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deepagents-python-quickstart

GitHub

指导用户基于官方文档，使用 Provider 原生搜索替代 Tavily，在本地快速搭建并运行最小化 Deep Agent。

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

触发场景

想要快速构建或尝试 Deep Agent 需要本地初始化 Deep Agent 项目

安装

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

不安装直接使用

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

指定 Agent (Claude Code)

npx skills add langchain-ai/langchain-skills --skill deepagents-python-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-python-quickstart",
    "description": "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."
}

Deep Agents Python quickstart

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

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

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

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 any second search vendor). Replace the quickstart's internet_search / Tavily tool with the chosen provider's built-in web search. Look up the current tool shape on that provider's LangChain chat docs (examples as of writing — re-check if needed):

Provider Built-in search tool

Anthropic {"type": "web_search_20260209", "name": "web_search", "max_uses": 5}

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 deepagents (+ python-dotenv) and the provider package for their model — not tavily-python.
Run the research example, show output, then stop. Point to deep-agents-core / customization / Managed Deep Agents for next steps.