---
name: langgraph-typescript-quickstart
description: "Scaffold a minimal local LangGraph agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally."
title: langgraph-typescript-quickstart
canonical_url: https://skilld.dev/gh/langchain-ai/langchain-skills/langgraph-typescript-quickstart
last_updated: 2026-09-29T12:23:33.000Z
---

> **Skill from skilld.dev.** Follow the instructions below for this session. You do not need to install anything.
>
> If the user asked to install this Skill, run `npx skilld install langchain-ai/langchain-skills/langgraph-typescript-quickstart`. Install writes the Skill files into the project, so every session loads them.

# 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.