LlamaIndex's data-first approach to LLM orchestration, with best-in-class retrieval pipelines and document processing, makes it the go-to framework for RAG and knowledge-intensive applications.
Data framework for RAG pipelines, indexing, and agent retrieval.
Helps your AI work with your company's data — organizes documents so your AI can search, understand, and answer questions from them.
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.
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LlamaIndex is the best framework for building RAG applications, with sophisticated data ingestion, indexing, and retrieval capabilities. Less general-purpose than LangChain but significantly better for data-intensive knowledge retrieval workflows.
300+ community-contributed data loaders for databases (PostgreSQL, MongoDB), cloud storage (S3, GCS), SaaS tools (Notion, Slack, Salesforce), file formats (PDF, DOCX, CSV), and APIs. Loaders output standardized Document objects.
Use Case:
Building a corporate knowledge base that ingests data from Confluence, Google Drive, Salesforce, and internal databases through purpose-built loaders for each source.
Multiple indexing strategies: VectorStoreIndex (similarity search), TreeIndex (hierarchical summarization), KeywordTableIndex (keyword extraction), KnowledgeGraphIndex (entity relationships), and ComposableGraph (combines indices).
Use Case:
Using a TreeIndex for summarizing long documents (annual reports) while using VectorStoreIndex for specific fact retrieval — combining both in a ComposableGraph for comprehensive querying.
Query engines wrap indices with query logic. SubQuestionQueryEngine decomposes complex questions into sub-questions routed to appropriate data sources. RouterQueryEngine selects the best index for each query.
Use Case:
A financial analyst tool where 'Compare Q3 revenue across our top 3 products' is automatically decomposed into sub-queries for each product's data source.
Build multi-step AI applications using @step decorators and typed events. Steps process events and emit new events, creating flexible, composable pipelines. Supports parallel execution, error handling, and state management.
Use Case:
Building an automated research pipeline: ingestion step loads papers, analysis step extracts findings, synthesis step combines results, and output step generates a report.
Advanced document parsing service (via LlamaCloud) that handles complex PDFs with tables, charts, images, and multi-column layouts. Extracts structured content where standard PDF parsers fail.
Use Case:
Parsing financial reports with complex tables and charts into structured text that can be accurately indexed and queried by LLM applications.
Agents that use query engines as tools, enabling multi-step reasoning across data sources. Agents can combine retrieval with other tools (web search, calculation, code execution) in a ReAct loop.
Use Case:
Building a research agent that queries internal documentation, searches the web for external context, and synthesizes findings — using the best retrieval strategy for each data source.
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View Pricing Options →Building RAG applications that query complex, multi-source enterprise knowledge bases with sophisticated retrieval
Creating document Q&A systems that handle complex PDFs, tables, and structured data with accurate extraction
Developing multi-source query systems that decompose questions across different data sources and index types
Building data-heavy AI applications where the primary challenge is accurate retrieval from private data
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We believe in transparent reviews. Here's what LlamaIndex doesn't handle well:
Use LlamaIndex when your application is primarily about data retrieval — RAG, document Q&A, knowledge base search. Its indexing and query engine abstractions are more sophisticated. Use LangChain when you need broad integration with tools, agents, and general LLM orchestration. Many production systems use both: LlamaIndex for the data layer, LangChain for the application layer.
Not for basic use. The open-source framework handles standard documents well with community loaders. LlamaParse is valuable for complex documents (PDFs with tables, charts, multi-column layouts) where standard parsers fail. LlamaCloud's managed indices are useful for production deployments that want managed infrastructure.
Start with VectorStoreIndex for most use cases — it's the most versatile and well-supported. Use TreeIndex when you need document summarization. KeywordTableIndex for exact keyword matching. KnowledgeGraphIndex for relationship-based queries. In practice, 90% of applications use VectorStoreIndex. Combine indices with ComposableGraph when you need multiple strategies.
LlamaIndex supports incremental updates through document management: you can insert, delete, and update documents in indices without full re-indexing. Each document has a doc_id for tracking. The refresh mechanism detects changed documents and updates only affected embeddings. For production, combine this with a document tracking system for your data sources.
Redesigned data loader ecosystem with 500+ connectors and improved performance.
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In 2026, LlamaIndex expanded beyond RAG into a full data agent framework. Major additions include LlamaCloud for managed indexing and retrieval, Workflows for building complex data processing pipelines, improved agentic RAG patterns, and native multi-modal support. The framework now handles document parsing, chunking, indexing, and retrieval as an integrated pipeline.
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