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Langgraph · Python · Agent Development / AGENT SKILL
langgraph-python-quickstart
0
Scaffold a minimal local LangGraph agent in Python.
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.
Understand the trade-offs.
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The full skill.
Original instructions from the publisher’s SKILL.md
# 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.