real-ladybug
Highly scalable, extremely fast, easy-to-use embeddable graph database
Decision gist · record as of 2026-08-14
Yes, if you need an embeddable graph database with Cypher support and analytical query optimization. The permissive MIT license, active maintenance, zero runtime dependencies, and broad platform coverage make it low-risk to try. Medium install friction is acceptable for a compiled database engine. No known vulnerabilities. Best suited for applications that want graph querying without managing a separate database server.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Precompiled wheels available for macOS (Intel/ARM), Linux (glibc/musl), and Windows; specific Python version requirements not documented in the fact sheet.
- Medium install friction due to platform-specific wheels across multiple Python versions and architectures.
- No runtime dependencies simplifies deployment once installed.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and allows use in commercial and proprietary projects with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-04-01 (135 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 102,570 downloads/mo, #12,860 on PyPI
Alternatives
Verify before relying
pip install real_ladybug
import real_ladybug
# Create and query a graph database
db = real_ladybug.Database(':memory:')
conn = db.connect()
conn.execute('CREATE NODE TABLE Person(name STRING, age INT64)')- Exact Python version support and whether versions beyond those with available wheels are supported
- Performance characteristics of join algorithms and query processor relative to other graph databases
- Production-readiness guarantees and stability commitments beyond active maintenance status
- Typical use-case examples demonstrating scalability on real-world workloads
What it is and what it does
Real-ladybug is a Python binding for Ladybug, an embedded graph database designed to run directly within applications without a separate server. It implements a property graph model with Cypher query language support, allowing you to store and query complex relational data as nodes and edges. The database is optimized for analytical workloads on large datasets, combining columnar disk-based storage with specialized index structures (full-text search, vector indices, and sparse row-based adjacency lists) to accelerate query execution.
The package provides serverless, embeddable integration—you can create in-memory or file-backed databases and execute queries directly from Python without managing a separate database process. It includes multi-core query parallelism, ACID transactions, and WebAssembly bindings for browser execution. With no runtime dependencies beyond the precompiled binary, deployment is straightforward once the platform-specific wheel is installed.
Use it for
- Build knowledge graphs or recommendation engines with full-text and vector search on large node/edge datasets.
- Run analytical queries on property graphs in embedded applications without deploying a separate database server.
- Prototype or deploy graph-based machine learning pipelines that require join operations and columnar storage.
- Query complex relational data using Cypher in a serverless, single-process context for edge computing or desktop tools.
- Combine semantic search with structured graph queries in applications requiring both capabilities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need an embeddable graph database with Cypher support and analytical query optimization.
The permissive MIT license, active maintenance, zero runtime dependencies, and broad platform coverage make it low-risk to try. Medium install friction is acceptable for a compiled database engine. No known vulnerabilities. Best suited for applications that want graph querying without managing a separate database server.
Install
real-ladybug on PyPI
Before you install
Medium install friction due to platform-specific wheels across multiple Python versions and architectures. No runtime dependencies simplifies deployment once installed. Active maintenance status supports ongoing compatibility.
Precompiled wheels available for macOS (Intel/ARM), Linux (glibc/musl), and Windows; specific Python version requirements not documented in the fact sheet.
License in practice
MIT license is permissive and allows use in commercial and proprietary projects with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install real_ladybug
import real_ladybug
# Create and query a graph database
db = real_ladybug.Database(':memory:')
conn = db.connect()
conn.execute('CREATE NODE TABLE Person(name STRING, age INT64)')Verify before relying
- Exact Python version support and whether versions beyond those with available wheels are supported
- Performance characteristics of join algorithms and query processor relative to other graph databases
- Production-readiness guarantees and stability commitments beyond active maintenance status
- Typical use-case examples demonstrating scalability on real-world workloads
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 135 days since the last release |
| First released | |
| Downloads | 102,570 / month, #12,860 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: real_ladybug-0.15.3-cp310-cp310-macosx_13_0_arm64.whl; real_ladybug-0.15.3-cp310-cp310-macosx_13_0_x86_64.whl; real_ladybug-0.15.3-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; real_ladybug-0.15.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; real_ladybug-0.15.3-cp310-cp310-musllinux_1_2_aarch64.whl; real_ladybug-0.15.3-cp310-cp310-musllinux_1_2_x86_64.whl; real_ladybug-0.15.3-cp310-cp310-win_amd64.whl; real_ladybug-0.15.3-cp311-cp311-macosx_13_0_arm64.whl; real_ladybug-0.15.3-cp311-cp311-macosx_13_0_x86_64.whl; real_ladybug-0.15.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; real_ladybug-0.15.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; real_ladybug-0.15.3-cp311-cp311-musllinux_1_2_aarch64.whl; real_ladybug-0.15.3-cp311-cp311-musllinux_1_2_x86_64.whl; real_ladybug-0.15.3-cp311-cp311-win_amd64.whl; real_ladybug-0.15.3-cp312-cp312-macosx_13_0_arm64.whl; real_ladybug-0.15.3-cp312-cp312-macosx_13_0_x86_64.whl; real_ladybug-0.15.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; real_ladybug-0.15.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; real_ladybug-0.15.3-cp312-cp312-musllinux_1_2_aarch64.whl; real_ladybug-0.15.3-cp312-cp312-musllinux_1_2_x86_64.whl
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