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Agent connector tutorial: Python SDK

In this tutorial, you'll create a new Python project with uv, add a Pydantic AI agent, equip it to use one of Airbyte's agent connectors, and use natural language to explore your data. This tutorial uses GitHub, but if you don't have a GitHub account, you can use one of Airbyte's other agent connectors and perform different operations.

Using the Python SDK is more time-consuming than the Connector MCP server, but affords you the most control over the context you send to your agent.

Overview​

This tutorial is for AI engineers and other technical users who work with data and AI tools. You can complete it in about 15 minutes.

The tutorial assumes you have basic knowledge of the following tools, but most software engineers shouldn't struggle with anything that follows.

  • Python and package management with uv
  • Pydantic AI
  • GitHub, or a different third-party service you want to connect to

Before you start​

Before you begin this tutorial, ensure you have the following.

Part 1: Create a new Python project​

In this tutorial you initialize a basic Python project to work in. However, if you have an existing project you want to work with, feel free to use that instead.

  1. Create a new project using uv:

    uv init my-ai-agent --app
    cd my-ai-agent

    This creates a project with the following structure:

    my-ai-agent/
    ├── .gitignore
    ├── .python-version
    ├── README.md
    ├── main.py
    └── pyproject.toml

  2. Create an agent.py file for your agent definition:

    touch agent.py

You create .env and uv.lock files in later steps, so don't worry about them yet.

Part 2: Install dependencies​

Install the GitHub connector and Pydantic AI. This tutorial uses OpenAI as the LLM provider, but Pydantic AI supports many other providers.

uv add airbyte-agent-github pydantic-ai

This command installs:

  • airbyte-agent-github: The Airbyte agent connector for GitHub, which provides type-safe access to GitHub's API.
  • pydantic-ai: The AI agent framework, which includes support for multiple LLM providers including OpenAI, Anthropic, and Google.

The GitHub connector also includes python-dotenv, which you can use to load environment variables from a .env file.

note

If you want a smaller installation with only OpenAI support, you can use pydantic-ai-slim[openai] instead of pydantic-ai. See the Pydantic AI installation docs for more options.

Part 3: Import Pydantic AI and the GitHub agent connector​

Add the following imports to agent.py:

agent.py
import os

from dotenv import load_dotenv
from pydantic_ai import Agent
from airbyte_agent_github import GithubConnector
from airbyte_agent_github.models import GithubAuthConfig

These imports provide:

  • os: Access environment variables for your GitHub token and LLM API key.
  • load_dotenv: Load environment variables from your .env file.
  • Agent: The Pydantic AI agent class that orchestrates LLM interactions and tool calls.
  • GithubConnector: The Airbyte agent connector that provides type-safe access to GitHub's API.
  • GithubAuthConfig: The authentication configuration for the GitHub connector.

Part 4: Add a .env file with your secrets​

  1. Create a .env file in your project root and add your secrets to it. Replace the placeholder values with your actual credentials.

    .env
    GITHUB_ACCESS_TOKEN=your-github-personal-access-token
    OPENAI_API_KEY=your-openai-api-key
    warning

    Never commit your .env file to version control. If you do this by mistake, rotate your secrets immediately.

  2. Add the following line to agent.py after your imports to load the environment variables:

    agent.py
    load_dotenv()

    This makes your secrets available via os.environ. Pydantic AI automatically reads OPENAI_API_KEY from the environment, and you'll use os.environ["GITHUB_ACCESS_TOKEN"] to configure the connector in the next section.

Part 5: Configure your connector and agent​

Now that your environment is set up, add the following code to agent.py to create the GitHub connector and Pydantic AI agent.

Define the connector​

Define the agent connector for GitHub. It authenticates using your personal access token.

agent.py
connector = GithubConnector(
    auth_config=GithubAuthConfig(
        access_token=os.environ["GITHUB_ACCESS_TOKEN"]
    )
)

Define the agent​

Create a Pydantic AI agent with a system prompt that describes its purpose:

agent.py
agent = Agent(
    "openai:gpt-4o",
    system_prompt=(
        "You are a helpful assistant that can access GitHub repositories, issues, "
        "and pull requests. Use the available tools to answer questions about "
        "GitHub data. Be concise and accurate in your responses."
    ),
)
  • The "openai:gpt-4o" string specifies the model to use. You can use a different model by changing the model string. For example, use "openai:gpt-4o-mini" to lower costs, or see the Pydantic AI models documentation for other providers like Anthropic or Google.
  • The system_prompt parameter tells the LLM what role it should play and how to behave.

Part 6: Add tools to your agent​

Tools let your agent fetch real data from GitHub using Airbyte's agent connector. Without tools, the agent can only respond based on its training data. By registering connector operations as tools, the agent can decide when to call them based on natural language questions.

Add the following code to agent.py.

agent.py
# Tool to list issues in a repository
@agent.tool_plain
async def list_issues(owner: str, repo: str, limit: int = 10) -> str:
    """List open issues in a GitHub repository."""
    result = await connector.issues.list(owner=owner, repo=repo, states=["OPEN"], per_page=limit)
    return str(result.data)


# Tool to list pull requests in a repository
@agent.tool_plain
async def list_pull_requests(owner: str, repo: str, limit: int = 10) -> str:
    """List open pull requests in a GitHub repository."""
    result = await connector.pull_requests.list(owner=owner, repo=repo, states=["OPEN"], per_page=limit)
    return str(result.data)

The @agent.tool_plain decorator registers each function as a tool the agent can call. The docstring becomes the tool's description, which helps the LLM understand when to use it. The function parameters become the tool's input schema, so the LLM knows what arguments to provide.

With these two tools, your agent can answer questions about issues, pull requests, or both. For example, it can compare open issues against pending PRs to identify which issues might be resolved soon.

Part 7: Run your project​

Now that your agent is configured with tools, update main.py and run your project.

  1. Update main.py. This code creates a simple chat interface in your command line tool and allows your agent to remember your conversation history between prompts.

    main.py
    import asyncio
    from agent import agent

    async def main():
        print("GitHub Agent Ready! Ask questions about GitHub repositories.")
        print("Type 'quit' to exit.\n")

        history = None

        while True:
            prompt = input("You: ")
            if prompt.lower() in ('quit', 'exit', 'q'):
                break
            result = await agent.run(prompt, message_history=history)
            history = result.all_messages()  # Call the method
            print(f"\nAgent: {result.output}\n")

    if __name__ == "__main__":
        asyncio.run(main())

  2. Run the project.

    uv run main.py

Chat with your agent​

The agent waits for your input. Once you prompt it, the agent decides which tools to call based on your question, fetches the data from GitHub, and returns a natural language response. Try prompts like:

  • "List the 10 most recent open issues in airbytehq/airbyte"
  • "What are the 10 most recent pull requests that are still open in airbytehq/airbyte?"
  • "Are there any open issues that might be fixed by a pending PR?"

The agent has basic message history within each session, and you can ask followup questions based on its responses.

Troubleshooting​

If your agent fails to retrieve GitHub data, check the following:

  • HTTP 401 errors: Your GITHUB_ACCESS_TOKEN is invalid or expired. Generate a new token and update your .env file.
  • HTTP 403 errors: Your GITHUB_ACCESS_TOKEN doesn't have the required scopes. Ensure your token has repo scope for accessing repository data.
  • OpenAI errors: Verify your OPENAI_API_KEY is valid, has available credits, and won't exceed rate limits.

Summary​

In this tutorial, you learned how to:

  • Set up a new Python project with uv
  • Add Pydantic AI and Airbyte's GitHub agent connector to your project
  • Configure environment variables and authentication
  • Add tools to your agent using the GitHub connector
  • Run your project and use natural language to interact with GitHub data

Next steps​

  • Add more tools and agent connectors to your project. For GitHub, you can wrap additional operations (like search, comments, or commits) as tools. Explore other agent connectors in the Airbyte agent connectors catalog to give your agent access to more services.
  • Consider how you might like to expand your agent's capabilities. For example, you might want to trigger effects like sending a Slack message or an email based on the agent's findings. You aren't limited to the capabilities of Airbyte's agent connectors. You can use other libraries and integrations to build an increasingly robust agent ecosystem.
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