/langgraph-python-quickstart

name: langgraph-python-quickstart
description: "Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally."

LangGraph Python quickstart

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

**https://docs.langchain.com/oss/python/langgraph/quickstart**

Fetch that page (Docs MCP or HTTP) and implement what it shows (calculator / math agent with the Graph API). Prefer the Graph API path over the Functional API unless the user asks otherwise. Skip IPython graph visualization.

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 LangGraph works with any LangChain chat model. 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`**.

The docs often hardcode Anthropic — replace with `init_chat_model("<MODEL>")` (or equivalent) using their choice. If using Claude Sonnet 5+, omit `temperature` / `top_p` / `top_k` (unsupported).

2. Create a **new** directory (e.g. `langgraph-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 packages from the quickstart plus the provider package for their model.

5. Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to `langgraph-fundamentals` for next steps. For a higher-level agent API, use LangChain `create_agent` instead.