Airbyte Agent Connectors are packages that let AI agents call third‑party APIs through strongly typed, well‑documented tools. Each connector is a standalone Python package that you can use directly in your app or plug into an agent framework.
What this repo contains
- Python SDKs for specific SaaS APIs (for example Gong, Stripe, GitHub).
- A consistent connector layout so you can understand and extend any connector quickly.
- Ready‑to-use clients that validate auth, handle schemas, and expose typed methods for each operation.
Connector Structure
Each connector is a standalone Python package:
connectors/
├── gong/
│ ├── airbyte_agent_gong/
│ ├── pyproject.toml
│ ├── CHANGELOG.md
│ ├── README.md
│ └── REFERENCE.md
├── github/
│ └── ...
└── ...
Inside each connector folder, you’ll find:
- Generated Python client
- Connector-specific README with supported operations
- Typed methods generated from Airbyte’s connector definitions
- Validation + error handling
When to use these connectors
Use Airbyte Agent Connectors when you want:
- Agent‑friendly data access: Let AI agents call real SaaS APIs (e.g., CRM, billing, analytics) with guardrails and typed responses.
- Consistent auth and schemas: Reuse a uniform configuration and error‑handling pattern across many APIs. Use connectors inside frameworks like PydanticAI, LangChain, or any custom agent loop.
- Composable building blocks: Combine multiple connectors in a single agent to orchestrate multi‑system workflows. Compared to building ad‑hoc API wrappers, these connectors give you a shared structure, generated clients, and alignment with the rest of the Airbyte ecosystem.
Roadmap
We are actively expanding AI Connectors. Coming soon:
- Writes!
- MCP support (Model Context Protocol)
- Hosted execution, authentication, and search
- Demo apps + starter templates
- More connectors added weekly
Contributing / Feedback
We actively welcome feedback, ideas, and bug reports. If you're building AI agents with Airbyte Agent Connectors and want to share ideas or get help, join our community on slack. We'd love to hear what you're building and what connectors or capabilities you'd like to see next.
Agent Skill
This repo includes an agent skill for setup and usage guidance:
npx skills add airbytehq/airbyte-agent-connectors
Or in Claude Code:
/install airbytehq/airbyte-agent-connectors
See airbyte-agent-connectors for full usage details.
Tutorial using the 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.
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.
- Python version 3.13 or later
- uv
- A GitHub personal access token. For this tutorial, a classic token with
reposcope is sufficient. - An OpenAI API key. This tutorial uses OpenAI, but Pydantic AI supports other LLM providers if you prefer.