deepagents vs autogen
Python frameworks for building AI agents compared
deepagents and autogen are Python projects for building LLM agents and agent workflows. deepagents provides a ready-to-extend harness with planning, file tools, delegation, and context management, while autogen offers APIs for multi-agent systems and is now in maintenance mode.
deepagents: Build Agents with Planning and Tooling Built In
Deep Agents is a Python agent harness for building LLM agents with planning, delegation, file access, and context management built in. It suits developers who need a more capable starting point than a minimal agent loop, while retaining control over models and tools.
autogen: Build Multi-Agent AI Applications
AutoGen is a Python framework for building AI agents that work independently or alongside people. It supports multi-agent orchestration, model and tool integrations, and prototyping, but is now in maintenance mode.
| deepagents | autogen | |
|---|---|---|
| Language | Python | Python |
| License | MIT | CC-BY-4.0 |
| Stars | 29.9k | 61.3k |
| Forks | 4.2k | 9.3k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- deepagents bundles planning, file operations, sub-agent delegation, and context management; autogen provides layered APIs for message passing, agent interactions, and orchestration.
- deepagents is built on LangGraph and supports tool-calling models; autogen includes model integrations and resources for both Python and .NET.
- deepagents uses an MIT license; autogen uses CC-BY-4.0.
- deepagents is designed for production deployments on LangGraph, with tracing and monitoring paired with LangSmith; autogen Studio is intended for prototyping and demonstrations, not production deployment.
- deepagents is not archived; autogen is in maintenance mode, with new projects directed toward Microsoft Agent Framework.
Choose deepagents if you…
- need a ready-to-extend agent structure with planning, file access, and context management.
- want to delegate tasks to sub-agents or persist useful state across sessions.
- need to customize tools and execution boundaries for a LangGraph-based agent.
Choose autogen if you…
- maintain or migrate an existing application built with AutoGen APIs.
- want to prototype multi-agent workflows with AgentChat or AutoGen Studio.
- need AutoGen Bench to evaluate agent performance or its Core API for event-driven systems.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.