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Decision brief
PageIndex is a Python-based document indexing system that doesn't rely on vector databases. It's designed for agentic AI tasks where reasoning and context are key.
Good fit when
- When your agentic AI project requires a non-vector database solution for reasoning and retrieval-augmented generation.
- If you're working with tasks that need extensive context engineering and deep information retrieval without relying on traditional vector-based indexing.
Avoid when
- For projects that strictly require the efficiency of vector databases, as PageIndex operates independently of these technologies.
- When your application demands real-time indexing or quick data access methods that are more suited to vector database capabilities.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (1d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install PageIndexHow it fits your stack(9)
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Overview
PageIndex is a document indexing system designed specifically for agentic AI tasks that require reasoning and retrieval-augmented generation (RAG) without relying on vector databases.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 16, 2026
Categories
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README
🛠️ Deployment Options
- Self-host — run locally with this open-source repo (using standard PDF parsing).
- Cloud Service — production-grade pipeline with enhanced OCR, tree building, and retrieval for best results. Try instantly on our Chat Platform, or integrate via MCP or API.
- Enterprise — dedicated or private deployment (VPC, on-prem). Contact us or book a demo to learn more.
1. Install dependencies
pip3 install --upgrade -r requirements.txt
Install optional dependency
pip3 install openai-agents
For agents
This page has a .md twin and JSON over the API.