LlamaIndex Agents

LlamaIndex is a combined open-source framework and managed cloud platform for building AI agents and workflows over unstructured and semi-structured documents. The core open-source library (formerly GPT Index) provides indexing, retrieval, and agent orchestration primitives that let developers connect large language models to their own data sources, including PDFs, web pages, databases, and more. It focuses on document-centric applications, where agents need to read, reason, and act over complex document collections. On the commercial side, the LlamaParse platform offers a hosted, production-ready environment for document OCR, parsing, extraction, classification, splitting, and indexing through a unified credit-based billing system. Each operation—such as parsing pages, extracting structured fields, or classifying documents—consumes credits, and the system exposes multiple parsing tiers (Fast, Cost Effective, Agentic, Agentic Plus) tuned for different accuracy, speed, and cost tradeoffs. This enables teams to scale from lightweight parsing of simple text PDFs up to highly accurate, layout-aware processing of complex, multimodal documents. Developers typically use LlamaIndex to power agents that sit on top of their document repositories: internal knowledge bases, support documentation, research archives, and other high-value content. By combining the indexing and retrieval capabilities with LLM tools and workflows, these agents can answer questions, summarize long documents, extract structured data, and trigger downstream actions based on document content. The platform integrates with popular LLM providers and can be orchestrated via Python SDKs and APIs for seamless integration into existing applications and backends. A key distinction of LlamaIndex is its tight focus on document intelligence and agentic document workflows. While the open-source components give builders fine-grained control and can be self-hosted under a permissive license, the hosted LlamaParse environment adds production-grade OCR, robust parsing tiers, monitoring, and scaling, all wrapped in a transparent credit-based pricing model with a generous free allowance. This combination makes it attractive both to individual developers experimenting with AI agents and to enterprises looking to operationalize document-heavy workflows.

• ✓Build a chatbot over internal PDFs and knowledge bases
• ✓Extract invoice or contract fields into structured JSON
• ✓Classify and route incoming documents automatically
• ✓Index documents for semantic search and agent retrieval
• ✓Automate OCR-driven document workflow pipelines

Use the open-source library when you want to control your own retrieval and agent stack, and use the hosted platform when you need managed parsing and extraction. For document pipelines, start with simple parsing modes for clean text, then move to agentic or higher-accuracy parsing for complex layouts. Use the API key plus SDK pattern for integration, and account for credit consumption because parsing modes have different credit costs.

Pricing is credit-based rather than a simple flat subscription for usage-heavy document processing. The hosted platform is not open source, and the free allowance is limited. Complex or multimodal documents will consume more credits than simple text extraction, so costs can rise quickly for large volumes.