LangChain
The most popular framework for building applications with LLMs
Strengths
- Extensive ecosystem
- Rich documentation
- Active community
- Tool integrations
Considerations
- Can be complex
- Performance overhead
- Steep learning curve
Best For
RAG systemsChatbots
LlamaIndex
Data framework for LLM applications, focused on knowledge augmentation
Strengths
- Data-focused design
- Easy RAG setup
- Vector store abstractions
- Query optimization
Considerations
- Narrower scope
- Fewer tools
- Less flexibility
Best For
Knowledge basesDocument Q&A
CrewAI
Multi-agent framework for collaborative AI workflows
Strengths
- Multi-agent focus
- Role-based design
- Task coordination
- Simple API
Considerations
- Limited ecosystem
- Newer framework
- Less documentation
Best For
Team automationContent creation
AutoGen
Microsoft framework for multi-agent conversation systems
Strengths
- Conversation patterns
- Code generation
- Microsoft backing
- Research focus
Considerations
- Academic origins
- Complex setup
- Limited production use
Best For
Code generationResearch automation
LangGraph
State-based agent framework for complex workflows
Strengths
- Graph-based flows
- State management
- Debugging tools
- LangChain integration
Considerations
- Complex concepts
- Limited docs
- New framework
Best For
Complex workflowsState machines
Pydantic AI
Type-safe agent framework built on Pydantic validation
Strengths
- Type safety
- Validation
- Fast performance
- Python-native
Considerations
- New framework
- Limited features
- Small community
Best For
Type-safe agentsData validation