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LlamaIndex

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Coding & Development

Introduction

Data framework for LLM apps with document parsing capabilities

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LlamaIndex Product Information

LlamaIndex is a GenAI-native toolkit designed to help developers build AI knowledge assistants over enterprise data. It provides an end-to-end platform for parsing, indexing, orchestrating, and deploying AI-powered agents that can access, reason over, and act upon complex organizational data. The tool integrates with common data sources, supports multi-agent workflows, and emphasizes production-ready, enterprise-grade capabilities such as data governance, retrieval-augmented generation (RAG), and seamless deployment.

Overview

  • Builds production-grade AI agents that can find information, synthesize insights, generate reports, and take actions over large and complex enterprise datasets.
  • Combines data ingestion, parsing (via LlamaParse), indexing, and retrieval with LlamaCloud as a managed service to accelerate development and deployment.
  • Widely adopted in finance, manufacturing, IT, healthcare, and more; leveraged by large enterprises (e.g., KPMG, Salesforce) and startups alike.

How It Works

  1. Parse and index enterprise data. Use LlamaParse to extract structured and semi-structured information from documents (including nested tables and complex layouts) and store in an accessible index.
  2. Connect data sources. Seamlessly integrate with file-based sources (SharePoint, Box, S3, Google Drive, etc.) with built-in access control and incremental syncing.
  3. Build agent workflows. Orchestrate single and multi-agent pipelines using the Agent Framework and Build Agentic Workflows to perform RAG, reasoning, and actions over data.
  4. Deploy to production. Deploy agents and full-stack applications that leverage multimodal retrieval and enterprise data governance through LlamaCloud and related tooling.

Use Cases

  • Financial analysts and corporate reporting: extract, analyze, and summarize financial data from annual reports, 10-Ks, earnings decks, and PDFs.
  • IT and operations: build support and automation agents that retrieve, summarize, and act on internal documents and logs.
  • Knowledge management: create a centralized knowledge base that supports context-aware questions and automated reporting.
  • Healthcare and pharma: parse and index clinical and regulatory documents for compliant RAG workflows.

Core Capabilities

  • GenAI-native parser for complex enterprise data (LlamaParse)
  • Ingestion and indexing of unstructured and semi-structured data
  • Multi-source data connectivity (SharePoint, Box, S3, Google Drive, etc.) with access controls
  • Agent framework for building single- and multi-agent workflows
  • Workflows to deploy production-grade AI agents
  • LlamaCloud: managed cloud service for parsing, indexing, and deploying agents
  • RAG pipelines with optimized retrieval over enterprise data
  • Integration with 40+ vector stores, 40+ LLMs, and 160+ data sources
  • Community and ecosystem: LlamaHub, connectors, datasets, and extensible tooling
  • Enterprise-grade security, governance, and scale for production deployments

LlamaCloud & LlamaParse

  • LlamaCloud: Fast and secure knowledge management for AI agents; the backbone for deploying production agents over enterprise data.
  • LlamaParse: Advanced data parsing to correctly format text, tables, diagrams, and charts for LLM understanding; supports nested tables and complex layouts.
  • Both are designed to reduce engineering overhead and speed up RAG setup, iteration, and tuning, enabling focus on delivering business value.

Why Teams Choose LlamaIndex

  • Industry adoption and community: 2.8M+ monthly downloads, 20k+ community members, and a broad ecosystem of connectors and tools.
  • Proven enterprise impact: used by leading firms to standardize AI agent development and drive trusted AI adoption.
  • Flexible deployment: supports production-ready agents with multi-modal data, governance, and scalable pipelines.

Getting Started

  • Sign up to access LlamaCloud and LlamaIndex core libraries (Python and TypeScript tooling).
  • Start with LlamaParse to parse your documents, then connect data sources and build Agentic Workflows.
  • Deploy agents to production and monitor performance, latency, and ROI.

Safety and Governance

  • Designed for enterprise use with governance and access control; ensure compliant data handling and secure deployment.

Core Features

  • <strong>LlamaCloud</strong>: Fast, secure knowledge management to connect unstructured data to LLMs and deploy agents.
  • <strong>LlamaParse</strong>: Advanced parsing for text, tables, diagrams, and charts across complex documents.
  • <strong>LlamaIndex Core</strong>: Framework to orchestrate single and multi-agent workflows.
  • <strong>Data Source Connectors</strong>: Integration with SharePoint, Box, S3, Google Drive, and more with incremental syncing and access controls.
  • <strong>Agentic Workflows</strong>: End-to-end tooling to ship context-augmented AI agents to production.
  • <strong>Workflows & Full-Stack Apps</strong>: Build and deploy agent-driven apps over your data.
  • <strong>Broad Ecosystem</strong>: 40+ vector stores, 40+ LLMs, 160+ data sources, and extensive community resources (LlamaHub).