LlamaIndex

LlamaIndex (formerly GPT Index) is a data framework for building LLM applications that need to ingest, structure, and query private data. While LangChain is a general-purpose LLM toolkit, LlamaIndex is purpose-built for the data layer — connecting LLMs to your data sources with sophisticated indexing, retrieval, and query strategies.

The framework's core strength is its data connectors and indexing pipeline. LlamaHub provides 300+ data loaders for virtually any data source: databases, APIs, cloud storage, SaaS tools, file formats, and web content. Once loaded, documents flow through a configurable pipeline: chunking (with various splitting strategies), metadata extraction, embedding generation, and storage in your choice of vector store or index.

LlamaIndex's query engine is where it differentiates from simpler RAG frameworks. Beyond basic similarity search, it supports tree-based indices (hierarchical summarization), keyword table indices (structured retrieval), knowledge graph indices (relationship-based querying), and composable indices that combine multiple strategies. The SubQuestionQueryEngine automatically decomposes complex questions into sub-questions routed to different data sources.

The framework introduced Workflows in 2024 — an event-driven orchestration system for building multi-step AI applications. Workflows use @step decorators and typed events, providing a cleaner abstraction than LangChain's chains for complex, multi-step applications while remaining more flexible than rigid pipeline architectures.

LlamaIndex also provides agentic capabilities through the AgentRunner and various agent types (ReAct, OpenAI function calling) that can use query engines as tools. This means you can build agents that reason across multiple data sources, each with its own optimized retrieval strategy.

The ecosystem includes LlamaCloud for managed indexing and retrieval (LlamaParse for document parsing, managed indices), and LlamaHub for community-contributed data loaders and tools.

Honest assessment: LlamaIndex is the best choice for data-heavy LLM applications. If your primary challenge is connecting an LLM to proprietary data and getting accurate, well-sourced responses, LlamaIndex's indexing and query engine abstractions are more sophisticated than what you'll build ad-hoc. For applications that are primarily about agents, tool use, or general LLM orchestration, LangChain or dedicated agent frameworks are better fits.