AutoGen vs Microsoft Agent Framework: what changed

The rewrite of AutoGen into the Microsoft Agent Framework changed more than the package name. This head-to-head look at AutoGen vs Microsoft Agent Framework breaks down what actually moved in capabilities, cost, and developer ergonomics for teams shipping multi-agent systems.

Capabilities

AutoGen (classic, v0.2) gave you AssistantAgent and UserProxyAgent plus a GroupChat scheduler. It was synchronous, relied on a global llm_config, and treated code execution as a first-class tool via the user proxy. That design shipped plenty of demos but forced awkward patterns for anything event-driven. Memory was a Conversation object you manually appended to, and tool calls required wrapping functions in a register_function dance.

The Microsoft Agent Framework (MAF) absorbs AutoGen v0.4’s core and rebuilds it async-native. Agents are defined with explicit model clients, and teams (formerly group chats) are composable async objects. You get structured message streams, cancellation, and middleware hooks. Tool calling is typed: you pass a Callable with Python type hints and the framework generates the schema. Memory is a pluggable Memory interface, not a global list.

# Classic AutoGen
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant", llm_config={"model": "gpt-4o"})
user = UserProxyAgent("user", code_execution_config=False)
user.initiate_chat(assistant, message="Summarize this repo.")

# Microsoft Agent Framework (AutoGen v0.4 style)
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat

agent = AssistantAgent("assistant", model_client={"model": "gpt-4o"})
team = RoundRobinGroupChat([agent])
await team.run_stream(task="Summarize this repo.")

MAF also adds official adapters for Azure AI Projects and Semantic Kernel, so you can host agents as managed endpoints without hand-rolling FastAPI. The gap in AutoGen vs Microsoft Agent Framework shows up first in execution model: one blocks, one schedules.

Cost model

Neither framework charges a license fee; both are MIT/Apache-licensed open source. Your only hard cost is tokens. Classic AutoGen hides model config in a dict, making it easy to accidentally send expensive models to every agent. MAF pushes model clients per agent, so cost boundaries are explicit in code.

If you route either framework through an OpenAI-compatible gateway, the economics stay identical—you pay the gateway’s per-token rate. Because both emit standard chat completions requests, pointing them at a single endpoint such as n4n.ai (which fronts 240+ models with automatic fallback when a provider is rate-limited) is a one-line base_url change and nothing else. The gateway handles per-token usage metering and forwards provider cache-control hints, so your agent code doesn’t change when you switch from GPT-4o to Claude.

# Swap base_url in MAF model client
model_client = {"model": "anthropic/claude-3.5-sonnet", "base_url": "https://api.n4n.ai/v1"}

No framework-level markup exists. The only added cost in MAF is optional Azure hosting if you use the managed agent runtime. When weighing AutoGen vs Microsoft Agent Framework on cost, the framework is neutral; the model choice and routing policy decide your bill.

Latency and throughput

Classic AutoGen runs blocking initiate_chat calls. Concurrent sessions require threading or separate processes, and the framework does not batch requests. In practice, a 5-agent group chat serializes LLM calls unless you fork the loop. Streaming is limited to terminal output; you cannot easily pipe tokens to a websocket without monkey-patching.

MAF is built on asyncio. run_stream yields tokens as they arrive, and multiple teams can run concurrently in the same event loop. Throughput for fan-out workloads (e.g., 20 agents summarizing shards) improves by an order of magnitude simply because you’re not thread-switching. Backpressure is handled by the event loop, not by you.

We don’t quote numbers because your latency is dominated by the model provider, not the framework. The measurable win is concurrency headroom: MAF saturates a single process; classic AutoGen does not. If you need 100 parallel agent sessions, MAF is the only one that won’t make you manage a process pool.

Ergonomics

Classic AutoGen’s llm_config global state is a footgun. You mutate a dict to change models mid-session, and UserProxyAgent conflates human input with code execution. New engineers lose an hour to human_input_mode semantics. Debugging means printing messages lists because there is no structured trace.

MAF is code-first and typed. Agents take explicit model_client, system_message, and tools arguments. Teams are constructed objects, not singletons. The downside: you must understand async/await and the event loop. For a script that runs once, classic AutoGen is still less boilerplate.

# MAF explicit agent with tool
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

def get_weather(city: str) -> str:
    return f"Sunny in {city}"

client = OpenAIChatCompletionClient(model="gpt-4o-mini")
agent = AssistantAgent("helper", model_client=client, tools=[get_weather], system_message="Be terse.")

The migration pain is real: if your codebase is 2,000 lines of autogen.GroupChat, rewriting to MAF is a weekend, not a search-and-replace. Logging in MAF uses standard logging with structured events; you can ship traces to OpenTelemetry without custom sinks.

Ecosystem

AutoGen’s community built autogen.contrib extensions for vector stores, web search, and custom tools. Many are unmaintained post-v0.4 announcement. You’ll find a StackOverflow answer for most errors, but the code often pins openai==0.28.

MAF inherits the v0.4 extension system (autogen_ext) and adds first-party Azure AI Agents, Microsoft Graph connectors, and Semantic Kernel plugins. If you already use Azure OpenAI or Entra ID, MAF’s auth story is native. For local experimentation, the ecosystem is thinner because old contrib packages don’t port. Semantic Kernel interop means you can drop a Kernel skill into an MAF agent without rewriting the logic.

Limits

Classic AutoGen is in maintenance mode; security patches only. It requires Python 3.8+ but breaks on newer openai SDK versions beyond 1.x without pinning. Its GroupChat max round limit is easy to hit silently, causing agents to stop without error.

MAF requires Python 3.10+. It assumes async runtime, so blocking I/O inside tools will stall the loop unless you run_in_executor. Its managed Azure runtime is regional and tied to Azure AI Projects preview SLAs. The framework itself is stable for self-host, but the Azure hosting layer moves fast.

Comparison table

Which to choose

Greenfield project with concurrency needs. Use MAF. The async core saves you from building a task queue, and explicit model clients keep token spend auditable.

Existing AutoGen v0.2 production code. Stay on classic until you hit a concurrency wall. The migration is mechanical but not free; schedule it when you add a second simultaneous workflow. Pin openai<2.0 and isolate the agent module so you can swap later.

Azure-shop building internal agents. MAF wins by default. Entra ID, Azure AI Projects, and managed hosting remove glue code you’d otherwise write yourself. The Semantic Kernel bridge also lets you reuse existing skills.

Research prototypes and notebooks. Classic AutoGen’s synchronous initiate_chat is still the fastest path from idea to trace. Just pin dependencies and accept the maintenance risk. You can always port the prompt logic later.

Multi-provider routing without vendor lock. Both work. Point the base_url at any OpenAI-compatible gateway and swap models per agent. The AutoGen vs Microsoft Agent Framework decision here is irrelevant; your routing layer abstracts it.

High-throughput serving (100+ agents). MAF only. Classic AutoGen will force you into multiprocessing and shared state headaches. With MAF you write one async loop and scale via worker processes if needed.

The AutoGen vs Microsoft Agent Framework decision is less about features and more about runtime model and support horizon. Pick the async path unless you have a concrete reason not to.