Agent Framework Landscape
Navigate the ecosystem of agent frameworks and choose the right tools for your projects
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0 / 5 completedFeature Matrix Comparison
Choosing a framework isn't just about popularity—it's about feature fit for your specific use case. This interactive matrix compares core features, integrations, and developer experience across the top 4 frameworks.
Interactive: Feature Comparison Matrix
| Feature | LangChain | AutoGen | CrewAI | Semantic |
|---|---|---|---|---|
| Tool Calling | 5/5 | 5/5 | 4/5 | 5/5 |
| Memory Management | 5/5 | 4/5 | 3/5 | 4/5 |
| Multi-Agent Support | 3/5 | 5/5 | 5/5 | 3/5 |
| RAG Integration | 5/5 | 3/5 | 4/5 | 4/5 |
| Vector DB Support | 5/5 | 3/5 | 4/5 | 4/5 |
| LLM Provider Options | 5/5 | 4/5 | 4/5 | 3/5 |
| Documentation Quality | 4/5 | 3/5 | 3/5 | 4/5 |
| Learning Curve | 2/5 | 3/5 | 4/5 | 3/5 |
| Community Size | 5/5 | 4/5 | 3/5 | 3/5 |
💡 Scores are based on community feedback, documentation quality, and feature completeness (1=poor, 5=excellent)
🏆 LangChain Wins
Dominates in integrations and ecosystem maturity. Best if you need breadth and don't mind complexity.
👥 Multi-Agent Leaders
AutoGen and CrewAI excel at multi-agent coordination. CrewAI has better DX, AutoGen has more features.
📚 Learning Curve
CrewAI is easiest to learn, LangChain steepest. Semantic Kernel best for C# developers.
🔒 Enterprise Choice
Semantic Kernel wins for enterprise with Microsoft backing, multi-language support, and Azure integration.
🎯 Decision Framework
1.
Identify must-have features (e.g., multi-agent, RAG, specific LLM)
2.
Consider team expertise (Python vs C#, learning curve tolerance)
3.
Evaluate ecosystem (community size, integrations, documentation)
4.
Prototype quickly (spend 2-4 hours testing top 2 options)