#99th of 15 in Agent frameworks and SDKs

deepagents

Python agent harness with sub-agents, filesystem tools and context management

Stars
30.3k
License
MIT
Last commit
Oct 2026
Last release
Oct 2026
Language
Python
Stars, last 29 weeks+13,239
24 Mar 2026: 17,016 stars31 Mar 2026: 18,103 stars7 Apr 2026: 19,493 stars14 Apr 2026: 20,397 stars21 Apr 2026: 21,122 stars28 Apr 2026: 21,614 stars5 May 2026: 21,977 stars12 May 2026: 22,395 stars19 May 2026: 22,747 stars26 May 2026: 23,111 stars2 Jun 2026: 23,496 stars9 Jun 2026: 24,100 stars16 Jun 2026: 24,514 stars23 Jun 2026: 24,823 stars30 Jun 2026: 25,285 stars7 Jul 2026: 25,722 stars14 Jul 2026: 26,083 stars21 Jul 2026: 26,492 stars28 Jul 2026: 26,780 stars4 Aug 2026: 27,204 stars11 Aug 2026: 27,549 stars18 Aug 2026: 27,810 stars25 Aug 2026: 28,409 stars1 Sept 2026: 28,758 stars8 Sept 2026: 29,115 stars15 Sept 2026: 29,416 stars22 Sept 2026: 29,647 stars29 Sept 2026: 29,851 stars6 Oct 2026: 29,984 stars13 Oct 2026: 30,255 stars

17,016 on 24 Mar 202630,255 on 13 Oct 2026

From GitHub's daily star counts.

Checked on GitHub.

LangChain is hiring: 100 open roles

Overview

Deep Agents is a Python library whose create_deep_agent() returns a ready-to-run agent built on LangChain's create_agent and LangGraph. It bundles planning, sub-agents with isolated context, a pluggable filesystem and shell backend, context summarization, skills, human-in-the-loop approval and persistent memory. It works with any tool-calling LangChain chat model, including Ollama, vLLM and llama.cpp.

Who it is for: Python developers building long-running, tool-using agents on LangChain

Strengths

  • Sub-agents, filesystem, context summarization and skills are bundled and work out of the box
  • Any LangChain chat model with tool calling works, including Ollama, vLLM and llama.cpp
  • Any LangGraph CompiledStateGraph can be passed in as a sub-agent
  • Human-in-the-loop approval of tool calls; tools can be custom functions or MCP servers

Weaknesses

  • Opinionated defaults sit on top of LangChain and LangGraph, adding layers to learn
  • Trust-the-LLM security model: boundaries must be enforced via tools or sandbox
  • Tracing and evaluation are documented around LangSmith, a separate hosted product
  • Python only here; the JavaScript version is a separate repository

What it needs

  • no GPU
  • Needs LangChain, LangGraph, LLM provider or local model server
  • Models: OpenAI, Anthropic, Google, Baseten, Fireworks

Also in Agent frameworks and SDKs

See all 15
Also in Agent frameworks and SDKs
RankProjectScore
1langchainPython framework for building LLM agents and apps from interoperable components147.8k starsMIT88 out of 100
2browser-usePython library that lets an LLM agent control a web browser Live demo ↗ (opens in a new tab)117.9k starsMIT88 out of 100
3crewaiPython framework for multi-agent workflows with role-based Crews and event-driven Flows59.7k starsMIT80 out of 100
#4openai-agents-pythonPython SDK for multi-agent workflows with handoffs, guardrails and tracing30k starsMIT77 out of 100
#5agnoPython SDK and runtime for building and serving agents behind a REST API42.7k starsApache-2.076 out of 100