We built and open-sourced Caliby: An embedded, high-performance vector database for AI Agents (Beats pgvector by 4x, outperforms FAISS on disk)

Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for agent/LLM applications. It outperforms pgvector by 4x and significantly surpasses FAISS in disk-storage scenarios. It s DiskANN, HNSW, and IVF+PQ indexes, maintains high performance on disk, and - best of all - is just one pip install away. TL;DR Caliby is a high-performance, embedded vector retrieval library co-developed by Sea-Land AI and MIT’s Michael Stonebraker team. Core in C++ + Python bindings. Just pip install caliby.