.

2025-11-24 00:10:28 +08:00 · 2025-11-24 00:09:05 +08:00 · 2025-11-24 00:06:45 +08:00 · 2025-11-23 23:52:39 +08:00 · 2025-11-23 23:28:53 +08:00 · 2025-11-23 11:30:23 +08:00
7 changed files with 256 additions and 71 deletions
@@ -7,6 +7,8 @@ on:
      - Examples - Spider
      - Examples - APO
      - Examples - Unsloth
+      - Examples - Tinker
+      - Examples - Azure
    types: [completed]

  workflow_dispatch:
@@ -31,5 +33,7 @@ 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'] },
            ];
            await badgeAggregation({ github, context, core, dependencies });
@@ -99,7 +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.

@@ -1,18 +1,98 @@
 # Contributing Guide

-Agent Lightning thrives on community improvements, whether you are polishing docs, fixing bugs, or building new features. This guide shows the shortest path from cloning the repository to shipping a polished pull request.
+Agent Lightning gets better every time someone files a clear bug, polishes docs, improves tests, or lands a new feature. This guide collects the expectations, checklists, and tips that help you go from “I have an idea” to “my pull request just merged.”

-## Step 1. Prepare Your Environment
+## Before You Start

-### Prerequisites
+Agent-lightning is built by a small Microsoft Research team with limited reviewer hours and GPU budget. For any sizeable change (new algorithm, example, or API surface) please first discuss scope with us in [Discord](https://discord.gg/RYk7CdvDR7). Early alignment keeps your effort from being blocked late in the process.

- **Python** 3.10 or newer (we test on 3.10–3.13).
- **uv** for dependency and virtual environment management. Install it from the [official uv docs](https://docs.astral.sh/uv/getting-started/installation/).
+## Where You Can Help
+
+Pick a lane, or combine several. Just keep the discussion-first principle in mind for anything non-trivial.
+
+### Documentation Improvements
+
+Documentation improvements are the easiest way to get started. You can find more about how to write good documentations and organize documentations in the following sections. Here are some general contribution points we can think of:
+
+- Tighten language, fix typos, clarify confusing sections, or add missing links. Fresh eyes catch docs gaps best.
+- Organize content using the directories listed below so readers can actually find it.
+- Avoid duplicate prose, unrelated “how-to” guides, or translations (we cannot maintain them today).
+
+!!! note "Changes that are usually rejected"
+
+    - Copy/pasting existing docs with shallow edits.
+    - Adding a `how-to` guide that is not tied to a new example.
+    - Adding doc translations to other languages (no capacity to review/maintain yet).
+
+### Bug Fixes
+
+Bug fixes are the fastest way to get familiar with the codebase. To get started, you can:
+
+- Browse the ["good first issue"](https://github.com/microsoft/agent-lightning/labels/good%20first%20issue) and ["bug"](https://github.com/microsoft/agent-lightning/labels/bug) labels; drop a comment before you start so we can mark it as taken.
+- For fresh bugs, open an issue with reproduction steps, logs, and expected behavior before submitting a fix.
+- Keep each pull request focused, ideally avoiding breaking API changes. Larger refactors should be discussed via RFC or maintainer sync.
+
+### New Examples
+
+Examples must be curated so that we can maintain them. We generally merge only those that meet at least one (ideally several) of these criteria:
+
+- Demonstrates an agent framework or workflow that is materially different from what already exists. ([LangChain](https://www.langchain.com/) vs. [LlamaIndex](https://www.llamaindex.ai/) is not different enough; [LangChain](https://www.langchain.com/) vs. [n8n](https://n8n.io/) or [Vercel AI SDK](https://ai-sdk.dev/) is, because they either have different orchestration paradigms or differ in programming languages.)
+- Shows measurable performance gains on a **real-world** problem with a **real-world** dataset, such as tuning a search agent with Google Search API or improving a coding agent’s (e.g., Claude Code) SWE-Bench score.
+- Integrates a new algorithm, training backend, or serving stack (see “New Algorithms” below).
+- Validates scenarios that are rarely tested, such as multi-modality agents or long-lived memory/workflow agents.
+
+Bonus points for examples that:
+
+- Ship CI or self-test coverage so we know they still work as the core evolves.  **Otherwise, we would have to mark the example as unmaintained because we won't be able to test the examples manually before each release.**
+- Include a [`docs/how-to/`]({{ src("docs/how-to/") }}) guide (or a detailed README if no how-to exists) without duplicating content in multiple places.
+- Favor simple, dependency-light code over heavy abstractions.
+
+!!! warning "Please discuss first"
+
+    Examples tend to be the most time-consuming contributions for both you and reviewers. Sync with us on Discord or through an issue before diving into a new one.
+
+### Fresh Implementations of Core Modules
+
+If you are looking to extend [`Runner`][agentlightning.Runner], [`Tracer`][agentlightning.Tracer], [`Adapter`][agentlightning.Adapter], [`LightningStore`][agentlightning.LightningStore], or another core interface, here are the steps:
+
+1. File an issue or proposal first.
+2. Explain which interface you are extending, why existing implementations are insufficient, and how you intend to test compatibility with the rest of the stack (unit tests, documentation updates, example refreshes, etc.).
+3. Any API changes must be reviewed up front. DO NOT begin coding large changes before the discussion lands!
+
+### New Algorithms
+
+If you are integrating a new training/serving backend, check whether it already lives in the [Algorithm Zoo](../algorithm-zoo/index.md) or is covered in the [Examples Catalog](../how-to/examples-catalog.md). We especially welcome:
+
+- Currently unsupported or under-tested algorithms such as Supervised Fine-tuning (SFT), Direct Policy Optimization (DPO), or Monte Carlo Tree Search (MCTS).
+- Tuning [Resource][agentlightning.Resource]s that are not supported yet, such as workflows or memory.
+- Expansions of supported stacks, e.g., adding multi-modality to APO or multi-agent prompt tuning.
+- Reinforcement-learning integrations beyond our current stack of [VERL](https://github.com/volcengine/verl), [vLLM](https://vllm.ai/), [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-foundry/models/openai), and [Tinker](https://tinker-docs.thinkingmachines.ai/). Contributions using [SGLang](https://github.com/sgl-project/sglang), [TRL](https://github.com/huggingface/trl), [SkyRL](https://github.com/NovaSky-AI/SkyRL), [RLinf](https://github.com/RLinf/RLinf), [litgpt](https://github.com/Lightning-AI/litgpt), or similar are welcome.
+
+Most brand-new algorithms ultimately land as “new examples,” so read that section too. Post an issue or design doc to scope the work, reuse existing utilities, and avoid duplicating efforts. Mature, battle-tested examples graduate into the [Algorithm Zoo](../algorithm-zoo/index.md).
+
+### Ecosystem Projects
+
+Have a project that builds on Agent-lightning but does not belong in the main repo? Fork it or depend on it externally, then let us know. We can showcase notable projects in [Community Projects](../index.md) and the main [README]({{ src("README.md") }}).
+
+### Other Contribution Ideas
+
+- **Tests.** Add or improve cases in [`tests/`]({{ src("tests") }}) (unit, integration, or end-to-end).
+- **Benchmarks.** Expand [`tests/benchmark`]({{ src("tests/benchmark") }}) to stress large-scale training or rollouts.
+- **Issue triage.** Reproduce bugs, confirm whether they reproduce on `main`, or suggest short-term mitigations so maintainers can prioritize.
+
+## Contribution Workflow
+
+The steps below keep changes reviewable and CI-friendly. Follow them in order; rerun the relevant pieces if you revisit a branch later.
+
+### 1. Prepare Your Environment
+
+Minimum tooling:
+
+- **Python** 3.10+ (3.12 recommended).
+- **uv** for dependency and virtual-environment management. Install it using the [official uv docs](https://docs.astral.sh/uv/getting-started/installation/).
 - **Git** configured with your GitHub credentials.

-### Clone the Repository
-
-Fork the repo, then clone your fork and register the upstream remote so you can stay current:
+Clone your fork and point `upstream` at the official repo:

 ```bash
 git clone git@github.com:<your-username>/agent-lightning.git
@@ -20,15 +100,13 @@ cd agent-lightning
 git remote add upstream https://github.com/microsoft/agent-lightning.git
 ```

-### Install Dependencies
-
-Install the standard development toolchain:
+Install the default development stack:

 ```bash
 uv sync --group dev
 ```

-Want GPU extras, example dependencies, or other optional features? Pin everything in one pass:
+Need GPU extras or specific optional dependencies? Lock them in with one command:

 ```bash
 uv sync --frozen \
@@ -41,28 +119,22 @@ uv sync --frozen \
    --no-default-groups
 ```

-After `uv sync`, run commands with `uv run ...` (or `uv run --no-sync` once the environment is locked), or activate the virtual environment in `.venv/`.
+After `uv sync`, run commands via `uv run ...` (add `--no-sync` once the environment is locked) or activate `.venv/`.

---
+### 2. Install and Run Pre-commit

-## Step 2. Install and Run Pre-commit
-
-We enforce formatting and linting with [pre-commit](https://pre-commit.com/). Install the hooks once, then run them before every push:
+Formatting and linting are enforced through [pre-commit](https://pre-commit.com/). Install once, then run before each push:

 ```bash
 uv run pre-commit install
-
-# The following will auto-run if you have set up the pre-commit hooks to run automatically on commit.
 uv run pre-commit run --all-files --show-diff-on-failure --color=always
 ```

-Running them locally saves a CI round-trip and keeps diffs tidy.
+Once installed, the hooks run automatically on every `git commit`. Running the pre-commit hooks locally keeps CI green and diffs manageable.

---
+### 3. Branch From a Fresh `main`

-## Step 3. Branching Workflow
-
-Start from a fresh `main`, then branch for your change:
+Start all work from the latest upstream state:

 ```bash
 git fetch upstream
@@ -70,20 +142,32 @@ git checkout main
 git merge upstream/main
 ```

-Create a topic branch with one of these prefixes:
+Branch naming convention:

- `feature/<short-description>` for new features
- `fix/<short-description>` for bug fixes
- `docs/<short-description>` for documentation-only work
- `chore/<short-description>` for tooling or maintenance
+- `feature/<short-description>` for new features.
+- `fix/<short-description>` for bug fixes.
+- `docs/<short-description>` for documentation-only updates.
+- `chore/<short-description>` for tooling or maintenance.

-Stick to lowercase words separated by hyphens, e.g. `feature/async-runner-hooks`.
+Use lowercase with hyphens, e.g., `feature/async-runner-hooks`.

---
+!!! note "Where should docs or examples live?"

-## Step 4. Test Your Changes
+    Many new contributors get confused about what to put in the `docs/how-to/` directory and what to put in the `examples/` directory (particularly README files). Here is a quick reference you can refer to:

-Most updates should ship with automated checks. Preface commands with `uv run` so they use the project environment.
+    | Location | Description |
+    | --- | --- |
+    | `docs/algorithm-zoo/` | Documentation for **built-in algorithms** shipped with Agent-lightning. |
+    | `docs/how-to/` | Step-by-step **how-to guides**, usually tied to an example in `examples/`. |
+    | `docs/tutorials/` | Conceptual walkthroughs for components or workflows. See [debugging](../tutorials/debug.md) or [parallelization](../tutorials/parallelize.md) for examples. |
+    | `docs/deep-dive/` | Advanced explanations and in-depth concepts. |
+    | `examples/<name>/README.md` | Example-specific README. If any related how-to if that exists, link to it avoid duplicating the same instructions twice; write only brief instructions on how to install and run the example. Otherwise, you can make the README more detailed and self-explanatory. |
+
+    Remember to register new docs in [`mkdocs.yml`]({{ src("mkdocs.yml") }}), add examples to [examples/README]({{ src("examples/README.md") }}), and update the [Examples Catalog](../how-to/examples-catalog.md).
+
+### 4. Test and Validate
+
+Most contributions require automated checks. Prefix commands with `uv run` so they use the project environment.

 **Full test suite**

@@ -97,53 +181,43 @@ uv run pytest -v
 uv run pytest tests/path/to/test_file.py -k test_name
 ```

-**Optional/gated tests**
+**Optional/gated tests:** GPU-specific suites or API-dependent tests run automatically when the required hardware or environment variables (such as `OPENAI_API_KEY`) are present.

-GPU-specific suites or API-dependent tests run automatically when the required hardware or environment variables (such as `OPENAI_API_KEY`) are present.
-
-**Static analysis**
+**Static analysis:**

 ```bash
 uv run pyright
 ```

-Touching code under `examples/`? Each directory includes a README with example-specific smoke tests—run those too.
+If you have touched code under `examples/`, you should run the example-specific smoke tests. Each directory includes a README with example-specific smoke tests—run those too.

---
+!!! note "Build documentation when needed"

-## Step 5. Build Documentation (When Applicable)
+    Keep API references under [docs/reference]({{ src("docs/reference/") }}) up to date. Doc-only changes should still build cleanly:

-Doc changes should build cleanly before you push:
+    ```bash
+    uv run mkdocs serve --strict   # live reload
+    uv run mkdocs build --strict   # CI-equivalent
+    ```

-```bash
-uv run mkdocs serve --strict  # live reload while editing
-uv run mkdocs build --strict  # CI-equivalent validation
-```
+    `--strict` elevates warnings to errors so you catch issues before CI.

-`--strict` matches CI and promotes warnings to errors so you catch them early.
-
---
-
-## Step 6. Final Local Checks
+Before opening a PR, double-check the basics:

+- Run `uv lock` if you changed dependencies.
 - Run `uv run pre-commit run --all-files` (hooks installed via `pre-commit install` run automatically on `git commit`, but rerun them if you amended history).
- Execute the relevant test commands from Step 4.
- Validate any affected examples by following the instructions in `examples/<name>/README`.
+- Execute the relevant commands from the test list above.
+- Validate each affected example via its README instructions.

---
+### 5. Open a Pull Request

-## Step 7. Open a Pull Request
-
-1. Push your branch to your fork:
+1. Push your branch:
   ```bash
   git push origin <branch-name>
   ```
 2. Open a PR against `microsoft/agent-lightning:main`.
-3. Complete the PR template with:
-    - A concise summary of the change.
-    - The tests or commands you ran (copy from Step 4/6).
-    - Linked issues (use `Fixes #123` to auto-close).
-4. Attach screenshots or terminal output when it clarifies behavior.
-5. Address review feedback promptly. Use focused commits, and consider `git commit --fixup` for follow-up adjustments.
+3. Fill out the template with a concise summary, the commands/tests you ran, and linked issues (use `Fixes #123` syntax to auto-close).
+4. Include screenshots or logs if they clarify behavior.
+5. Address review feedback promptly. Follow-up tweaks work best as focused commits; `git commit --fixup` is handy for reviewer-suggested edits.

-Thanks for contributing—every improvement grows the Agent Lightning community!
+Thanks for contributing! every improvement strengthens the Agent Lightning community!
@@ -0,0 +1,81 @@
+# Examples Catalog
+
+!!! tip "Want to Contribute?"
+
+    We welcome contributions to the examples catalog! Please refer to the [Contributing](../community/contributing.md) guide for more details.
+
+<div class="grid cards" markdown>
+
+-   :material-robot:{ .lg .middle } __APO room selector__
+
+    ---
+
+    Prompt-optimize a room-booking agent with the built-in APO algorithm, then contrast it with the write-your-own algorithm and debugging workflows in the tutorials. Pairs well with the [Train the First Agent how-to]({{ src("docs/how-to/train-first-agent.md") }}) and the [Write the First Algorithm guide]({{ src("docs/how-to/write-first-algorithm.md") }}).
+
+    [:octicons-repo-24: Browse source]({{ src("examples/apo") }})
+
+-   :material-cloud-sync:{ .lg .middle } __Azure OpenAI SFT__
+
+    ---
+
+    Run a supervised fine-tuning loop against Azure OpenAI: roll out the capital-lookup agent, turn traces into JSONL, launch fine-tunes, and redeploy the resulting checkpoints through Azure CLI.
+
+    [:octicons-repo-24: Browse source]({{ src("examples/azure") }})
+
+-   :material-calculator:{ .lg .middle } __Calc-X VERL math__
+
+    ---
+
+    VERL-based reinforcement learning setup for a math-reasoning agent that uses AutoGen plus an MCP calculator tool to solve Calc-X problems end to end.
+
+    [:octicons-repo-24: Browse source]({{ src("examples/calc_x") }})
+
+-   :material-view-grid:{ .lg .middle } __Minimal building blocks__
+
+    ---
+
+    Bite-sized scripts that isolate Agent-lightning primitives (e.g., LightningStore usage, LLM proxying, minimal vLLM host) so you can study each part before composing larger workflows.
+
+    [:octicons-repo-24: Browse source]({{ src("examples/minimal") }})
+
+-   :material-book-open-page-variant:{ .lg .middle } __RAG (MuSiQue)__
+
+    ---
+
+    Retrieval-Augmented Generation pipeline that preps a Wikipedia retriever via MCP and trains a MuSiQue QA agent with GRPO. Documented for historical reference (verified on Agent-lightning v0.1.x).
+
+    [:octicons-repo-24: Browse source]({{ src("examples/rag") }})
+
+-   :material-magnify:{ .lg .middle } __Search-R1 RL__
+
+    ---
+
+    Reproduction of the Search-R1 workflow that prepares its own retrieval backend, runs the rollout script, and coordinates GRPO-style training without extra orchestration layers (last validated on v0.1.x).
+
+    [:octicons-repo-24: Browse source]({{ src("examples/search_r1") }})
+
+-   :material-database:{ .lg .middle } __Spider SQL agent__
+
+    ---
+
+    LangGraph-powered text-to-SQL workflow for the Spider benchmark, combining LangChain tooling with Agent-lightning rollouts; follow along with the [how-to for training SQL agents]({{ src("docs/how-to/train-sql-agent.md") }}).
+
+    [:octicons-repo-24: Browse source]({{ src("examples/spider") }})
+
+-   :material-thought-bubble:{ .lg .middle } __Tinker integration__
+
+    ---
+
+    Adapter package ([`agl_tinker`]({{ src("examples/tinker/agl_tinker") }})) with Tinker plus sample CrewAI/OpenAI agents that feed Agent-lightning traces into Tinker’s reinforcement-learning backend for both toy and 20-Questions-style workflows.
+
+    [:octicons-repo-24: Browse source]({{ src("examples/tinker") }})
+
+-   :material-fast-forward:{ .lg .middle } __Unsloth SFT__
+
+    ---
+
+    Supervised fine-tuning loop that ranks math-agent rollouts, fine-tunes with Unsloth’s 4-bit LoRA stack, and mirrors the [Fine-tune with Unsloth recipe]({{ src("docs/how-to/unsloth-sft.md") }}).
+
+    [:octicons-repo-24: Browse source]({{ src("examples/unsloth") }})
+
+</div>
@@ -84,3 +84,24 @@ canvas[data-chart] {
  width: 100%;
  display: block;
 }
+
+/* Grid behavior */
+.md-typeset .grid {
+  grid-template-columns: repeat(auto-fit, minmax(24rem, 1fr));
+}
+
+/* Make cards fill equal height and push footer link to bottom */
+.md-typeset .grid.cards > ul > li {
+  display: flex;
+  flex-direction: column;
+  gap: 0;
+}
+
+.md-typeset .grid.cards > ul > li > hr {
+  margin: 0.5em 0;
+}
+
+.md-typeset .grid.cards > ul > li > :last-child {
+  margin-top: auto; /* pushes the last element (Browse source) to bottom */
+  padding-top: 0.5em;
+}
@@ -4,14 +4,14 @@ This catalog highlights the examples shipped with Agent-lightning.

 | Example | Description | CI Maintenance |
 |---------|-------------|----------------|
-| [apo](./apo) | Automatic Prompt Optimization tutorials covering built-in, custom, and debugging workflows. | [![apo workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-apo.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-apo.yml) |
-| [azure](./azure) | Supervised fine-tuning with Azure OpenAI. | **Unmaintained** — last verified with Agent-lightning v0.2.1 |
-| [calc_x](./calc_x) | VERL-powered math reasoning agent training that uses AutoGen with an MCP calculator tool. | [![calc_x workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-calc-x.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-calc-x.yml) |
+| [apo](./apo) | Automatic Prompt Optimization tutorials covering built-in, custom, and debugging workflows. | [![apo workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-apo.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-apo.yml) |
+| [azure](./azure) | Supervised fine-tuning with Azure OpenAI. | [![azure workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-azure.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-azure.yml) |
+| [calc_x](./calc_x) | VERL-powered math reasoning agent training that uses AutoGen with an MCP calculator tool. | [![calc_x workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-calc-x.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-calc-x.yml) |
 | [minimal](./minimal) | Bite-sized programs that demonstrate how individual Agent-lightning building blocks behave in isolation. | [![minimal 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) |
 | [rag](./rag) | Retrieval-Augmented Generation pipeline targeting the MuSiQue dataset with Wikipedia retrieval. | **Unmaintained** — last verified with Agent-lightning v0.1.1 |
 | [search_r1](./search_r1) | Framework-free Search-R1 reinforcement learning training workflow with a retrieval backend. | **Unmaintained** — last verified with Agent-lightning v0.1.2 |
-| [spider](./spider) | Text-to-SQL reinforcement learning training on the Spider dataset using LangGraph. | [![spider workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-spider.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-spider.yml) |
-| [tinker](./tinker) | Reinforcement learning with Tinker as the backend training service. | **Unmaintained** — last verified with Agent-lightning v0.2.2 |
-| [unsloth](./unsloth) | Supervised fine-tuning example powered by Unsloth with 4-bit quantization and LoRA. | [![unsloth workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unsloth.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unsloth.yml) |
+| [spider](./spider) | Text-to-SQL reinforcement learning training on the Spider dataset using LangGraph. | [![spider workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-spider.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml) |
+| [tinker](./tinker) | Reinforcement learning with Tinker as the backend training service. | [![tinker workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-tinker.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-tinker.yml) |
+| [unsloth](./unsloth) | Supervised fine-tuning example powered by Unsloth with 4-bit quantization and LoRA. | [![unsloth workflow status](https://github.com/microsoft/agent-lightning/actions/workflows/badge-unsloth.yml/badge.svg)](https://github.com/microsoft/agent-lightning/actions/workflows/examples-unsloth.yml) |

-*NOTE: CI status avoid taking any workflow running with latest dependencies into account. That's why we reference the corresponding `badge-*` workflows instead. Each example's own README also displays its `examples-*` workflow status whenever the project is maintained by CI.*
+*NOTE: CI status avoids taking any workflow running with latest dependencies into account. That's why we reference the corresponding `badge-*` workflows instead. Each example's own README also displays its `examples-*` workflow status whenever the project is maintained by CI.*
@@ -49,6 +49,10 @@ markdown_extensions:
  - toc:
      permalink: true
  - attr_list
+  - md_in_html
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg

 plugins:
  - search
@@ -98,6 +102,7 @@ nav:
    - Train the First Agent: how-to/train-first-agent.md
    - Write the First Algorithm: how-to/write-first-algorithm.md
  - How-To Recipes:
+    - Examples Catalog: how-to/examples-catalog.md
    - SFT with Unsloth: how-to/unsloth-sft.md
    - Train SQL Agent with RL: how-to/train-sql-agent.md
  - Learning More:
Author	SHA1	Message	Date
Yuge Zhang	7b6e763079	.	2025-11-24 00:10:28 +08:00
Yuge Zhang	03ed353da9	.	2025-11-24 00:09:05 +08:00
Yuge Zhang	87f459886a	.	2025-11-24 00:06:45 +08:00
Yuge Zhang	a791ef6447	update contributing guide	2025-11-23 23:52:39 +08:00
Yuge Zhang	215cc8fe74	.	2025-11-23 23:28:53 +08:00
Yuge Zhang	bda205128b	.	2025-11-23 11:30:23 +08:00
Yuge Zhang	c71a58fa08	how to contribute	2025-11-23 11:23:13 +08:00
Yuge Zhang	bbc4d35c7f	Add examples catalog	2025-11-23 10:57:51 +08:00