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.
- Skill ID
- langchain-ai/langchain-skills/deepagents-python-quickstart
- Publisher
- langchain-ai
- Repository
- langchain-skills
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
langchain-ai/langchain-skills/deepagents-python-quickstartInstalls these files- SKILL.md
What this skill tells the agent
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 aprovider: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_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.
More from langchain-ai
Use this skill in a project
Open any RiverX project, search the Skills panel for langchain-ai/langchain-skills/deepagents-python-quickstart, and install it in one click.