Skip to content
Marketplace

langgraph-typescript-quickstart

by langchain-ai

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.

Overview

SKILL.mdRead at 16a992f

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.

Files 1 file

Install it by hand

Deska runs this for you, for every agent you pick. To run it yourself in a terminal:

npx skills add langchain-ai/langchain-skills --skill langgraph-typescript-quickstart

More from langchain-ai

  • deep-agents-memorylangchain-aiINVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.17.9K installs on skills.sh0
  • langgraph-persistencelangchain-aiINVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.16.8K installs on skills.sh0
  • langgraph-fundamentalslangchain-aiINVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.16.6K installs on skills.sh0
  • langchain-raglangchain-aiINVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).16.4K installs on skills.sh0
  • langchain-fundamentalslangchain-aiCreate LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.16.3K installs on skills.sh0
All skills by langchain-ai