Keywords AI

CrewAI vs Semantic Kernel

Compare CrewAI and Semantic Kernel side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

	CrewAI	Semantic Kernel
Category	Agent Frameworks	Agent Frameworks
Pricing	Open Source	Open Source
Best For	Developers who want to build multi-agent systems where specialized agents collaborate	Enterprise .NET developers building AI applications on Microsoft infrastructure
Website	crewai.com	learn.microsoft.com
Key Features	Multi-agent role-based collaboration Simple YAML or Python configuration Built-in task delegation Memory and tool integration Process types: sequential, hierarchical, parallel	Enterprise AI orchestration by Microsoft C# and Python SDKs Plugin architecture for tools Memory and planning capabilities Azure and Microsoft 365 integration
Use Cases	Multi-agent team workflows Automated research and content pipelines Business process automation with AI teams Customer service agent hierarchies Collaborative problem-solving	Enterprise AI on Microsoft stack Copilot-style applications Integrating AI into .NET applications Microsoft 365 automation Enterprise workflow orchestration

When to Choose CrewAI vs Semantic Kernel

Choose CrewAI if you need

Multi-agent team workflows
Automated research and content pipelines
Business process automation with AI teams

Pricing: Open Source

Choose Semantic Kernel if you need

Enterprise AI on Microsoft stack
Copilot-style applications
Integrating AI into .NET applications

Pricing: Open Source

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.

Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.

Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.

ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.

Production readinessError handling, retries, streaming, and deployment options for production use.

About CrewAI

CrewAI is a framework for orchestrating multi-agent AI systems where specialized agents collaborate to complete complex tasks. It provides abstractions for defining agent roles, goals, tools, and workflows, enabling teams of AI agents to work together like a human crew. CrewAI supports sequential, parallel, and hierarchical task execution patterns and integrates with all major LLM providers.

View CrewAI profile →Visit website

About Semantic Kernel

Semantic Kernel is Microsoft's enterprise SDK for integrating AI into applications. It provides planners for multi-step task execution, plugin architectures for tool use, memory systems, and connectors for all major LLM providers. Available in C#, Python, and Java, Semantic Kernel is designed for enterprise .NET shops building AI-powered features into existing applications.

View Semantic Kernel profile →Visit website

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?▾

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?▾

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?▾

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.