CozoDB, Hybrid Relational-Graph-Vector Database
Hi HN! We're thrilled to share CozoDB v0.6, a monumental update to our FOSS database, which already unifies relational and graph features. With the addition of vector search, CozoDB becomes an even better companion for LLMs like ChatGPT. This release introduces vector search using HNSW indices within Datalog, enabling seamless integration with powerful features such as ad-hoc joins, recursive Datalog, and classical whole-graph algorithms. This update significantly broadens CozoDB's capabilities. Check out the linked release note for an in-depth look at the new features, comparisons to other…
CozoDB, Hybrid Relational-Graph-Vector Database launched on April 20, 2023 with 137 votes, #296 of 4,853 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
CozoDB is an open-source database that combines relational, graph, and vector search capabilities in a single system. It is designed for developers building applications with large language models and complex data queries. Version 0.6 adds vector search using HNSW indices integrated with Datalog, allowing users to perform ad-hoc joins, recursive queries, and graph algorithms alongside semantic search functionality.
written from the facts on this page · September 2026
From the sources
After a long repose, today we are very excited to bring you the release of CozoDB v0.6! The singular feature addition in this release is the introduction of vector search within Datalog . For those who are unfamiliar with the concept: vector search refers to searching through large collections of usually high-dimensional numeric vectors, with the vectors representing data points in a metric space. Vector search algorithms find vectors that are closest to a given query vector, based on some distance metric. This is useful for tasks like recommendation systems, duplicate detection, and clustering similar data points, and has recently become an extremely hot topic since large language models…from docs.cozodb.org
In the maker’s words, at launch
Hi HN! We're thrilled to share CozoDB v0.6, a monumental update to our FOSS database, which already unifies relational and graph features. With the addition of vector search, CozoDB becomes an even better companion for LLMs like ChatGPT. This release introduces vector search using HNSW indices within Datalog…
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Cozo – new Graph DB with Datalog, embedded like SQLite2022 · github.com · ▲425Hi HN, I have been making this Cozo database since half a year ago, and now it is ready for public release. My initial motivation is that I want a graph database. Lightweight and easy to use, like SQLite. Powerful and performant, like Postgres. I found none of the existing solutions good enough. Deciding to roll my own, I need to choose a query language. I am familiar with Cypher but consider it not much of an improvement over CTE in SQL (Cypher is sometimes notationally more convenient, but not more expressive). I like Gremlin but would prefer something more declarative. Experimentations…
HelixDB – Open-source vector-graph database for AI applications (Rust)2025 · github.com · ▲237Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
LatticeDB – Like SQLite but for graph databasesAug 2026 · github.com · ▲190We have been using graph DBs more and more at work. I found them painful to work with locally and decided to try and build something better.
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OctaneDB – Fast, Open-Source Vector Database for Python2025 · github.com · ▲33OctaneDB is an open-source vector database for Python that focuses on ultra-fast similarity search for high-dimensional data—perfect for AI/ML, semantic search, and large-scale document or embedding retrieval. What does it do? Store, index, and search millions of embeddings (text, images, etc.) with sub-millisecond query time. Supports in-memory and efficient HDF5 persistent storage. Integrates seamlessly with sentence-transformers for automatic text embedding. Key Features: 10x faster than Pinecone or ChromaDB for vector search and batch insertions. Advanced indexing: HNSW (approximate…
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Source: Show HN post ↗ · launch text quoted in part
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