Comparisons / AutoGen vs ControlFlow

AutoGen vs ControlFlow: Which Agent Framework to Use?

AutoGen vs ControlFlow, head to head

AutoGen and ControlFlow 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.

ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them.

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; ControlFlow will feel like translation if they don't.

Full AutoGencomparison →

Pick ControlFlow if

Pick ControlFlow if controlFlow's task-centric model is a genuinely different way to think about agent orchestration — define what you want, not how to get it. The Prefect integration adds real production value. But if your workflow is linear and your tasks are simple, plain function composition does the same job with less ceremony. The tradeoffs in its intro should match how your team already thinks about agents; AutoGen will feel like translation if they don't.

Full ControlFlowcomparison →

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; ControlFlow 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

ControlFlow

GitHub Stars

1.5k

Forks

120

Language

Python

License

Apache-2.0

Created

2024-05-01

Created by

Prefect

github.com/PrefectHQ/ControlFlow

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

ConceptAutoGenControlFlow
Agent`ConversableAgent` with `system_message`, `llm_config``cf.Agent()` with name, model, instructions, and tool access
Tools`register_for_llm()` and `register_for_execution()`Python functions passed to `Task()` or `Agent()` as tool lists
ConversationTwo-agent chat with `initiate_chat()`, message history
Multi-Agent`GroupChat` with `GroupChatManager`, speaker selectionMultiple `cf.Agent()` instances assigned to different tasks in one flow
Nested Chats`register_nested_chats()` for sub-task handling
Termination`is_termination_msg` callback, `max_consecutive_auto_reply`
Task`cf.Task()` with `result_type`, `instructions`, `agents`, and `dependencies`
Flow`@cf.flow` decorator composing tasks with dependency resolution
ObservabilityBuilt-in Prefect integration for logging, retries, and monitoring

Or build your own in 60 lines

Both AutoGen and ControlFlow 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 →