How to install CozoDB for use in a Python project?

Install via pip with `pip install pycozo`. It supports Linux, Mac, and Windows with backends like RocksDB and SQLite. Check the README's install table for platform-specific details and storage options.

CozoDB vs Neo4j: which is better for graph databases?

CozoDB offers multi-model capabilities including vectors and relational data with Datalog queries, while Neo4j is specialized for graphs with Cypher. CozoDB is more embeddable and scalable, but Neo4j has a larger ecosystem and maturity.

Can CozoDB handle time-series data effectively?

Yes, with its time travel feature, you can track historical changes per relation. However, for high-frequency time-series, ensure the storage backend is tuned for performance, and note that it's not optimized specifically for time-series like dedicated databases.

How to perform vector similarity search in CozoDB?

Create HNSW indices on vector relations using Datalog, then query with vector unification. The README shows examples integrating vector search with recursive Datalog for complex operations like ad-hoc joins.

Is CozoDB suitable for production use?

It's still pre-1.0, so not recommended for critical production without extensive testing. The project is active, but the README warns that stability and compatibility aren't guaranteed until version 1.0.

What are the backup and restore options in CozoDB?

Uses SQLite format for backup, allowing data exchange between different storage backends. Backup speed is around 1M rows per second, and restore is 400K rows per second, as per the performance benchmarks in the README.

Open-Awesome

CozoDB

MPL-2.0Rustv0.7.6

A transactional, relational-graph-vector database that uses Datalog for query, designed as the hippocampus for AI.

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4.0k stars155 forks0 contributors

What is CozoDB?

CozoDB is a transactional, multi-model database that integrates relational, graph, and vector data, using Datalog as its query language. It solves the problem of managing interconnected and high-dimensional data in a single system, enabling complex queries like recursive graph traversals and vector similarity searches seamlessly. It is designed to be embeddable across environments—from browsers to servers—while scaling for performance and concurrency.

Target Audience

Developers building AI applications, graph analytics, or data-intensive systems that require unified relational, graph, and vector operations in an embeddable or scalable database.

Value Proposition

Developers choose CozoDB for its unique combination of Datalog's expressiveness, multi-model flexibility, and embeddability—offering graph algorithms, vector search, and time travel in a single transactional database without sacrificing performance.

Overview

A transactional, relational-graph-vector database that uses Datalog for query. The hippocampus for AI!

Use Cases

Best For

Building AI applications that require integrated vector similarity search and graph reasoning
Embedding a transactional database in mobile apps or browsers with WebAssembly
Performing complex recursive graph queries and algorithms like PageRank
Developing systems that need historical data querying (time travel) for auditing or analysis
Unifying relational, graph, and vector data models in a single database backend
Running high-concurrency OLTP workloads with embedded or client-server deployment

Not Ideal For

Projects heavily reliant on SQL and its ecosystem of ORMs, migration tools, and BI integrations
Teams requiring guaranteed long-term stability and backward compatibility, as pre-1.0 versions lack these promises
Simple applications needing only basic key-value or document storage without advanced querying
Environments where developers are unwilling to learn Datalog and prefer more common query languages like SQL

Pros & Cons

Pros

Expressive Datalog Querying

Uses a powerful Datalog dialect for recursion and composability, enabling complex graph operations like reachability and shortest paths naturally, as demonstrated in the tutorial examples.

Multi-Model Integration

Unifies relational, graph, and vector data in a single database with integrated HNSW vector search, allowing seamless joins and searches across data types without separate systems.

Wide Embeddability

Runs embedded in browsers via WASM, on mobile devices, and servers with multiple storage engines like RocksDB and SQLite, supporting diverse environments without setup.

Time Travel Capability

Offers optional immutable history tracking per relation for querying past data states, useful for auditing or analysis, as highlighted in the feature list.

Cons

Pre-1.0 Instability

Versions before 1.0 do not promise syntax/API stability or storage compatibility, risking breaking changes and making it unsuitable for production-critical applications without thorough testing.

Datalog Learning Curve

Requires learning Datalog, a niche query language compared to SQL, which can increase onboarding time and limit adoption in teams familiar with traditional databases.

Ecosystem Limitations

Has a smaller community and fewer third-party tools, libraries, and documentation compared to established databases, potentially increasing development effort for integrations.

Storage Engine Complexity

Supports multiple backends like RocksDB that require tuning via options files, and misconfiguration can lead to performance issues or corruption, as noted in the tuning section.

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