langgraph-python-quickstart

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha a78b2e2257e3 · 31 Oct 2026Report an issue or request a re-scan

• Publisherlangchain-ai GitHub organizationOwner of the repository this skill is read from
• Source folderlangchain-ai/langchain-skills/config/skills/langgraph-python-quickstartThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdicta78b2e2257e3A new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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.

---
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.

Excerpt, first 1,812 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/langchain-ai/langchain-skills
git -C langchain-skills checkout a78b2e2257e3

Works in

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source a78b2e2257e3

Questions about langgraph-python-quickstart

• github.com/langchain-ai/langchain-skills/tree/main/config/skills — the audited source at commit a78b2e2257e3 and every finding aboveObserved · 31 Oct 2026

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