The batteries included agent harness. Deep Agents is an open source agent harness — an opinionated agent that runs out of the box. Extend, override, or replace any piece. Principles: Opinionated — defaults tuned for long horizon, multi step work Extensible — override or replace any piece without forking Model agnostic — works with any LLM that supports tool calling: frontier, open weight, or local Production ready — built on LangGraph (streaming, persistence, checkpointing) with first class tracing, evaluation, and deployment via LangSmith Features include: Sub agents — delegate tasks to agents with isolated context windows Filesystem — read, write, edit, or search over pluggable local, sandboxed, or remote backends Context management — summarize long threads and offload tool outputs to disk Shell access — run commands in your sandbox of choice Persistent memory — pluggable state and store backends for cross session recall Human in the loop — approve, edit, or reject tool calls before they run Skills — reusable behaviors the agent can load on demand Tools — bring your own functions or any MCP server Deep Agents is available as a JavaScript/TypeScript library — see deepagents.js. [!NOTE] Deep Agents Code — a pre built coding agent in your terminal, similar to Claude Code or Cursor, powered by any LLM. Install with curl LsSf https://langch.in/dcode bash . See the documentation for the full feature set. Quickstart [安装/使用命令见下方] [安装/使用命令见下方] 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. FAQ 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 fil