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Llama Index

Free

LlamaIndex is the leading document agent and OCR platform

FreeFree tier
Type
Open Source
Visit Website GitHub (49.6k stars)

About Llama Index

LlamaIndex is an open-source data framework designed to connect LLMs with external data sources, offering a comprehensive platform for document parsing, extraction, classification, and indexing. It includes LlamaParse for parsing complex PDFs (with tables, charts, multimodal content), LlamaExtract for structured data extraction with schema design, LlamaClassify for document classification, LlamaSplit for splitting concatenated documents, and LlamaSheets for spreadsheet analysis. The platform provides REST APIs, web UI, batch processing, webhooks, and supports self-hosting (BYOC). It is widely used for building RAG applications, processing financial reports, resumes, contracts, and more.

Key Features

LlamaParse: Parse PDFs, tables, charts, and multimodal documents with granular bounding boxes
LlamaExtract: Extract structured data from documents with schema design and citations
LlamaClassify: Classify documents (e.g., contract types) using saved configurations
LlamaSplit: Split concatenated documents into individual files
LlamaSheets: Analyze and extract data from spreadsheets with coding agents
Index V2: Build vector indexes for retrieval-augmented generation (RAG) with sync and chat capabilities
Batch processing for scalable document handling
REST API and webhook integration for automation
Self-hosting (BYOC) with Azure deployment guides and LLM integrations (OpenAI, Anthropic, Gemini, etc.)
Supports multiple document types including PDFs, spreadsheets, and more

Pros & Cons

Pros
  • Open source with MIT license, free to use and modify
  • Comprehensive document processing (parsing, extraction, classification, splitting, spreadsheet analysis)
  • Supports multimodal parsing (PDFs with tables, charts, graphs)
  • Provides REST API, web UI, and SDK for flexible integration
  • Self-hosting option (BYOC) for data sovereignty and compliance
  • Integrates with major LLM providers (OpenAI, Anthropic, Google, AWS)
  • Active community with over 49,000 stars on GitHub
Cons
  • Primarily developer-focused, requires programming knowledge (Python/TypeScript) for full integration
  • Self-hosting setup can be complex, involving Azure deployment and configuration
  • Advanced features (e.g., multimodal parsing) may have learning curve for non-technical users

Best For

Building RAG applications that parse and index complex documentsExtracting structured data from financial reports with tables and citationsParsing and analyzing resumes for recruitment processingClassifying contract types in legal document workflowsSplitting concatenated PDFs or documents into separate filesAnalyzing spreadsheet data with agents for business intelligenceAutomating document ingestion pipelines with batch processing and webhooks