A Graph Database for HTAP Workloads

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NeuG (pronounced "new-gee") is a graph database for HTAP (Hybrid Transactional/Analytical Processing) workloads. It provides two modes that you can switch between based on your needs:

• Embedded Mode: Optimized for analytical workloads including bulk data loading, complex pattern matching, and graph analytics
• Service Mode: Optimized for transactional workloads for real-time applications and concurrent user access

For more information, please refer to the NeuG documentation.

News

• 2026-06 — NeuG v0.1.3: GDS extensions, COPY TEMP, Node.js client
• 2026-05 — NeuG v0.1.2: LOAD FROM, Parquet & HTTPFS extensions
• 2026-03 — NeuG v0.1 released
• 2025-06 — Shattered LDBC SNB Interactive Benchmark world record with 80,000+ QPS

Installation

The packages work on Linux, macOS, and Windows (via WSL2). For more detailed instructions (including C++ from source), see the installation guide.

Python  ·  requires Python 3.8+

pip install neug

Node.js  ·  requires Node.js 18+  (since v0.1.3)

npm install @graphscope-neug/neug

Quick Example

Python

import neug

db = neug.Database("/path/to/database")
db.load_builtin_dataset("tinysnb")

conn = db.connect()

# Find triangles in the graph
result = conn.execute("""
    MATCH (a:person)-[:knows]->(b:person)-[:knows]->(c:person),
          (a)-[:knows]->(c)
    RETURN a.fName, b.fName, c.fName
""")

for record in result:
    print(f"{record[0]}, {record[1]}, {record[2]} are mutual friends")

# Switch to service mode
conn.close()
db.serve(port=8080)

Node.js

const { Database } = require('@graphscope-neug/neug');

const db = new Database({ databasePath: '', mode: 'w' });
const conn = db.connect();

conn.execute("CREATE NODE TABLE person(id INT64, fName STRING, PRIMARY KEY(id));");
conn.execute("CREATE REL TABLE knows(FROM person TO person);");
conn.execute("CREATE (:person {id: 1, fName: 'Alice'}), (:person {id: 2, fName: 'Bob'}), (:person {id: 3, fName: 'Carol'});");
conn.execute("MATCH (a:person {id: 1}), (b:person {id: 2}), (c:person {id: 3}) CREATE (a)-[:knows]->(b), (b)-[:knows]->(c), (a)-[:knows]->(c);");

// Find triangles in the graph
const result = conn.execute(`
    MATCH (a:person)-[:knows]->(b:person)-[:knows]->(c:person),
          (a)-[:knows]->(c)
    RETURN a.fName, b.fName, c.fName
`);

for (const record of result) {
    console.log(`${record[0]}, ${record[1]}, ${record[2]} are mutual friends`);
}

conn.close();
db.close();

Development & Contributing

For building NeuG from source, see the Development Guide. We welcome contributions — please read the Contributing Guide before submitting issues or pull requests.

AI-Assisted Workflow

We apply an AI-assisted Spec-Driven workflow inspired by GitHub Spec-Kit:

• 🐛 Bug Reports: Use /create-issue command in your IDE, or submit an issue manually
• 💻 Pull Requests: Use /create-pr command in your IDE, or submit a PR manually

For more details, see the AI-Assisted Development Guide.

Acknowledgements

NeuG builds upon the excellent work of the open-source community. We would like to acknowledge:

• Kùzu: Our C++ Cypher compiler is adapted from Kùzu's implementation
• DuckDB: Our runtime value system and extension framework are inspired by DuckDB's architecture

License

NeuG is distributed under the Apache License 2.0.