CrewAI vs Semantic Kernel
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python framework for building teams of role-playing AI agents that work together on tasks.
Open source · MIT
Open-source SDK for building AI agents and multi-agent apps in Python, .NET and Java.
The short answer
Choose CrewAI if you need…
- Python developers prototyping multi-agent workflows
- Teams that want role-based agents collaborating on a task
- Developers who want both autonomous agent teams and tighter step-by-step control via Flows
- Builders who want low-level control over prompts and execution logic
Choose Semantic Kernel if you need…
- Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents
- Teams working in .NET, Python or Java who want one framework across languages
- Projects that need plugins built from native code, prompt templates, OpenAPI specs or MCP
- Existing Semantic Kernel users maintaining or migrating older code
Side-by-side
| CrewAI | Semantic Kernel | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 60.4k | 28.7k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | C# |
| Last code update | 4 days ago | 4 days ago |
What each one is
CrewAI
CrewAI is an open-source Python framework for orchestrating multiple AI agents. You give each agent a role and goal, and they collaborate through what the project calls Crews, while Flows provide event-driven, more precisely controlled workflows. It is aimed at developers building multi-step automations with LLMs.
Semantic Kernel
Semantic Kernel is a model-agnostic SDK from Microsoft that lets developers connect large language models (such as OpenAI or Azure OpenAI models) to their own apps, add tools and plugins, and orchestrate one or more AI agents. The README says it is now succeeded by Microsoft Agent Framework, so new projects should check that project first.
Best for
CrewAI
- Python developers prototyping multi-agent workflows
- Teams that want role-based agents collaborating on a task
- Developers who want both autonomous agent teams and tighter step-by-step control via Flows
- Builders who want low-level control over prompts and execution logic
Semantic Kernel
- Developers already using Azure OpenAI or OpenAI who want a structured SDK for agents
- Teams working in .NET, Python or Java who want one framework across languages
- Projects that need plugins built from native code, prompt templates, OpenAPI specs or MCP
- Existing Semantic Kernel users maintaining or migrating older code
Strengths
CrewAI
- Open source under the MIT license with an active repository
- Offers both autonomous Crews and deterministic event-driven Flows
- Includes documentation, courses, and an official skills package for coding agents
Semantic Kernel
- Supports several models and providers, including OpenAI, Azure OpenAI, Hugging Face and NVidia, per the README
- Offers examples in Python and C# with matching quickstarts
- Permissive MIT license and a large public project with about 28,700 stars
Not ideal for
CrewAI
- Non-programmers, since the project is a Python framework and requires writing code
- Projects needing a fully managed hosted product without setup; the README's commercial AMP Suite is a separate paid offering
- Simple single-prompt tasks that do not need multiple agents or workflow control
Semantic Kernel
- Non-programmers: it is a developer SDK and needs code to use, so no-code users should look elsewhere
- Teams starting fresh who want the newest path: the README directs users to Microsoft Agent Framework as the successor
- Simple scripts that only need one direct API call, where an SDK adds more structure than needed
Trade-offs
CrewAI
- The README's performance and 'production-ready' claims are promotional and not backed by benchmarks in the excerpt
- Core concepts (Crews, Flows, Agents) add a learning curve for newcomers
- Some advanced features and a control plane are part of a commercial suite
Semantic Kernel
- The README states the project is succeeded by Microsoft Agent Framework, so long-term direction may point elsewhere
- Quickstarts assume Azure or OpenAI credentials and a configured environment, so setup needs some engineering work
- Java support is pointed to a separate build guide rather than documented here
Getting started
CrewAI
Install CrewAI (from PyPI, per the project's PyPI badge)
pip install crewaiSemantic Kernel
Set Azure OpenAI API key
export AZURE_OPENAI_API_KEY=AAA....Set OpenAI API key (alternative)
export OPENAI_API_KEY=sk-...Frequently asked questions
What is the difference between CrewAI and Semantic Kernel?
CrewAI: Python framework for building teams of role-playing AI agents that work together on tasks. Semantic Kernel: Open-source SDK for building AI agents and multi-agent apps in Python, .NET and Java. CrewAI suits python developers prototyping multi-agent workflows, while Semantic Kernel suits developers already using azure openai or openai who want a structured sdk for agents.
Are CrewAI and Semantic Kernel free?
CrewAI: Free and open source. Semantic Kernel: Free and open source.
Is CrewAI or Semantic Kernel more popular?
CrewAI has more GitHub stars (60.4k) than Semantic Kernel (28.7k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to CrewAI and Semantic Kernel?
Other options people compare them with include LangGraph, LlamaIndex, PageIndex, Stagehand, OpenAI Agents SDK.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.