Comparisons / AutoGen vs Semantic Kernel
AutoGen vs Semantic Kernel: Which Agent Framework to Use?
AutoGen by Microsoft models agents as ConversableAgents that chat with each other. 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
AutoGen
56.7k
8.5k
Python
CC-BY-4.0
2023-08-18
Microsoft Research
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 | AutoGen | Semantic Kernel |
|---|---|---|
| Agent | `ConversableAgent` with `system_message`, `llm_config` | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | `register_for_llm()` and `register_for_execution()` | — |
| Conversation | Two-agent chat with `initiate_chat()`, message history | — |
| Multi-Agent | `GroupChat` with `GroupChatManager`, speaker selection | — |
| Nested Chats | `register_nested_chats()` for sub-task handling | — |
| Termination | `is_termination_msg` callback, `max_consecutive_auto_reply` | — |
| 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 |
AutoGen vs Semantic Kernel, head to head
AutoGen AutoGen by Microsoft models agents as ConversableAgents that chat with each other.
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 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. AutoGen 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; AutoGen would force you to translate.
What both add
Both AutoGen 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 AutoGen 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 →