INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.
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langchain-ai/langchain-skills12972026年10月19日 更新
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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.
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查看安装方法包含的文件（1）
- SKILL.md1.8 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
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):
-
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:modelstring — 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+, omittemperature/top_p/top_k(unsupported). -
Create a new directory (e.g.
langgraph-agent/) and do all work there — do not pollute the open project. -
Only secret: the provider API key in
.env(gitignored). No LangSmith / Tavily unless they ask. Prefer they edit.envthemselves — don't paste keys into chat. -
Install packages from the quickstart plus the provider package for their model.
-
Run the example (e.g. “Add 3 and 4.”), show output, then stop. Point to
langgraph-fundamentalsfor next steps. For a higher-level agent API, use LangChaincreateAgentinstead.
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同一仓库的技能
INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12972026年10月19日 更新
INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12972026年10月19日 更新
Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12972026年10月19日 更新
Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12972026年10月19日 更新
INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12972026年10月19日 更新