LangGraph Python quickstart

Local setup constraints

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.