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LlamaIndex

Python framework for connecting LLMs to your documents, with ingestion, indexing, retrieval and event-driven agent Workflows.

Reviewed by Sheela · Updated

View on GitHub Open source

Our rating

4.3/5

Still a deep toolkit for document-heavy RAG, but its maker now says plainly that the framework is no longer its priority.

What it is

LlamaIndex is an open-source Python framework for retrieval-augmented generation (RAG): loading documents, splitting and indexing them, and retrieving the right pieces to answer questions. The README says more than 300 integration packages plug into its core for models, embeddings and vector stores.

Its orchestration layer is Workflows, an event-driven system of typed steps that powers its prebuilt agents, and LlamaAgents deploys those workflows as services.

The company has changed course. Its README now states that its primary focus has shifted to LlamaParse, a paid document parsing and extraction platform, and that the framework remains available as an open toolkit. The code is still updated, but buyers should read that note before betting a roadmap on it.

Our review

LlamaIndex remains a capable data layer for RAG, and the risk is no longer technical. In 2026 the company rewrote its README to say its primary focus has shifted to LlamaParse, with the open framework kept available as a toolkit. Development has not stopped: v0.14.25 shipped on 21 September 2026, commits landed in October, and the repo has about 52,600 stars. But a team adopting it today is adopting a project its owner treats as the on-ramp to a paid product.

The breadth is still the reason to use it. Loaders, node parsers, many index types, retrievers and rerankers sit behind one API, and the split between llama-index-core and separate integration packages means you install only the vector store and model client you need. Indexes live in memory by default and persist to a ./storage folder with one call, which keeps prototypes simple.

Parsing is where the commercial line falls. The free readers extract text; scanned and complex layouts are what LlamaParse is sold for, billed at 1 credit per page on its Fast tier and 45 credits per page on Agentic Plus. At $1.25 per 1,000 credits, a 100,000-page backlog costs about $125 on Fast and roughly $5,600 on Agentic Plus, so the tier choice matters more than the plan.

API churn is the everyday cost. Query Pipelines were deprecated in favour of Workflows and llama-deploy gave way to LlamaAgents, so guides from 2024 often fail. GitHub's advisory database lists several 2025 and 2026 issues in llama-index-core, including a path traversal in encode_image and denial-of-service bugs in JSON parsing, all of which argue for pinning versions and upgrading deliberately. LangGraph has the cleaner story for agent control flow; LlamaIndex still wins when the hard part is the documents.

Where it earns its place

  • A Python engineer at a 30-person insurance broker

    Answer staff questions over 20,000 policy PDFs

    Ingest with the free PDF reader, index into an existing Postgres pgvector store and serve a query engine behind an internal API. The framework costs nothing; the bill is embeddings and LLM calls, plus LlamaParse credits if scanned documents need OCR.

  • A platform engineer at a legal-tech start-up

    Build an agent that extracts clauses and flags risky terms

    Write a Workflow with parse, extract and review steps, calling LlamaParse for layout and a local model for classification. Typed steps make the pipeline testable; expect to rework it when the Workflows API moves.

  • An operations manager budgeting a document backlog

    Digitise 100,000 pages of archived contracts

    LlamaParse at 1 credit per page on Fast costs about $125 for the lot; Agentic Plus at 45 credits per page runs to about $5,600. Testing a sample on each tier before committing avoids paying for accuracy the documents do not need.

Before you commit

Setup
`pip install llama-index` (Python 3.10+) gives core plus starter integrations; the quick start needs an OpenAI key unless you install Ollama or Hugging Face packages instead.
Integrations
More than 300 integration packages for LLMs, embeddings and vector stores; MCP servers can be used as tools or exposed from Workflows; LlamaParse plugs in for document parsing.
Data and privacy
The framework sends text only to the model and vector-store providers you configure; indexes stay in memory or in ./storage. LlamaParse is a hosted service listing SOC 2 Type II, HIPAA and GDPR, with hybrid or VPC deployment on Enterprise.
Cost at scale
No paid edition of the framework. LlamaParse costs $1.25 per 1,000 credits; Fast parsing is 1 credit per page, Agentic 10 and Agentic Plus 45, with re-parses within 48 hours free.
Limits
About 312 open issues; frequent deprecations; core security advisories in 2025 and 2026 (path traversal, JSON-parsing denial of service); weaker than LangGraph for complex multi-agent control flow.

What's good

  • Many index types, retrievers and node parsers behind one API
  • 300+ integration packages; install only what you use
  • Workflows give typed, event-driven agent steps
  • Active releases; v0.14.25 in September 2026

What's not

  • Maker says its focus has moved to paid LlamaParse
  • Good parsing of complex PDFs means LlamaParse credits
  • Deprecations (Query Pipelines, llama-deploy) break old tutorials
  • Several core security advisories in 2025 and 2026

Best for

Python developers building RAG or document agents over large, varied data sources.

Alternatives

Haystack

RAG & dataPython27k stars

4.2/5

deepset's open-source Python framework for production RAG pipelines and agents, built from explicit, typed components.

LangChain

Agent frameworksPython148k stars

4.2/5

Python and TypeScript framework for building LLM agents, with a standard agent loop, middleware and hundreds of model integrations.

RAGFlow

RAG & dataGo93k stars

4.0/5

Self-hosted RAG engine with layout-aware document parsing, a web UI and an agent builder, midway through a rewrite in Go.

LlamaIndex: common questions

Is LlamaIndex worth it?
Still a deep toolkit for document-heavy RAG, but its maker now says plainly that the framework is no longer its priority. We rate it 4.3 out of 5.
How much does LlamaIndex cost?
Framework free under MIT. LlamaParse (paid cloud): Free with 10k credits, Starter $50/month (40k credits), Pro $500/month (400k credits), Enterprise custom; 1,000 credits = $1.25.
What are the downsides of LlamaIndex?
Maker says its focus has moved to paid LlamaParse; good parsing of complex PDFs means LlamaParse credits; deprecations (Query Pipelines, llama-deploy) break old tutorials; several core security advisories in 2025 and 2026.
Who is LlamaIndex best for?
Python developers building RAG or document agents over large, varied data sources.
How much does LlamaIndex cost for a team or in production?
No paid edition of the framework. LlamaParse costs $1.25 per 1,000 credits; Fast parsing is 1 credit per page, Agentic 10 and Agentic Plus 45, with re-parses within 48 hours free.
Is LlamaIndex safe for company data?
The framework sends text only to the model and vector-store providers you configure; indexes stay in memory or in ./storage. LlamaParse is a hosted service listing SOC 2 Type II, HIPAA and GDPR, with hybrid or VPC deployment on Enterprise.
What are the best LlamaIndex alternatives?
The closest alternatives we have reviewed are Haystack, LangChain and RAGFlow.

Answers checked Oct 23, 2026.

Make LlamaIndex? Show this rating on your siteLlamaIndex rated 4.3 out of 5 on ReviewRamble