LlamaIndex

What is LlamaIndex?

LlamaIndex is a developer-focused platform for building AI-powered document processing agents. It combines agentic OCR and document automation with a workflow engine so you can parse documents (such as PDFs, spreadsheets, and images), extract structured information, and orchestrate multi-step processes that include agents and retrieval.

The core purpose of LlamaIndex is to help teams move from unstructured document inputs to reliable, production-oriented document workflows—using modular components for parsing, schema-based extraction, indexing for retrieval (RAG), and event-driven orchestration.

Key Features

• LlamaParse agentic OCR and parsing: Parses 90+ unstructured file types, including embedded images, complex layouts, multi-page tables, and handwritten notes—supporting layout-aware document understanding.
• Schema-based extraction with citations and confidence: Uses extraction agents to transform unstructured content into structured outputs based on defined schemas, with page citations and confidence scores to support validation.
• Indexing optimized for retrieval: Provides an enterprise-grade chunking and embedding pipeline designed to deliver precision and relevance during retrieval calls for RAG.
• Workflows event-driven, async-first engine: Orchestrates multi-step AI processes (agents and document pipelines) with the ability to chain steps, loop, and branch in parallel paths.
• Stateful launch/pause/resume for workflows: Supports event-driven execution where workflows can be controlled and resumed statefully.
• Developer-first agent framework (LlamaIndex): Offers Python and TypeScript SDKs with low and high-level abstractions for agents, RAG, custom workflows, and integrations, including building blocks such as memory and human-in-the-loop review.

How to Use LlamaIndex

1. Start with LlamaParse to parse your source documents (e.g., PDFs or images) and obtain structured representations suitable for downstream processing.
2. Define a schema for the fields you want to extract, then run schema-based extraction to produce structured outputs with citations and confidence scores.
3. Index for retrieval using LlamaIndex’s chunking and embedding pipeline so you can support RAG-style queries over your documents.
4. Orchestrate the end-to-end flow with Workflows by connecting parsing, extraction, indexing, and any agent steps into an async-first, event-driven workflow that can be launched and resumed.

Use Cases

• Automated invoice or document review pipelines: Parse documents, extract defined fields into a schema, and assemble results into downstream steps that match business logic (e.g., validation, routing, or follow-up actions).
• Financial research and due diligence support: Convert complex, unstructured materials into structured insights and enable retrieval over indexed content for agent-driven analysis workflows.
• Underwriting, audits, and claims operations: Process risk and protection documents to extract relevant information from unstructured sources such as handwritten notes or structured tables, supporting administrative and review workflows.
• Manufacturing extraction from technical documentation: Extract insights from specifications, manuals, and inspection reports that include complex layouts and tables to support faster information retrieval.
• Customer support knowledge and agent assistance: Use indexed document content and retrieval to power internal knowledge base queries and support agents with extracted, cited answers.

FAQ

What documents can LlamaIndex process?

LlamaParse supports parsing for 90+ unstructured file types, including PDFs and other unstructured sources, with handling for embedded images, complex layouts, multi-page tables, and handwritten notes.

How does LlamaIndex produce structured outputs?

It uses schema-based, LLM-powered extraction agents to turn unstructured content into structured insights. The platform also supports page citations and confidence scores.

Is Workflows required to build document agents?

LlamaIndex provides a developer-first agent framework (LlamaIndex) and a separate workflow engine (Workflows). The platform is positioned as an end-to-end approach, but specific combinations depend on the workflow you build.

What is Workflows used for?

Workflows is used to orchestrate multi-step AI processes—such as chaining parsing, extraction, and agent steps—with an event-driven, async-first model that can launch, pause, and resume statefully.

Does LlamaIndex support RAG?

Yes. The platform includes an indexing and retrieval pipeline (chunking and embeddings) designed for RAG-style retrieval calls, and the LlamaIndex framework is described as optimized for agents and RAG.

Alternatives

• General-purpose document OCR + custom pipelines: Use OCR engines to extract text, then build your own extraction, indexing, and orchestration logic. This can offer flexibility, but requires more engineering to handle layout-aware parsing and multi-step workflows.
• RAG frameworks without document parsing modules: Choose an agent/RAG framework and connect external document parsing/OCR services. This shifts responsibility for OCR layout handling and document-specific extraction to components outside the core framework.
• Workflow orchestration platforms for LLM apps: Build a custom document processing pipeline using a workflow/orchestration tool and integrate separate parsing and indexing components. This may fit teams already standardized on their orchestration stack, but you may need more integration work to achieve end-to-end document automation.