Comparisons / AutoGen vs AutoGPT

AutoGen vs AutoGPT: Which Agent Framework to Use?

AutoGen vs AutoGPT, head to head

AutoGen and AutoGPT both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

AutoGen by Microsoft models agents as ConversableAgents that chat with each other.

AutoGPT was one of the first autonomous agent projects, spawning 165k+ GitHub stars.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick AutoGen if

Pick AutoGen if autoGen excels at complex multi-agent workflows where agents need to debate or collaborate. For single-agent use cases or simple tool-calling agents, the plain Python version is significantly simpler. The tradeoffs in its intro should match how your team already thinks about agents; AutoGPT will feel like translation if they don't.

Full AutoGencomparison →

Pick AutoGPT if

Pick AutoGPT if autoGPT pioneered the autonomous agent pattern, but most of its complexity comes from managing an unbounded loop — not from the core agent logic. For bounded tasks, a plain while loop with tool dispatch gives you the same capability with full control over when to stop. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full AutoGPTcomparison →

What both add

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. AutoGen has its own class hierarchy and tool registration conventions; AutoGPT has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.

By the numbers

By the numbers

AutoGen

GitHub Stars

56.7k

Forks

8.5k

Language

Python

License

CC-BY-4.0

Created

2023-08-18

Created by

Microsoft Research

github.com/microsoft/autogen

AutoGPT

GitHub Stars

183.1k

Forks

46.2k

Language

Python

License

MIT

Created

2023-03-16

Created by

Toran Bruce Richards

github.com/Significant-Gravitas/AutoGPT

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAutoGenAutoGPT
Agent`ConversableAgent` with `system_message`, `llm_config`AutoGPT `Agent` class with goal decomposition and self-prompting loop
Tools`register_for_llm()` and `register_for_execution()`Plugin system with web browsing, file I/O, code execution, Google search
ConversationTwo-agent chat with `initiate_chat()`, message history
Multi-Agent`GroupChat` with `GroupChatManager`, speaker selection
Nested Chats`register_nested_chats()` for sub-task handling
Termination`is_termination_msg` callback, `max_consecutive_auto_reply`
Agent LoopAutonomous loop: think → plan → act → observe → repeat until goal met
MemoryVector DB (Pinecone/local) for long-term memory, message history for short-term
PlanningGPT-4 generates multi-step plans, stores in task queue, revises on failure
Self-CritiqueBuilt-in self-evaluation prompt that critiques each action before executing

Or build your own in 60 lines

Both AutoGen and AutoGPT implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →