langchain-ai/deepagents

Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are “shallow” and fail to plan and act over longer, more complex tasks.

Applications like “Deep Research”, "Manus", and “Claude Code” have gotten around this limitation by implementing a combination of four things: a planning tool, sub agents, access to a file system, and a detailed prompt.

deepagents is a Python package that implements these in a general purpose way so that you can easily create a Deep Agent for your application. For a full overview and quickstart of deepagents, the best resource is our docs.

Acknowledgements: This project was primarily inspired by Claude Code, and initially was largely an attempt to see what made Claude Code general purpose, and make it even more so.

(To run the example below, you will need to pip install tavily-python).

Make sure to set TAVILY_API_KEY in your environment. You can generate one here.

See examples/research/research_agent.py for a more complex example.

The agent created with create_deep_agent is just a LangGraph graph - so you can interact with it (streaming, human-in-the-loop, memory, studio) in the same way you would any LangGraph agent.