LangChain vs LlamaIndex: Which AI Framework?

LangChain vs LlamaIndex: Choosing the Right AI Framework

Core Philosophy

LangChain is a general-purpose framework for building LLM applications. It excels at chaining together multiple LLM calls, tool usage, and agent-based workflows. Think of it as the Swiss Army knife of LLM development.

LlamaIndex (formerly GPT Index) is specialized for data retrieval and RAG (Retrieval Augmented Generation). It excels at connecting LLMs to your data sources — documents, databases, APIs — and making them searchable.

When to Use LangChain

• Building AI agents that take actions (send emails, update databases, browse the web)
• Complex multi-step reasoning chains
• Chatbots with tool integration
• Workflows that combine multiple AI models
• Applications needing conversation memory and context management

When to Use LlamaIndex

• Document Q&A systems ("ask questions about your data")
• Knowledge base search over internal documents
• Structured data extraction from unstructured sources
• RAG pipelines where retrieval quality is critical
• When you need fine-grained control over indexing, chunking, and retrieval strategies

Can You Use Both?

Yes — and many production applications do. LlamaIndex handles the data layer (indexing, retrieval), while LangChain handles the application layer (agents, chains, tools). They complement each other well.

Need help building an AI application? I work with both frameworks. Get in touch to discuss your project.