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120 lines
4.6 KiB
Markdown
120 lines
4.6 KiB
Markdown
# Agent Framework Lab
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This is the experimental package for Microsoft Agent Framework, `agent-framework-lab`, which contains
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various lab modules built on top of the core framework.
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Lab modules are not part of the core framework and may experience breaking changes or be deprecated in the future.
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## What are Lab Modules?
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Lab modules are extensions to the core Agent Framework that fall into
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one of the following categories:
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1. Incubation of new features that may get incorporated by the core framework.
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2. Research prototypes built on the core framework.
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3. Benchmarks and experimentation tools.
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## Lab Modules
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- [**gaia**](./gaia/): Evaluate your agents using the GAIA benchmark for general assistant tasks
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- [**tau2**](./tau2/): Evaluate your agents using the TAU2 benchmark for customer support tasks
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- [**lightning**](./lightning/): RL training for agents using Agent Lightning
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## Repository Structure
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```
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agent-framework-lab/
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├── pyproject.toml # Single package configuration for agent-framework-lab
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├── README.md # This file
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├── LICENSE # License file
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├── namespace/ # Centralized namespace package files
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│ └── agent_framework/
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│ └── lab/
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│ ├── gaia/ # Re-exports from agent_framework_lab_gaia
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│ ├── lightning/ # Re-exports from agent_framework_lab_lightning
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│ └── tau2/ # Re-exports from agent_framework_lab_tau2
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├── gaia/ # GAIA module implementation
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│ └── agent_framework_lab_gaia/
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├── lightning/ # Lightning module implementation
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│ └── agent_framework_lab_lightning/
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└── tau2/ # TAU2 module implementation
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└── agent_framework_lab_tau2/
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```
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This structure maintains a single PyPI package `agent-framework-lab` while supporting modular imports through the namespace package mechanism.
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## Installation
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To install each lab module, use the extras syntax with `pip`:
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```bash
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pip install "agent-framework-lab[gaia]"
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pip install "agent-framework-lab[tau2]"
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pip install "agent-framework-lab[lightning]"
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```
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## Usage
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Import and use lab modules from the `agent_framework.lab` namespace.
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For example, to use the GAIA module:
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```python
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# Using GAIA module
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from agent_framework.lab.gaia import GAIA
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```
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## Running Tests Locally
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For machine-safe local runs, prefer package-scoped commands first:
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```bash
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uv run --directory packages/lab poe test
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uv run --directory packages/lab pytest -q -m "not integration"
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```
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When you need to run lab tests from the repository root, scope the root task to the lab package:
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```bash
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uv run poe test -P lab
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```
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Lightning observability tests intentionally exercise heavier tracing paths and are marked as `resource_intensive`:
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```bash
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uv run --directory packages/lab pytest lightning/tests/test_lightning.py -m "resource_intensive" -q
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```
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## Should I consume Lab Modules?
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If you are looking for stable and production-ready features, you should not use lab modules. Stick to the core framework.
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If you are looking for experimentation, research, or want to
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benchmark different approaches -- most importantly, if you don't mind breaking changes and potential deprecations --
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then lab modules are for you.
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## Contributing to Lab Modules
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### Microsoft-maintained modules
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For Microsoft-maintained modules in this repository, please follow standard contribution guidelines and submit pull requests directly to this repository.
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### Community modules
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If you want to contribute a community-maintained lab module:
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1. Create a new repository on GitHub for your module
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2. Tag your repository with `agent-framework-lab` for discoverability
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3. Submit a PR to add a link to your repository in the [Lab Modules](#lab-modules) section above
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4. Use the PR title format: `[New Lab Module] Your Module Name`
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We will review your submission based on the guidelines below.
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### Guidelines
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1. **Purpose**: Community modules should fit into one of the three categories of lab modules (incubation, research, benchmarks)
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2. **Namespace**: Community modules should avoid the `agent_framework.lab` namespace (reserved for modules maintained in this repository)
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3. **Dependencies**: Minimize external dependencies, always include `agent-framework` as a base dependency
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4. **Documentation**: Include comprehensive README with installation instructions and usage examples
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5. **Tests**: Write comprehensive tests with good coverage
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6. **Type hints**: Always include type hints and a `py.typed` file
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7. **Versioning**: Use semantic versioning, start with `0.1.0` for initial releases
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