Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
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---
name: langchain-python-quickstart
description: "Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally."
---
# LangChain Python quickstart
Follow the live docs — do not invent an alternate API from memory:
**https://docs.langchain.com/oss/python/langchain/quickstart**
Fetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + `create_agent`).
## Local setup constraints
Apply these on top of the quickstart (they keep setup minimal and model-agnostic):
1. **Ask** which provider/model to use. Showcase that LangChain is model-agnostic. Suggested prompt:
> Which model should this agent use? Pass a `provider:model` string — e.g. `openai:gpt-5.5`, `anthropic:claude-sonnet-5`, `google_genai:gemini-2.5-flash-lite`. Default if you're unsure: **`anthropic:claude-sonnet-5`**.
Swap the quickstart's model string for their choice (or the default).
2. Create a **new** directory (e.g. `langchain-agent/`) and do all work there — do not pollute the open project.
3. Only secret: the provider API key in `.env` (gitignored). No LangSmith / Tavily unless they ask. Prefer they edit `.env` themselves — don't paste keys into chat.
4. Install the provider package needed for their model if the quickstart's base install isn't enough.
5. Run the example, show output, then stop. Point to `langchain-fundamentals` for next steps.
