Github Connector for AI Agents

Give agents tools for secure, real-time access to fetch, search, write, and sync across every system, with replication, entity mapping, and auth built-in.

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community members
6,000+
daily active companies
2PB+
synced/month
900+
contributors

About this Connector

GitHub is a platform for version control and collaborative software development using Git. This connector provides access to repositories, branches, commits, issues, pull requests, reviews, comments, releases, organizations, teams, and users for development workflow analysis and project management insights.

CRM

Sales Analytics

Customer Data

developer tools, engineering productivity, source control data

Version Information

Package version

0.18.75

Connector version

0.1.9

SDK commit

5b20f488dec0e8f29410823753106603c23a4b65

Support Open Source

Star us on GitHub to help grow the Airbyte community

Github

Installation & Usage

Get started with the Github connector in minutes

1

Install Package

Using uv or pip

bash

Copy
uv pip install airbyte-agent-github

2

Import

Initialize and use

python

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from airbyte_agent_github import GithubConnector
from airbyte_agent_github.models import GithubPersonalAccessTokenAuthConfig

connector = GithubConnector(
    auth_config=GithubPersonalAccessTokenAuthConfig(
        token="<GitHub personal access token (fine-grained or classic)>"
    )
)

3

Tool

Add tools to your agent

python

Copy
@agent.tool_plain # assumes you're using Pydantic AI
@GithubConnector.tool_utils
async def github_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Supported Entities & Actions

Access all your Github data through a unified API

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| Entity | Actions |\n|--------|---------|\n| Repositories | [Get](./REFERENCE.md#repositories-get), [List](./REFERENCE.md#repositories-list), [API Search](./REFERENCE.md#repositories-api_search) |\n| Org Repositories | [List](./REFERENCE.md#org-repositories-list) |\n| Branches | [List](./REFERENCE.md#branches-list), [Get](./REFERENCE.md#branches-get) |\n| Commits | [List](./REFERENCE.md#commits-list), [Get](./REFERENCE.md#commits-get) |\n| Releases | [List](./REFERENCE.md#releases-list), [Get](./REFERENCE.md#releases-get) |\n| Issues | [List](./REFERENCE.md#issues-list), [Get](./REFERENCE.md#issues-get), [API Search](./REFERENCE.md#issues-api_search) |\n| Pull Requests | [List](./REFERENCE.md#pull-requests-list), [Get](./REFERENCE.md#pull-requests-get), [API Search](./REFERENCE.md#pull-requests-api_search) |\n| Reviews | [List](./REFERENCE.md#reviews-list) |\n| Comments | [List](./REFERENCE.md#comments-list), [Get](./REFERENCE.md#comments-get) |\n| Pr Comments | [List](./REFERENCE.md#pr-comments-list), [Get](./REFERENCE.md#pr-comments-get) |\n| Labels | [List](./REFERENCE.md#labels-list), [Get](./REFERENCE.md#labels-get) |\n| Milestones | [List](./REFERENCE.md#milestones-list), [Get](./REFERENCE.md#milestones-get) |\n| Organizations | [Get](./REFERENCE.md#organizations-get), [List](./REFERENCE.md#organizations-list) |\n| Users | [Get](./REFERENCE.md#users-get), [List](./REFERENCE.md#users-list), [API Search](./REFERENCE.md#users-api_search) |\n| Teams | [List](./REFERENCE.md#teams-list), [Get](./REFERENCE.md#teams-get) |\n| Tags | [List](./REFERENCE.md#tags-list), [Get](./REFERENCE.md#tags-get) |\n| Stargazers | [List](./REFERENCE.md#stargazers-list) |\n| Viewer | [Get](./REFERENCE.md#viewer-get) |\n| Viewer Repositories | [List](./REFERENCE.md#viewer-repositories-list) |\n| Projects | [List](./REFERENCE.md#projects-list), [Get](./REFERENCE.md#projects-get) |\n| Project Items | [List](./REFERENCE.md#project-items-list) |

Example Prompts

The Github connector is optimized to handle prompts like these

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Show me all open issues in my repositories this month - List the top 5 repositories I've starred recently - Analyze the commit trends in my main project over the last quarter - Find all pull requests created by \{team_member\} in the past two weeks - Search for repositories related to machine learning in my organizations - Compare the number of contributors across my different team projects - Identify the most active branches in my main repository - Get details about the most recent releases in my organization - List all milestones for our current development sprint - Show me insights about pull request review patterns in our team

Why Airbyte for AI Agents?

Built for production AI workloads with enterprise-grade reliability

Agent-Native Design

Structured, LLM-friendly schemas optimized for AI agent consumption with natural language query support.

Secure Authentication

Built-in OAuth 2.0 handling with automatic token refresh. No hard-coded credentials.

Production Ready

Battle-tested connectors with comprehensive error handling, logging, and retry logic.

Open Source

Fully open source under the MIT license. Contribute, customize, and extend freely.

Works with your favorite frameworks

Use the Github connector with any AI agent framework

🦜

LangChain

🦙

LlamaIndex

🤖

CrewAI

AutoGen

🧠

OpenAI Agents SDK

🔮

Claude Agents SDK

Frequently Asked Questions

Didn't find your answer? Please don't hesitate to reach out.

How do I authenticate with Github?

The GitHub connector supports authentication via Personal Access Tokens (both fine-grained and classic). You provide your GitHub personal access token in the GithubPersonalAccessTokenAuthConfig when initializing the connector.

Can I use this connector with any AI agent framework?

The connector is compatible with any Python-based AI agent framework including LangChain, LlamaIndex, CrewAI, Pydantic AI, and custom implementations.

Does this connector support write operations?

Currently, the GitHub connector focuses on read operations for development workflow analysis and project management insights. Write operations (such as creating issues, updating pull requests, or deleting branches) are not currently supported, as indicated in the Unsupported questions section.

How is this different from the Airbyte data connector?

Agent connectors are specifically designed for AI agents and LLM applications. They provide natural language interfaces, optimized response formats, and seamless integration with agent frameworks, unlike traditional ETL-focused connectors.

Will there be a platform for agent connectors?

The hosted version with secure credential storage through Airbyte Cloud is already available. See the hosted usage section in the documentation for setup instructions.

20,000+
community members
6,000+
daily active companies
2PB+
synced/month
900+
contributors