airbyte-agent-sdk 0.1.162

Airbyte Agent SDK: typed connectors that give AI agents access to 50+ third-party APIs

Airbyte Agent SDK

Type-safe connector execution framework with blessed connectors and full IDE autocomplete.

Overview

The Airbyte Agent SDK gives AI agents access to 50+ third-party APIs through strongly typed, well-documented tools. Connectors can run through the Airbyte platform (which manages credentials, rate limiting, and execution) or locally in OSS mode.

How to install

uv pip install airbyte-agent-sdk

Documentation

Full documentation is available at docs.airbyte.com/ai-agents/about/.

Tool integration

The SDK ships two decorators for turning a connector call into an LLM tool with retry-aware exception translation, output-size guards, and framework-specific error signalling.

• @<Connector>.tool_utils — preferred for typed connectors. Auto-detects the installed framework (pydantic-ai, LangChain, OpenAI Agents, or FastMCP) and composes translate_exceptions under the hood. Pass framework="..." to override auto-detection. Forwards update_docstring, max_output_chars, framework, internal_retries, should_internal_retry, and exhausted_runtime_failure_message.
• @translate_exceptions — same translation behaviour for any callable that is not a generated Connector (custom helpers, eval harnesses, ad-hoc tools).

Both decorators preserve sync/async, __name__, and __doc__. Transient runtime failures (429/5xx, network, timeout) can be retried silently via internal_retries=N on either decorator. Output exceeding max_output_chars (default 100 KB) is converted to the framework's retry signal so the LLM can narrow the query.

Pick one decorator per tool. Stacking @translate_exceptions over @<Connector>.tool_utils (or vice versa) is detected at decoration time: the inner layer is preserved and the outer layer logs a warning and short-circuits, so double-translation is impossible.

pydantic-ai

from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.stripe import StripeConnector

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@StripeConnector.tool_utils
async def list_customers(limit: int = 10) -> list[dict]:
    async with StripeConnector(connector_id="src_123") as stripe:
        result = await stripe.execute("customers", "list", params={"limit": limit})
        return result.data

Failures raise pydantic_ai.ModelRetry so the agent can retry with corrected arguments.

LangChain

from langchain_core.tools import StructuredTool
from airbyte_agent_sdk.connectors.stripe import StripeConnector

@StripeConnector.tool_utils(framework="langchain")
async def list_customers(limit: int = 10) -> list[dict]:
    async with StripeConnector(connector_id="src_123") as stripe:
        result = await stripe.execute("customers", "list", params={"limit": limit})
        return result.data

tool = StructuredTool.from_function(
    coroutine=list_customers,
    name="list_customers",
    description="List Stripe customers.",
    handle_tool_error=True,  # surfaces ToolException as the tool's string result
)

Failures raise langchain_core.tools.ToolException; handle_tool_error=True turns that into the tool's string result for the LLM.

Alternative for non-typed callables: replace @StripeConnector.tool_utils(framework="langchain") with @translate_exceptions(framework="langchain") from airbyte_agent_sdk.

OpenAI Agents

from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.stripe import StripeConnector

@function_tool
@StripeConnector.tool_utils(framework="openai_agents")
async def list_customers(limit: int = 10) -> list[dict]:
    async with StripeConnector(connector_id="src_123") as stripe:
        result = await stripe.execute("customers", "list", params={"limit": limit})
        return result.data

agent = Agent(name="stripe", tools=[list_customers])

Note: the OpenAI Agents strategy uses catch-and-return-string semantics — tool_utils catches the failure and returns a string (e.g. "ConnectorValidationError: entity must be one of: ...") instead of raising. The OpenAI runner serialises this string verbatim into the tool result the LLM sees.

Alternative for non-typed callables: replace @StripeConnector.tool_utils(framework="openai_agents") with @translate_exceptions(framework="openai_agents") from airbyte_agent_sdk.

FastMCP

from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.stripe import StripeConnector

mcp = FastMCP("stripe-tools")

@mcp.tool()
@StripeConnector.tool_utils(framework="mcp")
async def list_customers(limit: int = 10) -> list[dict]:
    async with StripeConnector(connector_id="src_123") as stripe:
        result = await stripe.execute("customers", "list", params={"limit": limit})
        return result.data

Failures raise fastmcp.exceptions.ToolError, which FastMCP serialises as an MCP error response to the client.

See the translate_exceptions reference for advanced kwargs (internal_retries, should_internal_retry, exhausted_runtime_failure_message).

How to install the skills

The repo ships skills that walk agents through setting up and using the connectors. Three install paths:

skills.sh (works for Claude Code, Codex, Cursor, OpenCode, and 40+ other agents):

npx skills add airbytehq/airbyte-agent-sdk

Claude Code (native plugin):

/plugin marketplace add airbytehq/airbyte-agent-sdk
/plugin install airbyte-agent-sdk@airbyte-agent-sdk

Codex (clone + symlink):

git clone https://github.com/airbytehq/airbyte-agent-sdk ~/.codex/skills/airbyte-agent-sdk-src
ln -s ~/.codex/skills/airbyte-agent-sdk-src/connector-sdk/.claude/skills/* ~/.codex/skills/

See docs.airbyte.com/ai-agents/about/ for full documentation.