CONTRIBUTING.md

Contributing to agno

Agno is an open-source project and we welcome contributions.

👩‍💻 How to contribute

Please follow the fork and pull request workflow:

• Fork the repository.
• Create a new branch for your feature.
• Add your feature or improvement.
• Send a pull request.
• We appreciate your support & input!

Development setup

1. Clone the repository.
2. Check if you have uv installed by running uv --version.
   • If you have uv installed, you can skip this step.
   • If you don't have uv installed, you can install it by running pip install uv.
3. Create a virtual environment:
   • For Unix, use ./scripts/dev_setup.sh.
   • For Windows, use .\scripts\dev_setup.bat.
   • This setup will:
     • Create a .venv virtual environment in the current directory.
     • Install the required packages.
     • Install the agno package in editable mode.
4. Activate the virtual environment:
   • On Unix: source .venv/bin/activate
   • On Windows: .venv\Scripts\activate

From here on you have to use uv pip install to install missing packages

Formatting and validation

Ensure your code meets our quality standards by running the appropriate formatting and validation script before submitting a pull request:

• For Unix:
  • ./scripts/format.sh
  • ./scripts/validate.sh
• For Windows:
  • .\scripts\format.bat
  • .\scripts\validate.bat

These scripts will perform code formatting with ruff and static type checks with mypy.

Local testing

Before submitting a pull request, ensure all tests pass locally:

1. Do the development setup above.

2. Run the test suite ./scripts/test.sh

3. Run specific test files or test cases: pytest ./libs/agno/tests/unit/utils/test_string.py or whatever file you want to test.

Make sure all tests pass before submitting your pull request. If you add new features, include appropriate test coverage.

Adding a new Vector Database

1. Setup your local environment by following the Development setup.
2. Create a new directory under libs/agno/agno/vectordb for the new vector database.
3. Create a Class for your VectorDb that implements the VectorDb interface
   • Your Class will be in the libs/agno/agno/vectordb/<your_db>/<your_db>.py file.
   • The VectorDb interface is defined in `libs/agno/agno/vectordb/base
   • Import your VectorDb Class in libs/agno/agno/vectordb/<your_db>/__init__.py.
   • Checkout the libs/agno/agno/vectordb/pgvector/pgvector file for an example.
4. Add a recipe for using your VectorDb under cookbook/agent_concepts/knowledge/vector_dbs/<your_db>.
   • Checkout cookbook/agent_concepts/knowledge/vector_dbs/pg_vector for an example.
5. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
6. Submit a pull request.

Adding a new Model Provider

1. Setup your local environment by following the Development setup.
2. Create a new directory under libs/agno/agno/models for the new Model provider.
3. If the Model provider supports the OpenAI API spec:
   • Create a Class for your LLM provider that inherits the OpenAILike Class from libs/agno/agno/models/openai/like.py.
   • Your Class will be in the libs/agno/agno/models/<your_model>/<your_model>.py file.
   • Import your Class in the libs/agno/agno/models/<your_model>/__init__.py file.
   • Checkout the agno/models/xai/xai.py file for an example.
4. If the Model provider does not support the OpenAI API spec:
   • Reach out to us on Discord or open an issue to discuss the best way to integrate your LLM provider.
   • Checkout agno/models/anthropic/claude.py or agno/models/cohere/chat.py for inspiration.
5. Add a recipe for using your Model provider under cookbook/providers/<your_model>.
   • Checkout agno/cookbook/provider/claude for an example.
6. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
7. Submit a pull request.

Adding a new Tool.

1. Setup your local environment by following the Development setup.
2. Create a new directory under libs/agno/agno/tools for the new Tool.
3. Create a Class for your Tool that inherits the Toolkit Class from libs/agno/agno/tools/toolkit/.py.
   • Your Class will be in libs/agno/agno/tools/<your_tool>.py.
   • Make sure to register all functions in your class via a flag.
   • Checkout the agno/tools/youtube_tools.py file for an example.
   • If your tool requires an API key, checkout the agno/tools/serpapi_tools.py as well.
4. Add a recipe for using your Tool under cookbook/tools/<your_tool>.
   • Checkout agno/cookbook/tools/youtube_tools for an example.
5. Important: Format and validate your code by running ./scripts/format.sh and ./scripts/validate.sh.
6. Submit a pull request.

Message us on Discord or post on Discourse if you have any questions or need help with credits.

📚 Resources

• Documentation
• Discord
• Discourse

📝 License

This project is licensed under the terms of the MPL-2.0 license