AutoGen Review: Microsoft's Multi-Agent Framework
TL;DR: AutoGen is one of the more capable multi-agent frameworks going around, and Microsoft's backing means it isn't going to disappear next year. Its conversational agent pattern is genuinely flexible. The catch: the learning curve is steep and the framework is complex, so it's not where a beginner should start.
Most "AI agent" tools you've seen so far are a single assistant doing one job at a time. AutoGen, built by Microsoft Research, takes a different bet: instead of one agent, you run a small team of them, and they talk to each other to get work done.
Picture a product manager, an architect, a developer and a tester sitting in a chat. Each one is an AI agent with a defined job. They pass the work around, argue a bit, write code, test it, and hand back a result. A separate "manager" agent keeps the conversation moving and decides who speaks next. That's the core idea, and it's what makes AutoGen interesting for businesses thinking about more than a chatbot.
The trade-off is real, though. This is power-user territory. Setting it up, debugging it when agents talk past each other, and keeping the LLM bill under control all take effort. If your need is simple, you'll get there faster with something else. If you're building a genuine multi-agent system, AutoGen is one of the strongest options on the table.
We tested AutoGen v0.4, the version Microsoft Research rebuilt from the ground up for scale and reliability. Here's how it held up.
What Is AutoGen?
AutoGen is a Microsoft Research framework for building LLM applications out of multiple agents that talk to each other:
- Conversational agents, agents talk to each other
- Code execution, agents write and run code
- Group chat, multiple agents in one conversation
- Nested chat, agents can spawn sub-conversations
- Human proxy, humans participate in agent conversations
- Custom agents, define agent behaviour in Python
Those capabilities are documented in AutoGen's conversation patterns guide.
Price: Free and open source. The code ships under the MIT licence (microsoft/autogen on GitHub); note that the repo separately licenses its documentation under CC BY 4.0, so "MIT" covers the code rather than every file in the repo.
Group Chat
Group chat is where AutoGen earns its reputation. We set up four agents:
- Product Manager, defines requirements
- Architect, designs the solution
- Developer, writes the code
- Tester, reviews and tests
A fifth agent, the group chat manager, decides who speaks next based on what's happening in the conversation. That's how AutoGen documents it too: the GroupChatManager acts as the conductor, picking the next speaker and broadcasting messages to the rest. In our run, the team reportedly worked through 12 rounds of discussion and produced a working Python script with tests.
Quality: the author rated it 8/10, good, though it needed a human to step in once.
Code Execution
AutoGen agents can write code and actually run it. The code execution agent:
- Writes Python in a markdown block
- Executes in a Docker container
- Returns output to the conversation
- Retries on errors
Running code inside Docker by default is built in, per AutoGen's own writeup. We had the agents write, test and debug a data processing script. By the author's account it took 5 attempts, but it got there in the end with no human help.
Pros and Cons
| Pros | Cons |
|---|---|
| Most flexible multi-agent framework | Very steep learning curve |
| Code execution is powerful | Complex to configure |
| Microsoft backing | Debugging is difficult |
| Group chat is innovative | Can get expensive (many LLM calls) |
| Highly extensible | Documentation is scattered |
Verdict
Score: 8.5/10
AutoGen suits teams building serious multi-agent systems. The conversational pattern is hard to beat for collaborative problem-solving, and the complexity pays off once you're working on enterprise-scale problems. For simpler jobs, we'd point you at CrewAI as an easier place to begin.
One thing worth flagging: since late 2025, Microsoft has been folding AutoGen and Semantic Kernel together into a unified Microsoft Agent Framework. This review covers AutoGen v0.4 on its own and doesn't account for that shift, so keep an eye on where the project lands.
*Published June 20, 2026 | AutoGen v0.4 tested*