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-skills12952026年10月9日 更新
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deepagents-python-quickstart
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.
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集成工具OpenAI
查看安装方法包含的文件（1）
- SKILL.md2.2 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
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:modelstring — 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 — nottavily-python. -
Run the research example, show output, then stop. Point to
deep-agents-core/ customization / Managed Deep Agents for next steps.
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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-skills12952026年10月9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.
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langchain-ai/langchain-skills12952026年10月9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.
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langchain-ai/langchain-skills12952026年10月9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.
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langchain-ai/langchain-skills12952026年10月9日 更新
Inspect an agent repository and optional traces, interview the user, write reviewed Task Specs, build and audit Harbor tasks, and bootstrap reusable project World Knowledge Skills. Use for agent evals, benchmark design, Task generation, controlled Environments, synthetic data, Verifiers, Harbor runs, calibration, or continuous benchmark maintenance.
中文简介正在准备中。当前显示原文说明。
langchain-ai/langchain-skills12952026年10月9日 更新