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.
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.
By the numbers
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | OpenAI 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 |
| Conversation | Two-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 |
| Context | — | Typed 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 →