All comparisons
AI FRAMEWORKS
LangChain vs LlamaIndex
LangChain for agents and complex workflows; LlamaIndex for RAG and business data access.
Pros and Cons
LangChain
Strengths
- General-purpose framework for any LLM application
- AI agents with advanced tool use
- LangGraph for complex, stateful AI workflows
- Huge community and documentation
- LangSmith for production monitoring and debugging
Limitations
- Complexity that can be excessive for simple cases
- Abstractions that sometimes obscure how things work
- Frequent updates that break compatibility
LlamaIndex
Strengths
- Specialized and optimized for RAG
- Data connectors for 100+ sources (PDF, DB, web)
- Intelligent indexing and chunking
- Simpler for standard RAG use cases
- Powerful query engine for interrogating data
Limitations
- Less versatile for agents and complex workflows
- Smaller community than LangChain
- Fewer options for monitoring and debugging
Which to choose?
LangChain for complex AI agents, multi-step workflows, and custom applications. LlamaIndex for business chatbots and RAG on internal documents.
Our verdict
For SMEs building a business chatbot on internal documents (FAQs, manuals, contracts), LlamaIndex is more direct and simple. For more complex applications like agents that execute actions (bookings, database queries, sending emails), LangChain offers more power and flexibility. Many projects use both: LlamaIndex for the RAG part, LangChain for orchestration.
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