🧠🤖 Deep Agents
Looking for the JS/TS version? Check out Deep Agents.js.
To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.
Quick Install
uv add deepagents
🤔 What is this?
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
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. For a full overview and quickstart of Deep Agents, the best resource is our docs.
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.
❓ 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 that supports tool calling works — frontier APIs (OpenAI, Anthropic, Google), open-weight models hosted on providers like Baseten or Fireworks, and self-hosted models via Ollama, vLLM, or llama.cpp. Use any LangChain chat model.
Can I use this in production?
Yes! Deep Agents is built on LangGraph, designed for production agent deployments. Pair it with LangSmith for tracing, evaluation, and monitoring. See Going to production for the full guide.
When should I use Deep Agents vs. LangChain or LangGraph directly?
All three are layers in the same stack — see the LangChain ecosystem overview for how they relate. Use Deep Agents when you want the full harness — planning, context management, delegation — out of the box. Use LangChain's create_agent when you want a lighter harness without the bundled middleware. Drop to LangGraph when the agent loop itself isn't the right shape and you need a custom graph.
The layers compose: any LangGraph CompiledStateGraph can be passed in as a sub-agent to a Deep Agent, so custom orchestration plugs in alongside the harness's defaults.
📖 Resources
- Documentation — Full documentation
- LangChain ecosystem overview — how Deep Agents, LangChain, LangGraph, and LangSmith fit together
- API Reference — Full SDK reference documentation
- Examples — Working agents and patterns
- Discussions — Community forum for technical questions, ideas, and feedback
- LangChain Academy — Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.
- Code of Conduct — community guidelines and standards
📕 Releases & Versioning
See our Releases and Versioning policies.
🔒 Security
Deep Agents follows a "trust the LLM" model. The agent can do anything its tools allow. Enforce boundaries at the tool/sandbox level, not by expecting the model to self-police. See the security policy for more information.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file deepagents-0.7.13.tar.gz.
File metadata
- Download URL: deepagents-0.7.13.tar.gz
- Upload date:
- Size: 297.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via:
twine/7.0.0 CPython/3.13.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
99fc1855b1387c1c6e6035ba3600edbd933f59c39a4863ef3a7d5a0272ea67ab
|
|
| MD5 |
fb813b8871da19b16a3e728f104b6b6c
|
|
| BLAKE2b-256 |
d2a718d5479456c5c4d47d6074a8af615f8b844d91ec4e7bd548f2b047953903
|
File details
Details for the file deepagents-0.7.13-py3-none-any.whl.
File metadata
- Download URL: deepagents-0.7.13-py3-none-any.whl
- Upload date:
- Size: 324.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via:
twine/7.0.0 CPython/3.13.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d717ee8ee092a91c475a6124ed578d5e4154d54120769a1081bfe1aece87c248
|
|
| MD5 |
4f44962fe385e8ee2b5c4746d2c67dd7
|
|
| BLAKE2b-256 |
9d646881d1c040433a3c48a1556afa5e70bcd5a761b0f67f9162a311ca8ed91c
|
