LlamaIndex AI Agents: Features & Pricing

What is LlamaIndex?

LlamaIndex provides document-processing tools for teams that need to turn complex files into usable inputs for AI systems. Its LlamaParse offering focuses on parsing and extracting information from unstructured documents.

The platform can parse more than 50 unstructured file types, including files with embedded images, complex layouts, tables, and handwritten notes. It is designed to convert those inputs into clean outputs that are ready for language-model workflows.

Schema-based extraction agents turn unstructured content into structured information without model training. Teams can also split documents into logical sections and classify them with natural-language rules.

For retrieval-oriented applications, LlamaIndex includes a chunking and embedding pipeline for indexing documents. This makes it a fit for teams building RAG systems or other applications that need relevant context from source files.

A free LlamaParse plan includes monthly credits and tools such as layout-aware OCR and structured extraction. The available workflow spans initial ingestion through to deploying document agents.

LlamaIndex Features

Complex document parsing

LlamaIndex parses more than 50 types of unstructured files, including documents with complex layouts, embedded images, multi-page tables, and handwriting. This gives AI workflows a way to work from source material that is difficult to process with simple text extraction.

Schema-based extraction

LlamaIndex uses LLM-powered extraction agents to turn unstructured material into structured insights. The workflow is schema based, so teams can define the information they want without training a custom model.

Document splitting and classification

The platform can split a document into logical sections using natural-language descriptions. It can also classify documents automatically from natural-language rules, helping organize incoming material before it is used elsewhere.

Indexing for retrieval

LlamaIndex includes chunking and embedding tooling for document indexes. It is built for retrieval use cases where systems need precise and relevant context from stored source material.

LlamaIndex