Comparisons / ControlFlow vs Semantic Kernel
ControlFlow vs Semantic Kernel: Which Agent Framework to Use?
ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them. Semantic Kernel is Microsoft's enterprise SDK for building AI agents. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
ControlFlow
1.5k
120
Python
Apache-2.0
2024-05-01
Prefect
Semantic Kernel
27.6k
4.5k
C#
MIT
2023-02-27
Microsoft
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | ControlFlow | Semantic Kernel |
|---|---|---|
| Agent | `cf.Agent()` with name, model, instructions, and tool access | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | Python functions passed to `Task()` or `Agent()` as tool lists | — |
| Task | `cf.Task()` with `result_type`, `instructions`, `agents`, and `dependencies` | — |
| Flow | `@cf.flow` decorator composing tasks with dependency resolution | — |
| Multi-Agent | Multiple `cf.Agent()` instances assigned to different tasks in one flow | — |
| Observability | Built-in Prefect integration for logging, retries, and monitoring | — |
| Tools / Plugins | — | `KernelPlugin` with `@kernel_function` decorators, typed parameters |
| Planning | — | `StepwisePlanner`, `HandlebarsPlanner` for multi-step decomposition |
| Memory | — | `SemanticTextMemory` with embeddings and vector stores |
| Orchestration | — | `Kernel.invoke()` with plugin resolution and filter pipeline |
| Multi-Language | — | C#, Python, Java SDKs with shared abstractions |
ControlFlow vs Semantic Kernel, head to head
ControlFlow ControlFlow by Prefect flips the typical agent framework: instead of defining agents that choose tasks, you define tasks and assign agents to them.
Semantic Kernel Semantic Kernel is Microsoft's enterprise SDK for building AI agents.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
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. ControlFlow is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Semantic Kernel would force you to translate.
Pick Semantic Kernel if
Pick Semantic Kernel if semantic Kernel earns its complexity in enterprise environments with Azure OpenAI, .NET backends, and existing Microsoft infrastructure. But the core agent pattern — LLM call, tool dispatch, loop — is identical to what you can build in 60 lines of Python. Semantic Kernel is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; ControlFlow would force you to translate.
What both add
Both ControlFlow and Semantic Kernel pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both ControlFlow and Semantic Kernel 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 →