langchain-ai/deepagentsjs

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.

💡 Tip: Looking for the Python version of this package? See here: langchain-ai/deepagents

deepagents is a TypeScript package that implements these in a general purpose way so that you can easily create a Deep Agent for your application.

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 npm install @langchain/tavily).

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

See examples/research/research-agent.ts for a more complex example.

The agent created with createDeepAgent 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.