LlamaIndex
Connect your own data to LLMs for retrieval-augmented generation. LlamaParse (formerly LlamaCloud) automates document parsing, extraction, and indexing for agentic workflows.
The verdict on LlamaIndex: Developers building retrieval-augmented generation apps that need to query private or enterprise data LlamaIndex is built around one specific conviction: retrieval quality is what actually breaks most AI applications, not the generation step. Pricing: Free (10K credits) / $5,000/mo Starter cap / Pro custom. Last reviewed: August 2026.
Best For
Developers building retrieval-augmented generation apps that need to query private or enterprise data
Standout Feature
A wide library of data connectors that ingest PDFs, wikis, and databases into a queryable index
TL;DR
The strongest RAG-specific framework available, expect real overlap and some redundant decisions against LangChain.
Alternatives
Overview
LlamaIndex is a data framework for building retrieval-augmented generation (RAG) and agentic AI applications that need to query private or enterprise data, document collections, databases, APIs, and knowledge bases, rather than relying solely on the LLM's training knowledge. Its core abstraction is the Index: ingest your data (PDFs, Notion pages, Confluence wikis, SQL databases, Slack exports, website content), build a structured index that enables semantic search, and query it through LLMs that answer from your actual data with source citations. LlamaIndex handles the pipeline from raw data ingestion through chunking, embedding, indexing, retrieval, and response generation, providing production-ready implementations of each step that teams can configure rather than build. The framework's Query Engines cover simple retrieval and generation, multi-document comparison, knowledge graph traversal, and structured SQL query generation from natural language.
Agents built on LlamaIndex can plan and execute multi-step research tasks across a connected set of data sources, querying multiple indexes, combining results, and generating synthesized responses. LlamaIndex is available in Python and TypeScript. The open-source framework is free; LlamaCloud provides managed ingestion pipelines and hosted indexes for production deployments. LlamaIndex is most commonly chosen over LangChain specifically for data-heavy RAG applications where the data ingestion pipeline, indexing strategies, and retrieval precision are the primary engineering concerns.
Our Take
Where a general-purpose framework gives you the tools to build everything, LlamaIndex narrows its focus to connecting private or enterprise data, PDFs, databases, wikis, APIs, to the model in a way that holds up under real queries. The wide library of data connectors is the standout here. If your application needs to answer questions from your own documents rather than from training data alone, this is the more focused choice over a general framework.
Key Features
- Data ingestion and indexing
- Advanced retrieval and query engines
- Agents and workflows
- LlamaCloud and LlamaParse
- • Best-in-class for RAG
- • Many data connectors
- • Good query abstractions
- • Overlaps with LangChain
- • Learning curve for advanced features
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Weights & Biases
Track, visualize, and compare machine learning experiments, with newer Weave and Inference tools for evaluating and monitoring LLM-based applications.
Workflows Using This Tool
Step-by-step playbooks that put LlamaIndex to work.