mcpField
L
langgraph-typescript-quickstart

langgraph-typescript-quickstart

github.com/langchain-aiAgent workflows1.3k5.2k installsUpdated 3d ago
Repository

About langgraph-typescript-quickstart

A skill for developers learning LangGraph in TypeScript through the official quickstart's calculator agent. It uses the Graph API and supports choosing a LangChain chat model, with work kept in a new project directory.

Key features

  1. 01Follow the official quickstart
  2. 02Build a calculator agent with the Graph API
  3. 03Choose a LangChain chat model
  4. 04Install dependencies in a separate directory
  5. 05Run the example and show output

Use cases

  1. 01Build a LangGraph TypeScript agent
  2. 02Implement a math flow with the Graph API
  3. 03Test the calculator agent example

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/langchain-ai/langchain-skills --skill langgraph-typescript-quickstart

After installing, type/langgraph-typescript-quickstartin Claude Code to confirm it loaded.

Details

From the project README

LangGraph TypeScript quickstart

Follow the live docs — do not invent an alternate API from memory:

https://docs.langchain.com/oss/javascript/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 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 initChatModel("<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 createAgent instead.

Comments

Markdown supported