llamaindex

llamaindex

LlamaIndex provides LlamaParse — an enterprise-grade document processing platform — and LiteParse — an open-source local parser — that use VLM/LLM-powered agents to parse, extract, index, and automate workflows across complex documents for teams in finance, insurance, healthcare, manufacturing, and more.

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Free
#524 in AI Agents (524 tools)
Added 0 year ago
Data reviewed Jul 15, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: AI Agents

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

Best fit

AI Agents

Pricing snapshot

Free from Free (includes 10,000 free credits per month, ~1000 pages)

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llamaindex

LlamaIndex offers LlamaParse, an enterprise-focused document automation platform that uses VLM- and LLM-powered agents to parse, extract, index, and build end-to-end document agents. It targets complex, multi-format documents (PDFs, Office docs, images, handwritten notes, charts, tables) and emphasizes accuracy, configurability, and scalability for production workloads. In parallel, LiteParse is an open-source local parser from the same team for fast, offline document parsing without cloud or LLM token usage, distributed via npm and GitHub.

A framework for building knowledge assistants with LLMs connected to enterprise data.

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Key Features

Agentic OCR / VLM-powered understanding

Agentic OCR that converts complex layouts into LLM-ready outputs using VLM-powered document understanding agents.

Schema-based LLM extraction

Structured extraction of defined schemas using LLM-powered extraction agents to turn unstructured content into structured insights.

Specialized expert agents

Task-specific agents break down content such as text, charts, tables, and more, routing it to the right expert.

Auto-correction loops

Recursive checks that detect and fix errors automatically to improve pass-through rates on messy scans and multi-modal documents.

Handwriting parsing

Capabilities to parse messy handwriting, extract structure, and make it usable for AI workflows.

Table extraction

Extract rows, columns, and relationships from dense or irregular table layouts.

Chart and graph parsing

Turn charts and graphs into structured data usable by downstream pipelines.

Document splitting and classification

Split documents into logical sections based on natural-language descriptions and automatically categorize documents using natural-language rules.

Enterprise-grade indexing and retrieval

Chunking and embedding pipeline built for precision and relevance in retrieval (RAG) and enterprise retrieval calls.

Open-source local parsing (LiteParse)

LiteParse provides fast local parsing of PDFs, Office docs, and images with bounding box outputs; available via npm and GitHub.

Enterprise reliability & deployment options

99.9% uptime infrastructure, granular access controls, enhanced data encryption, compliance (HIPAA, GDPR, SOC2), and options to run in LlamaIndex cloud or deploy fully in a VPC.

Pricing

Free Tier Available

Includes 10,000 free credits per month (approximately 1000 pages) with agentic OCR, schema-based extraction, and ability to build and deploy document agents.

Free

Free (includes 10,000 free credits per month, ~1000 pages)
  • 10,000 free credits per month (~1000 pages)
  • Agentic OCR for layout-aware document parsing
  • Structured extraction of defined schemas
  • Build and deploy end-to-end document agents

Use Cases

Financial Due Diligence

Speed up compliance reviews and financial research by extracting structured data and automating manual review workflows.

Invoice Processing

Automate manual invoice review and extraction to accelerate accounts payable and related workflows.

Technical Document Search

Find answers in complex technical documentation by indexing and retrieving relevant document chunks with high precision.

Customer Support

Provide instant, accurate responses by enabling agents to read and act on customer-facing documents and knowledge bases.

Context for AI Agents

Enable LLMs to read complex documents with human-level precision to power agentic workflows and multi-step document agents.

Replace Legacy IDP

Modernize document processing without custom templates by using schema-based extraction and agent routing.

Integrations

LiteParse (npm / GitHub)

Open-source local parser available via npm (npm install @llamaindex/liteparse) and GitHub for offline parsing and bounding-box outputs.

Indexing & embedding pipeline

Enterprise-grade chunking and embedding pipeline designed for retrieval-augmented generation (RAG) workflows (internal indexing/embedding integration).

Benefits

Dramatically reduce manual document processing time ("turns hours into seconds") using agentic OCR and automation.
High accuracy across complex document types including handwritten notes, multi-page tables, charts, and dense layouts.
Open-source local parsing option (LiteParse) for privacy-sensitive or offline use cases.
Enterprise readiness with 99.9% uptime, granular access controls, encryption, and HIPAA/GDPR/SOC2 compliance.
Flexible deployment models: secure cloud or fully in your VPC, plus dedicated support and SLAs.

Limitations

No verified limitations are available.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Step 1: Sign up for LlamaParse to receive the free plan (10,000 free credits per month).
  2. 2 Step 2: Try LlamaParse via the web site — Book a demo or Try for free to evaluate enterprise features.
  3. 3 Step 3: For local/open-source usage, install LiteParse (npm install @llamaindex/liteparse) or view the LiteParse GitHub repository.

Support

Docs

Documentation and resources available via the Docs and Resources sections on the website.

Sales / Demo

Book a demo or contact sales through the website for enterprise purchasing and SLAs.

Customer stories / Case studies

Customer stories and case studies (e.g., Jeppesen/Boeing example) available on the site to illustrate real-world deployments.

API

Available: No

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