langgraph-python-quickstart
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.
How do I install this agent skill?
npx skills add https://github.com/langchain-ai/langchain-skills --skill langgraph-python-quickstartIs this agent skill safe to install?
- Gen Agent Trust Hubpass
This skill provides a secure and standard procedure for scaffolding a local development environment for LangGraph agents using official documentation. It follows security best practices such as project isolation and proper credential management.
- Socketpass
No alerts
- Snykwarn
Risk: MEDIUM · 2 issues
What does this agent skill do?
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):
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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:modelstring — 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+, omittemperature/top_p/top_k(unsupported). -
Create a new directory (e.g.
langgraph-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.envthemselves — don't paste keys into chat. -
Install packages from the quickstart plus the provider package for their model.
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Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to
langgraph-fundamentalsfor next steps. For a higher-level agent API, use LangChaincreate_agentinstead.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/langchain-ai/langchain-skills/langgraph-python-quickstart">View langgraph-python-quickstart on skillZs</a>