deepagents

Deep Agents is LangChain's batteries-included agent harness. Where the base LangChain agent gives you a minimal loop, Deep Agents bundles the pieces you always end up building yourself: a filesystem, sub-agent delegation, context management, shell execution, persistent memory, human-approval steps, and reusable skills. It runs on any LLM that supports tool calling, MIT licensed and free.

It installs as a Python package (`uv add deepagents`) and one call to its `create_deep_agent` helper gives you a working agent with your model, tools, and prompt. Under the hood it is built on LangGraph, so you inherit streaming, persistence, and checkpointing. Running it yourself costs nothing beyond your model's token bill.

For a developer who wants a capable agent without wiring every subsystem by hand, this is a strong starting point, and the framework is free. Solo builders and teams use it the same way. The paid part of the LangChain ecosystem is LangSmith, which handles production tracing, monitoring, and deployment; you can run Deep Agents without it, but you will want observability once real traffic hits.

The catch is the LangChain gravity. Adopting Deep Agents pulls in LangGraph and nudges you toward LangSmith for the production story, and that ecosystem has a habit of abstracting things you might have wanted to control directly. If you like the batteries, it is a fast path. If you want a thin, transparent agent loop, this is more framework than you asked for; look at a lighter harness instead.