Microsoft AutoGen v0.4 is a complete architectural rewrite of the popular agent framework, shifting from a simple conversational loop to an event-driven actor model. While the original version (now forked as 'AG2' by its creators) popularized the idea of agents talking to each other, Microsoft’s new v0.4 is built for production scale, supporting asynchronous messaging and cross-language operations between Python and .NET. It is not a beginner’s toy anymore; it is a heavy-duty infrastructure layer for building complex, non-linear agentic systems.
Be warned: this is not the AutoGen you see in year-old YouTube tutorials. The new autogen-agentchat package abandons the simple .initiate_chat() loops of the past for a more robust, albeit complex, state management system. You define agents, registered tools, and termination conditions, then let them exchange messages asynchronously. This makes it far superior for long-running tasks where an agent might need to pause, wait for a human signal, or handle parallel streams of information—capabilities that linear chains simply cannot match.
The cost of this power is 'chatter.' In a test run of a simple coding task involving a UserProxy, an Engineer, and a Critic, the agents exchanged 15 messages to fix a single bug. If you are using GPT-4o at $2.50/1M input tokens, a single localized bug-fix session can easily cost $0.10–$0.20. That sounds negligible until you automate it to run 1,000 times a night. Unlike LangChain’s rigid chains where you pay for exactly what you predict, AutoGen’s conversational loops are nondeterministic. Agents can get stuck in 'thank you' loops or arguments unless you rigorously tune the termination conditions.
Use Microsoft AutoGen v0.4 if you are an enterprise developer building a 'Magentic-One' style generalist system where agents need to maintain state over long periods or across different compute environments. Avoid it if you just want a quick script to summarize news; the boilerplate required to set up the actor system is overkill. For simple flows, CrewAI is faster to ship. For strict graph control, LangGraph is more precise. AutoGen is for when you want to simulate a room full of experts and are willing to pay the architectural tax to manage them.
Pricing
The framework itself is open-source (MIT License) and free. The real cost is token consumption, which AutoGen amplifies by design. Because agents converse to self-correct, a task that takes 1 prompt in a standard chain might take 10+ turns here.
Hidden Cost: 'Context Stuffing.' Each reply in a group chat typically re-sends the entire conversation history to the next agent. On a 20-turn conversation using GPT-4o, you aren't paying for 20 messages; you're paying for message 1, then 1+2, then 1+2+3... effectively factorial cost growth. You must implement token management or summarization, or your API bill will explode.
Technical Verdict
The v0.4 rewrite brings strict typing and an event-driven core (autogen-core) that is far more robust than the old loop-based architecture. However, documentation is currently fragmented between the 'old' AutoGen (now AG2) and this new Microsoft version. Expect breaking changes and confusion when searching for help, as 90% of StackOverflow answers refer to the legacy version. Latency is higher due to the multi-turn nature, but the async support allows for high throughput in parallel workflows.
Quick Start
# pip install autogen-agentchat autogen-ext[openai]
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
async def main():
model = OpenAIChatCompletionClient(model="gpt-4o", api_key="sk-...")
agent = AssistantAgent("assistant", model)
print(await agent.run(task="Write a haiku about code."))
asyncio.run(main())Watch Out
- The 'AutoGen' you see on YouTube (v0.2) is now mostly 'AG2' (pyautogen); Microsoft's v0.4 (
autogen-agentchat) has a completely different API. - Default group chats can spiral into infinite 'Thank you' loops without strict termination conditions.
- Docker is required for safe code execution; running agents locally without it risks them deleting your files.
- Debugging async actor interactions is significantly harder than stepping through a linear Python script.