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):

1. 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).

2. Create a new directory (e.g. deep-agent/) and do all work there — do not pollute the open project.

3. 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.

4. Install deepagents (+ python-dotenv) and the provider package for their model — not tavily-python.

5. Run the research example, show output, then stop. Point to deep-agents-core / customization / Managed Deep Agents for next steps.