deepagents

uv add deepagents

from deepagents import create_deep_agent

agent = create_deep_agent(
    model="openai:gpt-5.5",
    tools=[my_custom_tool],
    system_prompt="You are a research assistant.",
)
result = agent.invoke({"messages": "Research LangGraph and write a summary"})

The agent can plan, read/write files, and manage its own context. Add your own tools, swap models, customize prompts, configure sub-agents, and more. See the documentation for full details.

[!TIP] For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

How is this different from LangGraph or LangChain?

LangGraph is the graph runtime. LangChain's create_agent is a minimal agent harness on top of it. Deep Agents is a more opinionated harness on top of create_agent — same building blocks, but with filesystem, sub-agents, context management, and skills bundled in. For how the three relate, see the LangChain ecosystem overview.

Does this work with open-weight or local models?

Yes. Any model that supports tool calling works — frontier APIs (OpenAI, Anthropic, Google), open-weight models hosted on providers like Baseten or Fireworks, and self-hosted models via Ollama, vLLM, or llama.cpp. Use any LangChain chat model.

Can I use this in production?

Yes! Deep Agents is built on LangGraph, designed for production agent deployments. Pair it with LangSmith for tracing, evaluation, and monitoring. See Going to production for the full guide.

When should I use Deep Agents vs. LangChain or LangGraph directly?

All three are layers in the same stack — see the LangChain ecosystem overview for how they relate. Use Deep Agents when you want the full harness — planning, context management, delegation — out of the box. Use LangChain's create_agent when you want a lighter harness without the bundled middleware. Drop to LangGraph when the agent loop itself isn't the right shape and you need a custom graph.