Comprehensive abstractions for LLM application development
Model-agnostic design supports all major LLMs
Rich ecosystem of pre-built chains and agents
Excellent documentation with many examples
Built-in memory and state management
Active community and rapid development
Cons
Steep learning curve for beginners
Abstractions can add unnecessary complexity
Performance overhead compared to direct API calls
Frequent breaking changes between versions
Debugging can be challenging with nested chains
Documentation sometimes lags behind releases
Use Cases
Best For:
Complex LLM applications with multiple componentsRAG (Retrieval Augmented Generation) systemsChatbots with memory and contextAgent-based autonomous systemsRapid prototyping of LLM ideas