LangChain Python quickstart

Follow the live docs — do not invent an alternate API from memory:

Fetch that page (Docs MCP or HTTP) and implement what it shows (weather agent + create_agent).

Apply these on top of the quickstart (they keep setup minimal and model-agnostic):

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).
Create a new directory (e.g. langchain-agent/) and do all work there — do not pollute the open project.
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.
Install the provider package needed for their model if the quickstart's base install isn't enough.
Run the example, show output, then stop. Point to langchain-fundamentals for next steps.

Third-party checks

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Gen Agent Trust Hub2mo
This skill provides a helpful way to scaffold a local LangChain agent by following official documentation. It includes security considerations such as external content retrieval and local command execution, which are appropriate for its intended purpose of setting up a development environment.
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