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
uv add deepagents
from deepagents import create_deep_agent
agent = create_deep_agent(
model="openai:gpt-5.5",
tools=[my_custom_tool],
system_prompt="You are a research assistant.",
)
result = agent.invoke({"messages": "Research LangGraph and write a summary"})
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 filesystem, sub-agents, context management, and skills bundled in. For how the three relate, see the LangChain ecosystem overview.
Does this work with open-weight or local models?
Yes. Any model