LlamaIndex
The data framework for LLM apps, RAG-first, now with agent workflows and a document-parsing cloud.
Best forDevelopers building document Q&A, enterprise search or RAG-heavy agents who want the deepest retrieval toolkit and optional managed parsing.
Our verdict
LlamaIndex is the reference implementation for retrieval-augmented generation: if your application lives or dies on getting the right context into the model, its query engines, advanced retrieval patterns and 300-plus connectors are the deepest toolkit available, and the MIT-licensed core is genuinely free.
The deepest RAG toolkit, now with agent workflows, free and open-source at the core, with document parsing as the paid cloud upsell.
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What's great
- Best-in-class RAG and retrieval, with sophisticated patterns available out of the box.
- MIT-licensed free core plus an optional cloud, you are not forced into the paid product; LlamaParse handles genuinely hard documents (tables, scans, handwriting).
Watch-outs
- Advanced RAG has a steep learning curve, and the API has churned across very frequent releases.
- Retrieval-first design is less natural for real-time conversational or heavily agent-first apps, and the strongest document parsing (LlamaParse) is the paid cloud, not the free library.
Also note: The open-source library is retrieval-first; heavy agent orchestration or real-time chat may need a companion framework. · Top-tier document parsing lives in the paid LlamaCloud, not the free core.
What is LlamaIndex?
LlamaIndex is an open-source data framework for building LLM applications, best known as the leading toolkit for retrieval-augmented generation (RAG), connecting your documents and data sources to a large language model so it can answer questions grounded in them. Beyond retrieval it now offers event-driven agent workflows and a commercial cloud, LlamaCloud, whose LlamaParse service parses complex PDFs, tables and scanned documents for downstream indexing. The core library is MIT-licensed and free; it ships in Python and TypeScript with 300-plus integration packages available through LlamaHub.
What does LlamaIndex do?
At its heart LlamaIndex ingests data through connectors, splits and embeds it into indexes, and exposes query engines that retrieve the most relevant context before calling an LLM, the classic RAG pipeline, with advanced patterns like hybrid search, recursive retrieval and sub-question decomposition available out of the box. On top of that it provides Workflows, an event-driven, step-based abstraction for orchestrating multi-step and multi-agent processes with state, plus prebuilt agent tools so an agent can call retrievers and functions. Its commercial layer, LlamaCloud, adds managed parsing (LlamaParse for 50-plus file types, including handwriting and tables), extraction and hosted indexing behind an API, with a monthly free-credit allotment and usage-based paid plans for higher volume. You install the open-source packages (llama-index, or the leaner llama-index-core plus chosen integrations) and run them yourself, wiring in whichever model provider you like; or you offload document parsing and indexing to the cloud. Typical uses are document Q&A, enterprise search, knowledge-base chatbots and agentic document workflows that read, extract and act on unstructured files.
Key features
- RAG query engines & advanced retrieval: Indexes plus query engines with hybrid search, recursive retrieval and sub-question decomposition built in.
- Data connectors + LlamaHub: Hundreds of connectors and 300-plus integration packages for pulling in APIs, PDFs, SQL, SaaS apps and vector stores.
- Workflows: An event-driven, step-based abstraction for orchestrating multi-step and multi-agent processes with managed state.
- LlamaParse / LlamaCloud: Managed cloud parsing, extraction and hosted indexing for messy documents, behind an API with free monthly credits.
What are LlamaIndex's use cases?
- Document Q&A / enterprise search: A team grounds a chatbot on internal PDFs and wikis so it answers only from retrieved, cited context.
- Agentic document processing: An agent parses invoices or contracts with LlamaParse, extracts structured fields and routes them downstream.
What does LlamaIndex integrate with?
- Vector stores (Pinecone, Chroma, Qdrant, etc.)
- Model providers (OpenAI, Anthropic, local models)
- LlamaHub connectors (Notion, Slack, SQL, S3…)
- LangChain interoperability
- Model Context Protocol (MCP) — both a client (consume external MCP servers) and a server (publish LlamaIndex workflows as an MCP server)
Why use LlamaIndex?
- The deepest, most mature RAG toolkit, retrieval-first and often more token-efficient than general frameworks.
- MIT-licensed and free at the core, in both Python and TypeScript.
- Optional managed parsing and indexing via LlamaCloud when you don’t want to build it yourself.
- A huge integration ecosystem through LlamaHub (vector stores, model providers, data sources).
Pros & cons
Pros
- Best-in-class RAG and retrieval, with sophisticated patterns available out of the box.
- MIT-licensed free core plus an optional cloud, you are not forced into the paid product; LlamaParse handles genuinely hard documents (tables, scans, handwriting).
Cons
- Advanced RAG has a steep learning curve, and the API has churned across very frequent releases.
- Retrieval-first design is less natural for real-time conversational or heavily agent-first apps, and the strongest document parsing (LlamaParse) is the paid cloud, not the free library.
Limitations
- The open-source library is retrieval-first; heavy agent orchestration or real-time chat may need a companion framework.
- Top-tier document parsing lives in the paid LlamaCloud, not the free core.
LlamaIndex pricing
- Open sourceFree
- LlamaCloud FreeFree
- LlamaCloud Starter$50 / /month
- LlamaCloud Pro$500 / /month
- EnterpriseCustom
See current pricing on llamaindex.ai ↗Compare LlamaIndex alternatives →
LlamaIndex specs
Pricing
- Pricing model
- freemium
- Free tier
- ✓ Yes
Capabilities
- Model / LLM
- Model-agnostic
- Interface
- Library
- Public API
- ✓ Yes
- Open source
- ✓ Yes
Deployment
- Deployment
- Both
LlamaIndex review
LlamaIndex is the reference implementation for retrieval-augmented generation: if your application lives or dies on getting the right context into the model, its query engines, advanced retrieval patterns and 300-plus connectors are the deepest toolkit available, and the MIT-licensed core is genuinely free. Its expansion into event-driven Workflows and agent tooling means it is no longer just a RAG library, though agent-first orchestration is still more naturally LangGraph or CrewAI territory. The commercial LlamaCloud, chiefly LlamaParse, is a real asset for messy PDFs, tables and scans, and it comes with a monthly free-credit allotment, but the best parsing sits behind usage-based paid plans rather than in the free package. Be prepared for a learning curve on advanced retrieval and for an API that has churned across frequent releases. Pick LlamaIndex when retrieval quality is the core of your product; skip it as your primary tool if your app is chiefly a real-time conversational or multi-tool agent with little document grounding.
The deepest RAG toolkit, now with agent workflows, free and open-source at the core, with document parsing as the paid cloud upsell.
Frequently asked questions
Is LlamaIndex free and open-source?
What languages does LlamaIndex support?
How is LlamaIndex different from LangChain?
What are LlamaParse and LlamaCloud?
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