Comparisons / AutoGen vs OpenAI Agents SDK

AutoGen vs OpenAI Agents SDK: Which Agent Framework to Use?

AutoGen vs OpenAI Agents SDK, head to head

AutoGen and OpenAI Agents SDK 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.

OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.

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; OpenAI Agents SDK will feel like translation if they don't.

Full AutoGencomparison →

Pick OpenAI Agents SDK if

Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full OpenAI Agents SDKcomparison →

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; OpenAI Agents SDK 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

OpenAI Agents SDK

GitHub Stars

20.6k

Forks

3.4k

Language

Python

License

MIT

Created

2025-03-11

Created by

OpenAI

github.com/openai/openai-agents-python

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

ConceptAutoGenOpenAI Agents SDK
Agent`ConversableAgent` with `system_message`, `llm_config``Agent(name, instructions, model, tools)`
Tools`register_for_llm()` and `register_for_execution()`Python functions with type hints, auto-converted to schemas
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 Loop`Runner.run()` handles the loop internally
Handoffs`Handoff` between `Agent` objects for multi-agent routing
Guardrails`InputGuardrail` and `OutputGuardrail` with tripwire pattern
ContextTyped context object passed through the agent lifecycle

Or build your own in 60 lines

Both AutoGen and OpenAI Agents SDK 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 →