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langgraph-typescript-quickstart

引导获取官方 LangGraph TypeScript 快速入门内容，先询问用户选择 provider:model，在新目录安装依赖、用 initChatModel 替换硬编码模型、配置 .env 密钥，运行示例并展示输出；跳过图可视化，默认不接入 LangSmith 与 Tavily。

langchain-ai
热度 63.78
最近更新 2026-10-19 02:34:08
分类：开发依赖：代码执行 / 读写文件 / 第三方 API / MCP 服务 / 内部调用大模型应用场景：代码生成 / API 集成

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add langchain-ai/langchain-skills/langgraph-typescript-quickstart
Metadata
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.

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.