This repository provides examples of many popular Python AI agent frameworks using LLMs from GitHub Models. Those models are free to use for anyone with a GitHub account, up to a daily rate limit.

• Getting started
  • GitHub Codespaces
  • VS Code Dev Containers
  • Local environment
• Running the Python examples
• Guidance
  • Costs
  • Security guidelines
• Resources

You have a few options for getting started with this repository. The quickest way to get started is GitHub Codespaces, since it will setup everything for you, but you can also set it up locally.

You can run this repository virtually by using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:

1. Open the repository (this may take several minutes):

   

2. Open a terminal window

3. Continue with the steps to run the examples

A related option is VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:

1. Start Docker Desktop (install it if not already installed)

2. Open the project:

   

3. In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.

4. Continue with the steps to run the examples

1. Make sure the following tools are installed:

   • Python 3.10+
   • Git

2. Clone the repository:

   git clone https://github.com/Azure-Samples/python-ai-agent-frameworks-demos
   cd python-ai-agents-demos

3. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

4. Install the requirements:

   pip install -r requirements.txt

You can run the examples in this repository by executing the scripts in the examples directory. Each script demonstrates a different AI agent pattern or framework.

If you open this repository in GitHub Codespaces, you can run the scripts for free using GitHub Models without any additional steps, as your GITHUB_TOKEN is already configured in the Codespaces environment.

If you want to run the scripts locally, you need to set up the GITHUB_TOKEN environment variable with a GitHub personal access token (PAT). You can create a PAT by following these steps:

1. Go to your GitHub account settings.

2. Click on "Developer settings" in the left sidebar.

3. Click on "Personal access tokens" in the left sidebar.

4. Click on "Tokens (classic)" or "Fine-grained tokens" depending on your preference.

5. Click on "Generate new token".

6. Give your token a name and select the scopes you want to grant. For this project, you don't need any specific scopes.

7. Click on "Generate token".

8. Copy the generated token.

9. Set the GITHUB_TOKEN environment variable in your terminal or IDE:

   export GITHUB_TOKEN=your_personal_access_token

10. Optionally, you can use a model other than "gpt-4o" by setting the GITHUB_MODEL environment variable. Use a model that supports function calling, such as: gpt-4o, gpt-4o-mini, o3-mini, AI21-Jamba-1.5-Large, AI21-Jamba-1.5-Mini, Codestral-2501, Cohere-command-r, Ministral-3B, Mistral-Large-2411, Mistral-Nemo, Mistral-small

You can run all examples in this repository using GitHub Models. If you want to run the examples using models from Azure OpenAI instead, you need to provision the Azure AI resources, which will incur costs.

This project includes infrastructure as code (IaC) to provision Azure OpenAI deployments of "gpt-4o" and "text-embedding-3-large". The IaC is defined in the infra directory and uses the Azure Developer CLI to provision the resources.

1. Make sure the Azure Developer CLI (azd) is installed.

2. Login to Azure:

   azd auth login

   For GitHub Codespaces users, if the previous command fails, try:

    azd auth login --use-device-code

3. Provision the OpenAI account:

   azd provision

   It will prompt you to provide an azd environment name (like "agents-demos"), select a subscription from your Azure account, and select a location. Then it will provision the resources in your account.

4. Once the resources are provisioned, you should now see a local .env file with all the environment variables needed to run the scripts.

5. To delete the resources, run:

   azd down

• AutoGen Documentation
• LangGraph Documentation
• LlamaIndex Documentation
• OpenAI Agents Documentation
• OpenAI Function Calling Documentation
• PydanticAI Documentation
• Semantic Kernel Documentation
• SmolAgents Documentation