Comparisons / CrewAI vs Semantic Kernel
CrewAI vs Semantic Kernel: Which Agent Framework to Use?
CrewAI organizes work into Agents, Tasks, and Crews. 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
CrewAI
48.0k
6.5k
Python
MIT
2023-10-27
João Moura
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 | CrewAI | Semantic Kernel |
|---|---|---|
| Agent | `Agent(role, goal, backstory, tools, llm)` | `ChatCompletionAgent` with `Kernel`, instructions, and service config |
| Tools | Tool registration with `@tool` decorator, custom `Tool` classes | — |
| Agent Loop | Internal to `Agent` execution, hidden from user | — |
| Task Delegation | `Crew(agents, tasks, process=sequential/hierarchical)` | — |
| Memory | `ShortTermMemory`, `LongTermMemory`, `EntityMemory` | `SemanticTextMemory` with embeddings and vector stores |
| State | Task output passed between agents via `Crew` orchestration | — |
| Tools / Plugins | — | `KernelPlugin` with `@kernel_function` decorators, typed parameters |
| Planning | — | `StepwisePlanner`, `HandlebarsPlanner` for multi-step decomposition |
| Orchestration | — | `Kernel.invoke()` with plugin resolution and filter pipeline |
| Multi-Language | — | C#, Python, Java SDKs with shared abstractions |
CrewAI vs Semantic Kernel, head to head
CrewAI CrewAI organizes work into Agents, Tasks, and Crews.
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 CrewAI if
Pick CrewAI if crewAI shines for multi-agent setups where you want named roles ("researcher", "writer"). But the core mechanics — tool dispatch, the agent loop, task scheduling — are the same patterns you can build in plain Python. CrewAI 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; CrewAI would force you to translate.
What both add
Both CrewAI 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 CrewAI 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 →