DeepAgents

DeepAgents is the most feature-complete open-source agent harness we've seen from the LangChain ecosystem. It's explicitly designed for long-horizon, multi-step tasks — think research assistants that read multiple files, run shell commands, delegate sub-tasks, and remember context across sessions. The sub-agent feature is a standout: you can spin off isolated agents with their own context windows, useful for parallel research or sandboxed code execution. The filesystem abstraction is pluggable, supporting local, sandboxed, or remote backends, so you can enforce security at the tool level. If you're already using LangChain or LangGraph, this slots in naturally and composes with custom graphs. When should you pick DeepAgents? When you need a full harness out of the box: planning, context management, delegation, persistent memory, and human-in-the-loop. When should you pass? If you only need a simple tool-calling agent for a single-step task, LangChain's create_agent is lighter. Also avoid if you don't need the bundled middleware or if you require a graph runtime that isn't based on LangGraph. Compared to Claude Code: DeepAgents is more flexible (any LLM, any backend) but requires more setup. Real-world caveat: the 'trust the LLM' security model means you must sandbox tools yourself. No pricing info is provided, but as an open-source Python/TypeScript library, it's free to use; you'll pay for LLM API calls and optional LangSmith services.

DeepAgents is an open-source, opinionated agent harness built on LangGraph by LangChain. It runs out of the box with defaults tuned for long-horizon, multi-step work, and is designed to be extensible, model-agnostic, and production-ready. Whether you are building a research assistant, a coding agent, or a complex multi-agent system, DeepAgents bundles essential capabilities like sub-agents with isolated context, filesystem read/write/edit/search, context management (thread summarization and tool output offloading), shell access, persistent memory across sessions, human-in-the-loop approval, and reusable skills. It works with any LLM that supports tool calling—frontier APIs, open-weight models, or local models via Ollama, vLLM, or llama.cpp. Unlike LangChain's minimal create_agent, DeepAgents provides a full harness with middleware for planning, context, and delegation. For production use, pair it with LangSmith for tracing, evaluation, and monitoring. Compared to Claude Code or Cursor, DeepAgents is a customizable backbone that can be extended or replaced piece by piece without forking.