Papr – Predictive memory and context intelligence API for AI Agents

Summary: Papr is an API that combines retrieval-augmented generation (RAG) and memory into a unified system, enabling AI agents to access connected context with over 91% accuracy and retrieval times under 100ms. It structures data into a predictive memory graph accessible via GraphQL or natural language, supporting private, multi-tenant environments in both open-source and cloud editions.

What it does

Papr builds a predictive memory graph that links data across sources, enabling AI agents to query connected context and insights through a single API. It anticipates user queries to pre-cache relevant context, improving retrieval speed and accuracy as data scales.

Who it's for

It is designed for teams building AI agents and analytics interfaces that require accurate, fast access to integrated context from multiple data sources.

Why it matters

Papr addresses AI hallucinations caused by fragmented context by unifying conversation history, documents, and structured data into a connected knowledge graph, improving retrieval accuracy and enabling insight generation.