How does LlamaIndex differ from other LLM frameworks like LangChain?

LlamaIndex and LangChain serve distinct roles in building LLM-powered applications, focusing on different layers of the stack. LlamaIndex specializes in data indexing and retrieval, acting as a bridge between unstructured data and LLMs. It optimizes how external data (documents, databases) is organized, stored, and queried to improve the relevance of inputs fed to LLMs. For example, if you want to build a Q&A system over internal documents, LlamaIndex can chunk text, generate vector embeddings, and create searchable indexes to quickly find contextually relevant snippets. LangChain, on the other hand, is a broader framework for orchestrating multi-step LLM workflows. It provides tools to chain LLM calls with external APIs, databases, or custom logic, making it easier to build applications like chatbots that require sequential interactions or data transformations.

The key difference lies in their core features. LlamaIndex offers data connectors for ingesting diverse sources (PDFs, SQL databases), indexing strategies (vector stores, hierarchical summaries), and query interfaces optimized for retrieval-augmented generation (RAG). For instance, it might use a vector index to embed document chunks and a keyword index for fast lookup of specific terms. LangChain focuses on modular components like chains (predefined workflows), agents (LLM-driven decision-makers), and memory (context retention across interactions). A LangChain agent could decide to call a weather API based on a user’s query, then format the result using an LLM. While LlamaIndex streamlines the “data-to-context” step, LangChain handles the “context-to-action” flow, enabling complex logic like conditional branching or tool usage.

Integration is another differentiating factor. LlamaIndex is often used alongside LangChain as a specialized retrieval layer. For example, a LangChain pipeline might use LlamaIndex to fetch relevant document snippets, then pass them to an LLM for summarization. However, LlamaIndex can also work standalone for simpler RAG use cases. LangChain’s strength is its extensibility: it supports hundreds of third-party tools (Slack, Wikipedia) and LLM providers (OpenAI, Anthropic), whereas LlamaIndex focuses on depth in data handling. Developers might choose LlamaIndex for high-performance retrieval in data-heavy applications, while LangChain is better suited for building agentic systems that require dynamic interaction between LLMs, tools, and users.