Free trial
The free plan provides 10,000 credits monthly, roughly 1,000 pages, covering agentic OCR, structured extraction and end-to-end document agents. Sign-up is available in global and EU regions.
Learn moreLlamaindex
Agentic OCR that turns messy documents into AI-ready context
Advanced API
What is Llamaindex?
LlamaIndex builds LlamaParse, an agentic OCR and extraction service that converts complex PDFs, Office files and scans into structured, LLM-ready output. It handles tables, charts, handwriting and dense layouts, then chunks and indexes the results for retrieval, making it the ingestion layer behind document Q&A and agent workflows.
LlamaIndex is an applied AI lab whose product line centers on document intelligence. The flagship service, LlamaParse, is an agentic OCR layer built to convert unstructured files into clean output that language models and retrieval pipelines can actually use. The team positions it against two familiar options: legacy intelligent document processing suites that depend on hand-built templates, and raw vision model calls that struggle with dense layouts. Roughly 50+ unstructured file types are supported, including PDFs, Office documents and images, with handling for embedded images, multi-page tables, charts and handwriting. Under the hood, parsing runs as a chain of specialized agents. Task-specific experts break content apart by modality (text, tables, charts), an agentic understanding layer applies semantic reasoning to the layout, and recursive auto-correction loops check and repair errors before output. Beyond parsing, the platform adds schema-based extraction with no model training required, document splitting driven by natural language descriptions, rule-based classification, and an enterprise chunking and embedding pipeline aimed at RAG retrieval quality. For database Q&A and internal search work, this is the ingestion half of the stack. Teams feed parsed, chunked and indexed documents into their own retrieval or agent layer, so answers to questions about contracts, filings, manuals or records rest on accurate source text rather than garbled extraction. Published case work spans investment research, insurance, manufacturing and healthcare, and the vendor reports 1B+ documents processed, 300k+ LlamaParse users and 99.9% uptime commitments. A companion open-source project, LiteParse, handles local parsing of PDFs, Office files and images with no cloud dependency and no token costs, which suits privacy-sensitive or air-gapped jobs. Cloud deployment is available in global and EU regions, or fully inside a customer VPC. Expect a developer-first product: the value shows up inside a pipeline, not in a polished consumer interface.
How do you use Llamaindex?
1Create a free LlamaCloud account
Sign up in the global or EU cloud region. The free plan includes 10,000 credits per month, roughly 1,000 pages, with no card required.
2Upload a test document
Start with one of your genuinely difficult files, such as a scanned contract or a report with multi-page tables, so you can judge output quality on real material.
3Run parsing and inspect the output
Parse the file and compare the returned markdown or structured output against the source, paying attention to table boundaries, chart labels and handwritten sections.4Define an extraction schema
For repeatable fields, configure a schema so extraction agents return structured records instead of free text, and check the values against the original pages.
5Add splitting, classification and indexing
Segment long documents by natural-language descriptions, categorise them with rules, then run the chunking and embedding pipeline to prepare data for retrieval.
6Wire it into your Q&A stack
Connect the indexed output to your own retrieval and LLM layer so questions are answered from parsed source text; trial LiteParse locally if files cannot leave your network.
Pros and cons
Pros
- Parsing targets the hard cases: multi-page tables, charts, handwriting and embedded images, not just clean text PDFs.AI
- Schema-based extraction works without training a custom model, which shortens setup for new document types.AI
- LiteParse gives an open-source, local option with no cloud calls and no token spend for sensitive files.AI
- Splitting, classification and chunking are built in, so the output feeds RAG pipelines directly.AI
- Enterprise posture is explicit: 99.9% uptime, HIPAA, GDPR and SOC2 compliance, and VPC deployment.AI
Cons
- Only the free tier's 10,000 monthly credits (about 1,000 pages) are published; paid pricing requires a sales conversation.AI
- Accuracy claims lean on vendor-run benchmarks against unnamed VLM and IDP baselines, so independent testing is warranted.AI
- It is infrastructure, not an end-user Q&A app: you still need your own retrieval layer and interface on top.AI
- Best results depend on prompt and schema design, so teams should budget time for tuning extraction agents.AI
- Local parsing through LiteParse is lighter than the cloud service and will not match it on the messiest scans.AI
How much does Llamaindex cost?
Pricing
A free plan includes 10,000 credits per month (about 1,000 pages), agentic OCR, structured schema extraction and document agent building. Paid tiers and enterprise terms are not published on the homepage; pricing is discussed through a demo request.
Learn moreSupport
Enterprise plans include dedicated support, fast response times and SLAs tailored to production AI workloads. A contact form handles demos and sales questions; no public help centre page is listed.
Learn moreFeatures
Agentic OCR for 50+ unstructured file types, including tables, charts, handwriting and embedded images. Adds schema-based extraction without training, natural-language document splitting, rule-based classification, and an enterprise chunking and embedding pipeline for RAG. Recursive auto-correction loops improve pass-through on messy scans. LiteParse offers open-source local parsing.
Learn moreFrequently asked questions about Llamaindex
How much does Llamaindex cost?
A free plan includes 10,000 credits per month (about 1,000 pages), agentic OCR, structured schema extraction and document agent building. Paid tiers and enterprise terms are not published on the homepage; pricing is discussed through a demo request.Does Llamaindex offer a free trial?
The free plan provides 10,000 credits monthly, roughly 1,000 pages, covering agentic OCR, structured extraction and end-to-end document agents. Sign-up is available in global and EU regions.Does Llamaindex have an API?
Yes, Llamaindex offers an API.How do you use Llamaindex?
The walkthrough on this page covers 6 steps: 1. Create a free LlamaCloud account 2. Upload a test document 3. Run parsing and inspect the output 4. Define an extraction schema 5. Add splitting, classification and indexing 6. Wire it into your Q&A stack.What platforms does Llamaindex support?
Llamaindex is available on Web App, Linux, MacOS and Windows.What are the limitations of Llamaindex?
Only the free tier's 10,000 monthly credits (about 1,000 pages) are published; paid pricing requires a sales conversation. Accuracy claims lean on vendor-run benchmarks against unnamed VLM and IDP baselines, so independent testing is warranted. It is infrastructure, not an end-user Q&A app: you still need your own retrieval layer and interface on top.
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Added10/21/2026
Platforms
Web AppLinuxMacOSWindows
Pricing
FreemiumPaid
