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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SKILL.md
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.
Files
1- SKILL.md
a7a7c80cc32.2 KB
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