building-multi-connector-agent

name	building-multi-connector-agent
description	Builds a complete agent with multiple Airbyte connectors using PydanticAI or Claude SDK. Scaffolds project structure, wires up connectors, composes tools, and creates a run loop. Use when building an agent with multiple connectors or scaffolding a new agent project.

Building a Multi-Connector Agent

Use this when an agent needs two or more Airbyte connectors.

Going from Single to Multi

The bootstrapping-agent skill shows the single-connector pattern: direct class construction with AirbyteAuthConfig + @Connector.tool_utils decorator. Multi-connector agents use the same pattern, just repeated:

1. Build a single AirbyteAuthConfig(...) so credentials are shared across connectors.
2. Construct one typed connector per service (e.g. JiraConnector(auth_config=auth)).
3. Define one tool function per connector, each with its own @Connector.tool_utils decorator.

There are no new APIs — same install, same constructor, same classmethod decorator.

Install the SDK

uv pip install airbyte-agent-sdk

The single airbyte-agent-sdk package bundles every typed connector, so tool_utils, list_entities(), and entity_schema() are available on each one without per-connector installs.

Core Pattern (PydanticAI)

import os
from pydantic_ai import Agent
from airbyte_agent_sdk import AirbyteAuthConfig
from airbyte_agent_sdk.connectors.jira import JiraConnector
from airbyte_agent_sdk.connectors.slack import SlackConnector

# Shared credentials — all connectors reuse the same AirbyteAuthConfig
auth = AirbyteAuthConfig(
    airbyte_client_id=os.getenv("AIRBYTE_CLIENT_ID"),
    airbyte_client_secret=os.getenv("AIRBYTE_CLIENT_SECRET"),
    workspace_name=os.getenv("AIRBYTE_WORKSPACE_NAME", "default"),
)

jira = JiraConnector(auth_config=auth)
slack = SlackConnector(auth_config=auth)

If the workspace contains multiple connectors of the same type, pin one by passing connector_id=os.getenv("JIRA_CONNECTOR_ID") to the constructor.

One Tool Per Connector

Each connector gets its own tool function — don't combine them into a mega-tool. Separate tools give the LLM clear, independent tool descriptions.

tool_utils is a @classmethod — decorate with @JiraConnector.tool_utils, not @jira.tool_utils.

agent = Agent(
    "<provider:model>",
    system_prompt=(
        "You are a helpful assistant with access to Jira and Slack. "
        "Use the jira_execute tool to read Jira issues and the slack_execute tool to post messages. "
        "Ask for clarification if a request is ambiguous."
    ),
)

@agent.tool_plain
@JiraConnector.tool_utils
async def jira_execute(entity: str, action: str, params: dict | None = None):
    return await jira.execute(entity, action, params or {})

@agent.tool_plain
@SlackConnector.tool_utils
async def slack_execute(entity: str, action: str, params: dict | None = None):
    return await slack.execute(entity, action, params or {})

System Prompt

Describe the agent's purpose and what each connector does:

agent = Agent(
    "<provider:model>",
    system_prompt=(
        "You are a customer support assistant. "
        "Use the stripe tool to look up customer billing data. "
        "Use the jira tool to create and track support tickets. "
        "Use the slack tool to notify the support team."
    ),
)

Run Loop

PydanticAI

import asyncio

async def main():
    result = await agent.run("Find open P0 bugs and post a summary to #engineering")
    print(result.output)
    await jira.close()
    await slack.close()

asyncio.run(main())

Claude SDK (Anthropic Python)

See Claude SDK patterns for the full message loop with tool handling.

Project Structure

For a new agent project:

my-agent/
├── pyproject.toml       # dependencies: airbyte-agent-sdk, pydantic-ai or anthropic
├── .env                 # AIRBYTE_CLIENT_ID, AIRBYTE_CLIENT_SECRET, AIRBYTE_WORKSPACE_NAME
├── agent.py             # Entry point: auth config, connectors, agent + tools, run loop
└── README.md

pyproject.toml

[project]
name = "my-agent"
requires-python = ">=3.11"
dependencies = [
    "airbyte-agent-sdk",
    "pydantic-ai",
    "python-dotenv",
]

Environment Variables

AIRBYTE_CLIENT_ID=your_client_id
AIRBYTE_CLIENT_SECRET=your_client_secret
AIRBYTE_WORKSPACE_NAME=your_workspace_name

References

• SDK API reference — AirbyteAuthConfig, typed connector constructors, tool_utils
• PydanticAI patterns — multi-connector example
• Claude SDK patterns — multi-connector example
• Connector discovery — finding available connectors