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.
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.
By the numbers
By the numbers
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
ControlFlow
1.5k
120
Python
Apache-2.0
2024-05-01
Prefect
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | AutoGen | ControlFlow |
|---|---|---|
| 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 |
| Conversation | Two-agent chat with `initiate_chat()`, message history | — |
| Multi-Agent | `GroupChat` with `GroupChatManager`, speaker selection | Multiple `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 |
| Observability | — | Built-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 →