LangChain

Build a LangChain agent with Authlane tools scoped to one authenticated SaaS user.

The Python adapter returns LangChain StructuredTool instances with canonical input schemas.

Prerequisites

pip install 'authlane[langchain]' langchain

Implement the workflow

import os
from dataclasses import dataclass

from authlane import Authlane
from authlane.adapters import langchain
from langchain.agents import create_agent
from langchain_core.language_models import BaseChatModel

@dataclass(frozen=True)
class CurrentUser:
    id: str

def answer(current_user: CurrentUser, prompt: str, model: BaseChatModel):
    with Authlane(
        api_key=os.environ["AUTHLANE_API_KEY"],
        base_url="https://app.authlane.io",
    ) as authlane:
        user = authlane.user(current_user.id)
        result = user.tools.list(adapter=langchain())
        if result.error is not None:
            return result
        assert result.data is not None
        agent = create_agent(model=model, tools=result.data)
        return agent.invoke({"messages": [{"role": "user", "content": prompt}]})

Expected result

The agent receives one user-bound list of StructuredTool objects.

Handle errors

Branch on the Authlane Result before creating the agent. Treat model and framework exceptions as separate application errors.

Security boundary

Never share the tool list across user requests. Tool callbacks acquire fresh access-only material and call the provider from this Python process.

Next step

Use troubleshooting for tuple error recovery.