LadybugDB

LadybugDB is an embedded, in-process property graph database — no server process required. It uses the openCypher query language with a required, predefined schema (unlike Neo4j), columnar disk-based storage, vectorized query execution, and serializable ACID transactions.

Quick orientation

Schema-first: you must create node/rel tables before inserting data
One primary key per node table — automatically indexed, unique, non-null
Walk semantics: repeated edges allowed in MATCH (unlike Neo4j's trail semantics)
One write transaction at a time; multiple concurrent reads are fine
In-memory mode: use ":memory:" as the database path for ephemeral databases

Installation

# CLI
curl -s https://install.ladybugdb.com | bash   # Linux
brew install ladybug                             # macOS

# Python
pip install real_ladybug

# Node.js
npm install @ladybugdb/core

CLI basics

lbug mydb.lbug        # open/create on-disk DB
lbug                   # in-memory (ephemeral)
lbug mydb.lbug < schema.cypher   # batch mode

Key shell commands: :schema (show tables), :help, :quit, :mode [json|csv|markdown|...]

Reference files

Load only the sections you need:

File	Contents
`references/cypher-reference.md`	DDL, DML, MATCH queries, transactions, macros, LadybugDB vs Neo4j differences
`references/python.md`	Python (`real_ladybug`) — connection, query, DataFrame, transactions
`references/nodejs.md`	Node.js (`@ladybugdb/core`) — connection, query, streaming, transactions
`references/java.md`	Java — Maven setup, connection, query, transactions
`references/rust.md`	Rust — Cargo setup, connection, query, Value types
`references/go.md`	Go — module setup, connection, query, transactions
`references/swift.md`	Swift — SPM setup, connection, query, async/await
`references/import.md`	COPY FROM, LOAD FROM, DataFrame import, cloud storage, performance tips
`references/export.md`	COPY TO, DataFrame export (pandas/polars/arrow), DuckDB export
`references/graph-algorithms.md`	PageRank, Louvain, WCC, SCC, K-Core, shortest paths — PROJECT_GRAPH
`references/vector-search.md`	HNSW index, CREATE/QUERY/DROP_VECTOR_INDEX, RAG pattern
`references/full-text-search.md`	BM25, CREATE/QUERY/DROP_FTS_INDEX, stemmers
`references/llm-embeddings.md`	CREATE_EMBEDDING — OpenAI, Ollama, Google, Bedrock, Voyage AI
`references/attach.md`	ATTACH/DETACH — PostgreSQL, DuckDB, SQLite, Delta Lake, Iceberg, Neo4j
`references/cli.md`	`lbug` shell flags, commands, output modes, batch/scripting mode
`references/explorer.md`	Ladybug Explorer Docker GUI — launch, env vars, volume mount

Common task routing

Task	Read
Schema design, Cypher queries, differences from Neo4j	`cypher-reference.md`
Python integration	`python.md`
Node.js / TypeScript integration	`nodejs.md`
Java integration	`java.md`
Rust integration	`rust.md`
Go integration	`go.md`
Swift / iOS / macOS integration	`swift.md`
Bulk import — COPY FROM, LOAD FROM, DataFrames, cloud storage	`import.md`
Bulk export — COPY TO, DataFrame export, DuckDB	`export.md`
PageRank, Louvain, WCC, SCC, K-Core, shortest paths	`graph-algorithms.md`
HNSW vector similarity search, RAG	`vector-search.md`
Full-text search (BM25)	`full-text-search.md`
LLM embeddings (OpenAI, Ollama, Bedrock…)	`llm-embeddings.md`
ATTACH to PostgreSQL, DuckDB, Delta Lake, Neo4j	`attach.md`
CLI shell, batch scripts	`cli.md`
Ladybug Explorer browser GUI (Docker)	`explorer.md`

Key gotchas

SET n.prop = NULL to remove a property (not REMOVE)
label(n) not labels(n); id(n) not elementId(n)
UNWIND instead of FOREACH
List functions use list_ prefix: list_concat, list_sort, etc.
Variable-length paths must have an upper bound (default 30): [:Follows*1..5]
LOAD FROM (not LOAD CSV FROM) — supports CSV, Parquet, JSON, DataFrames
No manual index creation — primary key index is automatic; use FTS/vector extensions for search