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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…
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.
don't have the plugin yet? install it then click "run inline in claude" again.