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: $0 open-source framework and Workflows license / Model usage and hosting costs separate / LlamaParse is a separate commercial service; confirm its current pricing with the vendor.
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
Fit Score: use-case reference
How this is scoredLlamaIndex scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
Building RAG apps that need to query private or enterprise data reliably
3.8/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 3/5
- Ease
- 3/5
- Compatibility
- 4/5
LlamaIndex's bestFor and standout both describe exactly this use case, connecting private or enterprise data to a model via a wide connector library, confirmed by pros' 'best-in-class for RAG.'
General-purpose LLM app framework beyond retrieval
2.8/5- Capability
- 2/5
- Reliability
- 3/5
- Value
- 3/5
- Ease
- 3/5
- Compatibility
- 3/5
OurTake positions LlamaIndex as narrower than a general framework by design, focused specifically on retrieval quality; cons notes it 'overlaps with LangChain' for broader use, so a general-purpose framework need is a less distinct fit.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
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. LlamaIndex and its Workflows have a $0 open-source framework license fee; model usage and hosting costs remain separate. LlamaParse (formerly LlamaCloud) is a separately priced commercial product that provides managed document parsing, extraction, 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.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Best-in-class for RAG
- • Many data connectors
- • Good query abstractions
- • Overlaps with LangChain
- • Learning curve for advanced features
Key Features
- Data ingestion and indexing
- Advanced retrieval and query engines
- Agents and workflows
- LlamaCloud and LlamaParse
Trust & Data
Verified 2026-09
- Training on your data
- Not publishedThe privacy notice states customer content is used 'to improve our services' but makes no explicit statement about AI model training.
- Compliance
- Not published
- Retention
- Not published
- Export
- Not published
Sources: llamaindex.ai/privacy notice, llamaindex.ai/terms of service
As published by the vendor. Verify independently before purchase decisions.
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.
Workflows Using This Tool
Step-by-step playbooks whose entries name LlamaIndex. The step shown is where the workflow uses it.
- AdvancedOne-time setup: 3-4 hours, one-time setup. Planning estimate from the workflow entry, not measured time saved.
Build a Retrieval-Augmented Generation Pipeline Over Your Own Documents
Step 2 of 5: Chunk and Embed Your Documents
- AdvancedOne-time setup: 3-4 hours, one-time setup. Planning estimate from the workflow entry, not measured time saved.
Connect an Agent to Your Own Data Safely
Step 2 of 5: Exclude Sensitive Content Before It's Ever Indexed