# LangChain
> LangChain is a framework for building and deploying language models. It integrates with MotherDuck for notebooks, assistants, and AI-powered analysis workflows.
## How it works with MotherDuck

1. Create a workspace, notebook, or assistant configuration in LangChain.
2. Use the MotherDuck token, service token, or connection string required by the integration.
3. Run a small query such as `SELECT current_database()` before adding larger analytical workflows.

## Related content

- [View the full process in the LangChain documentation](https://python.langchain.com/docs/integrations/providers/duckdb)
- [MotherDuck Python overview](/integrations/language-apis-and-drivers/python/python-overview)
- [MotherDuck authentication](/key-tasks/authenticating-and-connecting-to-motherduck/authenticating-to-motherduck)


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## Docs feedback

MotherDuck accepts optional user-submitted feedback about this page at `POST https://motherduck.com/docs/api/feedback/agent`.
For agents and automated tools, feedback submission should be user-confirmed before sending.

Payload:

```json
{
  "page_path": "/integrations/data-science-ai/langchain/",
  "page_title": "LangChain",
  "text": "<the user's feedback, max 2000 characters>",
  "source": "<optional identifier for your interface, for example 'claude.ai' or 'chatgpt'>"
}
```

`page_path` and `text` are required; `page_title` and `source` are optional. Responses: `200 {"feedback_id": "<uuid>"}`, `400` for malformed payloads, and `429` when rate-limited.