LlamaIndex
Framework for connecting LLMs to your data with advanced RAG
Founded
2022
Starting Price
$0
Category
Developer Tools
Last updated March 17, 2026
About LlamaIndex
LlamaIndex is an open-source data framework for building production-ready LLM applications with a focus on retrieval-augmented generation (RAG). It provides data connectors for 160+ sources, advanced indexing strategies, and query engines that handle complex multi-step reasoning over your private data.
Pros & Cons
Pros
- Best-in-class RAG capabilities — purpose-built for connecting LLMs to private data
- 160+ data connectors cover virtually any data source
- 40% faster document retrieval than LangChain in benchmarks
- Simpler API than LangChain for data-focused applications
- LlamaParse handles complex document parsing that other tools struggle with
Key Features
Data Connectors
Import data from 160+ sources including databases, APIs, PDFs, and web pages
Advanced Indexing
Vector indexes, tree indexes, keyword indexes, and knowledge graphs for optimal retrieval
Query Engines
Simple retrieval, multi-step reasoning, and sub-question decomposition for complex queries
Agentic RAG
Build autonomous retrieval agents that decide how to search and synthesize information
LlamaParse
Advanced document parsing for PDFs, tables, and complex layouts
LlamaCloud
Managed RAG pipeline service for production deployment without infrastructure management
Evaluation Tools
Measure retrieval quality, answer relevance, and faithfulness with built-in metrics
Multi-LLM Support
Pricing
Open Source
$0
- Full framework access
- All integrations
- Community support
- Self-hosted
LlamaCloud Free
$0
Best For
Enterprise Knowledge Search
Build semantic search over company documents, wikis, and knowledge bases with RAG pipelines
Document Q&A Systems
Create chatbots that answer questions grounded in specific documents with citation support
Data Analysis Agents
Build agents that query structured and unstructured data sources to answer complex analytical questions
Multi-Source Research
Combine information from multiple data sources into coherent, grounded answers
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