Building the memory engine for the next generation of AI.

AI models have fundamentally changed how we interact with technology. But while models have grown increasingly capable, their ability to remember context across sessions has remained fragile, expensive, and difficult to scale.

Libro was born out of frustration with existing vector databases and RAG pipelines. We realized that developers shouldn't have to string together embedding APIs, Pinecone clusters, and complex chunking algorithms just to make an AI remember a user's name.

We've built an edge-native memory infrastructure that abstracts away the complexity. By bringing semantic vectorization to the edge and automating the chunking process, we allow developers to add infinite memory to their AI agents with a single line of code.