Caliby: Open-Source Embedded Vector Database for AI Agents with Hybrid Text+Vector Storage

Caliby is now open-source: an embedded, in-process vector database designed for AI Agent and RAG workloads. Developed by a team including a PhD from MIT’s DB Group (Michael Stonebraker’s team) and Sea-Land AI, it's a single C++ library with Python bindings.

Why Another Vector DB?

The team found existing solutions lacking for agent/LLM use cases:

• FAISS: Pure in-memory, no persistence — restart clears the index.
• pgvector: Performance ceiling due to PostgreSQL dependency.
• Chroma / Qdrant / Milvus: Require separate services, too heavy for embedded scenarios.
• LanceDB: Embedded but lacks advanced indexes like DiskANN, performance bottlenecks.

Caliby aims to be a lightweight, embeddable data engine like DuckDB, but for vector + text storage.

Architecture: Hybrid Text + Vector Storage

Caliby unifies text and vector data in a single system. Instead of juggling a vector DB and a relational DB, you store embeddings, raw text, and metadata in one library. The architecture uses a page-organized buffer pool for persistence.

Supported Indexes

• HNSW: General high-performance retrieval, CPU-optimized.
• DiskANN (Vamana Graph): Designed for disk-based scenarios, outperforms FAISS on disk.
• IVF+PQ: Inverted file with product quantization for compact indexes.

Caliby also supports brute-force search with SIMD (AVX-512, AVX2, SSE) distance functions (L2, InnerProduct, Cosine).

Performance Claims

Caliby beats pgvector by 4x and significantly surpasses FAISS in disk-storage scenarios. It handles millions to tens of millions of vectors on disk without requiring a separate service.

Getting Started

Simply install the package:

pip install caliby

The Python API exposes HnswIndex, DiskANN, and IVFPQIndex classes via pybind11. No dependencies, no server setup, no DevOps.

Who It's For

AI Agent developers and RAG pipeline builders who want an embeddable, zero-infrastructure vector database with hybrid text+vector capabilities and production-grade performance.