Agent Skills › langchain-ai/langchain-skills › deepagents-typescript-quickstart

deepagents-typescript-quickstart

GitHub

指导用户基于 LangChain 官方文档，在本地使用 TypeScript 快速搭建最小化 Deep Agent。支持模型无关配置，利用提供商内置搜索替代 Tavily，完成环境初始化、代码实现及运行验证。

config/skills/deepagents-typescript-quickstart/SKILL.md langchain-ai/langchain-skills

触发场景

想要快速尝试或构建本地 Deep Agent 需要基于 LangChain 创建 TypeScript 研究代理

安装

npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -g -y

更多选项

非标准路径

npx skills add https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deepagents-typescript-quickstart -g -y

不安装直接使用

npx skills use langchain-ai/langchain-skills@deepagents-typescript-quickstart

指定 Agent (Claude Code)

npx skills add langchain-ai/langchain-skills --skill deepagents-typescript-quickstart -a claude-code -g -y

安装 repo 全部 skill

npx skills add langchain-ai/langchain-skills --all -g -y

预览 repo 内 skill

npx skills add langchain-ai/langchain-skills --list

SKILL.md

Frontmatter

{
    "name": "deepagents-typescript-quickstart",
    "description": "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."
}

Deep Agents TypeScript quickstart

Follow the live docs — do not invent an alternate API from memory:

https://docs.langchain.com/oss/javascript/deepagents/quickstart

Fetch that page (Docs MCP or HTTP) and implement the research-agent shape it shows (createDeepAgent, research system prompt, invoke with a research question like “What is LangGraph?”). Requires Node 22+.

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 Deep Agents are model-agnostic. 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-3.5-flash. Default if you're unsure: anthropic:claude-sonnet-5.
We'll use that provider's built-in web search (no separate search API key).
Create a new directory (e.g. deep-agent/) and do all work there — do not pollute the open project.
Do not use Tavily (or @langchain/tavily). Replace the quickstart's search tool with the chosen provider's built-in web search. Look up the current export/tool shape on that provider's LangChain docs (examples as of writing — re-check if needed):

Provider Built-in search tool

Anthropic @langchain/anthropic tools.webSearch_*() (or equivalent dict)

OpenAI { type: "web_search" }

Google { google_search: {} }

Prefer Anthropic / OpenAI / Google so provider search is available. Only secret: that provider's API key in .env (gitignored). Skip LangSmith tracing unless they ask.
Install packages from the quickstart minus Tavily; add the provider package for their model.
Run the research example, show output, then stop. Point to deep-agents-core / customization / Managed Deep Agents for next steps.