LadybugDB is an embedded, serverless graph database that stores data in a columnar format and uses vectorized execution to run analytical Cypher queries up to ten times faster than traditional graph stores. It can be installed with a single command, has no external dependencies, and offers MIT‑licensed core code plus commercial support for security updates and maintenance, targeting regulated‑industry applications and agentic AI workloads.
Funding
Funding not disclosed
Founders
Product
Problem
Highly regulated industries need an embedded graph database that can be deployed quickly, runs without external dependencies, and receives ongoing security updates. Existing open‑source options have been abandoned, leaving teams with unpatched vulnerabilities and no support for mission‑critical agentic AI applications.
Solution
LadybugDB provides a production‑ready, embedded columnar graph database that can be installed in minutes via a single command. It stores graph data in a columnar format and uses vectorized execution to deliver analytical queries up to ten times faster than traditional graph stores. The database runs serverlessly on edge devices, containers, or cloud environments with zero external dependencies, making it suitable for tightly controlled environments. Built on research published at top‑tier conferences, LadybugDB offers an open‑source MIT‑licensed core plus commercial enterprise support to ensure long‑term maintenance, security patches, and feature updates. Developers can load data and query the graph using standard Cypher syntax through language SDKs (Python, Node.js, Rust), enabling rapid integration of graph capabilities into agentic AI solutions.
Target Audience
Primary customers are software teams building agentic AI or data‑intensive applications in regulated sectors such as finance, healthcare, and government, where embedded, secure graph processing is required.
Features
- Columnar storage with vectorized execution for high‑performance analytical graph queries
- Embedded, serverless deployment with no external services required (edge, container, or cloud)
- Full Cypher support and language SDKs (Python, Node.js, Rust) for easy data import and querying
- Open‑source MIT license guaranteeing perpetual availability of the core engine
- Commercial enterprise support contracts providing security updates, bug fixes, and roadmap planning
- One‑command installation via curl or Homebrew, enabling production readiness in under five minutes
- Optimized compression and scan performance for large, connected datasets typical in regulated domains