Saltar al contenido
Marketplace

deepagents-python-quickstart

de langchain-ai

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.

Resumen

SKILL.mdLeída en 16a992f

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):
    ProviderBuilt-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.

Archivos 1 archivo

Instálala a mano

Deska ejecuta esto por ti, para cada agente que elijas. Para ejecutarlo tú en una terminal:

npx skills add langchain-ai/langchain-skills --skill deepagents-python-quickstart

Más de langchain-ai

  • deep-agents-memorylangchain-aiINVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.17.9 k instalaciones en skills.sh0
  • langgraph-persistencelangchain-aiINVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.16.8 k instalaciones en skills.sh0
  • langgraph-fundamentalslangchain-aiINVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.16.6 k instalaciones en skills.sh0
  • langchain-raglangchain-aiINVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).16.4 k instalaciones en skills.sh0
  • langchain-fundamentalslangchain-aiCreate LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.16.3 k instalaciones en skills.sh0
Todas las skills de langchain-ai