langchain-ai/langchain-skills1 file

Langgraph Typescript Quickstart

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.

Specification
Skill ID
langchain-ai/langchain-skills/langgraph-typescript-quickstart
Publisher
langchain-ai
Repository
langchain-skills
Installs
0
Files
1
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

langchain-ai/langchain-skills/langgraph-typescript-quickstartInstalls these files
  • SKILL.md

What this skill tells the agent

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).

  1. Create a new directory (e.g. langgraph-agent/) and do all work there — do not pollute the open project.
  1. 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.
  1. Install packages from the quickstart plus the provider package for their model.
  1. 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.

Use this skill in a project

Open any RiverX project, search the Skills panel for langchain-ai/langchain-skills/langgraph-typescript-quickstart, and install it in one click.

Use this skill