Langgraph Python Quickstart

From the author

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.

— the description by LangChain

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow for setting up a basic LangGraph agent locally. It guides the user through the official quickstart process, ensuring a clean environment and model-agnostic configuration. The agent focuses on implementing a simple math-based agent using the Graph API while adhering to best practices for local development, such as environment variable management and dependency isolation.

What it does

Creates a dedicated project directory for the agent.
Prompts the user to select a preferred LLM provider and model.
Implements the official LangGraph quickstart math agent using the Graph API.
Configures the agent to use the selected model via standard initialization.
Sets up a gitignored .env file for secure API key management.
Executes a test query to verify the agent's functionality.

When to use it

When you want to start building with LangGraph for the first time.
When you need a clean, minimal boilerplate for a local agent project.
When you want to test LangGraph capabilities with a specific LLM provider.
When you need to follow the official documentation path without external dependencies like LangSmith.

Advantages

Ensures a clean, isolated development environment.
Provides flexibility in model selection rather than hardcoding providers.
Follows official documentation patterns for reliability.
Maintains security by keeping API keys out of the chat history.

Usage examples

“Help me set up a basic LangGraph agent.”: The agent will create a new directory, ask for your preferred model, install necessary packages, and scaffold the math agent code.
“I want to build a LangGraph agent using Gemini.”: The agent will configure the project to use google_genai:gemini-2.5-flash-lite and guide you through the setup process.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/langchain-ai/langgraph-python-quickstart/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip langgraph-python-quickstart.zip -d .agents/skills

Documentation by LangChain

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):

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).
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 .env themselves — don't paste keys into chat.
Install packages from the quickstart plus the provider package for their model.
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.

Files

ATTRIBUTION.mdAdded by AutoGineer916 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md1.8 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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