LadybugDB vs libSQL
LadybugDB
ladybugdb.comlibSQL
github.comSide by side
| Figure | LadybugDB | libSQL |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | — |
| Android | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Embedded Databases | 8.4 (#7) | 7.7 (#20) |
| Deployment | Ladybug runs embedded in an application process and does not require managing a separate database server. | The project provides Docker instructions for launching primary and replica server instances. |
| Extensions | Official extensions include support for ADBC sources, Azure storage, Delta Lake, DuckDB, full-text search, Iceberg, JSON, Neo4j migration, PostgreSQL, SQLite, Unity Catalog, and vector similarity search. | Listed SQLite extensions include ALTER TABLE changes to column types and constraints, randomized ROWID, WebAssembly user-defined functions, and a virtual write-ahead-log interface. |
| Integrations | The documentation describes integrations with Snowflake and PostgreSQL, and attaching to DuckDB, SQLite, Iceberg, Delta Lake, and Unity Catalog sources. | The repository lists GUI support through Beekeeper Studio, Outerbase, TablePlus, Dataflare, and libSQL Studio. |
| License | All source code and precompiled binaries are distributed under the MIT License. | The repository identifies the project license as MIT. |
| Product | Ladybug is an embedded graph database optimized for complex analytical workloads on very large graphs. | libSQL is an open-source, open-contribution fork of SQLite maintained by Turso. |
| Support | The site lists community support and says commercial enterprise support contracts are available. | The repository links to libSQL documentation, Discord, and Turso documentation for project resources and community. |
Plans
In their own words
LadybugDB
LadybugDB is an embedded columnar graph database for analytical workloads and agentic applications. It uses Cypher with a structured property graph model and can run on disk or in memory; in-memory data is lost when the process ends. Its query engine uses columnar disk storage, vectorized and factorized processing, multi-core parallelism, and join algorithms. Transactions are atomic, durable, and serializable. The project distributes source code and precompiled binaries under the MIT License, which permits commercial and proprietary applications. Imports support Parquet, CSV, JSON, NumPy, Pandas or Polars DataFrames, and PyArrow Tables. Official client APIs are listed for Python, Node.js, Java, Rust, Go, Swift, C, and C++, alongside a command-line interface. Ladybug Explorer provides a browser-based way to query and visualize a database, and the MCP Server exposes a database to LLMs and agents. Extensions cover sources and systems including Azure storage, Delta Lake, DuckDB, Iceberg, PostgreSQL, and vector similarity search. Concurrent access permits one read-write Database object or multiple read-only objects; multiple processes needing writes should use an API server pattern.