Getting Started with Airbyte Agent Connectors

Getting Started with Airbyte Agent Connectors

This guide walks you through installing your first connector, setting up authentication, and executing your first operation.

Contents

• Prerequisites
• Installation
• Environment Setup
• Your First Connector: GitHub Example
• Quick Reference: Common Operations
• Next Steps
• Connector-Specific Documentation
• File Placement
• Version Management

Prerequisites

• Python 3.11 or later
• uv (recommended) or pip
• API credentials for the service you want to connect to

Installation

Install connectors individually based on the services you need:

# Using uv (recommended)
uv add airbyte-agent-github
uv add airbyte-agent-stripe
uv add airbyte-agent-salesforce

# Or using pip in a virtual environment
python3 -m venv .venv && source .venv/bin/activate
pip install airbyte-agent-github
pip install airbyte-agent-stripe
pip install airbyte-agent-salesforce

Note: When working in the airbyte-agent-connectors repo, packages are already available—no installation needed.

All connectors follow the naming pattern airbyte-agent-{service}:

Version Pinning (Recommended for Production)

For production stability, pin connector versions in your pyproject.toml or requirements.txt:

# Pin to specific version
uv add airbyte-agent-github==0.18.78
pip install airbyte-agent-stripe==0.5.75

# Or specify in requirements.txt
airbyte-agent-github==0.18.78
airbyte-agent-stripe==0.5.75
airbyte-agent-salesforce>=0.1.0,<0.2.0

Check connector versions in their CHANGELOG.md files or on PyPI.

Environment Setup

Using .env Files

Create a .env file in your project root:

# GitHub
GITHUB_ACCESS_TOKEN=ghp_your_token_here

# Stripe
STRIPE_API_KEY=sk_live_your_key_here

# Salesforce (OAuth)
SALESFORCE_CLIENT_ID=your_client_id
SALESFORCE_CLIENT_SECRET=your_client_secret
SALESFORCE_REFRESH_TOKEN=your_refresh_token

# OpenAI (for LLM-based agents)
OPENAI_API_KEY=sk-your_key_here

Load environment variables in your code:

from dotenv import load_dotenv
import os

load_dotenv()

github_token = os.environ["GITHUB_ACCESS_TOKEN"]

Obtaining Credentials

Each connector's AUTH.md file documents how to obtain credentials:

For detailed authentication instructions, see the connector's AUTH.md:

• GitHub AUTH.md
• Stripe AUTH.md
• Salesforce AUTH.md

Your First Connector: GitHub Example

Step 1: Create Project

uv init my-agent-app --app
cd my-agent-app
uv add airbyte-agent-github python-dotenv

Step 2: Configure Credentials

Create .env:

GITHUB_ACCESS_TOKEN=ghp_your_personal_access_token

Step 3: Basic Usage (No Agent Framework)

Create main.py:

import asyncio
import os
from dotenv import load_dotenv
from airbyte_agent_github import GithubConnector
from airbyte_agent_github.models import GithubPersonalAccessTokenAuthConfig

load_dotenv()

async def main():
    # Initialize connector
    connector = GithubConnector(
        auth_config=GithubPersonalAccessTokenAuthConfig(
            token=os.environ["GITHUB_ACCESS_TOKEN"]
        )
    )

    # List repositories for a user
    result = await connector.execute("repositories", "list", {
        "username": "airbytehq",
        "per_page": 5
    })

    if result.success:
        for repo in result.data:
            print(f"- {repo['name']}: {repo.get('description', 'No description')}")
    else:
        print(f"Error: {result.error}")

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

Run:

uv run main.py

Step 4: With PydanticAI Agent

Create agent.py:

import asyncio
import os
from dotenv import load_dotenv
from pydantic_ai import Agent
from airbyte_agent_github import GithubConnector
from airbyte_agent_github.models import GithubPersonalAccessTokenAuthConfig

load_dotenv()

# Initialize connector
connector = GithubConnector(
    auth_config=GithubPersonalAccessTokenAuthConfig(
        token=os.environ["GITHUB_ACCESS_TOKEN"]
    )
)

# Create agent
agent = Agent(
    "openai:gpt-4o",
    system_prompt=(
        "You help users explore GitHub repositories. "
        "Use the available tools to answer questions about repos, issues, and PRs."
    )
)

# Register tools
@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.execute("issues", "list", {
        "owner": owner,
        "repo": repo,
        "states": ["OPEN"],
        "per_page": limit
    })
    return str(result.data) if result.success else f"Error: {result.error}"

@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.execute("pull_requests", "list", {
        "owner": owner,
        "repo": repo,
        "states": ["OPEN"],
        "per_page": limit
    })
    return str(result.data) if result.success else f"Error: {result.error}"

@agent.tool_plain
async def get_repository(owner: str, repo: str) -> str:
    """Get details about a specific GitHub repository."""
    result = await connector.execute("repositories", "get", {
        "owner": owner,
        "repo": repo
    })
    return str(result.data) if result.success else f"Error: {result.error}"

async def main():
    print("GitHub Agent Ready! Ask questions about repositories.")
    print("Example: 'List open issues in airbytehq/airbyte'")
    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()
        print(f"\nAgent: {result.output}\n")

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

Add dependencies and run:

uv add pydantic-ai
uv run agent.py

Quick Reference: Common Operations

GitHub

# List user's repositories
await connector.execute("repositories", "list", {"username": "octocat"})

# Get specific repo
await connector.execute("repositories", "get", {"owner": "org", "repo": "name"})

# List open issues
await connector.execute("issues", "list", {
    "owner": "org", "repo": "name", "states": ["OPEN"]
})

# Search repositories
await connector.execute("repositories", "api_search", {
    "query": "language:python stars:>1000"
})

Stripe

from airbyte_agent_stripe import StripeConnector
from airbyte_agent_stripe.models import StripeAuthConfig

connector = StripeConnector(
    auth_config=StripeAuthConfig(api_key=os.environ["STRIPE_API_KEY"])
)

# List customers
await connector.execute("customers", "list", {"limit": 10})

# Get customer
await connector.execute("customers", "get", {"id": "cus_xxx"})

# Search customers
await connector.execute("customers", "api_search", {
    "query": "email:'user@example.com'"
})

Slack

from airbyte_agent_slack import SlackConnector
from airbyte_agent_slack.models import SlackAuthConfig

connector = SlackConnector(
    auth_config=SlackAuthConfig(token=os.environ["SLACK_BOT_TOKEN"])
)

# List channels
await connector.execute("channels", "list", {})

# List messages in channel
await connector.execute("messages", "list", {"channel": "C01234567"})

HubSpot

from airbyte_agent_hubspot import HubspotConnector
from airbyte_agent_hubspot.models import HubspotPrivateAppAuthConfig

connector = HubspotConnector(
    auth_config=HubspotPrivateAppAuthConfig(
        access_token=os.environ["HUBSPOT_ACCESS_TOKEN"]
    )
)

# List contacts
await connector.execute("contacts", "list", {"limit": 100})

# List deals
await connector.execute("deals", "list", {"limit": 50})

Next Steps

• Entity-Action API - Understand the core API patterns
• Authentication - Deep dive into auth options
• MCP Integration - Set up MCP server for Claude
• Troubleshooting - Common issues and solutions

Connector-Specific Documentation

Each connector has detailed documentation in its directory:

connectors/{connector}/
├── README.md      # Overview, example questions, basic usage
├── AUTH.md        # All authentication options
└── REFERENCE.md   # Complete entity/action reference

For example:

• GitHub README
• Stripe README
• Salesforce README

File Placement

Important: When working in the airbyte-agent-connectors repo, place ALL files (.env, scripts, examples) in the specific connector directory, NOT the repo root.

In the airbyte-agent-connectors repo:

• .env files go in connectors/{connector}/.env
• Test scripts go in connectors/{connector}/
• Virtual environments should be created in the connector directory if needed
• Each connector directory is self-contained

Version Management

Checking for Updates

# Check current version
pip show airbyte-agent-stripe

# Check latest on PyPI
pip index versions airbyte-agent-stripe

Upgrade Strategy

1. Pin versions in production: airbyte-agent-stripe==0.5.75
2. Test upgrades in staging first
3. Check connector's CHANGELOG.md for breaking changes
4. Update one connector at a time

Breaking Changes

Connectors follow semantic versioning:

• Patch (0.5.74 → 0.5.75): Bug fixes, safe to upgrade
• Minor (0.5.x → 0.6.0): New features, usually backward compatible
• Major (0.x → 1.0): Breaking changes, review changelog