSpan
@@ -30,6 +44,7 @@ from agentlightning.types import (
RolloutRawResult,
Span,
)
from agentlightning.utils.system_snapshot import system_snapshot
if TYPE_CHECKING:
from agentlightning.execution.events import ExecutionEvent
@@ -52,7 +67,15 @@ class LitAgentRunner(Runner[T_task]):
worker_id: Identifier for the active worker process, if any.
"""
def __init__(self, tracer: Tracer, max_rollouts: Optional[int] = None, poll_interval: float = 5.0) -> None:
def __init__(
self,
tracer: Tracer,
max_rollouts: Optional[int] = None,
poll_interval: float = 5.0,
heartbeat_interval: float = 10.0,
interval_jitter: float = 0.1,
heartbeat_launch_mode: Literal["asyncio", "thread"] = "asyncio",
) -> None:
"""Initialize the agent runner.
Args:
@@ -60,11 +83,21 @@ class LitAgentRunner(Runner[T_task]):
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.
"""
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
@@ -105,7 +138,7 @@ class LitAgentRunner(Runner[T_task]):
self._store = store
self.worker_id = worker_id
self._tracer.init_worker(worker_id)
self._tracer.init_worker(worker_id, store)
def teardown(self, *args: Any, **kwargs: Any) -> None:
"""Teardown the runner and clean up all resources.
@@ -253,8 +286,9 @@ class LitAgentRunner(Runner[T_task]):
if isinstance(raw_result, float):
# Preserve the existing spans before another span is emitted
trace_spans = list(self._tracer.get_last_trace())
# This will emit another span to the tracer
reward_span = emit_reward(raw_result)
# This will NOT emit another span to the tracer
reward_span = emit_reward(raw_result, propagate=False)
# We add it to the store manually
await store.add_otel_span(rollout.rollout_id, rollout.attempt.attempt_id, reward_span)
trace_spans.append(reward_span)
@@ -304,6 +338,75 @@ class LitAgentRunner(Runner[T_task]):
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:
"""Sleep until the next poll interval, with optional event-based interruption.
@@ -314,11 +417,13 @@ class LitAgentRunner(Runner[T_task]):
event: Optional [`ExecutionEvent`][agentlightning.ExecutionEvent] 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(self._poll_interval)
await asyncio.sleep(interval)
return
current_time = time.time()
next_time = current_time + self._poll_interval
next_time = current_time + interval
while time.time() < next_time:
await asyncio.sleep(0.1)
if event.is_set():
@@ -364,7 +469,7 @@ class LitAgentRunner(Runner[T_task]):
start_time = time.time()
async with self._tracer.trace_context(
name=rollout_id, store=store, rollout_id=rollout_id, attempt_id=next_rollout.attempt.attempt_id
name=rollout_id, 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
@@ -450,39 +555,49 @@ class LitAgentRunner(Runner[T_task]):
logger.info(f"{self._log_prefix()} Started async rollouts (max: {self._max_rollouts or 'unlimited'}).")
store = self.get_store()
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()
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
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
return
if next_rollout is None:
return
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
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)
# 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'}")
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()
logger.info(f"{self._log_prefix()} Finished async rollouts. Processed {num_tasks_processed} tasks.")
@@ -526,6 +641,12 @@ class LitAgentRunner(Runner[T_task]):
resources_id = None
attempted_rollout = await self.get_store().start_rollout(input=input, mode=mode, resources_id=resources_id)
# Register the attempt as running by the current worker
await self.get_store().update_attempt(
attempted_rollout.rollout_id,
attempted_rollout.attempt.attempt_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)
+144
View File
@@ -0,0 +1,144 @@
# 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_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."""
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."""
+5 -1
View File
@@ -1,14 +1,18 @@
# Copyright (c) Microsoft. All rights reserved.
from .base import LightningStore
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",
]
+301 -17
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional, Sequence
from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, TypedDict
from opentelemetry.sdk.trace import ReadableSpan
@@ -17,6 +17,8 @@ from agentlightning.types import (
RolloutStatus,
Span,
TaskInput,
Worker,
WorkerStatus,
)
@@ -52,6 +54,51 @@ 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.
@@ -74,6 +121,38 @@ class LightningStore:
Unless stated otherwise, missing identifiers should result in a `ValueError`.
"""
@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()
async def start_rollout(
self,
input: TaskInput,
@@ -148,7 +227,7 @@ class LightningStore:
"""
raise NotImplementedError()
async def dequeue_rollout(self) -> Optional[AttemptedRollout]:
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.
@@ -161,6 +240,8 @@ class LightningStore:
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.
Returns:
The next attempt to execute, or `None` when no eligible rollouts are queued.
@@ -191,7 +272,15 @@ class LightningStore:
"""
raise NotImplementedError()
async def add_span(self, span: Span) -> Span:
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`,
@@ -208,6 +297,7 @@ class LightningStore:
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.
@@ -221,7 +311,7 @@ class LightningStore:
attempt_id: str,
readable_span: ReadableSpan,
sequence_id: int | None = None,
) -> Span:
) -> Optional[Span]:
"""Convert and persist an OpenTelemetry span for a particular attempt.
Implementations must transform the `readable_span` into a [`Span`][agentlightning.Span]
@@ -238,7 +328,7 @@ class LightningStore:
automatically.
Returns:
The stored span record.
The stored span record. Return `None` if the span was not added due to a duplicate.
Raises:
NotImplementedError: Subclasses must implement span persistence.
@@ -247,30 +337,77 @@ class LightningStore:
raise NotImplementedError()
async def query_rollouts(
self, *, status: Optional[Sequence[RolloutStatus]] = None, rollout_ids: Optional[Sequence[str]] = None
) -> List[Rollout]:
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: Optional whitelist of [`RolloutStatus`][agentlightning.RolloutStatus] values.
rollout_ids: Optional whitelist of rollout identifiers to include.
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 list of matching rollouts. Ordering is backend-defined but must be deterministic.
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.
"""
raise NotImplementedError()
async def query_attempts(self, rollout_id: str) -> List[Attempt]:
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:
Attempts sorted by `sequence_id` (oldest first). Returns an empty list when none exist.
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.
@@ -307,11 +444,35 @@ class LightningStore:
"""
raise NotImplementedError()
async def query_resources(self) -> List[ResourcesUpdate]:
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:
A chronological list of [`ResourcesUpdate`][agentlightning.ResourcesUpdate] objects.
[`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.
@@ -368,6 +529,20 @@ class LightningStore:
"""
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.
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.
"""
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.
@@ -394,19 +569,61 @@ class LightningStore:
"""
raise NotImplementedError()
async def query_spans(self, rollout_id: str, attempt_id: str | Literal["latest"] | None = None) -> List[Span]:
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.
Spans must be returned in ascending `sequence_id` order. Implementations may raise
a `RuntimeError` when spans were evicted or expired.
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.
@@ -508,6 +725,12 @@ class LightningStore:
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.
@@ -524,3 +747,64 @@ class LightningStore:
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).
"""
raise NotImplementedError()
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,18 @@
# Copyright (c) Microsoft. All rights reserved.
from .base import Collection, FilterOptions, KeyValue, LightningCollections, PaginatedResult, Queue, SortOptions
from .memory import DequeBasedQueue, DictBasedKeyValue, InMemoryLightningCollections, ListBasedCollection
__all__ = [
"Collection",
"Queue",
"KeyValue",
"FilterOptions",
"SortOptions",
"PaginatedResult",
"LightningCollections",
"ListBasedCollection",
"DequeBasedQueue",
"DictBasedKeyValue",
"InMemoryLightningCollections",
]
+356
View File
@@ -0,0 +1,356 @@
# 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")
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]) -> None:
"""Update the given items in the collection.
Raises:
ValueError: If an item with the primary keys does not exist.
"""
raise NotImplementedError()
async def upsert(self, items: Sequence[T]) -> None:
"""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.
"""
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, *args: Any, **kwargs: Any) -> AsyncContextManager[Self]:
"""Perform a atomic operation on the collections.
Subclass may use args and kwargs to support multiple levels of atomicity.
Args:
*args: Arguments to pass to the operation.
**kwargs: Keyword arguments to pass to the operation.
"""
raise NotImplementedError()
async def execute(self, callback: Callable[[Self], Awaitable[T]]) -> T:
"""Execute the given callback within an atomic operation."""
async with self.atomic() 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
+756
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@@ -0,0 +1,756 @@
# Copyright (c) Microsoft. All rights reserved.
from __future__ import annotations
import asyncio
import logging
import weakref
from collections import deque
from contextlib import asynccontextmanager
from typing import (
Any,
Deque,
Dict,
Iterable,
List,
Literal,
Mapping,
MutableMapping,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
from agentlightning.types import (
Attempt,
FilterField,
FilterOptions,
PaginatedResult,
ResourcesUpdate,
Rollout,
SortOptions,
Span,
Worker,
)
from .base import (
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) -> None:
"""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 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":
parent[final_key] = item
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]) -> None:
"""Update the given items.
Raises:
ValueError: If any item with the given primary keys does not exist.
"""
for item in items:
self._mutate_single(item, mode="update")
async def upsert(self, items: Sequence[T]) -> None:
"""Upsert the given items (insert if missing, otherwise update)."""
for item in items:
self._mutate_single(item, mode="upsert")
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):
self._lock = _LoopAwareAsyncLock()
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
@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, *args: Any, **kwargs: Any):
"""In-memory collections apply a lock outside. It doesn't need to manipulate the collections inside."""
async with self._lock:
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()
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+161
View File
@@ -0,0 +1,161 @@
# 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:
# Query the rollouts that are not finished in a single query
rollouts = await self.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("_many_rollouts_to_attempted_rollouts_unlocked")
async def _many_rollouts_to_attempted_rollouts_unlocked(
self, collections: MongoLightningCollections, rollouts: Sequence[Rollout]
) -> List[Union[Rollout, AttemptedRollout]]:
"""Query the latest attempts for the rollouts, and attach them to the rollout objects."""
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
]
+156 -12
View File
@@ -3,7 +3,7 @@
from __future__ import annotations
import threading
from typing import Any, Dict, List, Literal, Optional, Sequence
from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple
from opentelemetry.sdk.trace import ReadableSpan
@@ -18,9 +18,11 @@ from agentlightning.types import (
RolloutStatus,
Span,
TaskInput,
Worker,
WorkerStatus,
)
from .base import UNSET, LightningStore, Unset
from .base import UNSET, LightningStore, LightningStoreCapabilities, LightningStoreStatistics, Unset
class LightningStoreThreaded(LightningStore):
@@ -35,6 +37,21 @@ 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,
@@ -57,9 +74,9 @@ class LightningStoreThreaded(LightningStore):
with self._lock:
return await self.store.enqueue_rollout(input, mode, resources_id, config, metadata)
async def dequeue_rollout(self) -> Optional[AttemptedRollout]:
async def dequeue_rollout(self, worker_id: Optional[str] = None) -> Optional[AttemptedRollout]:
with self._lock:
return await self.store.dequeue_rollout()
return await self.store.dequeue_rollout(worker_id=worker_id)
async def start_attempt(self, rollout_id: str) -> AttemptedRollout:
with self._lock:
@@ -68,15 +85,48 @@ class LightningStoreThreaded(LightningStore):
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,
) -> List[Rollout]:
) -> Sequence[Rollout]:
with self._lock:
return await self.store.query_rollouts(status=status, rollout_ids=rollout_ids)
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,
)
async def query_attempts(self, rollout_id: str) -> List[Attempt]:
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]:
with self._lock:
return await self.store.query_attempts(rollout_id)
return await self.store.query_attempts(
rollout_id,
sort_by=sort_by,
sort_order=sort_order,
limit=limit,
offset=offset,
)
async def get_rollout_by_id(self, rollout_id: str) -> Optional[Rollout]:
with self._lock:
@@ -86,6 +136,26 @@ 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)
@@ -102,7 +172,11 @@ class LightningStoreThreaded(LightningStore):
with self._lock:
return await self.store.get_latest_resources()
async def add_span(self, span: Span) -> Span:
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]:
with self._lock:
return await self.store.add_span(span)
@@ -112,7 +186,7 @@ class LightningStoreThreaded(LightningStore):
attempt_id: str,
readable_span: ReadableSpan,
sequence_id: int | None = None,
) -> Span:
) -> Optional[Span]:
with self._lock:
return await self.store.add_otel_span(rollout_id, attempt_id, readable_span, sequence_id)
@@ -124,13 +198,47 @@ 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,
) -> List[Span]:
*,
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]:
with self._lock:
return await self.store.query_spans(rollout_id, attempt_id)
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,
)
async def update_rollout(
self,
@@ -171,3 +279,39 @@ 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,
)
+48
View File
@@ -9,6 +9,54 @@ UpdateRolloutStatus = Callable[[str, RolloutStatus], Awaitable[Rollout]]
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 propagate_status(
update_rollout_status: UpdateRolloutStatus, # this should be unlocked
attempt: Attempt,
+73 -180
View File
@@ -2,25 +2,24 @@
from __future__ import annotations
import asyncio
import logging
import os
import threading
import warnings
from contextlib import asynccontextmanager, contextmanager
from typing import TYPE_CHECKING, Any, AsyncGenerator, Awaitable, Iterator, List, Optional
from typing import TYPE_CHECKING, Any, AsyncGenerator, Iterator, List, Optional
import agentops
import agentops.sdk.core
import opentelemetry.trace as trace_api
from agentops.sdk.core import TracingCore
from agentops.sdk.processors import SpanProcessor
from opentelemetry.instrumentation.utils import suppress_instrumentation
from opentelemetry.sdk.trace import ReadableSpan
from opentelemetry.sdk.trace import TracerProvider as TracerProviderImpl
from opentelemetry.trace import get_tracer_provider
from opentelemetry.trace.status import StatusCode
from agentlightning.instrumentation import instrument_all, uninstrument_all
from agentlightning.store.base import LightningStore
from .base import Tracer
from .otel import LightningSpanProcessor, OtelTracer
if TYPE_CHECKING:
from agentops.integration.callbacks.langchain import LangchainCallbackHandler
@@ -29,7 +28,7 @@ if TYPE_CHECKING:
logger = logging.getLogger(__name__)
class AgentOpsTracer(Tracer):
class AgentOpsTracer(OtelTracer):
"""Traces agent execution using AgentOps.
This tracer provides functionality to capture execution details using the
@@ -67,9 +66,8 @@ class AgentOpsTracer(Tracer):
def uninstrument(self, worker_id: int):
uninstrument_all()
def init_worker(self, worker_id: int):
super().init_worker(worker_id)
logger.info(f"[Worker {worker_id}] Setting up tracer...") # worker_id included in process name
def _initialize_tracer_provider(self, worker_id: int):
logger.info(f"[Worker {worker_id}] Setting up AgentOps tracer...") # worker_id included in process name
if self.instrument_managed:
self.instrument(worker_id)
@@ -85,16 +83,9 @@ class AgentOpsTracer(Tracer):
self._lightning_span_processor = LightningSpanProcessor()
try:
# new versions
instance = agentops.sdk.core.tracer
# TODO: The span processor cannot be deleted once added.
# This might be a problem if the tracer is entered and exited multiple times.
instance.provider.add_span_processor(self._lightning_span_processor) # type: ignore
except AttributeError:
# old versions
instance = TracingCore.get_instance() # type: ignore
instance._provider.add_span_processor(self._lightning_span_processor) # type: ignore
# TODO: The span processor cannot be deleted once added.
# This might be a problem if the tracer is entered and exited multiple times.
self._get_tracer_provider().add_span_processor(self._lightning_span_processor) # type: ignore
def teardown_worker(self, worker_id: int) -> None:
super().teardown_worker(worker_id)
@@ -111,7 +102,7 @@ class AgentOpsTracer(Tracer):
store: Optional[LightningStore] = None,
rollout_id: Optional[str] = None,
attempt_id: Optional[str] = None,
) -> AsyncGenerator[LightningSpanProcessor, None]:
) -> AsyncGenerator[trace_api.Tracer, None]:
"""
Starts a new tracing context. This should be used as a context manager.
@@ -122,12 +113,18 @@ class AgentOpsTracer(Tracer):
attempt_id: Optional attempt ID to add the spans to.
Yields:
The [`LightningSpanProcessor`][agentlightning.tracer.agentops.LightningSpanProcessor] instance to collect spans.
The OpenTelemetry tracer instance to collect spans.
"""
with self._trace_context_sync(
name=name, store=store, rollout_id=rollout_id, attempt_id=attempt_id
) as processor:
yield processor
if store is not None:
warnings.warn(
"store is deprecated in favor of init_worker(). It will be removed in the future.",
DeprecationWarning,
stacklevel=3,
)
else:
store = self._store
with self._trace_context_sync(name=name, store=store, rollout_id=rollout_id, attempt_id=attempt_id) as tracer:
yield tracer
@contextmanager
def _trace_context_sync(
@@ -137,47 +134,52 @@ class AgentOpsTracer(Tracer):
store: Optional[LightningStore] = None,
rollout_id: Optional[str] = None,
attempt_id: Optional[str] = None,
) -> Iterator[LightningSpanProcessor]:
) -> Iterator[trace_api.Tracer]:
"""Implementation of `trace_context` for synchronous execution."""
if not self._lightning_span_processor:
raise RuntimeError("LightningSpanProcessor is not initialized. Call init_worker() first.")
tracer_provider = self._get_tracer_provider()
kwargs: dict[str, Any] = {}
if name is not None:
kwargs["trace_name"] = name
elif rollout_id is not None:
kwargs["trace_name"] = rollout_id
if store is not None and rollout_id is not None and attempt_id is not None:
if store.capabilities.get("otlp_traces", False) is True:
logger.debug(f"Tracing to LightningStore rollout_id={rollout_id}, attempt_id={attempt_id}")
self._enable_native_otlp_exporter(store, rollout_id, attempt_id)
else:
self._disable_native_otlp_exporter()
ctx = self._lightning_span_processor.with_context(store=store, rollout_id=rollout_id, attempt_id=attempt_id)
with ctx:
# AgentOps end_trace and start_trace must live inside the lightning span processor context.
# Otherwise some traces might not be recorded.
with self._agentops_trace_context(rollout_id, attempt_id, kwargs):
yield trace_api.get_tracer(__name__, tracer_provider=tracer_provider)
elif store is None and rollout_id is None and attempt_id is None:
# TODO: Add tests to cover both paths
self._disable_native_otlp_exporter()
with self._lightning_span_processor:
with self._agentops_trace_context(None, None, kwargs):
yield trace_api.get_tracer(__name__, tracer_provider=tracer_provider)
else:
raise ValueError("store, rollout_id, and attempt_id must be either all provided or all None")
@contextmanager
def _agentops_trace_context(self, rollout_id: Optional[str], attempt_id: Optional[str], kwargs: dict[str, Any]):
trace = agentops.start_trace(**kwargs)
status = StatusCode.OK # type: ignore
try:
if store is not None and rollout_id is not None and attempt_id is not None:
ctx = self._lightning_span_processor.with_context(
store=store, rollout_id=rollout_id, attempt_id=attempt_id
)
with ctx as processor:
yield processor
elif store is None and rollout_id is None and attempt_id is None:
with self._lightning_span_processor:
yield self._lightning_span_processor
else:
raise ValueError("store, rollout_id, and attempt_id must be either all provided or all None")
yield
except Exception as e:
# This will catch errors in user code.
status = StatusCode.ERROR # type: ignore
logger.error(f"Trace failed for rollout_id={rollout_id}, attempt_id={attempt_id}, error={e}")
logger.error(f"Trace failed for rollout_id={rollout_id}, attempt_id={attempt_id}: {e}")
raise # should reraise the error here so that runner can handle it
finally:
agentops.end_trace(trace, end_state=status) # type: ignore
def get_last_trace(self) -> List[ReadableSpan]:
"""
Retrieves the raw list of captured spans from the most recent trace.
Returns:
A list of OpenTelemetry `ReadableSpan` objects.
"""
if not self._lightning_span_processor:
raise RuntimeError("LightningSpanProcessor is not initialized. Call init_worker() first.")
return self._lightning_span_processor.spans()
def get_langchain_handler(self, tags: List[str] | None = None) -> LangchainCallbackHandler:
"""
Get the Langchain callback handler for integrating with Langchain.
@@ -204,135 +206,26 @@ class AgentOpsTracer(Tracer):
get_langchain_callback_handler = get_langchain_handler # alias
def _get_tracer_provider(self) -> TracerProviderImpl:
try:
# new versions
instance = agentops.sdk.core.tracer
if instance.provider is None:
raise RuntimeError("AgentOps TracerProvider is not initialized.")
class LightningSpanProcessor(SpanProcessor):
"""Span processor that subclasses OpenTelemetry's `SpanProcessor` and adds support to dump traces
to a [`LightningStore`][agentlightning.LightningStore].
"""
if get_tracer_provider() is not instance.provider:
logger.error(
"Mismatch between global singleton TracerProvider and AgentOps TracerProvider. "
"AgentOps might not work properly."
)
def __init__(self):
self._spans: List[ReadableSpan] = []
if not isinstance(instance.provider, TracerProviderImpl): # type: ignore
raise RuntimeError("Unsupported TracerProvider type for AgentOps instrumentation.")
# Store related context and states
self._store: Optional[LightningStore] = None
self._rollout_id: Optional[str] = None
self._attempt_id: Optional[str] = None
self._lock = threading.Lock()
# private asyncio loop running in a daemon thread
self._loop_ready = threading.Event()
self._loop: Optional[asyncio.AbstractEventLoop] = None
self._loop_thread = threading.Thread(target=self._loop_runner, name="otel-loop", daemon=True)
self._loop_thread.start()
self._loop_ready.wait() # loop is ready
def _loop_runner(self):
loop = asyncio.new_event_loop()
self._loop = loop
asyncio.set_event_loop(loop)
self._loop_ready.set()
loop.run_forever()
loop.close()
def __enter__(self):
self._last_trace = None
self._spans = []
return self
def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):
self._store = None
self._rollout_id = None
self._attempt_id = None
def _await_in_loop(self, coro: Awaitable[Any], timeout: Optional[float] = None) -> Any:
# submit to the dedicated loop and wait synchronously
if self._loop is None:
raise RuntimeError("Loop is not initialized. This should not happen.")
# If already on the exporter loop thread, schedule and return immediately.
# ---------------------------------------------------------------------------
# WHY THIS CONDITIONAL EXISTS:
# In rare cases, span.end() is triggered from a LangchainCallbackHandler.__del__
# (or another finalizer) while the Python garbage collector is running on the
# *same thread* that owns our exporter event loop ("otel-loop").
#
# When that happens, on_end() executes on the exporter loop thread itself.
# If we were to call `asyncio.run_coroutine_threadsafe(...).result()` here,
# it would deadlock immediately — because the loop cannot both wait on and run
# the same coroutine. The Future stays pending forever and the loop stops
# processing scheduled callbacks.
#
# To avoid that self-deadlock, we detect when on_end() runs on the exporter
# loop thread. If so, we *schedule* the coroutine on the loop (fire-and-forget)
# instead of blocking with .result().
#
# This situation can occur because Python calls __del__ in whatever thread
# releases the last reference, which can easily be our loop thread if the
# object is dereferenced during loop._run_once().
# ---------------------------------------------------------------------------
if threading.current_thread() is self._loop_thread:
self._loop.call_soon_threadsafe(asyncio.create_task, coro) # type: ignore
return None
fut = asyncio.run_coroutine_threadsafe(coro, self._loop) # type: ignore
return fut.result(timeout=timeout) # raises on error # type: ignore
def shutdown(self) -> None:
if self._loop:
self._loop.call_soon_threadsafe(self._loop.stop)
self._loop_thread.join(timeout=5)
self._loop = None
def force_flush(self, timeout_millis: int = 30000) -> bool:
return True
def spans(self) -> List[ReadableSpan]:
"""
Get the list of spans collected by this processor.
This is useful for debugging and testing purposes.
Returns:
List of ReadableSpan objects collected during tracing.
"""
return self._spans
def with_context(self, store: LightningStore, rollout_id: str, attempt_id: str):
# simple context manager without nesting into asyncio
class _Ctx:
def __enter__(_): # type: ignore
with self._lock:
self._store, self._rollout_id, self._attempt_id = store, rollout_id, attempt_id
self._last_trace = None
self._spans = []
return self
def __exit__(_, exc_type, exc, tb): # type: ignore
with self._lock:
self._store = self._rollout_id = self._attempt_id = None
return _Ctx()
def on_end(self, span: ReadableSpan) -> None:
"""
Process a span when it ends.
Args:
span: The span that has ended.
"""
# Skip if span is not sampled
if not span.context or not span.context.trace_flags.sampled:
return
if self._store and self._rollout_id and self._attempt_id:
try:
# Submit add_otel_span to the event loop and wait for it to complete
with suppress_instrumentation():
self._await_in_loop(
self._store.add_otel_span(self._rollout_id, self._attempt_id, span),
timeout=60.0,
)
except Exception:
# log; on_end MUST NOT raise
logger.exception(f"Error adding span to store: {span.name}")
self._spans.append(span)
self._tracer_provider = instance.provider
return self._tracer_provider
except AttributeError:
# old versions
instance = TracingCore.get_instance() # type: ignore
self._tracer_provider = instance._provider # type: ignore
return self._tracer_provider # type: ignore
+41 -4
View File
@@ -3,6 +3,7 @@
from __future__ import annotations
import logging
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any, AsyncContextManager, Awaitable, Callable, ContextManager, List, Optional
from opentelemetry.sdk.trace import ReadableSpan
@@ -51,6 +52,18 @@ class Tracer(ParallelWorkerBase):
```
"""
_store: Optional[LightningStore] = None
def init_worker(self, worker_id: int, store: Optional[LightningStore] = None) -> None:
"""Initialize the tracer for a worker.
Args:
worker_id: The ID of the worker.
store: The store to add the spans to. If it's provided, traces will be added to the store when tracing.
"""
super().init_worker(worker_id)
self._store = store
def trace_context(
self,
name: Optional[str] = None,
@@ -67,11 +80,9 @@ class Tracer(ParallelWorkerBase):
within the `with` block are collected and made available via
[`get_last_trace`][agentlightning.Tracer.get_last_trace].
If a store is provided, the spans will be added to the store when tracing.
Args:
name: The name for the root span of this trace context.
store: The store to add the spans to.
store: The store to add the spans to. Deprecated in favor of passing store to init_worker().
rollout_id: The rollout ID to add the spans to.
attempt_id: The attempt ID to add the spans to.
"""
@@ -81,7 +92,6 @@ class Tracer(ParallelWorkerBase):
self,
name: Optional[str] = None,
*,
store: Optional[LightningStore] = None,
rollout_id: Optional[str] = None,
attempt_id: Optional[str] = None,
) -> ContextManager[Any]:
@@ -138,3 +148,30 @@ class Tracer(ParallelWorkerBase):
"""
logger.warning(f"{self.__class__.__name__} does not provide a LangChain callback handler.")
return None
@contextmanager
def lifespan(self, store: Optional[LightningStore] = None):
"""A context manager to manage the lifespan of the tracer.
This can be used to set up and tear down any necessary resources
for the tracer, useful for debugging purposes.
Args:
store: The store to add the spans to. If it's provided, traces will be added to the store when tracing.
"""
has_init = False
has_init_worker = False
try:
self.init()
has_init = True
self.init_worker(0, store)
has_init_worker = True
yield
finally:
if has_init_worker:
self.teardown_worker(0)
if has_init:
self.teardown()
+5 -2
View File
@@ -19,6 +19,8 @@ from opentelemetry.trace.span import (
TraceState,
)
from agentlightning.store import LightningStore
from .base import Tracer
logger = logging.getLogger(__name__)
@@ -68,14 +70,15 @@ class HttpTracer(Tracer):
self.subprocess_mode = subprocess_mode
self.subprocess_timeout = subprocess_timeout
def init_worker(self, worker_id: int) -> None:
def init_worker(self, worker_id: int, store: Optional[LightningStore] = None) -> None:
"""
Initialize the tracer in a worker process.
Args:
worker_id: The ID of the worker process.
store: The store to add the spans to.
"""
super().init_worker(worker_id)
super().init_worker(worker_id, store)
logger.info(f"[Worker {worker_id}] HttpTracer initialized.")
@asynccontextmanager
+310 -21
View File
@@ -2,16 +2,27 @@
from __future__ import annotations
import asyncio
import logging
import threading
import warnings
from contextlib import asynccontextmanager
from typing import AsyncGenerator, List, Optional
from typing import Any, AsyncGenerator, Awaitable, List, Optional
import opentelemetry.trace as trace_api
from opentelemetry.sdk.trace import ReadableSpan, TracerProvider
from agentops.sdk.core import BatchSpanProcessor
from opentelemetry.instrumentation.utils import suppress_instrumentation
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import ReadableSpan, SpanProcessor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace import TracerProvider as TracerProviderImpl
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from agentlightning.semconv import LightningResourceAttributes
from agentlightning.store.base import LightningStore
from agentlightning.utils.otel import get_tracer_provider
from agentlightning.utils.otlp import LightningStoreOTLPExporter
from .agentops import LightningSpanProcessor # FIXME: This import should be from otel to agentops
from .base import Tracer
logger = logging.getLogger(__name__)
@@ -29,25 +40,43 @@ class OtelTracer(Tracer):
# This provider is only initialized when the worker is initialized.
self._tracer_provider: Optional[TracerProvider] = None
self._lightning_span_processor: Optional[LightningSpanProcessor] = None
self._simple_span_processor: Optional[SimpleSpanProcessor] = None
self._otlp_span_exporter: Optional[LightningStoreOTLPExporter] = None
self._initialized: bool = False
def init_worker(self, worker_id: int):
super().init_worker(worker_id)
def init_worker(self, worker_id: int, store: Optional[LightningStore] = None):
super().init_worker(worker_id, store)
self._initialize_tracer_provider(worker_id)
def _initialize_tracer_provider(self, worker_id: int):
logger.info(f"[Worker {worker_id}] Setting up OpenTelemetry tracer...")
if self._initialized:
logger.error("Tracer provider is already initialized. OpenTelemetry may not work as expected.")
logger.info(f"[Worker {worker_id}] Tracer provider is already initialized. Skipping initialization.")
return
tracer_provider = TracerProvider()
trace_api.set_tracer_provider(tracer_provider)
try:
get_tracer_provider()
logger.error(
f"[Worker {worker_id}] Tracer provider is already initialized but not by OtelTracer. OpenTelemetry may not work as expected."
)
except RuntimeError:
logger.debug(f"[Worker {worker_id}] Tracer provider is not initialized by OtelTracer. Initializing it now.")
self._tracer_provider = TracerProvider()
trace_api.set_tracer_provider(self._tracer_provider)
self._lightning_span_processor = LightningSpanProcessor()
tracer_provider.add_span_processor(self._lightning_span_processor)
self._tracer_provider.add_span_processor(self._lightning_span_processor)
self._otlp_span_exporter = LightningStoreOTLPExporter()
self._simple_span_processor = SimpleSpanProcessor(self._otlp_span_exporter)
self._tracer_provider.add_span_processor(self._simple_span_processor)
self._initialized = True
logger.info(f"[Worker {worker_id}] OpenTelemetry tracer provider initialized.")
def teardown_worker(self, worker_id: int):
super().teardown_worker(worker_id)
logger.info(f"[Worker {worker_id}] Tearing down OpenTelemetry tracer...")
self._tracer_provider = None
logger.info(f"[Worker {worker_id}] Tearing down OpenTelemetry tracer does NOT remove the tracer provider.")
@asynccontextmanager
async def trace_context(
@@ -57,7 +86,7 @@ class OtelTracer(Tracer):
store: Optional[LightningStore] = None,
rollout_id: Optional[str] = None,
attempt_id: Optional[str] = None,
) -> AsyncGenerator[LightningSpanProcessor, None]:
) -> AsyncGenerator[trace_api.Tracer, None]:
"""
Starts a new tracing context. This should be used as a context manager.
@@ -68,20 +97,37 @@ class OtelTracer(Tracer):
attempt_id: Optional attempt ID to add the spans to.
Yields:
The LightningSpanProcessor instance to collect spans.
The OpenTelemetry tracer instance to collect spans.
"""
if not self._lightning_span_processor:
raise RuntimeError("LightningSpanProcessor is not initialized. Call init_worker() first.")
if store is not None and rollout_id is not None and attempt_id is not None:
ctx = self._lightning_span_processor.with_context(store=store, rollout_id=rollout_id, attempt_id=attempt_id)
with ctx as processor:
yield processor
elif store is None and rollout_id is None and attempt_id is None:
with self._lightning_span_processor:
yield self._lightning_span_processor
if store is not None:
warnings.warn(
"store is deprecated in favor of init_worker(). It will be removed in the future.",
DeprecationWarning,
stacklevel=3,
)
else:
raise ValueError("store, rollout_id, and attempt_id must be either all provided or all None")
store = self._store
if rollout_id is not None and attempt_id is not None:
if store is None:
raise ValueError("store is required to be initialized when rollout_id and attempt_id are provided")
if store.capabilities.get("otlp_traces", False) is True:
logger.debug(f"Tracing to LightningStore rollout_id={rollout_id}, attempt_id={attempt_id}")
self._enable_native_otlp_exporter(store, rollout_id, attempt_id)
else:
self._disable_native_otlp_exporter()
ctx = self._lightning_span_processor.with_context(store=store, rollout_id=rollout_id, attempt_id=attempt_id)
with ctx:
yield trace_api.get_tracer(__name__, tracer_provider=self._tracer_provider)
elif rollout_id is None and attempt_id is None:
self._disable_native_otlp_exporter()
with self._lightning_span_processor:
yield trace_api.get_tracer(__name__, tracer_provider=self._tracer_provider)
else:
raise ValueError("rollout_id and attempt_id must be either all provided or all None")
def get_last_trace(self) -> List[ReadableSpan]:
"""
@@ -93,3 +139,246 @@ class OtelTracer(Tracer):
if not self._lightning_span_processor:
raise RuntimeError("LightningSpanProcessor is not initialized. Call init_worker() first.")
return self._lightning_span_processor.spans()
def _get_tracer_provider(self) -> TracerProviderImpl:
if self._tracer_provider is None:
raise RuntimeError("TracerProvider is not initialized. Call init_worker() first.")
return self._tracer_provider
def _enable_native_otlp_exporter(self, store: LightningStore, rollout_id: str, attempt_id: str):
tracer_provider = self._get_tracer_provider()
active_span_processor = tracer_provider._active_span_processor # pyright: ignore[reportPrivateUsage]
# Override the resources so that the server knows where the request comes from.
tracer_provider._resource = tracer_provider._resource.merge( # pyright: ignore[reportPrivateUsage]
Resource.create(
{
LightningResourceAttributes.ROLLOUT_ID.value: rollout_id,
LightningResourceAttributes.ATTEMPT_ID.value: attempt_id,
}
)
)
instrumented = False
candidates: List[str] = []
for processor in active_span_processor._span_processors: # pyright: ignore[reportPrivateUsage]
if isinstance(processor, LightningSpanProcessor):
# We don't need the LightningSpanProcessor any more.
logger.debug("LightningSpanProcessor already present in TracerProvider, disabling it.")
processor.disable_store_submission = True
elif isinstance(processor, (SimpleSpanProcessor, BatchSpanProcessor)):
# Instead, we rely on the OTLPSpanExporter to send spans to the store.
if isinstance(processor.span_exporter, LightningStoreOTLPExporter):
processor.span_exporter.enable_store_otlp(store.otlp_traces_endpoint(), rollout_id, attempt_id)
logger.debug(f"Set LightningStoreOTLPExporter endpoint to {store.otlp_traces_endpoint()}")
instrumented = True
else:
candidates.append(
f"{processor.__class__.__name__} with {processor.span_exporter.__class__.__name__}"
)
else:
candidates.append(f"{processor.__class__.__name__}")
if not instrumented:
raise RuntimeError(
"Failed to enable native OTLP exporter: no BatchSpanProcessor or SimpleSpanProcessor with "
"LightningStoreOTLPExporter found in TracerProvider. Please try using a non-OTLP store."
"Candidates are: " + ", ".join(candidates)
)
def _disable_native_otlp_exporter(self):
tracer_provider = self._get_tracer_provider()
active_span_processor = tracer_provider._active_span_processor # pyright: ignore[reportPrivateUsage]
tracer_provider._resource = tracer_provider._resource.merge( # pyright: ignore[reportPrivateUsage]
Resource.create(
{
LightningResourceAttributes.ROLLOUT_ID.value: "",
LightningResourceAttributes.ATTEMPT_ID.value: "",
}
)
) # reset resource
for processor in active_span_processor._span_processors: # pyright: ignore[reportPrivateUsage]
if isinstance(processor, LightningSpanProcessor):
# We will be in need of the LightningSpanProcessor again.
logger.debug("Enabling LightningSpanProcessor in TracerProvider.")
processor.disable_store_submission = False
class LightningSpanProcessor(SpanProcessor):
"""Span processor that subclasses OpenTelemetry's `SpanProcessor` and adds support to dump traces
to a [`LightningStore`][agentlightning.LightningStore].
It serves two purposes:
1. Records all the spans in a local buffer.
2. Submits the spans to the event loop to be added to the store.
"""
def __init__(self, disable_store_submission: bool = False):
self._disable_store_submission: bool = disable_store_submission
self._spans: List[ReadableSpan] = []
# Store related context and states
self._store: Optional[LightningStore] = None
self._rollout_id: Optional[str] = None
self._attempt_id: Optional[str] = None
self._lock = threading.Lock()
# private asyncio loop running in a daemon thread
self._loop_ready = threading.Event()
self._loop: Optional[asyncio.AbstractEventLoop] = None
self._loop_thread: Optional[threading.Thread] = None
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
+ f"disable_store_submission={self.disable_store_submission}, "
+ f"store={self.store!r}, "
+ f"rollout_id={self.rollout_id!r}, "
+ f"attempt_id={self.attempt_id!r})"
)
@property
def store(self) -> Optional[LightningStore]:
"""The store to submit the spans to."""
return self._store
@property
def rollout_id(self) -> Optional[str]:
"""The rollout ID to submit the spans to."""
return self._rollout_id
@property
def attempt_id(self) -> Optional[str]:
"""The attempt ID to submit the spans to."""
return self._attempt_id
@property
def disable_store_submission(self) -> bool:
"""Whether to disable submitting spans to the store."""
return self._disable_store_submission
@disable_store_submission.setter
def disable_store_submission(self, value: bool) -> None:
self._disable_store_submission = value
def _ensure_loop(self) -> None:
if self._loop_thread is None or self._loop is None:
self._loop_ready.clear()
self._loop_thread = threading.Thread(target=self._loop_runner, name="otel-loop", daemon=True)
self._loop_thread.start()
self._loop_ready.wait() # loop is ready
def _loop_runner(self):
loop = asyncio.new_event_loop()
self._loop = loop
asyncio.set_event_loop(loop)
self._loop_ready.set()
loop.run_forever()
loop.close()
def __enter__(self):
self._last_trace = None
self._spans = []
return self
def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):
self._store = None
self._rollout_id = None
self._attempt_id = None
def _await_in_loop(self, coro: Awaitable[Any], timeout: Optional[float] = None) -> Any:
# submit to the dedicated loop and wait synchronously
self._ensure_loop()
if self._loop is None:
raise RuntimeError("Loop is not initialized. This should not happen.")
# If already on the exporter loop thread, schedule and return immediately.
# ---------------------------------------------------------------------------
# WHY THIS CONDITIONAL EXISTS:
# In rare cases, span.end() is triggered from a LangchainCallbackHandler.__del__
# (or another finalizer) while the Python garbage collector is running on the
# *same thread* that owns our exporter event loop ("otel-loop").
#
# When that happens, on_end() executes on the exporter loop thread itself.
# If we were to call `asyncio.run_coroutine_threadsafe(...).result()` here,
# it would deadlock immediately — because the loop cannot both wait on and run
# the same coroutine. The Future stays pending forever and the loop stops
# processing scheduled callbacks.
#
# To avoid that self-deadlock, we detect when on_end() runs on the exporter
# loop thread. If so, we *schedule* the coroutine on the loop (fire-and-forget)
# instead of blocking with .result().
#
# This situation can occur because Python calls __del__ in whatever thread
# releases the last reference, which can easily be our loop thread if the
# object is dereferenced during loop._run_once().
# ---------------------------------------------------------------------------
if threading.current_thread() is self._loop_thread:
self._loop.call_soon_threadsafe(asyncio.create_task, coro) # type: ignore
return None
fut = asyncio.run_coroutine_threadsafe(coro, self._loop) # type: ignore
return fut.result(timeout=timeout) # raises on error # type: ignore
def shutdown(self) -> None:
if self._loop:
self._loop.call_soon_threadsafe(self._loop.stop)
self._loop = None
if self._loop_thread:
self._loop_thread.join(timeout=5)
def force_flush(self, timeout_millis: int = 30000) -> bool:
return True
def spans(self) -> List[ReadableSpan]:
"""
Get the list of spans collected by this processor.
This is useful for debugging and testing purposes.
Returns:
List of ReadableSpan objects collected during tracing.
"""
return self._spans
def with_context(self, store: LightningStore, rollout_id: str, attempt_id: str):
# simple context manager without nesting into asyncio
class _Ctx:
def __enter__(_): # type: ignore
# Use _ instead of self to avoid shadowing the instance method.
with self._lock:
self._store, self._rollout_id, self._attempt_id = store, rollout_id, attempt_id
self._last_trace = None
self._spans = []
return self
def __exit__(_, exc_type, exc, tb): # type: ignore
with self._lock:
self._store = self._rollout_id = self._attempt_id = None
return _Ctx()
def on_end(self, span: ReadableSpan) -> None:
"""
Process a span when it ends.
Args:
span: The span that has ended.
"""
# Skip if span is not sampled
if not span.context or not span.context.trace_flags.sampled:
return
if not self._disable_store_submission and self._store and self._rollout_id and self._attempt_id:
try:
# Submit add_otel_span to the event loop and wait for it to complete
with suppress_instrumentation():
self._ensure_loop()
self._await_in_loop(
self._store.add_otel_span(self._rollout_id, self._attempt_id, span),
timeout=60.0,
)
except Exception:
# log; on_end MUST NOT raise
logger.exception(f"Error adding span to store: {span.name}")
self._spans.append(span)
+138
View File
@@ -10,14 +10,19 @@ from typing import (
Callable,
Dict,
Generic,
Iterator,
List,
Literal,
Mapping,
Optional,
Protocol,
Sequence,
SupportsIndex,
TypedDict,
TypeVar,
Union,
cast,
overload,
)
from opentelemetry.sdk.trace import ReadableSpan
@@ -49,6 +54,12 @@ __all__ = [
"Attempt",
"AttemptedRollout",
"Hook",
"Worker",
"WorkerStatus",
"PaginatedResult",
"FilterOptions",
"SortOptions",
"FilterField",
]
T_co = TypeVar("T_co", covariant=True)
@@ -200,6 +211,32 @@ class AttemptedRollout(Rollout):
return self
WorkerStatus = Literal["idle", "busy", "unknown"]
class Worker(BaseModel):
"""Worker information. This is actually the same as Runner info."""
worker_id: str
"""The ID of the worker."""
status: WorkerStatus = "unknown"
"""The status of the worker."""
heartbeat_stats: Optional[Dict[str, Any]] = None
"""Statistics about the worker's heartbeat."""
last_heartbeat_time: Optional[float] = None
"""The last time when the worker has reported the stats."""
last_dequeue_time: Optional[float] = None
"""The last time when the worker has tried to dequeue a rollout."""
last_busy_time: Optional[float] = None
"""The last time when the worker has started an attempt and became busy."""
last_idle_time: Optional[float] = None
"""The last time when the worker has triggered the end of an attempt and became idle."""
current_rollout_id: Optional[str] = None
"""The ID of the current rollout that the worker is processing."""
current_attempt_id: Optional[str] = None
"""The ID of the current attempt that the worker is processing."""
TaskInput = Any
"""Task input type. Accepts arbitrary payloads."""
@@ -393,3 +430,104 @@ class Hook(ParallelWorkerBase):
Subclasses can override this method for cleanup or additional
logging. By default, this is a no-op.
"""
class FilterField(TypedDict, total=False):
"""An operator dict for a single field."""
exact: Any
within: Sequence[Any]
contains: str
FilterOptions = Mapping[
Union[str, Literal["_aggregate", "_must"]],
Union[FilterField, Literal["and", "or"], Mapping[str, FilterField]],
]
"""A mapping of field name -> operator dict.
Each operator dict can contain:
- "exact": value for exact equality.
- "within": iterable of allowed values.
- "contains": substring to search for in string fields.
The filter can also have a special field called "_aggregate" that can be used to specify the logic
to combine the results of the filters:
- "and": all conditions must match. This is the default value if not specified.
- "or": at least one condition must match.
All conditions within a field and between different fields are
stored in a unified pool and combined using `_aggregate`.
The filter can also have a special group called "_must", which is a mapping of filters that must all match,
no matter whether the aggregate logic is "and" or "or".
Example:
```json
{
"_aggregate": "or",
"_must": {
"city": {"exact": "New York"},
"timezone": {"within": ["America/New_York", "America/Los_Angeles"]},
},
"status": {"exact": "active"},
"id": {"within": [1, 2, 3]},
"name": {"contains": "foo"},
}
```
"""
class SortOptions(TypedDict):
"""Options for sorting the collection."""
name: str
"""The name of the field to sort by."""
order: Literal["asc", "desc"]
"""The order to sort by."""
T_item = TypeVar("T_item")
class PaginatedResult(BaseModel, Sequence[T_item]):
"""Result of a paginated query.
Behaves like a sequence, but also carries pagination metadata (limit, offset, total).
"""
items: Sequence[T_item]
"""Items in the result."""
limit: int
"""Limit of the result."""
offset: int
"""Offset of the result."""
total: int
"""Total number of items in the collection."""
def __len__(self) -> int:
return len(self.items)
@overload
def __getitem__(self, index: int) -> T_item: ...
@overload
def __getitem__(self, index: slice) -> Sequence[T_item]: ...
def __getitem__(self, index: Union[int, slice]) -> Union[T_item, Sequence[T_item]]:
return self.items[index]
# Overriding __iter__ enables list(paginated_result) to work as expected,
# but changes Pydantic's default dict iteration behavior (which would otherwise
# iterate over field names).
def __iter__(self) -> Iterator[T_item]: # type: ignore
return iter(self.items)
def __repr__(self) -> str:
first_item_repr = repr(self.items[0]) if self.items else "empty"
items_repr = f"[{first_item_repr}, ...]" if len(self.items) > 1 else first_item_repr
slice_repr = f"{self.offset}:" if self.limit == -1 else f"{self.offset}:{self.offset + self.limit}"
return f"<PaginatedResult ({slice_repr} of {self.total}) {items_repr}>"
+11 -3
View File
@@ -16,6 +16,8 @@ from opentelemetry.sdk.trace.id_generator import RandomIdGenerator
from opentelemetry.trace.status import Status as OtelStatus
from pydantic import BaseModel, ConfigDict
from agentlightning.semconv import AGL_VIRTUAL
__all__ = [
"AttributeValue",
"Attributes",
@@ -379,7 +381,7 @@ class Span(BaseModel):
is_remote=False,
trace_state={},
),
name=name or SpanNames.VIRTUAL.value,
name=name or AGL_VIRTUAL,
resource=resource or OtelResource(attributes={}, schema_url=""),
attributes=attributes,
status=TraceStatus(status_code="OK"),
@@ -399,7 +401,7 @@ class Span(BaseModel):
class SpanNames(str, Enum):
"""Enumerated span names recognised by Agent-lightning."""
"""Enumerated span names recognised by Agent-lightning. Deprecated in favor of [semconv][agentlightning.semconv]."""
REWARD = "agentlightning.reward"
"""The name of the reward span."""
@@ -411,10 +413,16 @@ class SpanNames(str, Enum):
"""The name of the exception span."""
VIRTUAL = "agentlightning.virtual"
"""The name of the virtual span. It represents derived spans without concrete operations."""
ROLLOUT_ID = "agentlightning.rollout_id"
"""The name of the rollout ID."""
ATTEMPT_ID = "agentlightning.attempt_id"
"""The name of the attempt ID."""
SPAN_SEQUENCE_ID = "agentlightning.span_sequence_id"
"""The name of the span sequence ID."""
class SpanAttributeNames(str, Enum):
"""Canonical attribute names written by Agent Lightning emitters."""
"""Canonical attribute names written by Agent Lightning emitters. Deprecated in favor of [semconv][agentlightning.semconv]."""
MESSAGE = "message"
"""The name of the message attribute."""
+401
View File
@@ -0,0 +1,401 @@
# Copyright (c) Microsoft. All rights reserved.
"""Utilities shared for OpenTelemetry span (attributes) support."""
import logging
from typing import Any, Dict, List, Sequence, Union, cast
from warnings import filterwarnings
import opentelemetry.trace as trace_api
from agentops.sdk.exporters import OTLPSpanExporter
from opentelemetry.sdk.trace import ReadableSpan, SpanLimits, SynchronousMultiSpanProcessor, Tracer
from opentelemetry.sdk.trace import TracerProvider as TracerProviderImpl
from opentelemetry.sdk.trace.export import BatchSpanProcessor, SimpleSpanProcessor
from opentelemetry.sdk.util.instrumentation import InstrumentationInfo, InstrumentationScope
from opentelemetry.trace import get_tracer_provider as otel_get_tracer_provider
from pydantic import TypeAdapter
from agentlightning.env_var import LightningEnvVar, resolve_bool_env_var
from agentlightning.semconv import LightningSpanAttributes, LinkAttributes, LinkPydanticModel
from agentlightning.types import SpanLike
from agentlightning.utils.otlp import LightningStoreOTLPExporter
logger = logging.getLogger(__name__)
__all__ = [
"full_qualified_name",
"get_tracer_provider",
"get_tracer",
"make_tag_attributes",
"extract_tags_from_attributes",
"make_link_attributes",
"query_linked_spans",
"extract_links_from_attributes",
"filter_attributes",
"filter_and_unflatten_attributes",
"flatten_attributes",
"unflatten_attributes",
]
def full_qualified_name(obj: type) -> str:
if str(obj.__module__) == "builtins":
return obj.__qualname__
return f"{obj.__module__}.{obj.__qualname__}"
def get_tracer_provider(inspect: bool = True) -> TracerProviderImpl:
"""Get the OpenTelemetry tracer provider configured for Agent Lightning.
Args:
inspect: Whether to inspect the tracer provider and log its configuration.
When it's on, make sure you also set the logger level to DEBUG to see the logs.
"""
from agentlightning.tracer.otel import LightningSpanProcessor
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 = otel_get_tracer_provider()
if not isinstance(tracer_provider, TracerProviderImpl):
logger.error(
"Tracer provider is expected to be an instance of opentelemetry.sdk.trace.TracerProvider, found: %s",
full_qualified_name(type(tracer_provider)),
)
return cast(TracerProviderImpl, tracer_provider)
if not inspect:
return tracer_provider
emitter_debug = resolve_bool_env_var(LightningEnvVar.AGL_EMITTER_DEBUG, fallback=None)
logger_effective_level = logger.getEffectiveLevel()
if emitter_debug is True and logger_effective_level > logging.DEBUG:
logger.warning(
"Emitter debug logging is enabled but logging level is not set to DEBUG. Nothing will be logged."
)
if emitter_debug is None:
# Set to true by default if the logging level is lower than DEBUG
emitter_debug = logging.DEBUG >= logger_effective_level
if emitter_debug:
active_span_processor = tracer_provider._active_span_processor # pyright: ignore[reportPrivateUsage]
processors: List[str] = []
active_span_processor_cls = active_span_processor.__class__.__name__
for processor in active_span_processor._span_processors: # pyright: ignore[reportPrivateUsage]
if isinstance(processor, LightningSpanProcessor):
# The legacy case for tracers without OTLP support.
processors.append(f"{active_span_processor_cls} - {processor!r}")
elif isinstance(processor, (SimpleSpanProcessor, BatchSpanProcessor)):
processor_cls = processor.__class__.__name__
if isinstance(processor.span_exporter, LightningStoreOTLPExporter):
# This should be the main path now.
processors.append(f"{active_span_processor_cls} - {processor_cls} - {processor.span_exporter!r}")
elif isinstance(processor.span_exporter, OTLPSpanExporter):
# You need to be careful if the code goes into this path.
endpoint = processor.span_exporter._endpoint # pyright: ignore[reportPrivateUsage]
processors.append(
f"{active_span_processor_cls} - {processor_cls} - "
f"{processor.span_exporter.__class__.__name__}(endpoint={endpoint!r})"
)
else:
# Other cases like Console Span Exporter.
processors.append(
f"{active_span_processor_cls} - {processor_cls} - {processor.span_exporter.__class__.__name__}"
)
else:
processors.append(f"{active_span_processor_cls} - {processor.__class__.__name__}")
logger.debug(f"Tracer provider: {tracer_provider!r}. Active span processors:")
for processor in processors:
logger.debug(" * " + processor)
return tracer_provider
def get_tracer(use_active_span_processor: bool = True) -> trace_api.Tracer:
"""Resolve the OpenTelemetry tracer configured for Agent Lightning.
Args:
use_active_span_processor: Whether to use the active span processor.
Returns:
OpenTelemetry tracer tagged with the `agentlightning` instrumentation name.
Raises:
RuntimeError: If OpenTelemetry was not initialized before calling this helper.
"""
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(inspect=True) # inspection is on by default
if use_active_span_processor:
return tracer_provider.get_tracer("agentlightning")
else:
filterwarnings(
"ignore",
message=r"You should use InstrumentationScope. Deprecated since version 1.11.1.",
category=DeprecationWarning,
module="opentelemetry.sdk.trace",
)
return Tracer(
tracer_provider.sampler,
tracer_provider.resource,
# We use an empty span processor to avoid emitting spans to the tracer
SynchronousMultiSpanProcessor(),
tracer_provider.id_generator,
InstrumentationInfo("agentlightning", "", ""), # type: ignore
SpanLimits(),
InstrumentationScope(
"agentlightning",
"",
"",
{},
),
)
def make_tag_attributes(tags: List[str]) -> Dict[str, Any]:
"""Convert a list of tags into flattened attributes for span tagging.
There is no syntax enforced for tags, they are just strings. For example:
```python
["gen_ai.model:gpt-4", "reward.extrinsic"]
```
"""
return flatten_attributes({LightningSpanAttributes.TAG.value: tags})
def extract_tags_from_attributes(attributes: Dict[str, Any]) -> List[str]:
"""Extract tag attributes from flattened span attributes.
Args:
attributes: A dictionary of flattened span attributes.
"""
maybe_tag_list = filter_and_unflatten_attributes(attributes, LightningSpanAttributes.TAG.value)
return TypeAdapter(List[str]).validate_python(maybe_tag_list)
def make_link_attributes(links: Dict[str, str]) -> Dict[str, Any]:
"""Convert a dictionary of links into flattened attributes for span linking.
Links example:
```python
{
"gen_ai.response.id": "response-123",
"span_id": "abcd-efgh-ijkl",
}
```
"""
link_list: List[Dict[str, str]] = []
for key, value in links.items():
if not isinstance(value, str): # pyright: ignore[reportUnnecessaryIsInstance]
raise ValueError(f"Link value must be a string, got {type(value)} for key '{key}'")
link_list.append({LinkAttributes.KEY_MATCH.value: key, LinkAttributes.VALUE_MATCH.value: value})
return flatten_attributes({LightningSpanAttributes.LINK.value: link_list})
def query_linked_spans(spans: Sequence[SpanLike], links: List[LinkPydanticModel]) -> List[SpanLike]:
"""Query spans that are linked by the given link attributes.
Args:
spans: A sequence of spans to search.
links: A list of link attributes to match.
Returns:
A list of spans that match the given link attributes.
"""
matched_spans: List[SpanLike] = []
for span in spans:
span_attributes = span.attributes or {}
is_match = True
for link in links:
# trace_id and span_id must be full match.
if link.key_match == "trace_id":
if isinstance(span, ReadableSpan):
trace_id = trace_api.format_trace_id(span.context.trace_id) if span.context else None
else:
trace_id = span.trace_id
if trace_id != link.value_match:
is_match = False
break
elif link.key_match == "span_id":
if isinstance(span, ReadableSpan):
span_id = trace_api.format_span_id(span.context.span_id) if span.context else None
else:
span_id = span.span_id
if span_id != link.value_match:
is_match = False
break
else:
attribute = span_attributes.get(link.key_match)
# attributes must also be a full match currently.
if attribute != link.value_match:
is_match = False
break
if is_match:
matched_spans.append(span)
return matched_spans
def extract_links_from_attributes(attributes: Dict[str, Any]) -> List[LinkPydanticModel]:
"""Extract link attributes from flattened span attributes.
Args:
attributes: A dictionary of flattened span attributes.
"""
maybe_link_list = filter_and_unflatten_attributes(attributes, LightningSpanAttributes.LINK.value)
return TypeAdapter(List[LinkPydanticModel]).validate_python(maybe_link_list)
def filter_attributes(attributes: Dict[str, Any], prefix: str) -> Dict[str, Any]:
"""Filter attributes that start with the given prefix.
The attribute must start with `prefix.` or be exactly `prefix` to be included.
Args:
attributes: A dictionary of span attributes.
prefix: The prefix to filter by.
Returns:
A dictionary of attributes that start with the given prefix.
"""
return {k: v for k, v in attributes.items() if k.startswith(prefix + ".") or k == prefix}
def filter_and_unflatten_attributes(attributes: Dict[str, Any], prefix: str) -> Union[Dict[str, Any], List[Any]]:
"""Filter attributes that start with the given prefix and unflatten them.
The prefix will be removed during unflattening.
Args:
attributes: A dictionary of span attributes.
prefix: The prefix to filter by.
Returns:
A nested dictionary or list of attributes that start with the given prefix.
"""
filtered_attributes = filter_attributes(attributes, prefix)
stripped_attributes: Dict[str, Any] = {}
for k, v in filtered_attributes.items():
if k == prefix:
raise ValueError(f"Cannot unflatten attribute with key exactly equal to prefix: {prefix}")
else:
stripped_key = k[len(prefix) + 1 :] # +1 to remove the dot
stripped_attributes[stripped_key] = v
return unflatten_attributes(stripped_attributes)
def flatten_attributes(nested_data: Union[Dict[str, Any], List[Any]]) -> Dict[str, Any]:
"""Flatten a nested dictionary or list into a flat dictionary with dotted keys.
This function recursively traverses dictionaries and lists, producing a flat
key-value mapping where nested paths are represented via dot-separated keys.
Lists are indexed numerically.
Example:
>>> flatten_attributes({"a": {"b": 1, "c": [2, 3]}})
{"a.b": 1, "a.c.0": 2, "a.c.1": 3}
Args:
nested_data: A nested structure composed of dictionaries, lists, or
primitive values.
Returns:
A flat dictionary mapping dotted-string paths to primitive values.
"""
flat: Dict[str, Any] = {}
def _walk(value: Any, prefix: str = "") -> None:
if isinstance(value, dict):
for k, v in cast(Dict[Any, Any], value).items():
if not isinstance(k, str):
raise ValueError(
f"Only string keys are supported in dictionaries, got '{k}' of type {type(k)} in {prefix}"
)
new_prefix = f"{prefix}.{k}" if prefix else k
_walk(v, new_prefix)
elif isinstance(value, list):
for idx, item in enumerate(cast(List[Any], value)):
new_prefix = f"{prefix}.{idx}" if prefix else str(idx)
_walk(item, new_prefix)
else:
flat[prefix] = value
_walk(nested_data)
return flat
def unflatten_attributes(flat_data: Dict[str, Any]) -> Union[Dict[str, Any], List[Any]]:
"""Reconstruct a nested dictionary/list structure from a flat dictionary.
Keys are dot-separated paths. Segments that are digit strings will only
become list indices if *all* keys in that dict form a consecutive
0..n-1 range. Otherwise they remain dict keys.
Example:
>>> unflatten_attributes({"a.b": 1, "a.c.0": 2, "a.c.1": 3})
{"a": {"b": 1, "c": [2, 3]}}
Args:
flat_data: A dictionary whose keys are dot-separated paths and whose
values are primitive data elements.
Returns:
A nested dictionary (and lists where appropriate) corresponding to
the flattened structure.
"""
# 1) Build a pure dict tree first (no lists yet)
root: Dict[str, Any] = {}
for flat_key, value in flat_data.items():
parts = flat_key.split(".")
curr: Dict[str, Any] = root
for part in parts[:-1]:
# Ensure intermediate node is a dict
if part not in curr or not isinstance(curr[part], dict):
curr[part] = {}
curr = curr[part] # type: ignore[assignment]
curr[parts[-1]] = value
# 2) Recursively convert dicts-with-consecutive-numeric-keys into lists
def convert(node: Union[Dict[str, Any], List[Any]]) -> Union[Dict[str, Any], List[Any]]:
if isinstance(node, dict):
# First convert children
for k, v in list(node.items()):
node[k] = convert(v)
if not node:
# empty dict stays dict
return node
# Check if keys are all numeric strings
keys = list(node.keys())
if all(isinstance(k, str) and k.isdigit() for k in keys): # pyright: ignore[reportUnnecessaryIsInstance]
indices = sorted(int(k) for k in keys)
# Must be exactly 0..n-1
if indices == list(range(len(indices))):
return [node[str(i)] for i in range(len(indices))]
return node
if isinstance(node, list): # pyright: ignore[reportUnnecessaryIsInstance]
return [convert(v) for v in node]
# Keep as is
return node
return convert(root)
+474
View File
@@ -0,0 +1,474 @@
# Copyright (c) Microsoft. All rights reserved.
from __future__ import annotations
import gzip
import logging
from typing import Any, Awaitable, Callable, Dict, List, Optional, Sequence, Tuple, Type, TypeVar
from fastapi import Request, Response
from google.protobuf import json_format
from google.rpc.status_pb2 import Status
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.proto.collector.logs.v1.logs_service_pb2 import (
ExportLogsServiceRequest,
ExportLogsServiceResponse,
)
from opentelemetry.proto.collector.metrics.v1.metrics_service_pb2 import (
ExportMetricsServiceRequest,
ExportMetricsServiceResponse,
)
from opentelemetry.proto.collector.trace.v1.trace_service_pb2 import (
ExportTraceServiceRequest,
ExportTraceServiceResponse,
)
from opentelemetry.proto.common.v1.common_pb2 import AnyValue, KeyValue
from opentelemetry.proto.resource.v1.resource_pb2 import Resource as ProtoResource
from opentelemetry.proto.trace.v1.trace_pb2 import Span as ProtoSpan
from opentelemetry.proto.trace.v1.trace_pb2 import Status as ProtoStatus
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import ReadableSpan
from opentelemetry.sdk.trace.export import SpanExportResult
from opentelemetry.util.types import AttributeValue
from agentlightning.semconv import LightningResourceAttributes
from agentlightning.types.tracer import (
Attributes,
Event,
Link,
OtelResource,
Span,
SpanContext,
TraceStatus,
convert_timestamp,
)
PROTOBUF_CT = "application/x-protobuf"
logger = logging.getLogger(__name__)
T_request = TypeVar("T_request", ExportLogsServiceRequest, ExportMetricsServiceRequest, ExportTraceServiceRequest)
T_response = TypeVar("T_response", ExportLogsServiceResponse, ExportMetricsServiceResponse, ExportTraceServiceResponse)
async def handle_otlp_export(
request: Request,
request_message_cls: Type[T_request],
response_message_cls: Type[T_response],
message_callback: Optional[Callable[[T_request], Awaitable[None]]],
signal_name: str,
) -> Response:
"""
Generic handler for /v1/traces, /v1/metrics, /v1/logs.
Convert the OTLP Protobuf request to a JSON-like object.
"""
content_type = request.headers.get("Content-Type", "").split(";")[0].strip()
if content_type != PROTOBUF_CT:
# For brevity we only support binary protobuf here.
return _bad_request_response(
request,
f"Unsupported Content-Type '{content_type}', expected '{PROTOBUF_CT}'",
content_type=PROTOBUF_CT,
)
raw_body = await request.body()
body = _read_body_maybe_gzip(request, raw_body)
# Empty request is allowed and should still succeed.
if not body:
req_msg = request_message_cls()
else:
req_msg = request_message_cls()
try:
req_msg.ParseFromString(body)
except Exception as exc:
return _bad_request_response(request, f"Unable to parse OTLP {signal_name} payload: {exc}")
if message_callback is not None:
await message_callback(req_msg)
# Build success response. Partial success field is left unset.
resp_msg = response_message_cls()
# Encode response in the same Content-Type as request.
if content_type == PROTOBUF_CT:
resp_bytes = resp_msg.SerializeToString()
else:
resp_bytes = json_format.MessageToJson(resp_msg).encode("utf-8")
resp_bytes, headers = _maybe_gzip_response(request, resp_bytes)
return Response(
content=resp_bytes,
media_type=content_type,
status_code=200,
headers=headers,
)
async def spans_from_proto(
request: ExportTraceServiceRequest,
sequence_id_bulk_issuer: Callable[[Sequence[Tuple[str, str]]], Awaitable[Sequence[int]]],
) -> List[Span]:
"""Parse an OTLP proto payload into List[Span].
A store is needed here for generating a sequence ID for each span.
"""
output_spans: List[Span] = []
for resource_spans in request.resource_spans:
# Resource-level attributes & IDs
resource_attrs = _kv_list_to_dict(resource_spans.resource.attributes)
# rollout_id, attempt_id from resource attributes when present.
rollout_id_resource = resource_attrs.get(LightningResourceAttributes.ROLLOUT_ID.value)
attempt_id_resource = resource_attrs.get(LightningResourceAttributes.ATTEMPT_ID.value)
# If sequence id is provided, all the spans will share the same sequence ID.
# unless otherwise overridden by span-level attributes.
sequence_id_resource = resource_attrs.get(LightningResourceAttributes.SPAN_SEQUENCE_ID.value)
otel_resource = _resource_from_proto(resource_spans.resource, getattr(resource_spans, "schema_url", ""))
# Each ScopeSpans contains multiple spans
for scope_spans in resource_spans.scope_spans:
for proto_span in scope_spans.spans:
trace_id_hex = _bytes_to_trace_id_hex(proto_span.trace_id)
span_id_hex = _bytes_to_span_id_hex(proto_span.span_id)
parent_id_hex = _bytes_to_span_id_hex(proto_span.parent_span_id) if proto_span.parent_span_id else None
# Status
status_code_str = _STATUS_CODE_MAP.get(proto_span.status.code, "UNSET")
status = TraceStatus(
status_code=status_code_str,
description=proto_span.status.message or None,
)
# Attributes
span_attrs = _kv_list_to_dict(proto_span.attributes)
# Context
context = SpanContext(
trace_id=trace_id_hex,
span_id=span_id_hex,
is_remote=False,
trace_state={},
)
# Try to get if span attributes contain something like rollout_id or attempt_id
# Override the resource-level attributes with the span-level attributes if present.
rollout_id_span = span_attrs.get(LightningResourceAttributes.ROLLOUT_ID.value)
attempt_id_span = span_attrs.get(LightningResourceAttributes.ATTEMPT_ID.value)
sequence_id_span = span_attrs.get(LightningResourceAttributes.SPAN_SEQUENCE_ID.value)
# Normalize to regular strings and ints
rollout_id_raw = rollout_id_span if rollout_id_span is not None else rollout_id_resource
attempt_id_raw = attempt_id_span if attempt_id_span is not None else attempt_id_resource
sequence_id_raw = sequence_id_span if sequence_id_span is not None else sequence_id_resource
rollout_id, attempt_id = _normalize_rollout_attempt_id(rollout_id_raw, attempt_id_raw)
sequence_id = _normalize_sequence_id(sequence_id_raw)
if rollout_id is None or attempt_id is None:
logger.warning(
"Both rollout_id and attempt_id must be present in resource attributes. "
"Spans will not be able to log to the store because of missing IDs: rollout_id=%s, attempt_id=%s, sequence_id=%s",
rollout_id,
attempt_id,
sequence_id,
)
continue
# Generate a new sequence ID if not provided
if sequence_id is None:
current_sequence_id = -1
elif sequence_id < 0:
logger.error(
"Invalid sequence_id value in resource attributes: %r. Must be a positive integer. Regenerating one.",
sequence_id,
)
current_sequence_id = -1
else:
current_sequence_id = sequence_id
# Build Span
span = Span(
rollout_id=rollout_id,
attempt_id=attempt_id,
sequence_id=current_sequence_id,
trace_id=trace_id_hex,
span_id=span_id_hex,
parent_id=parent_id_hex,
name=proto_span.name,
status=status,
attributes=span_attrs,
events=_events_from_proto(proto_span),
links=_links_from_proto(proto_span),
start_time=convert_timestamp(proto_span.start_time_unix_nano),
end_time=convert_timestamp(proto_span.end_time_unix_nano),
context=context,
parent=None, # OTLP only has parent_span_id; we don't have full SpanContext
resource=otel_resource,
)
output_spans.append(span)
# Finalize the sequence IDs
bulk_issue_requests = [(span.rollout_id, span.attempt_id) for span in output_spans if span.sequence_id < 0]
bulk_sequence_ids = await sequence_id_bulk_issuer(bulk_issue_requests)
for span, sequence_id in zip(
[span for span in output_spans if span.sequence_id < 0], bulk_sequence_ids, strict=True
):
span.sequence_id = sequence_id
return output_spans
class LightningStoreOTLPExporter(OTLPSpanExporter):
"""OTLP Exporter that write to a LightningStore-compatible backend.
The backend requires two special attributes on each span:
- `agentlightning.rollout_id`: The rollout ID to associate the span with.
- `agentlightning.attempt_id`: The attempt ID to associate the span with.
It can optionally use the following attribute to sequence spans:
- `agentlightning.span_sequence_id`: A decimal string representing the sequence ID of the span.
"""
_default_endpoint: Optional[str] = None
_rollout_id: Optional[str] = None
_attempt_id: Optional[str] = None
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}("
+ f"endpoint={self.endpoint!r}, "
+ f"rollout_id={self.rollout_id!r}, "
+ f"attempt_id={self.attempt_id!r}, "
+ f"should_bypass={self.should_bypass()!r})"
)
@property
def endpoint(self) -> Optional[str]:
"""The endpoint to submit the spans to."""
if hasattr(self, "_endpoint"):
return self._endpoint
return None
@property
def rollout_id(self) -> Optional[str]:
"""The rollout ID to submit the spans to."""
if hasattr(self, "_rollout_id"):
return self._rollout_id
return None
@property
def attempt_id(self) -> Optional[str]:
"""The attempt ID to submit the spans to."""
if hasattr(self, "_attempt_id"):
return self._attempt_id
return None
def enable_store_otlp(self, endpoint: str, rollout_id: str, attempt_id: str) -> None:
"""Enable storing OTLP data to a specific LightningStore rollout/attempt."""
self._rollout_id = rollout_id
self._attempt_id = attempt_id
self._default_endpoint = self._endpoint
self._endpoint = endpoint
def disable_store_otlp(self) -> None:
"""Disable storing OTLP data to LightningStore."""
self._rollout_id = None
self._attempt_id = None
if self._default_endpoint is not None:
self._endpoint = self._default_endpoint
def should_bypass(self) -> bool:
"""Check if the exporter should bypass the default export if rollout_id and attempt_id are not set."""
return True
def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult:
if self._rollout_id is not None and self._attempt_id is not None:
# rollout_id and attempt_id are present in resource attributes
# It means that the server supports OTLP endpoint.
for span in spans:
# Override the resources so that the server knows where the request comes from.
span._resource = span._resource.merge( # pyright: ignore[reportPrivateUsage]
Resource.create(
{
LightningResourceAttributes.ROLLOUT_ID.value: self._rollout_id,
LightningResourceAttributes.ATTEMPT_ID.value: self._attempt_id,
}
)
)
return super().export(spans)
elif not self.should_bypass():
logger.debug("Rollout ID and Attempt ID not set; using default OTLP exporter behavior.")
return super().export(spans)
else:
logger.debug("Rollout ID and Attempt ID not set; bypassing export.")
return SpanExportResult.SUCCESS
def _read_body_maybe_gzip(request: Request, raw_body: bytes) -> bytes:
"""
Decompress body if Content-Encoding: gzip; otherwise return as is.
"""
encoding = request.headers.get("Content-Encoding", "").lower()
if encoding == "gzip":
return gzip.decompress(raw_body)
return raw_body
def _maybe_gzip_response(request: Request, payload: bytes) -> Tuple[bytes, Dict[str, str]]:
"""
If Accept-Encoding includes gzip, gzip the payload and set Content-Encoding header.
"""
ae = request.headers.get("Accept-Encoding", "")
tokens = [token.split(";")[0].strip().lower() for token in ae.split(",") if token.strip()]
headers: Dict[str, str] = {}
if "gzip" in tokens:
payload = gzip.compress(payload)
headers["Content-Encoding"] = "gzip"
return payload, headers
def _bad_request_response(request: Request, message: str, content_type: str = PROTOBUF_CT) -> Response:
"""
Build a 400 response whose body is a protobuf Status message, encoded
in the same Content-Type as the request (OTLP/HTTP requirement).
"""
status_msg = Status(message=message)
if content_type == PROTOBUF_CT:
body = status_msg.SerializeToString()
else:
# Fallback: JSON representation of Status.
body = json_format.MessageToJson(status_msg).encode("utf-8")
body, headers = _maybe_gzip_response(request, body)
return Response(
content=body,
status_code=400,
media_type=content_type,
headers=headers,
)
def _normalize_rollout_attempt_id(
rollout_id: Optional[AttributeValue], attempt_id: Optional[AttributeValue]
) -> Tuple[Optional[str], Optional[str]]:
"""Normalize a rollout or attempt ID to a string."""
rollout_id_str = str(rollout_id) if rollout_id is not None else None
attempt_id_str = str(attempt_id) if attempt_id is not None else None
return rollout_id_str, attempt_id_str
def _normalize_sequence_id(sequence_id: Optional[AttributeValue]) -> Optional[int]:
"""Normalize a sequence ID to an integer."""
if sequence_id is None:
return None
try:
sequence_id_int = int(str(sequence_id))
except (ValueError, TypeError):
logger.warning(
"Invalid sequence_id value in resource attributes: %r. Must be an integer or string representing an integer. Assuming None.",
sequence_id,
)
sequence_id_int = None
return sequence_id_int
def _any_value_to_python(value: AnyValue) -> Any:
"""Convert OTLP AnyValue -> plain Python value."""
kind = value.WhichOneof("value")
if kind is None:
return None
if kind == "string_value":
return value.string_value
if kind == "bool_value":
return value.bool_value
if kind == "int_value":
return int(value.int_value)
if kind == "double_value":
return float(value.double_value)
if kind == "array_value":
return [_any_value_to_python(v) for v in value.array_value.values]
if kind == "kvlist_value":
# Map<string, AnyValue> -> dict
return {kv.key: _any_value_to_python(kv.value) for kv in value.kvlist_value.values}
if kind == "bytes_value":
# Serialize bytes as hex string to stay JSON-friendly
return value.bytes_value.hex()
return None
def _kv_list_to_dict(kvs: Sequence[KeyValue]) -> Attributes:
"""Convert repeated KeyValue -> Attributes dict."""
return {kv.key: _any_value_to_python(kv.value) for kv in kvs}
_STATUS_CODE_MAP = {
ProtoStatus.STATUS_CODE_UNSET: "UNSET",
ProtoStatus.STATUS_CODE_OK: "OK",
ProtoStatus.STATUS_CODE_ERROR: "ERROR",
}
def _bytes_to_trace_id_hex(b: bytes) -> str:
# OTLP uses 16-byte trace IDs; format as 32-char hex
if not b:
return "0" * 32
return b.hex().rjust(32, "0")
def _bytes_to_span_id_hex(b: bytes) -> str:
# OTLP uses 8-byte span IDs; format as 16-char hex
if not b:
return "0" * 16
return b.hex().rjust(16, "0")
def _events_from_proto(span: ProtoSpan) -> List[Event]:
"""Event converter from OTLP ProtoSpan to List[Event]."""
return [
Event(
name=e.name,
attributes=_kv_list_to_dict(e.attributes),
timestamp=convert_timestamp(e.time_unix_nano),
)
for e in span.events
]
def _links_from_proto(span: ProtoSpan) -> List[Link]:
"""Link converter from OTLP ProtoSpan to List[Link]."""
links: List[Link] = []
for link in span.links:
trace_id_hex = _bytes_to_trace_id_hex(link.trace_id)
span_id_hex = _bytes_to_span_id_hex(link.span_id)
ctx = SpanContext(
trace_id=trace_id_hex,
span_id=span_id_hex,
is_remote=False,
trace_state={}, # OTLP trace_state is currently a string; you can parse if needed
)
links.append(
Link(
context=ctx,
attributes=_kv_list_to_dict(link.attributes) or None,
)
)
return links
def _resource_from_proto(resource: ProtoResource, schema_url: str = "") -> OtelResource:
return OtelResource(
attributes=_kv_list_to_dict(resource.attributes),
schema_url=schema_url or "",
)
+127 -23
View File
@@ -6,15 +6,17 @@ import asyncio
import inspect
import logging
import multiprocessing
import os
import queue
import signal
import socket
import threading
import time
import traceback
from contextlib import asynccontextmanager, suppress
from dataclasses import dataclass
from multiprocessing.process import BaseProcess
from typing import Any, AsyncContextManager, AsyncIterator, Dict, Literal, Optional
from typing import Any, AsyncContextManager, AsyncIterator, Dict, Literal, Optional, cast
import aiohttp
import requests
@@ -24,18 +26,21 @@ from gunicorn.app.base import BaseApplication
from gunicorn.arbiter import Arbiter
from portpicker import pick_unused_port
__all__ = ["PythonServerLauncher", "PythonServerLauncherArgs"]
__all__ = ["PythonServerLauncher", "PythonServerLauncherArgs", "LaunchMode"]
LaunchMode = Literal["asyncio", "thread", "mp"]
"""The launch mode for the server."""
@dataclass
class PythonServerLauncherArgs:
port: Optional[int] = None
"""The TCP port to listen on. If not provided, the server will use a random available port."""
host: str = "127.0.0.1"
host: Optional[str] = None
"""The hostname or IP address to bind the server to."""
access_host: Optional[str] = None
"""The hostname or IP address to advertise to the client. If not provided, the server will use the default outbound IPv4 address for this machine."""
launch_mode: LaunchMode = "asyncio"
"""The launch mode. `asyncio` is the default mode to runs the server in the current thread.
`thread` runs the server in a separate thread. `mp` runs the server in a separate process."""
@@ -49,6 +54,8 @@ class PythonServerLauncherArgs:
"""
log_level: int = logging.INFO
"""The log level to use."""
access_log: bool = False
"""Whether to turn on access logs."""
startup_timeout: float = 60.0
"""The timeout to wait for the server to start up."""
kill_unhealthy_server: bool = True
@@ -59,6 +66,8 @@ class PythonServerLauncherArgs:
"""The timeout to wait for the thread to join."""
process_join_timeout: float = 10.0
"""The timeout to wait for the process to join."""
timeout_keep_alive: int = 30
"""The timeout to keep the connection alive."""
@dataclass
@@ -152,21 +161,30 @@ async def run_uvicorn_asyncio(
if not uvicorn_server.started:
# Normally, the program will not reach this point, as the server will throw the exception itself earlier.
raise RuntimeError(f"Server did not start up within {timeout:.2f} seconds.") from server_start_exception
raise RuntimeError(
f"Server did not start up within {time.time() - start_time:.2f} seconds."
) from server_start_exception
logger.debug(f"Server started up in {time.time() - start_time:.2f} seconds.")
logger.info(f"Server started up in {time.time() - start_time:.2f} seconds.")
# Check for health endpoint status if provided
if health_url is not None:
logger.info(f"Probing health endpoint {health_url}...")
async with aiohttp.ClientSession() as session:
while time.time() < deadline:
with suppress(Exception):
try:
async with session.get(health_url) as resp:
if resp.status == 200:
logger.debug(
logger.info(
f"Server is healthy at {health_url} in {time.time() - start_time:.2f} seconds."
)
return
else:
logger.debug(
f"Server is NOT healthy at {health_url} in {time.time() - start_time:.2f} seconds. Got status {resp.status}."
)
except Exception as e:
logger.debug(f"Error probing health endpoint {health_url}: {str(e)}")
await asyncio.sleep(0.1)
# If the server is not healthy, kill it if requested.
@@ -187,7 +205,7 @@ async def run_uvicorn_asyncio(
)
else:
logger.debug("Server does not provide a health check endpoint. Skipping health check.")
logger.info("Server does not provide a health check endpoint. Skipping health check.")
async def _serve_server() -> None:
nonlocal server_start_exception
@@ -555,6 +573,27 @@ def run_gunicorn(
watchdog_thread.join(timeout=5.0)
def _get_default_ipv4_address() -> str:
"""Determine the default outbound IPv4 address for this machine.
Implementation:
Opens a UDP socket and "connects" to a public address to force route
selection, then inspects the socket's local address. No packets are sent.
Returns:
str: Best-guess IPv4 like `192.168.x.y`. Falls back to `127.0.0.1`.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# Doesn't actually contact 8.8.8.8; just forces the OS to pick a route.
s.connect(("8.8.8.8", 80))
return s.getsockname()[0]
except Exception:
return "127.0.0.1"
finally:
s.close()
class PythonServerLauncher:
"""Unified launcher for FastAPI, using uvicorn or gunicorn per mode/worker count.
@@ -573,7 +612,16 @@ class PythonServerLauncher:
self.app = app
self.args = args
self.serve_context = serve_context
self._host: Optional[str] = self.args.host
self._port: Optional[int] = self.args.port
self._access_host: Optional[str] = self.args.access_host
self.initialize()
def initialize(self):
# ensure the host/port/access_host are set
self._ensure_host()
self._ensure_port()
self._ensure_access_host()
# uvicorn (in-proc asyncio)
self._uvicorn_server: Optional[uvicorn.Server] = None
@@ -592,17 +640,35 @@ class PythonServerLauncher:
# is_running flag
self._is_running: bool = False
def __getstate__(self):
"""Control pickling to prevent server state from being sent to subprocesses."""
return {
"app": self.app,
"args": self.args,
"serve_context": self.serve_context,
"_host": self._host,
"_port": self._port,
"_access_host": self._access_host,
}
def __setstate__(self, state: Dict[str, Any]):
self.app = state["app"]
self.args = cast(PythonServerLauncherArgs, state["args"])
self.serve_context = state["serve_context"]
self._host = state["_host"]
self._port = state["_port"]
self._access_host = state["_access_host"]
self.initialize()
@property
def endpoint(self) -> str:
"""Return the externally advertised host:port pair regardless of accessibility."""
return f"http://{self.args.host}:{self._ensure_port()}"
return f"http://{self._ensure_host()}:{self._ensure_port()}"
@property
def access_url(self) -> str:
def access_endpoint(self) -> str:
"""Return a loopback-friendly URL so health checks succeed even when binding to 0.0.0.0."""
# Probe host normalization for 0.0.0.0
host_for_probe = "127.0.0.1" if self.args.host in ("0.0.0.0", "::") else self.args.host
return f"http://{host_for_probe}:{self._ensure_port()}"
return f"http://{self._ensure_access_host()}:{self._ensure_port()}"
@property
def health_url(self) -> Optional[str]:
@@ -612,7 +678,7 @@ class PythonServerLauncher:
path = self.args.healthcheck_url
if not path.startswith("/"):
path = "/" + path
return f"{self.access_url}{path}"
return f"{self.access_endpoint}{path}"
async def start(self):
"""Starts the server according to launch_mode and n_workers."""
@@ -699,19 +765,41 @@ class PythonServerLauncher:
return f"{module}:app"
return "unknown:app"
def _ensure_host(self) -> str:
if self._host is None:
logger.warning("No host provided, using 0.0.0.0.")
self._host = "0.0.0.0"
return self._host
def _ensure_port(self) -> int:
if self._port is None:
logger.warning("No port provided, using pick_unused_port to pick a random unused port.")
self._port = pick_unused_port()
return self._port
def _ensure_access_host(self) -> str:
if self._access_host is None:
if self.args.access_host is None:
if self._ensure_host() in ("0.0.0.0", "::"):
# Probe host normalization for 0.0.0.0
logger.warning("No access host provided, using default outbound IPv4 address for this machine.")
self._access_host = _get_default_ipv4_address()
else:
logger.warning("No access host provided, using the host provided.")
self._access_host = self._ensure_host()
else:
self._access_host = self.args.access_host
return self._access_host # type: ignore
def _create_uvicorn_server(self) -> uvicorn.Server:
config = uvicorn.Config(
app=self.app,
host=self.args.host,
host=self._ensure_host(),
port=self._ensure_port(),
log_level=self.args.log_level,
access_log=self.args.access_log,
loop="asyncio",
timeout_keep_alive=self.args.timeout_keep_alive,
)
return uvicorn.Server(config)
@@ -782,15 +870,20 @@ class PythonServerLauncher:
try:
evt: ChildEvent = await asyncio.to_thread(self._thread_event_queue.get, True, timeout)
except queue.Empty:
logger.error("Threaded server failed to start and sends no event. This should not happen.")
if not self._thread.is_alive():
raise RuntimeError("Threaded server failed to start and is not alive. No error event was received.")
logger.error(
"Threaded server failed to start and sends no event. This should not happen. Shutting down server."
)
await self._stop_uvicorn_thread()
return
raise RuntimeError("Threaded server failed to start and sends no event. This should not happen.")
if evt.kind == "error":
logger.error("Threaded server failed to start (%s): %s\n%s", evt.exc_type, evt.message, evt.traceback)
await asyncio.to_thread(self._thread.join, self.args.thread_join_timeout)
if self._thread.is_alive():
raise RuntimeError(evt.message or "Threaded server failed to start and refused to shut down.")
logger.error("Threaded server failed to start and refused to shut down.")
raise RuntimeError(evt.message)
else:
logger.info("Threaded server started successfully.")
self._is_running = True
@@ -819,6 +912,7 @@ class PythonServerLauncher:
if self.is_running():
raise RuntimeError("Server process is already running. Stopping it first.")
host = self._ensure_host()
port = self._ensure_port()
try:
@@ -834,17 +928,22 @@ class PythonServerLauncher:
if self.args.n_workers > 1:
logger.info(f"Starting Gunicorn server...")
options = {
"bind": f"{self.args.host}:{port}",
"bind": f"{host}:{port}",
"workers": int(self.args.n_workers),
"worker_class": "uvicorn_worker.UvicornWorker",
"loglevel": logging.getLevelName(self.args.log_level).lower(),
"accesslog": None,
"accesslog": "-" if self.args.access_log else None,
"errorlog": "-",
"preload_app": True,
"graceful_timeout": int(
self.args.process_join_timeout / 2
), # Allow half the timeout for graceful shutdown
}
if "PROMETHEUS_MULTIPROC_DIR" in os.environ:
from prometheus_client import multiprocess
options["child_exit"] = lambda server, worker: multiprocess.mark_process_dead(worker.pid) # type: ignore
self._gunicorn_app = GunicornApp(self.app, options)
self._proc = ctx.Process(
@@ -883,9 +982,13 @@ class PythonServerLauncher:
try:
evt: ChildEvent = await asyncio.to_thread(self._mp_event_queue.get, True, timeout)
except queue.Empty:
logger.error("Server process failed to start and sends no event. This should not happen.")
if not self._proc.is_alive():
raise RuntimeError("Server process failed to start and is not alive. No error event was received.")
logger.error(
"Server process failed to start and sends no event. This should not happen. Shutting down server."
)
await self._stop_serving_process()
return
raise RuntimeError("Server process failed to start and sends no event. This should not happen.")
if evt.kind == "error":
logger.error(
@@ -897,7 +1000,8 @@ class PythonServerLauncher:
)
await asyncio.to_thread(self._proc.join, self.args.process_join_timeout)
if self._proc.is_alive():
raise RuntimeError(evt.message or "Server process failed to start and refused to shut down.")
logger.error("Server process failed to start and refused to shut down.")
raise RuntimeError(evt.message)
else:
logger.info("Subprocess server started successfully.")
self._is_running = True
+72
View File
@@ -0,0 +1,72 @@
# Copyright (c) Microsoft. All rights reserved.
from __future__ import annotations
import platform
import socket
from contextlib import suppress
from datetime import datetime
from typing import Any, Dict, List, cast
import psutil
from gpustat import GPUStat, GPUStatCollection
def system_snapshot(include_gpu: bool = False) -> Dict[str, Any]:
# CPU
cpu = {
"cpu_name": platform.processor(),
"cpu_cores": psutil.cpu_count(logical=False),
"cpu_threads": psutil.cpu_count(logical=True),
"cpu_usage_pct": psutil.cpu_percent(0.05),
}
# Memory
vm = psutil.virtual_memory()
mem = {
"mem_used_gb": round(vm.used / (2**30), 2),
"mem_total_gb": round(vm.total / (2**30), 2),
"mem_pct": vm.percent,
}
# Disk
du = psutil.disk_usage("/")
disk = {
"disk_used_gb": round(du.used / (2**30), 2),
"disk_total_gb": round(du.total / (2**30), 2),
"disk_pct": du.percent,
}
# GPU
gpus: List[Dict[str, Any]] = []
with suppress(Exception):
for g in GPUStatCollection.new_query().gpus: # type: ignore
g = cast(GPUStat, g)
gpus.append(
{
"gpu": g.name, # type: ignore
"util_pct": g.utilization,
"mem_used_mb": g.memory_used,
"mem_total_mb": g.memory_total,
"temp_c": g.temperature,
}
)
# Network
net = psutil.net_io_counters()
netinfo = {
"bytes_sent_mb": round(net.bytes_sent / (2**20), 2),
"bytes_recv_mb": round(net.bytes_recv / (2**20), 2),
}
# OS / meta
return {
"timestamp": datetime.now().isoformat(timespec="seconds"),
"host": socket.gethostname(),
"os": platform.platform(),
**cpu,
**mem,
**disk,
**netinfo,
**({"gpus": gpus} if include_gpu else {}),
}
+17 -13
View File
@@ -18,14 +18,12 @@ from flask import Flask, Response, abort, request
from tensordict import TensorDict
from verl import DataProto
from agentlightning import LLM, AgentLightningServer, NamedResources, RolloutLegacy, configure_logger
from agentlightning import LLM, AgentLightningServer, NamedResources, RolloutLegacy
from agentlightning.adapter.triplet import TracerTraceToTriplet, TraceToTripletBase
from agentlightning.llm_proxy import LLMProxy, ModelConfig
from agentlightning.store.base import LightningStore
from agentlightning.types import Rollout, RolloutConfig, Task
configure_logger()
__all__ = [
"AgentModeDaemon",
"get_left_padded_ids_and_attention_mask",
@@ -294,7 +292,7 @@ class AgentModeDaemon:
self._proxy_thread.start()
print(f"Proxy server running on port {self.proxy_port}")
def _update_proxy_server_v1(self):
async def _update_proxy_server_v1(self):
model_name = self.train_information.get("model")
if not model_name:
raise ValueError("Model name is not set.")
@@ -313,12 +311,7 @@ class AgentModeDaemon:
],
)
if self.llm_proxy.is_running():
# FIXME: Need to switch to a different port right now
# because the forked processes carried the old fd
self.llm_proxy.restart(_port=_find_available_port())
else:
self.llm_proxy.start()
await self.llm_proxy.restart()
def start(self):
"""Starts the main AgentLightningServer and the proxy server."""
@@ -352,7 +345,7 @@ class AgentModeDaemon:
if server_addresses != self.backend_llm_server_addresses:
self.backend_llm_server_addresses = server_addresses
if self.mode == "v1" and not self.llm_proxy.is_running():
self._update_proxy_server_v1()
await self._update_proxy_server_v1()
self.is_train = is_train
# 1. Update resources on the server for clients to use
@@ -564,14 +557,17 @@ class AgentModeDaemon:
) # FIXME: Evaluate whether grouping stats by source is actually needed.
for rollout_id, rollout in self._completed_rollouts_v0.items():
final_reward_raw: Optional[float] = rollout.final_reward
final_reward = self._fillna_reward(rollout)
if not rollout.triplets:
print(f"Warning: No triplets found for test rollout {rollout.rollout_id}.")
sample_stat_list.append({"reward": final_reward})
sample_stat_list.append({"reward": final_reward, "has_reward": final_reward_raw is not None})
continue
response_length_list = [len(triplet.response.get("token_ids", [])) for triplet in rollout.triplets]
if "data_source" in self._task_id_to_original_sample[rollout_id]:
# When a test sample includes a 'data_source' field, record per-source statistics for test results.
# TODO: This is a flawed design. We should have a better way to handle this.
data_source = self._task_id_to_original_sample[rollout_id]["data_source"]
sample_stat_list_by_source[data_source].append(
{
@@ -579,6 +575,7 @@ class AgentModeDaemon:
"mean_response_length": np.mean(response_length_list) if response_length_list else 0,
"turn_count": len(rollout.triplets),
"reward": final_reward,
"has_reward": final_reward_raw is not None,
}
)
sample_stat_list.append(
@@ -587,6 +584,7 @@ class AgentModeDaemon:
"mean_response_length": np.mean(response_length_list) if response_length_list else 0,
"turn_count": len(rollout.triplets),
"reward": final_reward,
"has_reward": final_reward_raw is not None,
}
)
metric_dict: Dict[str, Any] = {}
@@ -601,6 +599,9 @@ class AgentModeDaemon:
{
f"val/{data_source}/n_rollouts": len(sample_stats),
f"val/{data_source}/n_rollouts_w_trace": len(stats_w_trace_by_source[data_source]),
f"val/{data_source}/n_rollouts_w_reward": len(
[stat for stat in sample_stats if stat["has_reward"]]
),
f"val/{data_source}/reward": np.mean(
[stat["reward"] for stat in sample_stats]
), # each rollout must have a reward (fillna if missing)
@@ -619,6 +620,7 @@ class AgentModeDaemon:
{
"val/n_rollouts": len(sample_stat_list),
"val/n_rollouts_w_trace": len(stats_w_trace),
"val/n_rollouts_w_reward": len([stat for stat in sample_stat_list if stat["has_reward"]]),
"val/reward": np.mean(
[stat["reward"] for stat in sample_stat_list]
), # each rollout must have a reward (fillna if missing)
@@ -643,9 +645,10 @@ class AgentModeDaemon:
# 1. Reconstruct the `finished_id_to_sample_info` structure from completed rollouts
finished_id_to_sample_info: Dict[str, Dict[str, Any]] = {}
finished_id_to_final_reward: Dict[str, float] = {}
sample_with_reward_count = 0
for rollout_id, rollout in self._completed_rollouts_v0.items():
original_sample = self._task_id_to_original_sample[rollout_id]
sample_with_reward_count += int(rollout.final_reward is not None)
final_reward = self._fillna_reward(rollout)
if not rollout.triplets:
@@ -764,6 +767,7 @@ class AgentModeDaemon:
"training/reward": np.mean(list(finished_id_to_final_reward.values())),
"training/n_rollouts": len(finished_id_to_final_reward),
"training/n_rollouts_w_trace": len(finished_id_to_sample_info),
"training/n_rollouts_w_reward": sample_with_reward_count,
"training/n_truncated_triplets": n_trunc_sample_because_of_response,
"training/n_triplets": n_transition,
}
+11 -4
View File
@@ -12,6 +12,7 @@ from typing import Dict, Tuple
import numpy as np
import torch
import verl
from codetiming import Timer
from omegaconf import OmegaConf
from tqdm import tqdm
@@ -403,14 +404,20 @@ class AgentLightningTrainer(RayPPOTrainer):
assert self.async_rollout_mode, "If agent mode is enabled, async server must be enabled"
if self.adapter is not None and not isinstance(self.adapter, TraceToTripletBase):
raise ValueError("Adapter must be a TraceToTripletBase for currently VERL implementation.")
verl_version = verl.__version__
if verl_version == "0.5.0":
# Note (Zhiyuan): To avoid further patch into vllm async server, using the same sentence to get the naming here.
# However, it is possible that verl updates the naming and causes incompatibility.
# Reference: https://github.com/volcengine/verl/blob/5b5e09d9cc20625e436d01f69d9cc739ff681c54/verl/workers/rollout/vllm_rollout/vllm_async_server.py#L217
model = "/".join(self.config.actor_rollout_ref.model.path.split("/")[-2:])
else:
# For other versions (e.g., 0.6.0), we use the full path to the model.
model = self.config.actor_rollout_ref.model.path
self.agent_mode_daemon = AgentModeDaemon(
self.config.agentlightning.port,
self.config.actor_rollout_ref.rollout.n,
train_information={
# Note (Zhiyuan): To avoid further patch into vllm async server, using the same sentence to get the naming here.
# However, it is possible that verl updates the naming and causes incompatibility.
# Reference: https://github.com/volcengine/verl/blob/5b5e09d9cc20625e436d01f69d9cc739ff681c54/verl/workers/rollout/vllm_rollout/vllm_async_server.py#L217
"model": "/".join(self.config.actor_rollout_ref.model.path.split("/")[-2:]),
"model": model,
"temperature": self.config.actor_rollout_ref.rollout.temperature,
},
tokenizer=self.tokenizer,
+132
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@@ -0,0 +1,132 @@
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
lerna-debug.log*
.pnpm-debug.log*
# Diagnostic reports (https://nodejs.org/api/report.html)
report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
# Runtime data
pids
*.pid
*.seed
*.pid.lock
# Directory for instrumented libs generated by jscoverage/JSCover
lib-cov
# Coverage directory used by tools like istanbul
coverage
*.lcov
# nyc test coverage
.nyc_output
# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
.grunt
# Bower dependency directory (https://bower.io/)
bower_components
# node-waf configuration
.lock-wscript
# Compiled binary addons (https://nodejs.org/api/addons.html)
build/Release
# Dependency directories
node_modules/
jspm_packages/
# Snowpack dependency directory (https://snowpack.dev/)
web_modules/
# TypeScript cache
*.tsbuildinfo
# Optional npm cache directory
.npm
# Optional eslint cache
.eslintcache
# Optional stylelint cache
.stylelintcache
# Microbundle cache
.rpt2_cache/
.rts2_cache_cjs/
.rts2_cache_es/
.rts2_cache_umd/
# Optional REPL history
.node_repl_history
# Output of 'npm pack'
*.tgz
# Yarn Integrity file
.yarn-integrity
# dotenv environment variable files
.env
.env.development.local
.env.test.local
.env.production.local
.env.local
# parcel-bundler cache (https://parceljs.org/)
.cache
.parcel-cache
# Next.js build output
.next
out
# Nuxt.js build / generate output
.nuxt
dist
# Gatsby files
.cache/
# Comment in the public line in if your project uses Gatsby and not Next.js
# https://nextjs.org/blog/next-9-1#public-directory-support
# public
# vuepress build output
.vuepress/dist
# vuepress v2.x temp and cache directory
.temp
.cache
# Docusaurus cache and generated files
.docusaurus
# Serverless directories
.serverless/
# FuseBox cache
.fusebox/
# DynamoDB Local files
.dynamodb/
# TernJS port file
.tern-port
# Stores VSCode versions used for testing VSCode extensions
.vscode-test
# yarn v2
.yarn/cache
.yarn/unplugged
.yarn/build-state.yml
.yarn/install-state.gz
.pnp.*
.DS_Store
+47
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@@ -0,0 +1,47 @@
// Copyright (c) Microsoft. All rights reserved.
/** @type {import("@ianvs/prettier-plugin-sort-imports").PrettierConfig} */
const config = {
printWidth: 120,
singleQuote: true,
tabWidth: 2,
useTabs: false,
semi: true,
quoteProps: 'consistent',
jsxSingleQuote: true,
trailingComma: 'all',
bracketSpacing: true,
objectWrap: 'preserve',
arrowParens: 'always',
proseWrap: 'preserve',
endOfLine: 'lf',
plugins: ['@ianvs/prettier-plugin-sort-imports'],
importOrder: [
'.*styles.css$',
'',
'dayjs',
'^react$',
'^next$',
'^next/.*$',
'<BUILTIN_MODULES>',
'<THIRD_PARTY_MODULES>',
'^@mantine/(.*)$',
'^@mantinex/(.*)$',
'^@mantine-tests/(.*)$',
'^@docs/(.*)$',
'^@/.*$',
'^../(?!.*.css$).*$',
'^./(?!.*.css$).*$',
'\\.css$',
],
overrides: [
{
files: '*.mdx',
options: {
printWidth: 120,
},
},
],
};
export default config;
+12
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@@ -0,0 +1,12 @@
// Copyright (c) Microsoft. All rights reserved.
// Centralized constants that keep Storybook fixtures deterministic so Chromatic
// snapshots do not drift when the build environment changes.
export const STORY_DATE_NOW_MS = 1762775145209;
export const STORY_DATE_NOW_SECONDS = Math.floor(STORY_DATE_NOW_MS / 1000);
// Use a fixed origin so any code that would normally read window.location.*
// in the app can rely on the same value from Storybook fixtures. Prefer HTTPS
// so Chromatic (which is served over HTTPS) avoids mixed-content fetch errors.
export const STORY_BASE_URL = 'https://storybook.agentlightning.invalid';
export const STORY_LOCATION_HREF = `${STORY_BASE_URL}/storybook`;
+20
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@@ -0,0 +1,20 @@
// Copyright (c) Microsoft. All rights reserved.
import type { StorybookConfig } from '@storybook/react-vite';
const config: StorybookConfig = {
core: {
disableWhatsNewNotifications: true,
disableTelemetry: true,
enableCrashReports: false,
},
stories: ['../src/**/*.mdx', '../src/**/*.story.@(js|jsx|ts|tsx)'],
staticDirs: ['../static'],
addons: ['@storybook/addon-themes', '@storybook/addon-vitest'],
framework: {
name: '@storybook/react-vite',
options: {},
},
};
export default config;
+13
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@@ -0,0 +1,13 @@
// Copyright (c) Microsoft. All rights reserved.
export const allModes = {
MD: {
viewport: 'md',
},
LG: {
viewport: 'lg',
},
XL: {
viewport: 'xl',
},
} as const;
+79
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@@ -0,0 +1,79 @@
// Copyright (c) Microsoft. All rights reserved.
import '@mantine/core/styles.css';
import 'mantine-datatable/styles.css';
import '../src/styles/theme.css';
import '../src/styles/app.css';
import { initialize, mswLoader } from 'msw-storybook-addon';
import { ColorSchemeScript, MantineProvider } from '@mantine/core';
import { shadcnCssVariableResolver } from '../src/cssVariableResolver';
import { theme as mantineTheme } from '../src/theme';
import { STORY_DATE_NOW_MS } from './constants';
type ColorSchemeValue = 'light' | 'dark';
initialize({
onUnhandledRequest: 'bypass',
serviceWorker: {
url: '/mockServiceWorker.js',
},
});
const fixedDateNow = (() => {
const patched = Date.now as typeof Date.now & { __storybookPatched?: boolean };
if (patched.__storybookPatched) {
return patched;
}
const replacement = (() => STORY_DATE_NOW_MS) as typeof Date.now & { __storybookPatched?: boolean };
replacement.__storybookPatched = true;
return replacement;
})();
Date.now = fixedDateNow;
export const parameters = {
layout: 'fullscreen',
options: {
showPanel: false,
// @ts-expect-error storybook throws build error for (a: any, b: any)
storySort: (a, b) => a.title.localeCompare(b.title, undefined, { numeric: true }),
},
backgrounds: { disable: true },
viewport: {
options: {
md: { name: 'md', styles: { width: '1280px', height: '800px' } },
lg: { name: 'lg', styles: { width: '1920px', height: '1080px' } },
xl: { name: 'xl', styles: { width: '2560px', height: '1440px' } },
},
},
};
export const globalTypes = {
theme: {
name: 'Theme',
description: 'Mantine color scheme',
defaultValue: 'light',
toolbar: {
icon: 'mirror',
items: [
{ value: 'light', title: 'Light' },
{ value: 'dark', title: 'Dark' },
],
},
},
};
export const decorators = [
(Story: any, context: any) => {
const scheme = (context.parameters.theme ?? context.globals.theme ?? 'light') as ColorSchemeValue;
return (
<MantineProvider theme={mantineTheme} cssVariablesResolver={shadcnCssVariableResolver} forceColorScheme={scheme}>
<ColorSchemeScript />
<Story />
</MantineProvider>
);
},
];
export const loaders = [mswLoader];
+8
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@@ -0,0 +1,8 @@
// Copyright (c) Microsoft. All rights reserved.
import { setProjectAnnotations } from '@storybook/react-vite';
import * as projectAnnotations from './preview';
// This is an important step to apply the right configuration when testing your stories.
// More info at: https://storybook.js.org/docs/api/portable-stories/portable-stories-vitest#setprojectannotations
setProjectAnnotations([projectAnnotations]);
+5
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@@ -0,0 +1,5 @@
# Generated files
dist
# Theme files
theme.css
+28
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@@ -0,0 +1,28 @@
{
"extends": ["stylelint-config-standard-scss"],
"rules": {
"custom-property-pattern": null,
"selector-class-pattern": null,
"scss/no-duplicate-mixins": null,
"declaration-empty-line-before": null,
"declaration-block-no-redundant-longhand-properties": null,
"alpha-value-notation": null,
"custom-property-empty-line-before": null,
"property-no-vendor-prefix": null,
"color-function-notation": null,
"length-zero-no-unit": null,
"selector-not-notation": null,
"no-descending-specificity": null,
"comment-empty-line-before": null,
"scss/at-mixin-pattern": null,
"scss/at-rule-no-unknown": null,
"value-keyword-case": null,
"media-feature-range-notation": null,
"selector-pseudo-class-no-unknown": [
true,
{
"ignorePseudoClasses": ["global"]
}
]
}
}
+27
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@@ -0,0 +1,27 @@
# Agent-lightning Dashboard
This is the dashboard for Agent-lightning. It is a web application that allows you to inspect your Agent-lightning store and debug running experiments.
The dashboard is built with React, Mantine UI, and Storybook.
## npm scripts
## Build and dev scripts
- `dev` start development server
- `build` build production version of the app
- `preview` locally preview production build
### Testing scripts
- `eslint` - runs ESLint
- `stylelint` - runs Stylelint
- `prettier` - runs Prettier
- `typecheck` - runs TypeScript typecheck
- `vitest` runs vitest tests
- `chromatic` runs chromatic tests
### Other scripts
- `storybook` starts storybook dev server
- `build-storybook` build production storybook bundle to `storybook-static`
+50
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@@ -0,0 +1,50 @@
// Copyright (c) Microsoft. All rights reserved.
// @ts-check
import stylistic from '@stylistic/eslint-plugin';
import mantine from 'eslint-config-mantine';
import { defineConfig } from 'eslint/config';
import tseslint from 'typescript-eslint';
export default defineConfig([
// These are arrays → safe to spread
...tseslint.configs.recommended,
stylistic.configs.customize({ semi: true }),
// mantine is often a single object → include as-is (or spread only if it's actually an array)
...(Array.isArray(mantine) ? mantine : [mantine]),
// ignores go as their own entry
{ ignores: ['**/*.{mjs,cjs,js,d.ts,d.mts}'] },
// file-specific rules
{
files: ['**/*.story.tsx'],
rules: { 'no-console': 'off' },
},
// project/TS settings + your custom rules
{
languageOptions: {
parserOptions: {
tsconfigRootDir: process.cwd(),
project: ['./tsconfig.json'],
},
},
rules: {
// Disabling conflict rules with prettier
'@stylistic/brace-style': ['error', '1tbs', { allowSingleLine: false }],
'@stylistic/no-trailing-spaces': 'error',
'@stylistic/no-multiple-empty-lines': ['error', { max: 2, maxEOF: 1 }],
'@stylistic/jsx-quotes': ['error', 'prefer-single'],
'@stylistic/multiline-ternary': 'off',
'@stylistic/arrow-parens': ['error', 'always'],
'@stylistic/jsx-closing-bracket-location': 'off',
'@stylistic/operator-linebreak': 'off',
'@stylistic/jsx-newline': 'off',
'@stylistic/jsx-one-expression-per-line': 'off',
'@stylistic/indent': 'off',
'@stylistic/indent-binary-ops': 'off',
},
},
]);
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+75
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@@ -0,0 +1,75 @@
{
"name": "agent-lightning-dashboard",
"type": "module",
"version": "0.3.0",
"scripts": {
"dev": "vite",
"build": "tsc && vite build",
"preview": "vite preview",
"typecheck": "tsc --noEmit",
"eslint": "eslint .",
"stylelint": "stylelint '**/*.css'",
"prettier": "prettier --check \"**/*.{ts,tsx,mjs,cjs}\"",
"vitest": "vitest run --project unit",
"vitest-storybook": "vitest run --project storybook",
"storybook": "storybook dev -p 6006",
"build-storybook": "storybook build",
"chromatic": "chromatic"
},
"dependencies": {
"@mantine/core": "8.3.5",
"@mantine/hooks": "8.3.5",
"@monaco-editor/react": "^4.7.0",
"@reduxjs/toolkit": "^2.9.2",
"@tabler/icons-react": "^3.35.0",
"clsx": "^2.1.1",
"dayjs": "^1.11.18",
"mantine-datatable": "^8.2.0",
"react": "^19.2.0",
"react-dom": "^19.2.0",
"react-redux": "^9.2.0",
"react-router-dom": "^7.9.4"
},
"devDependencies": {
"@eslint/js": "^9.37.0",
"@ianvs/prettier-plugin-sort-imports": "^4.7.0",
"@storybook/addon-themes": "^9.1.10",
"@storybook/addon-vitest": "^9.1.16",
"@storybook/react": "^9.1.10",
"@storybook/react-vite": "^9.1.10",
"@stylistic/eslint-plugin": "^5.5.0",
"@testing-library/dom": "^10.4.1",
"@testing-library/jest-dom": "^6.9.1",
"@testing-library/react": "^16.3.0",
"@testing-library/user-event": "^14.6.1",
"@types/node": "^24.7.1",
"@types/react": "^19.2.2",
"@types/react-dom": "^19.2.1",
"@vitejs/plugin-react": "^5.0.4",
"chromatic": "^13.3.3",
"eslint": "^9.37.0",
"eslint-config-mantine": "^4.0.3",
"eslint-plugin-jsx-a11y": "^6.10.2",
"eslint-plugin-react": "^7.37.5",
"identity-obj-proxy": "^3.0.0",
"jsdom": "^27.0.0",
"msw": "^2.11.6",
"msw-storybook-addon": "^2.0.6",
"postcss": "^8.5.6",
"postcss-preset-mantine": "1.18.0",
"postcss-simple-vars": "^7.0.1",
"prettier": "^3.6.2",
"prop-types": "^15.8.1",
"storybook": "^9.1.10",
"stylelint": "^16.25.0",
"stylelint-config-standard-scss": "^16.0.0",
"typescript": "^5.9.3",
"typescript-eslint": "^8.46.0",
"vite": "^7.1.9",
"vite-tsconfig-paths": "^5.1.4",
"vitest": "^4.0.0",
"playwright": "^1.56.1",
"@vitest/browser-playwright": "4.0.4",
"@vitest/coverage-v8": "4.0.4"
}
}
+16
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@@ -0,0 +1,16 @@
// Copyright (c) Microsoft. All rights reserved.
module.exports = {
plugins: {
'postcss-preset-mantine': {},
'postcss-simple-vars': {
variables: {
'mantine-breakpoint-xs': '36em',
'mantine-breakpoint-sm': '48em',
'mantine-breakpoint-md': '62em',
'mantine-breakpoint-lg': '75em',
'mantine-breakpoint-xl': '88em',
},
},
},
};
+13
View File
@@ -0,0 +1,13 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="../src/favicon.svg" />
<meta name="viewport" content="minimum-scale=1, initial-scale=1, width=device-width, user-scalable=no" />
<title>Agent-lightning Dashboard</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/main.tsx"></script>
</body>
</html>
+3
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@@ -0,0 +1,3 @@
// Copyright (c) Microsoft. All rights reserved.
import '../src/main.js';
+30
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@@ -0,0 +1,30 @@
// Copyright (c) Microsoft. All rights reserved.
import '@mantine/core/styles.css';
import 'mantine-datatable/styles.css';
import './styles/theme.css';
import './styles/app.css';
import { MantineProvider } from '@mantine/core';
import { useColorScheme } from '@mantine/hooks';
import { shadcnCssVariableResolver } from './cssVariableResolver';
import { selectThemePreference } from './features/config/selectors';
import { Router } from './Router';
import { useAppSelector } from './store/hooks';
import { shadcnTheme } from './theme';
export default function App() {
const themePreference = useAppSelector(selectThemePreference);
const systemColorScheme = useColorScheme();
const resolvedColorScheme = themePreference === 'system' ? systemColorScheme : themePreference;
return (
<MantineProvider
theme={shadcnTheme}
cssVariablesResolver={shadcnCssVariableResolver}
forceColorScheme={resolvedColorScheme}
>
<Router />
</MantineProvider>
);
}
+46
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@@ -0,0 +1,46 @@
// Copyright (c) Microsoft. All rights reserved.
import { createBrowserRouter, Navigate, RouterProvider } from 'react-router-dom';
import { AppLayoutWithState } from './layouts/AppLayout';
import { ResourcesPage } from './pages/Resources.page';
import { RolloutsPage } from './pages/Rollouts.page';
import { SettingsPage } from './pages/Settings.page';
import { TracesPage } from './pages/Traces.page';
import { WorkersPage } from './pages/Workers.page';
const router = createBrowserRouter([
{
path: '/',
element: <AppLayoutWithState />,
children: [
{
index: true,
element: <Navigate to='/rollouts' replace />,
},
{
path: 'rollouts',
element: <RolloutsPage />,
},
{
path: 'resources',
element: <ResourcesPage />,
},
{
path: 'traces',
element: <TracesPage />,
},
{
path: 'runners',
element: <WorkersPage />,
},
{
path: 'settings',
element: <SettingsPage />,
},
],
},
]);
export function Router() {
return <RouterProvider router={router} />;
}
@@ -0,0 +1,91 @@
// Copyright (c) Microsoft. All rights reserved.
import type { Meta, StoryObj } from '@storybook/react';
import { Provider } from 'react-redux';
import { initialConfigState } from '@/features/config/slice';
import { initialRolloutsUiState } from '@/features/rollouts/slice';
import type { AlertsState, AlertTone } from '@/features/ui/alert';
import { initialDrawerState } from '@/features/ui/drawer/slice';
import { createAppStore } from '@/store';
import { STORY_BASE_URL, STORY_DATE_NOW_MS } from '../../.storybook/constants';
import { AppAlertBanner } from './AppAlertBanner';
const meta: Meta<typeof AppAlertBanner> = {
title: 'Components/AppAlertBanner',
component: AppAlertBanner,
parameters: {
layout: 'fullscreen',
},
};
export default meta;
type Story = StoryObj<typeof AppAlertBanner>;
function renderWithAlert(message: string, tone: AlertTone) {
const alertState: AlertsState = {
alerts: [
{
id: 'storybook-alert',
message,
tone,
isVisible: true,
createdAt: STORY_DATE_NOW_MS,
},
],
};
const store = createAppStore({
config: {
...initialConfigState,
baseUrl: STORY_BASE_URL,
},
drawer: initialDrawerState,
rollouts: initialRolloutsUiState,
alert: alertState,
});
return (
<Provider store={store}>
<div style={{ padding: 24 }}>
<AppAlertBanner />
</div>
</Provider>
);
}
export const InfoAlert: Story = {
render: () => renderWithAlert('Background synchronization completed successfully.', 'info'),
};
export const WarningAlert: Story = {
render: () =>
renderWithAlert('Rollout data may be stale. Check your network connection before continuing.', 'warning'),
};
export const ErrorAlert: Story = {
render: () =>
renderWithAlert('Unable to reach the Agent-lightning API. Retry or adjust the backend settings.', 'error'),
};
export const NoAlert: Story = {
render: () => {
const store = createAppStore({
config: {
...initialConfigState,
baseUrl: STORY_BASE_URL,
},
drawer: initialDrawerState,
rollouts: initialRolloutsUiState,
alert: { alerts: [] },
});
return (
<Provider store={store}>
<div style={{ padding: 24 }}>
<AppAlertBanner />
</div>
</Provider>
);
},
};
@@ -0,0 +1,84 @@
// Copyright (c) Microsoft. All rights reserved.
import { useEffect, useState } from 'react';
import { IconAlertCircle, IconAlertTriangle, IconInfoCircle } from '@tabler/icons-react';
import { Notification, Portal, Transition } from '@mantine/core';
import { hideAlert, selectHighestPriorityAlert, type AppAlert } from '@/features/ui/alert';
import { useAppDispatch, useAppSelector } from '@/store/hooks';
const ALERT_META = {
info: {
color: 'blue',
icon: IconInfoCircle,
},
warning: {
color: 'yellow',
icon: IconAlertTriangle,
},
error: {
color: 'red',
icon: IconAlertCircle,
},
} as const;
export function AppAlertBanner() {
const dispatch = useAppDispatch();
const alert = useAppSelector(selectHighestPriorityAlert);
const [transitionAlert, setTransitionAlert] = useState<AppAlert | null>(alert);
useEffect(() => {
if (alert) {
setTransitionAlert(alert);
}
}, [alert]);
const handleClose = (id?: string) => {
if (id) {
dispatch(hideAlert({ id }));
}
};
const currentAlert = alert ?? transitionAlert;
const mounted = Boolean(alert);
if (!currentAlert) {
return null;
}
const meta = ALERT_META[currentAlert.tone];
const IconComponent = meta.icon;
return (
<Portal>
<Transition
mounted={mounted}
transition='slide-down'
duration={200}
timingFunction='ease'
onExited={() => setTransitionAlert(null)}
>
{(styles) => (
<Notification
icon={<IconComponent size={18} />}
color={meta.color}
variant='light'
withCloseButton
onClose={() => handleClose(currentAlert.id)}
style={{
position: 'fixed',
top: 16,
right: 16,
maxWidth: 450,
width: 'calc(100% - 32px)',
zIndex: 2000,
boxShadow: 'var(--mantine-shadow-md)',
...styles,
}}
>
{currentAlert.message}
</Notification>
)}
</Transition>
</Portal>
);
}
@@ -0,0 +1,583 @@
// Copyright (c) Microsoft. All rights reserved.
import { useCallback, useEffect, useMemo, useRef, useState, type ReactNode } from 'react';
import { Editor } from '@monaco-editor/react';
import { IconCheck, IconCopy } from '@tabler/icons-react';
import type { DataTableSortStatus } from 'mantine-datatable';
import { createSearchParams, Link, useInRouterContext, useLocation } from 'react-router-dom';
import {
ActionIcon,
Anchor,
Badge,
Box,
CopyButton,
Drawer,
Group,
Stack,
Text,
Tooltip,
useMantineColorScheme,
} from '@mantine/core';
import { useGetSpansQuery } from '@/features/rollouts';
import { closeDrawer, openDrawer, selectDrawerContent, selectDrawerIsOpen } from '@/features/ui/drawer';
import { useAppDispatch, useAppSelector } from '@/store/hooks';
import type { Attempt, AttemptStatus, Rollout, RolloutStatus, Span, Worker } from '@/types';
import { formatStatusLabel } from '@/utils/format';
import { TracesTable, type TracesTableRecord } from './TracesTable.component';
const ATTEMPT_STATUS_COLORS: Record<AttemptStatus, string> = {
failed: 'red',
preparing: 'violet',
running: 'blue',
succeeded: 'teal',
timeout: 'orange',
unresponsive: 'orange',
};
const ROLLOUT_STATUS_COLORS: Record<RolloutStatus, string> = {
cancelled: 'gray',
failed: 'red',
preparing: 'violet',
queuing: 'blue',
requeuing: 'cyan',
running: 'blue',
succeeded: 'teal',
};
const SPAN_STATUS_COLORS: Record<Span['status']['status_code'], string> = {
UNSET: 'gray',
OK: 'teal',
ERROR: 'red',
};
const WORKER_STATUS_COLORS: Record<Worker['status'], string> = {
busy: 'orange',
idle: 'teal',
unknown: 'gray',
};
const TRACES_SORT_FIELD_MAP: Record<string, string> = {
name: 'name',
traceId: 'trace_id',
spanId: 'span_id',
parentId: 'parent_id',
statusCode: 'status_code',
startTime: 'start_time',
duration: 'duration',
};
type SortDirection = 'asc' | 'desc';
type LocalSortState = {
column: string;
direction: SortDirection;
};
function resolveTracesSortField(column: string): string {
return TRACES_SORT_FIELD_MAP[column] ?? 'start_time';
}
function getStatusBadgeColor(status: RolloutStatus | AttemptStatus, isAttempt: boolean) {
if (isAttempt) {
return ATTEMPT_STATUS_COLORS[status as AttemptStatus] ?? 'gray';
}
return ROLLOUT_STATUS_COLORS[status as RolloutStatus] ?? 'gray';
}
function formatJson(value: unknown) {
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}
export type AppDrawerProps = {
opened: boolean;
onClose: () => void;
title?: ReactNode;
body?: ReactNode;
};
export function AppDrawer({ opened, onClose, title, body }: AppDrawerProps) {
return (
<Drawer
position='right'
size='lg'
opened={opened}
onClose={onClose}
overlayProps={{ opacity: 0.5 }}
withinPortal
styles={{
content: {
display: 'flex',
flexDirection: 'column',
maxHeight: '100vh',
},
body: {
flex: 1,
display: 'flex',
flexDirection: 'column',
padding: 'var(--mantine-spacing-md)',
minHeight: 0,
overflow: 'hidden',
},
}}
title={title}
>
<Stack gap='md' h='100%' style={{ flex: 1, minHeight: 0 }}>
{body}
</Stack>
</Drawer>
);
}
type TraceDrawerTitleProps = {
span: Span;
};
export function TraceDrawerTitle({ span }: TraceDrawerTitleProps) {
const spanStatusCode = span.status?.status_code ?? null;
const spanBadgeColor = spanStatusCode ? (SPAN_STATUS_COLORS[spanStatusCode] ?? 'gray') : undefined;
return (
<Stack gap={3}>
<Group gap={6}>
<Text fw={600}>{span.name ?? span.spanId}</Text>
{spanStatusCode ? (
<Badge size='sm' variant='light' color={spanBadgeColor}>
{spanStatusCode}
</Badge>
) : null}
</Group>
<Group gap={6}>
<Text size='sm' c='dimmed'>
{span.spanId}
</Text>
<CopyButton value={span.spanId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy span ID ${span.spanId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
</Group>
<Group gap='xs'>
<Group gap={3}>
<Text size='sm' c='dimmed' fw={500}>
Rollout
</Text>
<Text size='sm' c='dimmed'>
{span.rolloutId}
</Text>
</Group>
<Group gap={3}>
<Text size='sm' c='dimmed' fw={500}>
Attempt
</Text>
<Text size='sm' c='dimmed'>
{span.attemptId ?? '—'}
</Text>
</Group>
</Group>
</Stack>
);
}
type RolloutAttemptDrawerTitleProps = {
rollout: Rollout;
attempt: Attempt | null;
};
export function RolloutAttemptDrawerTitle({ rollout, attempt }: RolloutAttemptDrawerTitleProps) {
const rolloutId = rollout.rolloutId;
const attemptId = attempt?.attemptId ?? null;
const rolloutStatus = rollout.status ?? null;
const attemptStatus = attempt?.status ?? null;
const rolloutStatusLabel = rolloutStatus ? formatStatusLabel(rolloutStatus) : null;
const attemptStatusLabel = attemptStatus ? formatStatusLabel(attemptStatus) : null;
const hasStatusMismatch = rolloutStatus !== null && attemptStatus !== null && rolloutStatus !== attemptStatus;
const rolloutBadgeColor = rolloutStatus ? getStatusBadgeColor(rolloutStatus, false) : undefined;
const attemptBadgeColor = attemptStatus ? getStatusBadgeColor(attemptStatus, true) : undefined;
const showRolloutBadgeInHeading = Boolean(rolloutStatusLabel && (!attemptStatus || hasStatusMismatch));
const showAttemptBadge = Boolean(attemptStatusLabel && attemptStatus);
return (
<Stack gap={3}>
<Group gap={6}>
<Text fw={600}>{rolloutId}</Text>
<CopyButton value={rolloutId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy rollout ID ${rolloutId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
{showRolloutBadgeInHeading && rolloutStatusLabel ? (
<Badge size='sm' variant='light' color={rolloutBadgeColor}>
{rolloutStatusLabel}
</Badge>
) : null}
</Group>
<Group gap='xs'>
{attemptId ? (
<Group gap={3}>
<Text size='sm' c='dimmed' fw={500}>
Attempt
</Text>
<Text size='sm' c='dimmed'>
{attemptId}
</Text>
</Group>
) : null}
{showAttemptBadge && attemptStatusLabel ? (
<Badge size='sm' variant='light' color={attemptBadgeColor}>
{attemptStatusLabel}
</Badge>
) : null}
{!showRolloutBadgeInHeading && !attemptStatus && rolloutStatusLabel ? (
<Badge size='sm' variant='light' color={rolloutBadgeColor}>
{rolloutStatusLabel}
</Badge>
) : null}
</Group>
</Stack>
);
}
type JsonEditorProps = {
value: unknown;
};
export function JsonEditor({ value }: JsonEditorProps) {
const { colorScheme } = useMantineColorScheme();
const editorTheme = colorScheme === 'dark' ? 'vs-dark' : 'vs-light';
return (
<Box data-testid='json-editor-container' style={{ flex: 1, minHeight: 0 }}>
<Editor
height='100%'
language='json'
value={formatJson(value)}
theme={editorTheme}
options={{
readOnly: true,
domReadOnly: true,
minimap: { enabled: false },
automaticLayout: true,
scrollBeyondLastLine: false,
fontSize: 13,
}}
/>
</Box>
);
}
type RolloutTracesDrawerBodyProps = {
rollout: Rollout;
attempt: Attempt | null;
onShowRollout: (record: TracesTableRecord) => void;
onShowSpanDetail: (record: TracesTableRecord) => void;
};
function RolloutTracesDrawerBody({ rollout, attempt, onShowRollout, onShowSpanDetail }: RolloutTracesDrawerBodyProps) {
const [page, setPage] = useState(1);
const [recordsPerPage, setRecordsPerPage] = useState(100);
const [sort, setSort] = useState<LocalSortState>({
column: 'startTime',
direction: 'desc',
});
useEffect(() => {
setPage(1);
}, [rollout.rolloutId, attempt?.attemptId]);
const queryArgs = useMemo(
() => ({
rolloutId: rollout.rolloutId,
attemptId: attempt?.attemptId ?? undefined,
limit: recordsPerPage,
offset: Math.max(0, (page - 1) * recordsPerPage),
sortBy: resolveTracesSortField(sort.column),
sortOrder: sort.direction,
}),
[rollout.rolloutId, attempt?.attemptId, recordsPerPage, page, sort],
);
const { data, isFetching, isError, error, refetch } = useGetSpansQuery(queryArgs);
const spans = data?.items ?? [];
const totalRecords = data?.total ?? 0;
const tracesLinkSearch = useMemo(() => {
const params = createSearchParams({
rolloutId: rollout.rolloutId,
...(attempt?.attemptId ? { attemptId: attempt.attemptId } : {}),
});
return params.toString();
}, [attempt?.attemptId, rollout.rolloutId]);
const tracesLinkHref = tracesLinkSearch ? `/traces?${tracesLinkSearch}` : '/traces';
const isWithinRouter = useInRouterContext();
const handleSortStatusChange = useCallback((status: DataTableSortStatus<TracesTableRecord>) => {
setSort({
column: status.columnAccessor as string,
direction: status.direction,
});
}, []);
const handlePageChange = useCallback((nextPage: number) => {
setPage(nextPage);
}, []);
const handleRecordsPerPageChange = useCallback((value: number) => {
setRecordsPerPage(value);
setPage(1);
}, []);
return (
<Stack gap='md' style={{ flex: 1, minHeight: 0 }}>
<Group justify='space-between' align='center' gap='sm' wrap='nowrap'>
<Text size='sm' style={{ flex: 1, minWidth: 0 }}>
Showing spans for{' '}
<Text component='span' fw={600}>
{rollout.rolloutId}
{attempt ? ` · Attempt ${attempt.sequenceId} (${attempt.attemptId})` : ' · Latest attempt'}
</Text>
</Text>
{isWithinRouter ? (
<Anchor
component={Link}
to={tracesLinkHref}
size='sm'
aria-label={`Open traces page for rollout ${rollout.rolloutId}${
attempt ? ` attempt ${attempt.sequenceId}` : ''
}`}
>
View full traces
</Anchor>
) : (
<Anchor
href={tracesLinkHref}
size='sm'
aria-label={`Open traces page for rollout ${rollout.rolloutId}${
attempt ? ` attempt ${attempt.sequenceId}` : ''
}`}
>
View full traces
</Anchor>
)}
</Group>
<Box data-testid='traces-drawer-table-container' style={{ flex: 1, minHeight: 0, overflow: 'auto' }}>
<TracesTable
spans={spans}
totalRecords={totalRecords}
isFetching={isFetching}
isError={isError}
error={error}
searchTerm=''
sort={sort}
page={page}
recordsPerPage={recordsPerPage}
onSortStatusChange={handleSortStatusChange}
onPageChange={handlePageChange}
onRecordsPerPageChange={handleRecordsPerPageChange}
onResetFilters={() => {}}
onRefetch={refetch}
onShowRollout={onShowRollout}
onShowSpanDetail={onShowSpanDetail}
recordsPerPageOptions={[50, 100, 200, 500]}
/>
</Box>
</Stack>
);
}
type WorkerDrawerTitleProps = {
worker: Worker;
};
function WorkerDrawerTitle({ worker }: WorkerDrawerTitleProps) {
const badgeColor = WORKER_STATUS_COLORS[worker.status] ?? 'gray';
return (
<Stack gap={3}>
<Group gap={6} align='center'>
<Text fw={600}>{worker.workerId}</Text>
<CopyButton value={worker.workerId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy worker ID ${worker.workerId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
<Badge size='sm' variant='light' color={badgeColor}>
{formatStatusLabel(worker.status)}
</Badge>
</Group>
<Group gap='xl'>
<Group gap={4}>
<Text size='sm' c='dimmed' fw={500}>
Rollout
</Text>
<Text size='sm' c='dimmed'>
{worker.currentRolloutId ?? '—'}
</Text>
</Group>
<Group gap={4}>
<Text size='sm' c='dimmed' fw={500}>
Attempt
</Text>
<Text size='sm' c='dimmed'>
{worker.currentAttemptId ?? '—'}
</Text>
</Group>
</Group>
</Stack>
);
}
export function AppDrawerContainer() {
const dispatch = useAppDispatch();
const isOpen = useAppSelector(selectDrawerIsOpen);
const content = useAppSelector(selectDrawerContent);
const isRouterAvailable = useInRouterContext();
const handleClose = useCallback(() => {
dispatch(closeDrawer());
}, [dispatch]);
const handleNavigation = useCallback(() => {
if (isOpen) {
dispatch(closeDrawer());
}
}, [dispatch, isOpen]);
const derivedContent = useMemo(() => {
if (!content) {
return null;
}
if (content.type === 'worker-detail') {
const { worker } = content;
const title = <WorkerDrawerTitle worker={worker} />;
const body = <JsonEditor value={worker} />;
return { title, body };
}
if (content.type === 'trace-detail') {
const { span } = content;
const title = <TraceDrawerTitle span={span} />;
const body = <JsonEditor value={span} />;
return { title, body };
}
const rollout = content.rollout;
const attempt = content.attempt;
const title = <RolloutAttemptDrawerTitle rollout={rollout} attempt={attempt} />;
if (content.type === 'rollout-json') {
const jsonValue = content.isNested && content.attempt ? content.attempt : rollout;
const body = jsonValue ? <JsonEditor value={jsonValue} /> : null;
return { title, body };
}
if (content.type === 'rollout-traces') {
const body = (
<RolloutTracesDrawerBody
rollout={rollout}
attempt={attempt}
onShowRollout={() => {
const attemptForRecord = attempt ?? rollout.attempt ?? null;
dispatch(
openDrawer({
type: 'rollout-json',
rollout,
attempt: attemptForRecord,
isNested: content.isNested,
}),
);
}}
onShowSpanDetail={(record) => {
const attemptForRecord = attempt ?? rollout.attempt ?? null;
dispatch(
openDrawer({
type: 'trace-detail',
span: record,
rollout,
attempt: attemptForRecord,
}),
);
}}
/>
);
return { title, body };
}
return null;
}, [content, dispatch]);
if (!content || !derivedContent) {
return null;
}
const { title, body } = derivedContent;
return (
<>
{isRouterAvailable ? <DrawerLocationWatcher onNavigation={handleNavigation} /> : null}
<AppDrawer opened={isOpen} onClose={handleClose} title={title} body={body} />
</>
);
}
type DrawerLocationWatcherProps = {
onNavigation: () => void;
};
function DrawerLocationWatcher({ onNavigation }: DrawerLocationWatcherProps) {
const location = useLocation();
const lastLocationKeyRef = useRef(location.key);
useEffect(() => {
if (lastLocationKeyRef.current === location.key) {
return;
}
lastLocationKeyRef.current = location.key;
onNavigation();
}, [location.key, onNavigation]);
return null;
}
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// Copyright (c) Microsoft. All rights reserved.
import type { Meta, StoryObj } from '@storybook/react';
import { Provider } from 'react-redux';
import { initialConfigState } from '@/features/config/slice';
import { initialResourcesUiState } from '@/features/resources/slice';
import { rolloutsApi } from '@/features/rollouts';
import { initialRolloutsUiState } from '@/features/rollouts/slice';
import { initialTracesUiState } from '@/features/traces/slice';
import type { DrawerContent } from '@/features/ui/drawer';
import { createAppStore } from '@/store';
import type { Attempt, Rollout, Span } from '@/types';
import { STORY_BASE_URL, STORY_DATE_NOW_SECONDS } from '../../.storybook/constants';
import { AppDrawerContainer } from './AppDrawer.component';
const meta = {
title: 'Components/AppDrawer',
component: AppDrawerContainer,
parameters: {
layout: 'fullscreen',
},
} satisfies Meta<typeof AppDrawerContainer>;
export default meta;
type Story = StoryObj<typeof AppDrawerContainer>;
const now = STORY_DATE_NOW_SECONDS;
const baseAttempt: Attempt = {
rolloutId: 'ro-story-001',
attemptId: 'at-story-001',
sequenceId: 1,
startTime: now - 3600,
endTime: null,
status: 'running',
workerId: 'worker-story',
lastHeartbeatTime: now - 42,
metadata: { info: 'Sample metadata', runId: 'run-123' },
};
const baseRollout: Rollout = {
rolloutId: 'ro-story-001',
input: {
task: 'Generate daily summary',
payload: { account: 'enterprise', date: '2024-02-19' },
},
startTime: now - 4000,
endTime: null,
mode: 'train',
resourcesId: 'rs-story-001',
status: 'running',
config: { retries: 1, priority: 'high' },
metadata: { owner: 'storybook' },
attempt: baseAttempt,
};
const noAttemptRollout: Rollout = {
...baseRollout,
status: 'queuing',
attempt: null,
};
const mismatchRollout: Rollout = {
...baseRollout,
status: 'running',
attempt: {
...baseAttempt,
status: 'failed',
endTime: now - 1200,
metadata: { info: 'Latest attempt failed', reason: 'Timeout' },
},
};
const sampleSpan: Span = {
rolloutId: 'ro-story-001',
attemptId: 'at-story-001',
sequenceId: 2,
traceId: 'tr-story-001',
spanId: 'sp-story-001',
parentId: null,
name: 'Fetch Resources',
status: { status_code: 'OK', description: 'Completed successfully' },
attributes: {
'http.method': 'GET',
'http.url': 'https://api.example.com/resources',
'duration_ms': 120,
},
startTime: now - 240,
endTime: now - 120,
events: [],
links: [],
context: {},
parent: null,
resource: {},
};
const sampleTraces: Span[] = [
sampleSpan,
{
...sampleSpan,
spanId: 'sp-story-002',
name: 'Process Response',
parentId: 'sp-story-001',
sequenceId: 3,
status: { status_code: 'ERROR', description: 'Unexpected response code' },
attributes: {
...sampleSpan.attributes,
duration_ms: 240,
},
startTime: now - 120,
endTime: now - 30,
},
];
function renderWithDrawer(content: DrawerContent, options?: { spans?: Span[] }) {
const store = createAppStore({
config: {
...initialConfigState,
baseUrl: STORY_BASE_URL,
},
rollouts: initialRolloutsUiState,
resources: initialResourcesUiState,
traces: initialTracesUiState,
drawer: {
isOpen: true,
content,
},
});
if (content.type === 'rollout-traces' && options?.spans) {
const defaultLimit = 100;
const queryArgs = {
rolloutId: content.rollout.rolloutId,
attemptId: content.attempt?.attemptId ?? undefined,
limit: defaultLimit,
offset: 0,
sortBy: 'start_time',
sortOrder: 'desc' as const,
};
store.dispatch(
rolloutsApi.util.upsertQueryData('getSpans', queryArgs, {
items: options.spans,
total: options.spans.length,
limit: defaultLimit,
offset: 0,
}),
);
}
return (
<Provider store={store}>
<AppDrawerContainer />
</Provider>
);
}
export const RolloutJson: Story = {
render: () =>
renderWithDrawer({
type: 'rollout-json',
rollout: baseRollout,
attempt: baseRollout.attempt,
isNested: false,
}),
};
export const NestedAttemptJson: Story = {
render: () =>
renderWithDrawer({
type: 'rollout-json',
rollout: baseRollout,
attempt: {
...baseAttempt,
attemptId: 'at-story-002',
sequenceId: 2,
status: 'failed',
endTime: now - 1200,
metadata: { info: 'Secondary attempt', reason: 'Timeout' },
},
isNested: true,
}),
};
export const RolloutTraces: Story = {
render: () =>
renderWithDrawer(
{
type: 'rollout-traces',
rollout: baseRollout,
attempt: baseRollout.attempt,
isNested: false,
},
{ spans: sampleTraces },
),
};
export const NoAttempt: Story = {
render: () =>
renderWithDrawer({
type: 'rollout-json',
rollout: noAttemptRollout,
attempt: null,
isNested: false,
}),
};
export const StatusMismatch: Story = {
render: () =>
renderWithDrawer({
type: 'rollout-json',
rollout: mismatchRollout,
attempt: mismatchRollout.attempt,
isNested: false,
}),
};
export const SpanDetail: Story = {
render: () =>
renderWithDrawer({
type: 'trace-detail',
span: sampleSpan,
rollout: mismatchRollout,
attempt: mismatchRollout.attempt,
}),
};
export const LightTheme: Story = {
render: () =>
renderWithDrawer({
type: 'rollout-json',
rollout: baseRollout,
attempt: baseRollout.attempt,
isNested: false,
}),
parameters: {
theme: 'light',
},
};
export const DarkTheme: Story = {
render: () =>
renderWithDrawer({
type: 'trace-detail',
span: {
...sampleSpan,
spanId: 'sp-story-002',
name: 'Process Response',
status: { status_code: 'ERROR', description: 'Unexpected response code' },
},
rollout: mismatchRollout,
attempt: mismatchRollout.attempt,
}),
parameters: {
theme: 'dark',
},
};
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// Copyright (c) Microsoft. All rights reserved.
import { useCallback, useEffect, useMemo, useState, type ReactNode, type SetStateAction } from 'react';
import { IconCheck, IconCopy, IconRefresh } from '@tabler/icons-react';
import { DataTable, type DataTableColumn, type DataTableSortStatus } from 'mantine-datatable';
import { ActionIcon, Box, Button, CopyButton, Group, Stack, Text, Tooltip } from '@mantine/core';
import { useElementSize, useViewportSize } from '@mantine/hooks';
import { getLayoutAwareWidth } from '@/layouts/helper';
import type { Resources } from '@/types';
import { getErrorDescriptor } from '@/utils/error';
import { formatDateTime, safeStringify } from '@/utils/format';
import { createResponsiveColumns, type ColumnVisibilityConfig } from '@/utils/table';
const DEFAULT_RECORDS_PER_PAGE_OPTIONS = [50, 100, 200, 500];
const COLUMN_VISIBILITY: Record<string, ColumnVisibilityConfig> = {
resourcesId: { fixedWidth: 12, priority: 0 },
version: { fixedWidth: 8, priority: 1 },
createTime: { fixedWidth: 14, priority: 2 },
updateTime: { fixedWidth: 14, priority: 2 },
resourceCount: { fixedWidth: 8, priority: 3 },
resourcesPreview: { minWidth: 16, priority: 4 },
};
export type ResourcesTableRecord = Resources & {
resourceCount: number;
canExpand: boolean;
resourcesPreview: string;
};
function buildResourcesRecord(resources: Resources): ResourcesTableRecord {
const resourceCount = Object.keys(resources.resources ?? {}).length;
const resourcesValue =
resources.resources === null || typeof resources.resources === 'undefined'
? '—'
: typeof resources.resources === 'string'
? resources.resources
: safeStringify(resources.resources);
return {
...resources,
resourceCount,
canExpand: resourceCount > 0,
resourcesPreview: resourcesValue,
};
}
type ResourcesColumnsOptions = Record<string, never>;
function createResourcesColumns(_options: ResourcesColumnsOptions): DataTableColumn<ResourcesTableRecord>[] {
return [
{
accessor: 'resourcesId',
title: 'Resources ID',
sortable: true,
render: ({ resourcesId }) => (
<Group gap={2}>
<Text fw={500} size='sm'>
{resourcesId}
</Text>
<CopyButton value={resourcesId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy resources ID ${resourcesId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
</Group>
),
},
{
accessor: 'version',
title: 'Version',
sortable: true,
textAlign: 'left',
render: ({ version }) => <Text size='sm'>{version}</Text>,
},
{
accessor: 'createTime',
title: 'Created',
sortable: true,
textAlign: 'left',
render: ({ createTime }) => <Text size='sm'>{formatDateTime(createTime)}</Text>,
},
{
accessor: 'updateTime',
title: 'Updated',
sortable: true,
textAlign: 'left',
render: ({ updateTime }) => <Text size='sm'>{formatDateTime(updateTime)}</Text>,
},
{
accessor: 'resourceCount',
title: 'Count',
sortable: true,
textAlign: 'left',
render: ({ resourceCount }) => <Text size='sm'>{resourceCount}</Text>,
},
{
accessor: 'resourcesPreview',
title: 'Preview',
render: ({ resourcesPreview }) => (
<Text size='sm' ff='monospace' c='dimmed' lineClamp={1} style={{ width: '100%' }}>
{resourcesPreview}
</Text>
),
},
];
}
type RowExpansionRenderer = (context: {
resources: Resources;
columns: DataTableColumn<ResourcesTableRecord>[];
}) => ReactNode;
export type ResourcesTableProps = {
resourcesList: Resources[] | undefined;
totalRecords: number;
isFetching: boolean;
isError: boolean;
error: unknown;
searchTerm: string;
sort: { column: string; direction: 'asc' | 'desc' };
page: number;
recordsPerPage: number;
onSortStatusChange: (status: DataTableSortStatus<ResourcesTableRecord>) => void;
onPageChange: (page: number) => void;
onRecordsPerPageChange: (value: number) => void;
onResetFilters: () => void;
onRefetch: () => void;
recordsPerPageOptions?: number[];
renderRowExpansion?: RowExpansionRenderer;
};
export function ResourcesTable({
resourcesList,
totalRecords,
isFetching,
isError,
error,
searchTerm,
sort,
page,
recordsPerPage,
onSortStatusChange,
onPageChange,
onRecordsPerPageChange,
onResetFilters,
onRefetch,
recordsPerPageOptions = DEFAULT_RECORDS_PER_PAGE_OPTIONS,
renderRowExpansion,
}: ResourcesTableProps) {
const [expandedRecordIds, setExpandedRecordIds] = useState<string[]>([]);
const { ref: tableContainerRef, width: containerWidth } = useElementSize();
const { width: viewportWidth } = useViewportSize();
const layoutAwareContainerWidth = useMemo(
() => getLayoutAwareWidth(containerWidth, viewportWidth),
[containerWidth, viewportWidth],
);
const resourcesRecords = useMemo<ResourcesTableRecord[]>(() => {
if (!resourcesList) {
return [];
}
return resourcesList.map((resourcesItem) => buildResourcesRecord(resourcesItem));
}, [resourcesList]);
const columns = useMemo(() => createResourcesColumns({}), []);
const responsiveColumns = useMemo(
() => createResponsiveColumns(columns, layoutAwareContainerWidth, COLUMN_VISIBILITY),
[columns, layoutAwareContainerWidth],
);
const totalPages = useMemo(
() => Math.max(1, Math.ceil(Math.max(0, totalRecords) / Math.max(1, recordsPerPage))),
[recordsPerPage, totalRecords],
);
useEffect(() => {
if (page > totalPages) {
onPageChange(totalPages);
}
}, [onPageChange, page, totalPages]);
useEffect(() => {
setExpandedRecordIds((current) =>
current.filter((id) => resourcesRecords.some((record) => record.resourcesId === id && record.canExpand)),
);
}, [resourcesRecords]);
const hasActiveFilters = searchTerm.trim().length > 0;
const sortStatus: DataTableSortStatus<ResourcesTableRecord> = {
columnAccessor: sort.column,
direction: sort.direction,
};
const handleSortStatusChange = useCallback(
(status: DataTableSortStatus<ResourcesTableRecord>) => {
onSortStatusChange(status);
},
[onSortStatusChange],
);
const errorDescriptor = isError ? getErrorDescriptor(error) : null;
const errorMessage = isError
? `Resources are temporarily unavailable${errorDescriptor ? ` (${errorDescriptor})` : ''}.`
: 'Resources are temporarily unavailable.';
const emptyState = (
<Stack gap='sm' align='center' py='lg'>
{isError ? (
<>
<Text fw={600} size='sm'>
{errorMessage}
</Text>
<Text size='sm' c='dimmed' ta='center'>
Use the retry button to try again, or adjust the filters to broaden the results.
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' color='gray' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Retry
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
) : (
<>
<Text fw={600} size='sm'>
No resources found
</Text>
<Text size='sm' c='dimmed' ta='center'>
{hasActiveFilters
? 'Try adjusting the search to see more results.'
: 'Try refreshing to fetch the latest resources.'}
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Refresh
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
)}
</Stack>
);
return (
<Box ref={tableContainerRef}>
<DataTable<ResourcesTableRecord>
classNames={{ root: 'resources-table' }}
withTableBorder
withColumnBorders
highlightOnHover
verticalAlign='center'
minHeight={resourcesRecords.length === 0 ? 500 : undefined}
idAccessor='resourcesId'
records={resourcesRecords}
columns={responsiveColumns}
totalRecords={totalRecords}
recordsPerPage={recordsPerPage}
page={page}
onPageChange={onPageChange}
onRecordsPerPageChange={onRecordsPerPageChange}
recordsPerPageOptions={recordsPerPageOptions}
sortStatus={sortStatus}
onSortStatusChange={handleSortStatusChange}
fetching={isFetching}
loaderSize='sm'
emptyState={resourcesRecords.length === 0 ? emptyState : undefined}
rowExpansion={
renderRowExpansion
? {
allowMultiple: true,
expandable: ({ record }) => record.canExpand,
expanded: {
recordIds: expandedRecordIds,
onRecordIdsChange: (nextRecordIds: SetStateAction<string[]>) => {
setExpandedRecordIds((previous) => {
const resolved =
typeof nextRecordIds === 'function'
? nextRecordIds(previous)
: ((nextRecordIds ?? []) as (string | number)[]);
return resolved
.map(String)
.filter((id) =>
resourcesRecords.some(
(tableRecord) => tableRecord.resourcesId === id && tableRecord.canExpand,
),
);
});
},
},
content: ({ record }) => renderRowExpansion({ resources: record, columns: responsiveColumns }),
}
: undefined
}
/>
</Box>
);
}
@@ -0,0 +1,312 @@
// Copyright (c) Microsoft. All rights reserved.
import { useMemo, useState } from 'react';
import type { Meta, StoryObj } from '@storybook/react';
import { IconSearch } from '@tabler/icons-react';
import { Box, Stack, TextInput, Title } from '@mantine/core';
import type { Resources } from '@/types';
import { ResourcesTable } from './ResourcesTable.component';
import { ResourcesTree } from './ResourcesTree.component';
const meta: Meta<typeof ResourcesTable> = {
title: 'Components/ResourcesTable',
component: ResourcesTable,
parameters: {
layout: 'fullscreen',
},
};
export default meta;
type Story = StoryObj<typeof ResourcesTable>;
const sampleResources: Resources[] = [
{
resourcesId: 'rs-story-001',
version: 1,
createTime: 1710806400,
updateTime: 1713412800,
resources: {
model: {
name: 'gpt-4',
version: '2024-01-01',
temperature: 0.7,
maxTokens: 2048,
topP: 0.9,
},
database: {
host: 'db.example.com',
port: 5432,
name: 'production',
pool: {
min: 2,
max: 10,
idle: 30000,
},
},
cache: {
type: 'redis',
host: 'cache.example.com',
port: 6379,
ttl: 3600,
},
},
},
{
resourcesId: 'rs-story-002',
version: 2,
createTime: 1712217600,
updateTime: 1714823200,
resources: {
model: {
name: 'claude-3-opus',
version: '2024-02-01',
temperature: 0.5,
maxTokens: 4096,
},
storage: {
type: 's3',
bucket: 'training-data',
region: 'us-east-1',
credentials: {
accessKeyId: 'AKIA***',
encrypted: true,
},
},
compute: {
instances: [
{ id: 'i-001', type: 't3.large', zone: 'us-east-1a' },
{ id: 'i-002', type: 't3.large', zone: 'us-east-1b' },
],
autoScaling: {
min: 2,
max: 10,
targetCpu: 70,
},
},
},
},
{
resourcesId: 'rs-story-003',
version: 3,
createTime: 1709251200,
updateTime: 1711856800,
resources: {
model: {
name: 'gpt-3.5-turbo',
version: '2023-12-01',
temperature: 0.8,
maxTokens: 1024,
},
monitoring: {
enabled: true,
interval: 60,
metrics: ['cpu', 'memory', 'disk', 'network'],
alerts: {
email: 'ops@example.com',
slack: '#alerts',
pagerduty: true,
},
},
},
},
{
resourcesId: 'rs-story-004',
version: 1,
createTime: 1706745600,
updateTime: 1709347200,
resources: {
apiKeys: {
openai: 'sk-***',
anthropic: 'sk-ant-***',
replicate: 'r8-***',
},
rateLimits: {
requestsPerMinute: 100,
tokensPerDay: 1000000,
concurrent: 5,
},
},
},
{
resourcesId: 'rs-story-005',
version: 1,
createTime: 1704067200,
updateTime: 1706668800,
resources: {},
},
];
type WrapperProps = {
maxWidth: number;
resourcesList?: Resources[] | undefined;
isFetching?: boolean;
isError?: boolean;
error?: unknown;
};
function ResourcesTableStoryWrapper({
maxWidth,
resourcesList = sampleResources,
isFetching = false,
isError = false,
error = null,
}: WrapperProps) {
const [searchTerm, setSearchTerm] = useState('');
const [page, setPage] = useState(1);
const [recordsPerPage, setRecordsPerPage] = useState(5);
const [sort, setSort] = useState<{ column: string; direction: 'asc' | 'desc' }>({
column: 'resourcesId',
direction: 'asc',
});
const baseResources = resourcesList ?? [];
const filteredResources = useMemo(() => {
const normalized = searchTerm.trim().toLowerCase();
if (normalized.length === 0) {
return baseResources;
}
return baseResources.filter((resource) => resource.resourcesId.toLowerCase().includes(normalized));
}, [baseResources, searchTerm]);
const sortedResources = useMemo(() => {
const items = filteredResources.slice();
const resolveSortValue = (resource: Resources, column: string) => {
switch (column) {
case 'version':
return resource.version;
case 'createTime':
return resource.createTime;
case 'updateTime':
return resource.updateTime;
case 'resourceCount':
return Object.keys(resource.resources ?? {}).length;
case 'resourcesId':
default:
return resource.resourcesId;
}
};
items.sort((a, b) => {
const aValue = resolveSortValue(a, sort.column);
const bValue = resolveSortValue(b, sort.column);
if (aValue === bValue) {
return 0;
}
if (typeof aValue === 'number' && typeof bValue === 'number') {
return aValue - bValue;
}
return String(aValue).localeCompare(String(bValue));
});
if (sort.direction === 'desc') {
items.reverse();
}
return items;
}, [filteredResources, sort]);
const totalRecordsValue = sortedResources.length;
const pagedResources = useMemo(() => {
const startIndex = (page - 1) * recordsPerPage;
return sortedResources.slice(startIndex, startIndex + recordsPerPage);
}, [page, recordsPerPage, sortedResources]);
return (
<Box mx='auto' style={{ maxWidth, width: '100%', padding: 16 }}>
<Stack gap='md'>
<Title order={2}>Resources</Title>
<TextInput
placeholder='Search by Resources ID'
value={searchTerm}
onChange={(event) => {
setSearchTerm(event.currentTarget.value);
setPage(1);
}}
leftSection={<IconSearch size={16} />}
data-testid='resources-search-input'
w='100%'
style={{ maxWidth: 360 }}
/>
<ResourcesTable
resourcesList={pagedResources}
totalRecords={totalRecordsValue}
isFetching={isFetching}
isError={isError}
error={error}
searchTerm={searchTerm}
sort={sort}
page={page}
recordsPerPage={recordsPerPage}
onSortStatusChange={(status) => {
setSort({
column: status.columnAccessor as string,
direction: status.direction,
});
}}
onPageChange={setPage}
onRecordsPerPageChange={(value) => {
setRecordsPerPage(value);
setPage(1);
}}
onResetFilters={() => {
setSearchTerm('');
setSort({ column: 'resourcesId', direction: 'asc' });
setPage(1);
}}
onRefetch={() => undefined}
recordsPerPageOptions={[5, 10, 20]}
renderRowExpansion={({ resources }) => <ResourcesTree resources={resources} />}
/>
</Stack>
</Box>
);
}
export const WideContainer: Story = {
render: () => <ResourcesTableStoryWrapper maxWidth={1280} />,
};
export const MediumContainer: Story = {
render: () => <ResourcesTableStoryWrapper maxWidth={960} />,
};
export const NarrowContainer: Story = {
render: () => <ResourcesTableStoryWrapper maxWidth={720} />,
};
export const DrawerWidth: Story = {
render: () => <ResourcesTableStoryWrapper maxWidth={520} />,
};
export const ErrorState: Story = {
render: () => (
<ResourcesTableStoryWrapper maxWidth={600} resourcesList={[]} isError error={new Error('Network unreachable')} />
),
};
export const EmptyResources: Story = {
render: () => (
<ResourcesTableStoryWrapper
maxWidth={960}
resourcesList={[
{
resourcesId: 'rs-empty-001',
version: 1,
createTime: 1702000000,
updateTime: 1704600000,
resources: {},
},
]}
/>
),
};
export const LoadingState: Story = {
render: () => <ResourcesTableStoryWrapper maxWidth={960} resourcesList={[]} isFetching />,
};
@@ -0,0 +1,129 @@
// Copyright (c) Microsoft. All rights reserved.
import { useMemo } from 'react';
import { IconAlertCircle, IconChevronRight } from '@tabler/icons-react';
import { Box, Group, Stack, Text, Tree, type TreeNodeData } from '@mantine/core';
import type { Resources } from '@/types';
import { safeStringify } from '@/utils/format';
function convertToTreeData(obj: any, key: string = 'root', parentPath = ''): TreeNodeData {
const isObject = obj !== null && typeof obj === 'object' && !Array.isArray(obj);
const isArray = Array.isArray(obj);
const currentPath = parentPath ? `${parentPath}.${key}` : key;
if (isObject) {
const children = Object.entries(obj).map(([childKey, childValue]) =>
convertToTreeData(childValue, childKey, currentPath),
);
return {
value: currentPath,
label: (
<Group gap={6}>
<Text size='sm' fw={500}>
{key}
</Text>
<Text size='xs' c='dimmed'>
(Object)
</Text>
</Group>
),
children: children.length > 0 ? children : undefined,
};
}
if (isArray) {
const children = obj.map((item: any, index: number) => convertToTreeData(item, `[${index}]`, currentPath));
return {
value: currentPath,
label: (
<Group gap={6}>
<Text size='sm' fw={500}>
{key}
</Text>
<Text size='xs' c='dimmed'>
(Array[
{obj.length}
])
</Text>
</Group>
),
children: children.length > 0 ? children : undefined,
};
}
// Primitive value
return {
value: currentPath,
label: (
<Group gap={6}>
<Text size='sm' fw={500}>
{key}:
</Text>
<Text size='sm' ff='monospace' c='dimmed'>
{safeStringify(obj)}
</Text>
</Group>
),
};
}
export type ResourcesTreeProps = {
resources: Resources;
};
export function ResourcesTree({ resources }: ResourcesTreeProps) {
const resourcesDict = resources.resources ?? {};
const treeData = useMemo<TreeNodeData[]>(() => {
const entries = Object.entries(resourcesDict);
if (entries.length === 0) {
return [];
}
return entries.map(([key, value]) => convertToTreeData(value, key));
}, [resourcesDict]);
if (treeData.length === 0) {
return (
<Stack gap='xs' align='center' py='md'>
<IconAlertCircle size={24} color='gray' />
<Text size='sm' c='dimmed'>
No resources found
</Text>
</Stack>
);
}
return (
<Box p='md' style={{ backgroundColor: 'var(--mantine-color-default-hover)' }}>
<Tree
data={treeData}
levelOffset={20}
expandOnClick
selectOnClick
renderNode={({ node, expanded, hasChildren, elementProps }) => (
<Group gap={4} {...elementProps}>
{hasChildren && (
<Box
style={{
minWidth: 14,
display: 'flex',
alignItems: 'center',
justifyContent: 'center',
transform: expanded ? 'rotate(90deg)' : 'rotate(0deg)',
transition: 'transform 150ms ease',
}}
>
<IconChevronRight size={14} />
</Box>
)}
{node.label}
</Group>
)}
/>
</Box>
);
}
@@ -0,0 +1,200 @@
// Copyright (c) Microsoft. All rights reserved.
import type { Meta, StoryObj } from '@storybook/react';
import { Box, Stack, Title } from '@mantine/core';
import type { Resources } from '@/types';
import { ResourcesTree } from './ResourcesTree.component';
const meta: Meta<typeof ResourcesTree> = {
title: 'Components/ResourcesTree',
component: ResourcesTree,
parameters: {
layout: 'fullscreen',
},
};
export default meta;
type Story = StoryObj<typeof ResourcesTree>;
const simpleResources: Resources = {
resourcesId: 'rs-simple-001',
version: 1,
createTime: 1704067200,
updateTime: 1706668800,
resources: {
apiKey: { value: 'sk-test-key-123', type: 'secret' },
maxRetries: { value: 3, description: 'Maximum retry attempts' },
timeout: { value: 30000, unit: 'ms' },
enabled: { value: true },
},
};
const nestedResources: Resources = {
resourcesId: 'rs-nested-001',
version: 2,
createTime: 1709251200,
updateTime: 1711856800,
resources: {
model: {
name: 'gpt-4',
version: '2024-01-01',
temperature: 0.7,
maxTokens: 2048,
topP: 0.9,
},
database: {
host: 'db.example.com',
port: 5432,
name: 'production',
pool: {
min: 2,
max: 10,
idle: 30000,
},
},
cache: {
type: 'redis',
host: 'cache.example.com',
port: 6379,
ttl: 3600,
},
},
};
const arrayResources: Resources = {
resourcesId: 'rs-array-001',
version: 3,
createTime: 1712217600,
updateTime: 1714823200,
resources: {
compute: {
instances: [
{ id: 'i-001', type: 't3.large', zone: 'us-east-1a', status: 'running' },
{ id: 'i-002', type: 't3.large', zone: 'us-east-1b', status: 'running' },
{ id: 'i-003', type: 't3.xlarge', zone: 'us-east-1c', status: 'stopped' },
],
autoScaling: {
min: 2,
max: 10,
targetCpu: 70,
},
},
tags: ['production', 'ml-training', 'auto-scale'],
ports: [80, 443, 8080],
},
};
const complexResources: Resources = {
resourcesId: 'rs-complex-001',
version: 4,
createTime: 1706745600,
updateTime: 1710000000,
resources: {
model: {
name: 'claude-3-opus',
version: '2024-02-01',
temperature: 0.5,
maxTokens: 4096,
providers: [
{ name: 'anthropic', priority: 1, enabled: true },
{ name: 'aws-bedrock', priority: 2, enabled: false },
],
},
storage: {
type: 's3',
bucket: 'training-data',
region: 'us-east-1',
credentials: {
accessKeyId: 'AKIA***',
encrypted: true,
},
lifecycle: {
transitionToIA: 30,
transitionToGlacier: 90,
expiration: 365,
},
},
monitoring: {
enabled: true,
interval: 60,
metrics: ['cpu', 'memory', 'disk', 'network'],
alerts: {
email: 'ops@example.com',
slack: '#alerts',
pagerduty: true,
thresholds: {
cpu: { warning: 70, critical: 90 },
memory: { warning: 80, critical: 95 },
disk: { warning: 75, critical: 90 },
},
},
},
apiKeys: {
openai: 'sk-***',
anthropic: 'sk-ant-***',
replicate: 'r8-***',
},
rateLimits: {
requestsPerMinute: 100,
tokensPerDay: 1000000,
concurrent: 5,
burstMultiplier: 1.5,
},
},
};
const emptyResources: Resources = {
resourcesId: 'rs-empty-001',
version: 1,
createTime: 1702000000,
updateTime: 1704600000,
resources: {},
};
type WrapperProps = {
resources: Resources;
maxWidth?: number;
};
function ResourcesTreeStoryWrapper({ resources, maxWidth = 800 }: WrapperProps) {
return (
<Box mx='auto' style={{ maxWidth, width: '100%', padding: 16 }}>
<Stack gap='md'>
<Title order={2}>
Resources:
{resources.resourcesId}
</Title>
<ResourcesTree resources={resources} />
</Stack>
</Box>
);
}
export const SimpleValues: Story = {
render: () => <ResourcesTreeStoryWrapper resources={simpleResources} />,
};
export const NestedObjects: Story = {
render: () => <ResourcesTreeStoryWrapper resources={nestedResources} />,
};
export const WithArrays: Story = {
render: () => <ResourcesTreeStoryWrapper resources={arrayResources} />,
};
export const ComplexStructure: Story = {
render: () => <ResourcesTreeStoryWrapper resources={complexResources} maxWidth={1000} />,
};
export const EmptyResources: Story = {
render: () => <ResourcesTreeStoryWrapper resources={emptyResources} />,
};
export const NarrowContainer: Story = {
render: () => <ResourcesTreeStoryWrapper resources={complexResources} maxWidth={500} />,
};
export const WideContainer: Story = {
render: () => <ResourcesTreeStoryWrapper resources={complexResources} maxWidth={1400} />,
};
@@ -0,0 +1,787 @@
// Copyright (c) Microsoft. All rights reserved.
import { useCallback, useEffect, useMemo, useState, type ReactNode, type SetStateAction } from 'react';
import {
IconAlertCircle,
IconCheck,
IconCopy,
IconFileDescription,
IconRefresh,
IconReload,
IconTimeline,
} from '@tabler/icons-react';
import { DataTable, type DataTableColumn, type DataTableSortStatus } from 'mantine-datatable';
import {
ActionIcon,
Alert,
Badge,
Box,
Button,
CopyButton,
Group,
MultiSelect,
Stack,
Text,
Tooltip,
} from '@mantine/core';
import { useElementSize, useViewportSize } from '@mantine/hooks';
import {
type Attempt,
type AttemptStatus,
type Rollout,
type RolloutMode,
type RolloutsSortState,
type RolloutStatus,
} from '@/features/rollouts';
import { getLayoutAwareWidth } from '@/layouts/helper';
import {
clampToNow,
formatDateTime,
formatDuration,
formatRelativeTime,
formatStatusLabel,
safeStringify,
toTimestamp,
} from '@/utils/format';
import { createResponsiveColumns, type ColumnVisibilityConfig } from '@/utils/table';
const ROLLOUT_STATUS_OPTIONS: RolloutStatus[] = [
'queuing',
'preparing',
'running',
'failed',
'succeeded',
'cancelled',
'requeuing',
];
const ATTEMPT_STATUS_COLORS: Record<AttemptStatus, string> = {
failed: 'red',
preparing: 'violet',
running: 'blue',
succeeded: 'teal',
timeout: 'orange',
unresponsive: 'orange',
};
const ROLLOUT_STATUS_COLORS: Record<RolloutStatus, string> = {
cancelled: 'gray',
failed: 'red',
preparing: 'violet',
queuing: 'gray',
requeuing: 'gray',
running: 'blue',
succeeded: 'teal',
};
const ROLLOUT_MODE_OPTIONS: RolloutMode[] = ['train', 'val', 'test'];
const DEFAULT_RECORDS_PER_PAGE_OPTIONS = [50, 100, 200, 500];
const COLUMN_VISIBILITY: Record<string, ColumnVisibilityConfig> = {
rolloutId: { fixedWidth: 12.5, priority: 0 },
actionsPlaceholder: { fixedWidth: 6.5, priority: 0 },
inputText: { minWidth: 14, priority: 1 },
statusValue: { fixedWidth: 10, priority: 1 },
startTimestamp: { fixedWidth: 12, priority: 2 },
durationSeconds: { fixedWidth: 10, priority: 2 },
attemptId: { fixedWidth: 12, priority: 3 },
resourcesId: { fixedWidth: 10, priority: 3 },
mode: { fixedWidth: 8, priority: 3 },
lastHeartbeatTimestamp: { fixedWidth: 10, priority: 3 },
workerId: { fixedWidth: 10, priority: 3 },
};
export type RolloutTableRecord = Rollout & {
attemptId: string | null;
attemptSequence: number | null;
isNested: boolean;
canExpand: boolean;
inputText: string;
attemptStatus?: AttemptStatus;
statusValue: string;
startTimestamp: number | null;
durationSeconds: number | null;
lastHeartbeatTimestamp: number | null;
workerId: string | null;
actionsPlaceholder?: null;
};
function selectHeartbeatTimestamp(attempt?: Attempt | null): number | null {
if (!attempt || attempt.lastHeartbeatTime == null || Number.isNaN(attempt.lastHeartbeatTime)) {
return null;
}
return attempt.lastHeartbeatTime;
}
export function buildRolloutRecord(rollout: Rollout): RolloutTableRecord {
const latestAttempt = rollout.attempt;
const inputValue =
rollout.input === null || typeof rollout.input === 'undefined'
? '—'
: typeof rollout.input === 'string'
? rollout.input
: safeStringify(rollout.input);
const startTimestamp = toTimestamp(latestAttempt?.startTime ?? rollout.startTime);
const endTimestamp = toTimestamp(latestAttempt?.endTime ?? rollout.endTime);
const durationSeconds = clampToNow(startTimestamp, endTimestamp);
const attemptStatus = latestAttempt?.status;
const sequenceId = latestAttempt?.sequenceId;
const statusValue =
attemptStatus && attemptStatus !== rollout.status ? `${rollout.status}-${attemptStatus}` : rollout.status;
return {
...rollout,
attempt: latestAttempt ?? null,
attemptId: latestAttempt?.attemptId ?? null,
attemptSequence: latestAttempt?.sequenceId ?? null,
isNested: false,
canExpand: Boolean(sequenceId && sequenceId > 1),
inputText: inputValue,
attemptStatus,
statusValue,
startTimestamp,
durationSeconds,
lastHeartbeatTimestamp: rollout.attempt?.lastHeartbeatTime ?? null,
workerId: latestAttempt?.workerId ?? null,
actionsPlaceholder: null,
};
}
function buildAttemptRecord(rollout: Rollout, attempt: Attempt): RolloutTableRecord {
const inputValue =
rollout.input === null || typeof rollout.input === 'undefined'
? '—'
: typeof rollout.input === 'string'
? rollout.input
: safeStringify(rollout.input);
const startTimestamp = toTimestamp(attempt.startTime ?? rollout.startTime);
const endTimestamp = toTimestamp(attempt.endTime);
const durationSeconds = clampToNow(startTimestamp, endTimestamp);
const lastHeartbeatTimestamp = selectHeartbeatTimestamp(attempt);
return {
...rollout,
attempt,
attemptId: attempt.attemptId,
attemptSequence: attempt.sequenceId,
isNested: true,
canExpand: false,
inputText: inputValue,
attemptStatus: attempt.status,
statusValue: attempt.status,
startTimestamp,
durationSeconds,
lastHeartbeatTimestamp,
workerId: attempt.workerId ?? null,
actionsPlaceholder: null,
};
}
function getStatusBadge(status: string, kind: 'rollout' | 'attempt') {
const color =
kind === 'rollout'
? (ROLLOUT_STATUS_COLORS[status as RolloutStatus] ?? 'gray')
: (ATTEMPT_STATUS_COLORS[status as AttemptStatus] ?? 'gray');
return (
<Badge size='sm' variant='light' color={color}>
{formatStatusLabel(status)}
</Badge>
);
}
type RolloutColumnsOptions = {
statusFilters: RolloutStatus[];
onStatusFilterChange: (values: RolloutStatus[]) => void;
onStatusFilterReset: () => void;
modeFilters: RolloutMode[];
onModeFilterChange: (values: RolloutMode[]) => void;
onModeFilterReset: () => void;
onViewRawJson?: (record: RolloutTableRecord) => void;
onViewTraces?: (record: RolloutTableRecord) => void;
};
function createRolloutColumns({
statusFilters,
onStatusFilterChange,
onStatusFilterReset,
modeFilters,
onModeFilterChange,
onModeFilterReset,
onViewRawJson,
onViewTraces,
}: RolloutColumnsOptions): DataTableColumn<RolloutTableRecord>[] {
const statusOptions = ROLLOUT_STATUS_OPTIONS.map((status) => ({
value: status,
label: formatStatusLabel(status),
}));
const modeOptions = ROLLOUT_MODE_OPTIONS.map((mode) => ({
value: mode,
label: formatStatusLabel(mode),
}));
return [
{
accessor: 'rolloutId',
title: 'Rollout',
sortable: true,
render: ({ rolloutId }) => (
<Group gap={2}>
<Text fw={500} size='sm'>
{rolloutId}
</Text>
<CopyButton value={rolloutId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy rollout ID ${rolloutId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
</Group>
),
},
{
accessor: 'attemptId',
title: 'Attempt',
sortable: true,
render: ({ attemptId, attemptSequence, isNested }) => (
<Group gap={2}>
<Text size='sm' c={attemptId ? undefined : 'dimmed'}>
{attemptId ?? '—'}
</Text>
{attemptId && (
<CopyButton value={attemptId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy attempt ID ${attemptId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
)}
{attemptSequence && (isNested || attemptSequence > 1) && (
<Badge leftSection={<IconReload size={12} />} pl={6} pr={6}>
{attemptSequence}
</Badge>
)}
</Group>
),
},
{
accessor: 'inputText',
title: 'Input',
render: ({ inputText }) => (
<Text
size='sm'
ff='monospace'
c='dimmed'
lineClamp={1}
title={inputText}
style={{ width: '100%', wordBreak: 'break-all', overflow: 'hidden' }}
>
{inputText}
</Text>
),
},
{
accessor: 'statusValue',
title: 'Status',
sortable: true,
filter: ({ close }) => (
<Stack gap='xs'>
<MultiSelect
label='Status'
description='Filter rollouts by status'
data={statusOptions}
value={statusFilters}
placeholder='Select statuses...'
searchable
clearable
comboboxProps={{ withinPortal: false }}
onChange={(values) => onStatusFilterChange(values as RolloutStatus[])}
/>
<Button
variant='light'
size='xs'
onClick={() => {
onStatusFilterReset();
close();
}}
disabled={statusFilters.length === 0}
>
Clear
</Button>
</Stack>
),
filtering: statusFilters.length > 0,
render: ({ status, attemptStatus, isNested }) => {
if (isNested) {
return <Group gap={4}>{getStatusBadge(attemptStatus ?? 'unknown', 'attempt')}</Group>;
}
if (attemptStatus && attemptStatus !== status) {
return (
<Group gap={4}>
{getStatusBadge(status, 'rollout')}
<Text size='sm' c='dimmed'>
</Text>
{getStatusBadge(attemptStatus, 'attempt')}
</Group>
);
}
return getStatusBadge(status, 'rollout');
},
},
{
accessor: 'resourcesId',
title: 'Resources',
sortable: true,
render: ({ resourcesId }) => (
<Text size='sm' c={resourcesId ? undefined : 'dimmed'}>
{resourcesId ?? '—'}
</Text>
),
},
{
accessor: 'mode',
title: 'Mode',
sortable: true,
filter: ({ close }) => (
<Stack gap='xs'>
<MultiSelect
label='Mode'
description='Filter rollouts by mode'
data={modeOptions}
value={modeFilters}
placeholder='Select modes...'
searchable
clearable
comboboxProps={{ withinPortal: false }}
onChange={(values) => onModeFilterChange(values as RolloutMode[])}
/>
<Button
variant='light'
size='xs'
onClick={() => {
onModeFilterReset();
close();
}}
disabled={modeFilters.length === 0}
>
Clear
</Button>
</Stack>
),
filtering: modeFilters.length > 0,
render: ({ mode }) => (
<Text size='sm' c={mode ? undefined : 'dimmed'}>
{mode ?? '—'}
</Text>
),
},
{
accessor: 'startTimestamp',
title: 'Start Time',
sortable: true,
textAlign: 'left',
render: ({ startTimestamp }) => <Text size='sm'>{formatDateTime(startTimestamp)}</Text>,
},
{
accessor: 'durationSeconds',
title: 'Duration',
sortable: true,
textAlign: 'left',
render: ({ durationSeconds }) => <Text size='sm'>{formatDuration(durationSeconds)}</Text>,
},
{
accessor: 'lastHeartbeatTimestamp',
title: 'Last Heartbeat',
sortable: true,
textAlign: 'left',
render: ({ lastHeartbeatTimestamp, attempt, isNested }) => {
if (!attempt && isNested) {
return (
<Text size='sm' c='dimmed'>
</Text>
);
}
return <Text size='sm'>{formatRelativeTime(lastHeartbeatTimestamp)}</Text>;
},
},
{
accessor: 'workerId',
title: 'Worker',
sortable: true,
render: ({ workerId }) => (
<Text size='sm' c={workerId ? undefined : 'dimmed'}>
{workerId ?? '—'}
</Text>
),
},
{
accessor: 'actionsPlaceholder',
title: 'Actions',
render: (record) => (
<Group gap={4}>
<Tooltip label='View raw JSON' withArrow disabled={!onViewRawJson}>
<ActionIcon
aria-label='View raw JSON'
variant='subtle'
color='gray'
onClick={(event) => {
event.stopPropagation();
onViewRawJson?.(record);
}}
>
<IconFileDescription size={16} />
</ActionIcon>
</Tooltip>
<Tooltip label='View traces' withArrow disabled={!onViewTraces}>
<ActionIcon
aria-label='View traces'
variant='subtle'
color='gray'
onClick={(event) => {
event.stopPropagation();
onViewTraces?.(record);
}}
>
<IconTimeline size={16} />
</ActionIcon>
</Tooltip>
</Group>
),
},
];
}
type RowExpansionRenderer = (context: {
rollout: Rollout;
columns: DataTableColumn<RolloutTableRecord>[];
}) => ReactNode;
export type RolloutTableProps = {
rollouts: Rollout[] | undefined;
totalRecords: number;
isFetching: boolean;
isError: boolean;
error: unknown;
searchTerm: string;
statusFilters: RolloutStatus[];
modeFilters: RolloutMode[];
sort: RolloutsSortState;
page: number;
recordsPerPage: number;
onStatusFilterChange: (values: RolloutStatus[]) => void;
onStatusFilterReset: () => void;
onModeFilterChange: (values: RolloutMode[]) => void;
onModeFilterReset: () => void;
onSortStatusChange: (status: DataTableSortStatus<RolloutTableRecord>) => void;
onPageChange: (page: number) => void;
onRecordsPerPageChange: (value: number) => void;
onResetFilters: () => void;
onRefetch: () => void;
onViewRawJson?: (record: RolloutTableRecord) => void;
onViewTraces?: (record: RolloutTableRecord) => void;
recordsPerPageOptions?: number[];
renderRowExpansion?: RowExpansionRenderer;
};
export function RolloutTable({
rollouts,
totalRecords,
isFetching,
isError,
error,
searchTerm,
statusFilters,
modeFilters,
sort,
page,
recordsPerPage,
onStatusFilterChange,
onStatusFilterReset,
onModeFilterChange,
onModeFilterReset,
onSortStatusChange,
onPageChange,
onRecordsPerPageChange,
onResetFilters,
onRefetch,
onViewRawJson,
onViewTraces,
recordsPerPageOptions = DEFAULT_RECORDS_PER_PAGE_OPTIONS,
renderRowExpansion,
}: RolloutTableProps) {
const [expandedRecordIds, setExpandedRecordIds] = useState<string[]>([]);
const { ref: tableContainerRef, width: containerWidth } = useElementSize();
const { width: viewportWidth } = useViewportSize();
const layoutAwareContainerWidth = useMemo(() => {
return getLayoutAwareWidth(containerWidth, viewportWidth);
}, [containerWidth, viewportWidth]);
const rolloutRecords = useMemo<RolloutTableRecord[]>(() => {
if (!rollouts) {
return [];
}
return rollouts.map((rolloutItem) => buildRolloutRecord(rolloutItem));
}, [rollouts]);
const columns = useMemo(
() =>
createRolloutColumns({
statusFilters,
onStatusFilterChange,
onStatusFilterReset,
modeFilters,
onModeFilterChange,
onModeFilterReset,
onViewRawJson,
onViewTraces,
}),
[
statusFilters,
onStatusFilterChange,
onStatusFilterReset,
modeFilters,
onModeFilterChange,
onModeFilterReset,
onViewRawJson,
onViewTraces,
],
);
const responsiveColumns = useMemo(
() => createResponsiveColumns(columns, layoutAwareContainerWidth, COLUMN_VISIBILITY),
[columns, layoutAwareContainerWidth],
);
const totalPages = useMemo(
() => Math.max(1, Math.ceil(Math.max(0, totalRecords) / Math.max(1, recordsPerPage))),
[recordsPerPage, totalRecords],
);
useEffect(() => {
if (page > totalPages) {
onPageChange(totalPages);
}
}, [onPageChange, page, totalPages]);
useEffect(() => {
setExpandedRecordIds((current) =>
current.filter((id) => rolloutRecords.some((record) => record.rolloutId === id && record.canExpand)),
);
}, [rolloutRecords]);
const hasActiveFilters = searchTerm.trim().length > 0 || statusFilters.length > 0 || modeFilters.length > 0;
const sortStatus: DataTableSortStatus<RolloutTableRecord> = {
columnAccessor: sort.column,
direction: sort.direction,
};
const handleSortStatusChange = useCallback(
(status: DataTableSortStatus<RolloutTableRecord>) => {
onSortStatusChange(status);
},
[onSortStatusChange],
);
const errorMessage =
isError && error && typeof error === 'object' && 'status' in (error as Record<string, unknown>)
? `Rollouts are temporarily unavailable (status: ${String((error as Record<string, unknown>).status)}).`
: 'Rollouts are temporarily unavailable.';
const emptyState = (
<Stack gap='sm' align='center' py='lg'>
{isError ? (
<>
<Text fw={600} size='sm'>
{errorMessage}
</Text>
<Text size='sm' c='dimmed' ta='center'>
Use the retry button to try again, or adjust the filters to broaden the results.
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' color='gray' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Retry
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
) : (
<>
<Text fw={600} size='sm'>
No rollouts found
</Text>
<Text size='sm' c='dimmed' ta='center'>
{hasActiveFilters
? 'Try adjusting the search or filters to see more results.'
: 'Try refreshing to fetch the latest rollouts.'}
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Refresh
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
)}
</Stack>
);
return (
<Box ref={tableContainerRef} data-testid='rollouts-table-container'>
<DataTable<RolloutTableRecord>
classNames={{ root: 'rollouts-table' }}
withTableBorder
withColumnBorders
highlightOnHover
verticalAlign='center'
minHeight={rolloutRecords.length === 0 ? 500 : undefined}
idAccessor='rolloutId'
records={rolloutRecords}
columns={responsiveColumns}
totalRecords={totalRecords}
recordsPerPage={recordsPerPage}
page={page}
onPageChange={onPageChange}
onRecordsPerPageChange={onRecordsPerPageChange}
recordsPerPageOptions={recordsPerPageOptions}
sortStatus={sortStatus}
onSortStatusChange={handleSortStatusChange}
fetching={isFetching}
loaderSize='sm'
emptyState={rolloutRecords.length === 0 ? emptyState : undefined}
rowExpansion={
renderRowExpansion
? {
allowMultiple: true,
expandable: ({ record }) => record.canExpand,
expanded: {
recordIds: expandedRecordIds,
onRecordIdsChange: (nextRecordIds: SetStateAction<string[]>) => {
setExpandedRecordIds((previous) => {
const resolved =
typeof nextRecordIds === 'function'
? nextRecordIds(previous)
: ((nextRecordIds ?? []) as (string | number)[]);
return resolved
.map(String)
.filter((id) =>
rolloutRecords.some((tableRecord) => tableRecord.rolloutId === id && tableRecord.canExpand),
);
});
},
},
content: ({ record }) => renderRowExpansion({ rollout: record, columns: responsiveColumns }),
}
: undefined
}
/>
</Box>
);
}
export type RolloutAttemptsTableProps = {
rollout: Rollout;
attempts: Attempt[] | undefined;
isFetching: boolean;
isError: boolean;
onRetry: () => void;
columns: DataTableColumn<RolloutTableRecord>[];
};
export function RolloutAttemptsTable({
rollout,
attempts,
isFetching,
isError,
onRetry,
columns,
}: RolloutAttemptsTableProps) {
const attemptRecords = useMemo<RolloutTableRecord[]>(() => {
if (!attempts) {
return [];
}
return attempts
.map((attempt) => buildAttemptRecord(rollout, attempt))
.sort((a, b) => (b.attemptSequence ?? 0) - (a.attemptSequence ?? 0))
.filter((record) => record.attemptSequence !== rollout.attempt?.sequenceId);
}, [attempts, rollout]);
if (isError && !attemptRecords.length) {
return (
<Alert color='red' variant='light' icon={<IconAlertCircle size={16} />}>
<Stack gap='xs'>
<Text size='sm'>Unable to load attempts for this rollout.</Text>
<Button size='xs' variant='light' leftSection={<IconRefresh size={14} />} onClick={onRetry}>
Retry
</Button>
</Stack>
</Alert>
);
}
const emptyState = (
<Stack gap='xs' align='center' py='md'>
<Text size='sm' c='dimmed'>
No attempts found for this rollout.
</Text>
<Button size='xs' variant='light' leftSection={<IconRefresh size={14} />} onClick={onRetry}>
Refresh
</Button>
</Stack>
);
return (
<DataTable<RolloutTableRecord>
classNames={{ root: 'rollouts-table rollouts-table--nested' }}
withColumnBorders
noHeader
minHeight={0}
idAccessor='attemptId'
verticalAlign='center'
fetching={isFetching}
loaderSize='sm'
records={attemptRecords}
columns={columns}
emptyState={attemptRecords.length === 0 ? emptyState : undefined}
/>
);
}
@@ -0,0 +1,307 @@
// Copyright (c) Microsoft. All rights reserved.
import { useMemo, useState } from 'react';
import type { Meta, StoryObj } from '@storybook/react';
import { IconSearch } from '@tabler/icons-react';
import { Box, Stack, TextInput, Title } from '@mantine/core';
import type { RolloutsSortState } from '@/features/rollouts';
import type { Rollout, RolloutMode, RolloutStatus } from '@/types';
import { compareRecords } from '@/utils/table';
import { STORY_DATE_NOW_SECONDS } from '../../.storybook/constants';
import { buildRolloutRecord, RolloutTable, type RolloutTableRecord } from './RolloutTable.component';
const meta: Meta<typeof RolloutTable> = {
title: 'Components/RolloutTable',
component: RolloutTable,
parameters: {
layout: 'fullscreen',
},
};
export default meta;
type Story = StoryObj<typeof RolloutTable>;
const now = STORY_DATE_NOW_SECONDS;
const sampleRollouts: Rollout[] = [
{
rolloutId: 'ro-story-001',
input: { task: 'Generate onboarding summary' },
startTime: now - 3200,
endTime: null,
mode: 'train',
resourcesId: 'rs-story-001',
status: 'running',
config: { retries: 1 },
metadata: { owner: 'alice' },
attempt: {
rolloutId: 'ro-story-001',
attemptId: 'at-story-010',
sequenceId: 1,
startTime: now - 3200,
endTime: null,
status: 'running',
workerId: 'worker-east',
lastHeartbeatTime: now - 45,
metadata: { info: 'Worker is processing' },
},
},
{
rolloutId: 'ro-story-002',
input: { task: 'Classify feedback tickets' },
startTime: now - 7200,
endTime: now - 5400,
mode: 'val',
resourcesId: 'rs-story-002',
status: 'succeeded',
config: { retries: 2 },
metadata: { owner: 'bob' },
attempt: {
rolloutId: 'ro-story-002',
attemptId: 'at-story-011',
sequenceId: 2,
startTime: now - 6200,
endTime: now - 5400,
status: 'succeeded',
workerId: 'worker-north',
lastHeartbeatTime: now - 5400,
metadata: { previousAttempt: 'at-story-010' },
},
},
{
rolloutId: 'ro-story-003',
input: { task: 'Analyze experiment results' },
startTime: now - 10800,
endTime: now - 9600,
mode: 'test',
resourcesId: 'rs-story-003',
status: 'failed',
config: { retries: 1 },
metadata: { owner: 'carol' },
attempt: {
rolloutId: 'ro-story-003',
attemptId: 'at-story-012',
sequenceId: 3,
startTime: now - 10200,
endTime: now - 9600,
status: 'failed',
workerId: 'worker-west',
lastHeartbeatTime: now - 9600,
metadata: { reason: 'Timeout' },
},
},
{
rolloutId: 'ro-story-004',
input: { task: 'Evaluate prompt variants' },
startTime: now - 3600,
endTime: null,
mode: 'train',
resourcesId: null,
status: 'preparing',
config: { retries: 0 },
metadata: { owner: 'dave' },
attempt: null,
},
{
rolloutId: 'ro-story-005',
input: { task: 'Generate quick answers' },
startTime: now - 1800,
endTime: null,
mode: 'val',
resourcesId: 'rs-story-004',
status: 'running',
config: { retries: 0 },
metadata: { owner: 'eva' },
attempt: {
rolloutId: 'ro-story-005',
attemptId: 'at-story-013',
sequenceId: 1,
startTime: now - 1800,
endTime: null,
status: 'running',
workerId: null,
lastHeartbeatTime: now - 75,
metadata: null,
},
},
{
rolloutId: 'ro-story-006',
input: { task: 'Compile release notes' },
startTime: now - 9600,
endTime: now - 9000,
mode: null,
resourcesId: 'rs-story-005',
status: 'cancelled',
config: { retries: 3 },
metadata: null,
attempt: {
rolloutId: 'ro-story-006',
attemptId: 'at-story-014',
sequenceId: 1,
startTime: now - 9600,
endTime: now - 9000,
status: 'timeout',
workerId: 'worker-south',
lastHeartbeatTime: now - 9000,
metadata: { info: 'Cancelled by operator' },
},
},
];
type WrapperProps = {
maxWidth: number;
rollouts?: Rollout[] | undefined;
isFetching?: boolean;
isError?: boolean;
error?: unknown;
};
function RolloutTableStoryWrapper({
maxWidth,
rollouts = sampleRollouts,
isFetching = false,
isError = false,
error = null,
}: WrapperProps) {
const [searchTerm, setSearchTerm] = useState('');
const [statusFilters, setStatusFilters] = useState<RolloutStatus[]>([]);
const [modeFilters, setModeFilters] = useState<RolloutMode[]>([]);
const [page, setPage] = useState(1);
const [recordsPerPage, setRecordsPerPage] = useState(5);
const [sort, setSort] = useState<RolloutsSortState>({
column: 'startTimestamp',
direction: 'desc',
});
const tableRecords = useMemo<RolloutTableRecord[]>(() => {
if (!rollouts) {
return [];
}
return rollouts.map((rolloutItem) => buildRolloutRecord(rolloutItem));
}, [rollouts]);
const filteredRecords = useMemo(() => {
const normalizedSearch = searchTerm.trim().toLowerCase();
return tableRecords.filter((record) => {
const matchesSearch = normalizedSearch.length === 0 || record.rolloutId.toLowerCase().includes(normalizedSearch);
const matchesStatus = statusFilters.length === 0 || statusFilters.includes(record.status);
const matchesMode = modeFilters.length === 0 || (record.mode !== null && modeFilters.includes(record.mode));
return matchesSearch && matchesStatus && matchesMode;
});
}, [modeFilters, searchTerm, statusFilters, tableRecords]);
const sortedRecords = useMemo(() => {
const sorted = filteredRecords.slice();
if (!sorted.length) {
return sorted;
}
const comparatorKey = sort.column as keyof RolloutTableRecord;
if (!(comparatorKey in sorted[0])) {
return sorted;
}
sorted.sort((a, b) => compareRecords(a, b, comparatorKey));
if (sort.direction === 'desc') {
sorted.reverse();
}
return sorted;
}, [filteredRecords, sort]);
const totalRecordsValue = sortedRecords.length;
const pagedRecords = useMemo(() => {
const startIndex = (page - 1) * recordsPerPage;
const endIndex = startIndex + recordsPerPage;
return sortedRecords.slice(startIndex, endIndex);
}, [page, recordsPerPage, sortedRecords]);
const pagedRollouts = useMemo(() => pagedRecords.map((record) => record as Rollout), [pagedRecords]);
return (
<Box mx='auto' style={{ maxWidth, width: '100%', padding: 16 }}>
<Stack gap='md'>
<Title order={2}>Rollouts</Title>
<TextInput
placeholder='Search by Rollout ID'
value={searchTerm}
onChange={(event) => setSearchTerm(event.currentTarget.value)}
leftSection={<IconSearch size={16} />}
data-testid='rollouts-search-input'
w='100%'
style={{ maxWidth: 360 }}
/>
<RolloutTable
rollouts={pagedRollouts}
totalRecords={totalRecordsValue}
isFetching={isFetching}
isError={isError}
error={error}
searchTerm={searchTerm}
statusFilters={statusFilters}
modeFilters={modeFilters}
sort={sort}
page={page}
recordsPerPage={recordsPerPage}
onStatusFilterChange={(values) => {
setStatusFilters(values);
setPage(1);
}}
onStatusFilterReset={() => {
setStatusFilters([]);
setPage(1);
}}
onModeFilterChange={(values) => {
setModeFilters(values);
setPage(1);
}}
onModeFilterReset={() => {
setModeFilters([]);
setPage(1);
}}
onSortStatusChange={(status) => {
setSort({
column: status.columnAccessor as string,
direction: status.direction,
});
}}
onPageChange={setPage}
onRecordsPerPageChange={(value) => {
setRecordsPerPage(value);
setPage(1);
}}
onResetFilters={() => {
setSearchTerm('');
setStatusFilters([]);
setModeFilters([]);
setSort({ column: 'startTimestamp', direction: 'desc' });
setPage(1);
}}
onRefetch={() => undefined}
recordsPerPageOptions={[5, 10, 20]}
/>
</Stack>
</Box>
);
}
export const WideContainer: Story = {
render: () => <RolloutTableStoryWrapper maxWidth={1280} />,
};
export const MediumContainer: Story = {
render: () => <RolloutTableStoryWrapper maxWidth={960} />,
};
export const NarrowContainer: Story = {
render: () => <RolloutTableStoryWrapper maxWidth={720} />,
};
export const DrawerWidth: Story = {
render: () => <RolloutTableStoryWrapper maxWidth={520} />,
};
export const ErrorState: Story = {
render: () => (
<RolloutTableStoryWrapper maxWidth={600} rollouts={[]} isError error={new Error('Network unreachable')} />
),
};
@@ -0,0 +1,469 @@
// Copyright (c) Microsoft. All rights reserved.
import { useCallback, useEffect, useMemo } from 'react';
import {
IconAlertCircle,
IconCheck,
IconCopy,
IconFileDescription,
IconRefresh,
IconRouteSquare,
} from '@tabler/icons-react';
import { DataTable, type DataTableColumn, type DataTableSortStatus } from 'mantine-datatable';
import { ActionIcon, Badge, Box, Button, CopyButton, Group, Stack, Text, Tooltip } from '@mantine/core';
import { useElementSize, useViewportSize } from '@mantine/hooks';
import { getLayoutAwareWidth } from '@/layouts/helper';
import type { Span } from '@/types';
import { getErrorDescriptor } from '@/utils/error';
import { formatDateTimeWithMilliseconds, formatDuration, toTimestamp } from '@/utils/format';
import { createResponsiveColumns, type ColumnVisibilityConfig } from '@/utils/table';
const DEFAULT_RECORDS_PER_PAGE_OPTIONS = [50, 100, 200, 500];
const COLUMN_VISIBILITY: Record<string, ColumnVisibilityConfig> = {
name: { minWidth: 12.5, priority: 0 },
sequenceId: { fixedWidth: 6, priority: 1 },
spanId: { fixedWidth: 14, priority: 1 },
traceId: { fixedWidth: 24, priority: 3 },
parentId: { fixedWidth: 12, priority: 2 },
statusCode: { fixedWidth: 8, priority: 2 },
attributeKeys: { minWidth: 12.5, priority: 2 },
startTime: { fixedWidth: 15, priority: 1 },
endTime: { fixedWidth: 15, priority: 1 },
duration: { fixedWidth: 10, priority: 3 },
actionsPlaceholder: { fixedWidth: 6, priority: 0 },
};
const STATUS_COLORS: Record<string, string> = {
UNSET: 'gray',
OK: 'teal',
ERROR: 'red',
};
export type TracesTableRecord = Span & {
statusCode: string;
attributeKeys: string;
duration: number;
actionsPlaceholder?: null;
};
export function buildTraceRecord(span: Span): TracesTableRecord {
const statusCode = span.status.status_code;
const attributeKeys = Object.keys(span.attributes ?? {}).join(', ') || '';
const startTimestamp = toTimestamp(span.startTime);
const endTimestamp = toTimestamp(span.endTime);
const duration = endTimestamp && startTimestamp ? endTimestamp - startTimestamp : 0;
return {
...span,
statusCode,
attributeKeys,
duration,
actionsPlaceholder: null,
};
}
type TracesColumnsOptions = {
onShowRollout?: (record: TracesTableRecord) => void;
onShowSpanDetail?: (record: TracesTableRecord) => void;
onParentIdClick?: (parentId: string) => void;
spanIds: Set<string>;
};
function createTracesColumns({
onShowRollout,
onShowSpanDetail,
onParentIdClick,
spanIds,
}: TracesColumnsOptions): DataTableColumn<TracesTableRecord>[] {
return [
{
accessor: 'name',
title: 'Name',
sortable: true,
render: ({ name }) => (
<Text size='sm' fw={500}>
{name}
</Text>
),
},
{
accessor: 'sequenceId',
title: 'Seq.',
sortable: true,
render: ({ sequenceId }) => <Text size='sm'>{sequenceId}</Text>,
},
{
accessor: 'traceId',
title: 'Trace ID',
sortable: true,
render: ({ traceId }) => (
<Group gap={2}>
<Text size='sm'>{traceId}</Text>
<CopyButton value={traceId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy trace ID ${traceId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
</Group>
),
},
{
accessor: 'spanId',
title: 'Span ID',
sortable: true,
render: ({ spanId }) => (
<Group gap={2}>
<Text size='sm'>{spanId}</Text>
<CopyButton value={spanId}>
{({ copied, copy }) => (
<Tooltip label={copied ? 'Copied' : 'Copy'} withArrow>
<ActionIcon
aria-label={`Copy span ID ${spanId}`}
variant='subtle'
color={copied ? 'teal' : 'gray'}
size='sm'
onClick={(event) => {
event.stopPropagation();
copy();
}}
>
{copied ? <IconCheck size={14} /> : <IconCopy size={14} />}
</ActionIcon>
</Tooltip>
)}
</CopyButton>
</Group>
),
},
{
accessor: 'parentId',
title: 'Parent ID',
sortable: true,
render: ({ parentId }) => {
if (!parentId) {
return (
<Text size='sm' c='dimmed'>
</Text>
);
}
const parentExists = spanIds.has(parentId);
const isInteractive = parentExists && typeof onParentIdClick === 'function';
return (
<Group gap={2}>
<Text
size='sm'
c={parentExists ? undefined : 'red'}
style={{ cursor: isInteractive ? 'pointer' : undefined }}
onClick={(event) => {
if (isInteractive) {
event.stopPropagation();
onParentIdClick?.(parentId);
}
}}
>
{parentId.slice(0, 8)}
</Text>
{!parentExists && (
<Tooltip label='Parent span not found in table' withArrow>
<IconAlertCircle size={14} color='red' />
</Tooltip>
)}
</Group>
);
},
},
{
accessor: 'statusCode',
title: 'Status',
sortable: true,
render: ({ statusCode }) => (
<Badge size='sm' variant='light' color={STATUS_COLORS[statusCode] ?? 'gray'}>
{statusCode}
</Badge>
),
},
{
accessor: 'attributeKeys',
title: 'Attribute Keys',
render: ({ attributeKeys }) =>
attributeKeys ? (
<Text size='sm' lineClamp={1}>
{/* TODO: dim "." and "," and other characters are just normal text */}
{attributeKeys}
</Text>
) : (
<Text size='sm' c='dimmed'>
</Text>
),
},
{
accessor: 'startTime',
title: 'Start Time',
sortable: true,
textAlign: 'left',
render: ({ startTime }) => <Text size='sm'>{formatDateTimeWithMilliseconds(toTimestamp(startTime))}</Text>,
},
{
accessor: 'endTime',
title: 'End Time',
sortable: true,
textAlign: 'left',
render: ({ endTime }) => <Text size='sm'>{formatDateTimeWithMilliseconds(toTimestamp(endTime))}</Text>,
},
{
accessor: 'duration',
title: 'Duration',
sortable: true,
textAlign: 'left',
render: ({ duration }) => <Text size='sm'>{formatDuration(duration)}</Text>,
},
{
accessor: 'actionsPlaceholder',
title: 'Actions',
render: (record) => (
<Group gap={2}>
<Tooltip label='Show rollout' withArrow disabled={!onShowRollout}>
<ActionIcon
aria-label='Show rollout'
variant='subtle'
color='gray'
onClick={(event) => {
event.stopPropagation();
onShowRollout?.(record);
}}
>
<IconRouteSquare size={16} />
</ActionIcon>
</Tooltip>
<Tooltip label='Show span detail' withArrow disabled={!onShowSpanDetail}>
<ActionIcon
aria-label='Show span detail'
variant='subtle'
color='gray'
onClick={(event) => {
event.stopPropagation();
onShowSpanDetail?.(record);
}}
>
<IconFileDescription size={16} />
</ActionIcon>
</Tooltip>
</Group>
),
},
];
}
export type TracesTableProps = {
spans: Span[] | undefined;
totalRecords: number;
isFetching: boolean;
isError: boolean;
error: unknown;
selectionMessage?: string;
searchTerm: string;
sort: { column: string; direction: 'asc' | 'desc' };
page: number;
recordsPerPage: number;
onSortStatusChange: (status: DataTableSortStatus<TracesTableRecord>) => void;
onPageChange: (page: number) => void;
onRecordsPerPageChange: (value: number) => void;
onResetFilters: () => void;
onRefetch: () => void;
onShowRollout?: (record: TracesTableRecord) => void;
onShowSpanDetail?: (record: TracesTableRecord) => void;
onParentIdClick?: (parentId: string) => void;
recordsPerPageOptions?: number[];
};
export function TracesTable({
spans,
totalRecords,
isFetching,
isError,
error,
selectionMessage,
searchTerm,
sort,
page,
recordsPerPage,
onSortStatusChange,
onPageChange,
onRecordsPerPageChange,
onResetFilters,
onRefetch,
onShowRollout,
onShowSpanDetail,
onParentIdClick,
recordsPerPageOptions = DEFAULT_RECORDS_PER_PAGE_OPTIONS,
}: TracesTableProps) {
const { ref: tableContainerRef, width: containerWidth } = useElementSize();
const { width: viewportWidth } = useViewportSize();
const traceRecords = useMemo<TracesTableRecord[]>(() => {
if (!spans) {
return [];
}
return spans.map((span) => buildTraceRecord(span));
}, [spans]);
const spanIds = useMemo(() => {
return new Set(traceRecords.map((record) => record.spanId));
}, [traceRecords]);
const columns = useMemo(
() =>
createTracesColumns({
onShowRollout,
onShowSpanDetail,
onParentIdClick,
spanIds,
}),
[onShowRollout, onShowSpanDetail, onParentIdClick, spanIds],
);
const layoutAwareContainerWidth = useMemo(
() => getLayoutAwareWidth(containerWidth, viewportWidth),
[containerWidth, viewportWidth],
);
const responsiveColumns = useMemo(
() => createResponsiveColumns(columns, layoutAwareContainerWidth, COLUMN_VISIBILITY),
[columns, layoutAwareContainerWidth],
);
const totalPages = useMemo(
() => Math.max(1, Math.ceil(Math.max(0, totalRecords) / Math.max(1, recordsPerPage))),
[recordsPerPage, totalRecords],
);
useEffect(() => {
if (page > totalPages) {
onPageChange(totalPages);
}
}, [onPageChange, page, totalPages]);
const hasActiveFilters = searchTerm.trim().length > 0;
const sortStatus: DataTableSortStatus<TracesTableRecord> = {
columnAccessor: sort.column,
direction: sort.direction,
};
const handleSortStatusChange = useCallback(
(status: DataTableSortStatus<TracesTableRecord>) => {
onSortStatusChange(status);
},
[onSortStatusChange],
);
const errorDescriptor = isError ? getErrorDescriptor(error) : null;
const errorMessage = isError
? `Traces are temporarily unavailable${errorDescriptor ? ` (${errorDescriptor})` : ''}.`
: 'Traces are temporarily unavailable.';
const selectionEmptyState = selectionMessage ? (
<Stack gap='sm' align='center' py='xl'>
<Text fw={600} size='sm'>
{selectionMessage}
</Text>
<Text size='sm' c='dimmed' ta='center'>
Choose a rollout and attempt from the controls above to load trace results.
</Text>
</Stack>
) : null;
const fallbackEmptyState = (
<Stack gap='sm' align='center' py='lg'>
{isError ? (
<>
<Text fw={600} size='sm'>
{errorMessage}
</Text>
<Text size='sm' c='dimmed' ta='center'>
Use the retry button to try again, or adjust the filters to broaden the results.
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' color='gray' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Retry
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
) : (
<>
<Text fw={600} size='sm'>
No traces found
</Text>
<Text size='sm' c='dimmed' ta='center'>
{hasActiveFilters
? 'Try adjusting the search to see more results.'
: 'Try refreshing to fetch the latest traces.'}
</Text>
<Group gap='xs'>
<Button size='xs' variant='light' leftSection={<IconRefresh size={14} />} onClick={onRefetch}>
Refresh
</Button>
{hasActiveFilters ? (
<Button size='xs' variant='subtle' onClick={onResetFilters}>
Clear filters
</Button>
) : null}
</Group>
</>
)}
</Stack>
);
const emptyState = selectionEmptyState ?? fallbackEmptyState;
return (
<Box ref={tableContainerRef}>
<DataTable<TracesTableRecord>
classNames={{ root: 'traces-table' }}
withTableBorder
withColumnBorders
highlightOnHover
verticalAlign='center'
minHeight={traceRecords.length === 0 ? 500 : undefined}
idAccessor='spanId'
records={traceRecords}
columns={responsiveColumns}
totalRecords={totalRecords}
recordsPerPage={recordsPerPage}
page={page}
onPageChange={onPageChange}
onRecordsPerPageChange={onRecordsPerPageChange}
recordsPerPageOptions={recordsPerPageOptions}
sortStatus={sortStatus}
onSortStatusChange={handleSortStatusChange}
fetching={isFetching}
loaderSize='sm'
emptyState={traceRecords.length === 0 ? emptyState : undefined}
/>
</Box>
);
}
@@ -0,0 +1,367 @@
// Copyright (c) Microsoft. All rights reserved.
import { useMemo, useState } from 'react';
import type { Meta, StoryObj } from '@storybook/react';
import { IconSearch } from '@tabler/icons-react';
import { Box, Stack, TextInput, Title } from '@mantine/core';
import type { Span } from '@/types';
import { compareRecords } from '@/utils/table';
import { buildTraceRecord, TracesTable, type TracesTableRecord } from './TracesTable.component';
const meta: Meta<typeof TracesTable> = {
title: 'Components/TracesTable',
component: TracesTable,
parameters: {
layout: 'fullscreen',
},
};
export default meta;
type Story = StoryObj<typeof TracesTable>;
const now = Math.floor(1762775145209 / 1000);
const sampleSpans: Span[] = [
{
rolloutId: 'ro-trace-001',
attemptId: 'at-trace-001',
sequenceId: 1,
traceId: 'trace-abc123def456',
spanId: 'span-root-001',
parentId: null,
name: 'main_task',
status: { status_code: 'OK', description: null },
attributes: {
'task.type': 'generation',
'task.priority': 'high',
'user.id': 'user-123',
},
startTime: now - 100,
endTime: now - 10,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-001',
attemptId: 'at-trace-001',
sequenceId: 1,
traceId: 'trace-abc123def456',
spanId: 'span-child-001',
parentId: 'span-root-001',
name: 'llm_call',
status: { status_code: 'OK', description: null },
attributes: {
'llm.model': 'gpt-4',
'llm.temperature': 0.7,
'llm.max_tokens': 2048,
},
startTime: now - 90,
endTime: now - 50,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-001',
attemptId: 'at-trace-001',
sequenceId: 1,
traceId: 'trace-abc123def456',
spanId: 'span-child-002',
parentId: 'span-root-001',
name: 'database_query',
status: { status_code: 'OK', description: null },
attributes: {
'db.system': 'postgresql',
'db.operation': 'SELECT',
'db.table': 'users',
},
startTime: now - 80,
endTime: now - 70,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-002',
attemptId: 'at-trace-002',
sequenceId: 1,
traceId: 'trace-xyz789ghi012',
spanId: 'span-error-001',
parentId: 'span-missing-parent',
name: 'failed_operation',
status: { status_code: 'ERROR', description: 'Connection timeout' },
attributes: {
'error.type': 'TimeoutError',
'error.message': 'Connection timed out after 30s',
},
startTime: now - 150,
endTime: now - 120,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-003',
attemptId: 'at-trace-003',
sequenceId: 1,
traceId: 'trace-unset123',
spanId: 'span-unset-001',
parentId: null,
name: 'pending_task',
status: { status_code: 'UNSET', description: null },
attributes: {},
startTime: now - 30,
endTime: now - 5,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-004',
attemptId: 'at-trace-004',
sequenceId: 2,
traceId: 'trace-nested456',
spanId: 'span-parent-001',
parentId: null,
name: 'workflow_execution',
status: { status_code: 'OK', description: null },
attributes: {
'workflow.name': 'data_processing',
'workflow.version': '2.1.0',
},
startTime: now - 200,
endTime: now - 50,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-004',
attemptId: 'at-trace-004',
sequenceId: 2,
traceId: 'trace-nested456',
spanId: 'span-child-nested-001',
parentId: 'span-parent-001',
name: 'step_1_validation',
status: { status_code: 'OK', description: null },
attributes: {
'step.name': 'validation',
'step.index': 1,
},
startTime: now - 195,
endTime: now - 180,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
{
rolloutId: 'ro-trace-004',
attemptId: 'at-trace-004',
sequenceId: 2,
traceId: 'trace-nested456',
spanId: 'span-child-nested-002',
parentId: 'span-parent-001',
name: 'step_2_processing',
status: { status_code: 'ERROR', description: 'Validation failed' },
attributes: {
'step.name': 'processing',
'step.index': 2,
'error.type': 'ValidationError',
},
startTime: now - 175,
endTime: now - 160,
events: [],
links: [],
context: {},
parent: null,
resource: {},
},
];
type WrapperProps = {
maxWidth: number;
spans?: Span[] | undefined;
isFetching?: boolean;
isError?: boolean;
error?: unknown;
};
function TracesTableStoryWrapper({
maxWidth,
spans = sampleSpans,
isFetching = false,
isError = false,
error = null,
}: WrapperProps) {
const [searchTerm, setSearchTerm] = useState('');
const [page, setPage] = useState(1);
const [recordsPerPage, setRecordsPerPage] = useState(10);
const [sort, setSort] = useState<{ column: string; direction: 'asc' | 'desc' }>({
column: 'startTime',
direction: 'desc',
});
const tableRecords = useMemo<TracesTableRecord[]>(() => {
if (!spans) {
return [];
}
return spans.map((span) => buildTraceRecord(span));
}, [spans]);
const filteredRecords = useMemo(() => {
const normalizedSearch = searchTerm.trim().toLowerCase();
if (normalizedSearch.length === 0) {
return tableRecords;
}
return tableRecords.filter(
(record) =>
record.traceId.toLowerCase().includes(normalizedSearch) ||
record.spanId.toLowerCase().includes(normalizedSearch) ||
record.name.toLowerCase().includes(normalizedSearch),
);
}, [searchTerm, tableRecords]);
const sortedRecords = useMemo(() => {
const sorted = filteredRecords.slice();
if (!sorted.length) {
return sorted;
}
const comparatorKey = sort.column as keyof TracesTableRecord;
if (!(comparatorKey in sorted[0])) {
return sorted;
}
sorted.sort((a, b) => compareRecords(a, b, comparatorKey));
if (sort.direction === 'desc') {
sorted.reverse();
}
return sorted;
}, [filteredRecords, sort]);
const totalRecordsValue = sortedRecords.length;
const pagedRecords = useMemo(() => {
const startIndex = (page - 1) * recordsPerPage;
const endIndex = startIndex + recordsPerPage;
return sortedRecords.slice(startIndex, endIndex);
}, [page, recordsPerPage, sortedRecords]);
const pagedSpans = useMemo(() => pagedRecords.map((record) => record as Span), [pagedRecords]);
const handleShowRollout = (record: any) => {
console.log('Show rollout for:', record.rolloutId);
};
const handleShowSpanDetail = (record: any) => {
console.log('Show span detail for:', record.spanId, record);
};
const handleParentIdClick = (parentId: string) => {
console.log('Navigate to parent span:', parentId);
setSearchTerm(parentId);
};
return (
<Box mx='auto' style={{ maxWidth, width: '100%', padding: 16 }}>
<Stack gap='md'>
<Title order={2}>Traces</Title>
<TextInput
placeholder='Search by Trace ID, Span ID, or Name'
value={searchTerm}
onChange={(event) => setSearchTerm(event.currentTarget.value)}
leftSection={<IconSearch size={16} />}
data-testid='traces-search-input'
w='100%'
style={{ maxWidth: 360 }}
/>
<TracesTable
spans={pagedSpans}
totalRecords={totalRecordsValue}
isFetching={isFetching}
isError={isError}
error={error}
searchTerm={searchTerm}
sort={sort}
page={page}
recordsPerPage={recordsPerPage}
onSortStatusChange={(status) => {
setSort({
column: status.columnAccessor as string,
direction: status.direction,
});
}}
onPageChange={setPage}
onRecordsPerPageChange={(value) => {
setRecordsPerPage(value);
setPage(1);
}}
onResetFilters={() => {
setSearchTerm('');
setSort({ column: 'startTime', direction: 'desc' });
setPage(1);
}}
onRefetch={() => undefined}
onShowRollout={handleShowRollout}
onShowSpanDetail={handleShowSpanDetail}
onParentIdClick={handleParentIdClick}
recordsPerPageOptions={[10, 20, 50]}
/>
</Stack>
</Box>
);
}
export const WideContainer: Story = {
render: () => <TracesTableStoryWrapper maxWidth={1400} />,
};
export const MediumContainer: Story = {
render: () => <TracesTableStoryWrapper maxWidth={960} />,
};
export const NarrowContainer: Story = {
render: () => <TracesTableStoryWrapper maxWidth={720} />,
};
export const DrawerWidth: Story = {
render: () => <TracesTableStoryWrapper maxWidth={520} />,
};
export const ErrorState: Story = {
render: () => <TracesTableStoryWrapper maxWidth={960} spans={[]} isError error={new Error('Network unreachable')} />,
};
export const LoadingState: Story = {
render: () => <TracesTableStoryWrapper maxWidth={960} spans={[]} isFetching />,
};
export const EmptyState: Story = {
render: () => <TracesTableStoryWrapper maxWidth={960} spans={[]} />,
};
export const WithMissingParent: Story = {
render: () => (
<TracesTableStoryWrapper maxWidth={1200} spans={sampleSpans.filter((s) => s.spanId === 'span-error-001')} />
),
};
export const NestedSpans: Story = {
render: () => (
<TracesTableStoryWrapper maxWidth={1200} spans={sampleSpans.filter((s) => s.traceId === 'trace-nested456')} />
),
};

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