PageIndex

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Category:AI Agent Development Frameworks

Overview

Reasoning-based vectorless RAG for long documents using a hierarchical tree index, available as open source plus cloud chat, MCP, and API.

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ai engineers machine learning engineers software developers data engineers researchers business analysts

PageIndex is a reasoning-based, vectorless RAG system for analyzing long professional documents without vector databases or fixed chunking. It builds a hierarchical “table-of-contents” style tree index from a document and performs retrieval via reasoning-driven tree search, aiming for more relevant, traceable results with page/section references. PageIndex can be self-hosted using the open-source repository, or used via a hosted chat platform and integrations such as MCP and an API, with enterprise deployment options for private/on-prem use cases.

Autonomy level

23%

Reasoning: PageIndex is fundamentally a retrieval-augmented generation (RAG) system designed for document analysis rather than an autonomous agent. While it incorporates reasoning capabilities through LLM-powered tree search and can make contextual navigation decisions within documents, it operates reactively based on user queries rather than independently pu...

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Some of the use cases of PageIndex:

Building document Q&A and analysis systems for long PDFs without using a vector database.
Improving retrieval relevance for professional documents by using reasoning-based tree search.
Providing traceable answers with page and section references for audits and reporting.
Integrating document analysis into agent workflows via MCP or a hosted API.

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Pricing model:

freemium

Code access:

open-source

Popularity level: 69%