Neumann

Stop juggling five databases for one AI app.

Neumann stores your tables, graphs, and vectors in one place. Query across all three in a single statement.

-- Find engineers similar to Alice who report to Bob
FIND NODE person
  WHERE role = 'engineer'
  SIMILAR TO 'user:alice'
  CONNECTED TO 'user:bob'

One query. Relational filter + vector similarity + graph traversal.

Why Neumann

Three engines, one system. Store a table, connect entities in a graph, and search by vector similarity -- without moving data between systems. No ETL, no sync, no glue code.

Semantic consensus. Concurrent writes to different fields auto-merge. The consensus layer classifies conflicts geometrically rather than treating all concurrent writes as errors.

Alice updates email, Bob updates photo (same user, same time).
Traditional DB: conflict, manual resolution.
Neumann: auto-merges (different fields = orthogonal changes).

AI-native by design. Built-in embedding storage, semantic caching for LLM responses, and encrypted vault for secrets. The query language understands similarity, not just equality.

Use Cases

RAG -- Store documents with embeddings and relationships. Semantic search follows graph links automatically.

Agent memory -- Conversation history with vector recall across sessions. Cache repeated LLM calls to cut API costs.

Knowledge graphs -- Combine structured data with semantic similarity. Find entities by what they mean, not just what they match.

Access control -- Graph-based permissions. Query results respect who's asking.

See Use Cases for worked examples with Python, TypeScript, and the CLI.

Quick Start

curl -sSfL https://raw.githubusercontent.com/Shadylukin/Neumann/main/install.sh | bash
neumann

-- Relational
CREATE TABLE users (id INT, name TEXT, role TEXT);
INSERT users id=1, name='Alice', role='engineer';
SELECT * FROM users WHERE role = 'engineer';

-- Graph
NODE CREATE person {name: 'Alice'};
NODE CREATE person {name: 'Bob'};
EDGE CREATE 1 -> 2 : reports_to;

-- Vector
EMBED STORE 'user:alice' [0.1, 0.2, 0.3];
SIMILAR 'user:alice' TOP 5;

More install methods (Homebrew, Cargo, Docker, source) in the Installation Guide. Full walkthrough in the Quick Start Tutorial.

SDKs

Python

pip install neumann-db

from neumann import NeumannClient
client = NeumannClient.connect("localhost:9200")
result = client.execute("SELECT * FROM users")

TypeScript

npm install @scrunchee/client

import { NeumannClient } from '@scrunchee/client';
const client = await NeumannClient.connect("localhost:9200");
const result = await client.execute("SELECT * FROM users");

Full tutorials: Python | TypeScript

Dashboard

Web dashboard with system status and query terminal

Interactive graph visualization with force-directed layout

Documentation

Full table of contents | Rustdoc API Reference

Performance

Benchmarked on Apple M-series silicon:

• 3.8M reads/sec, 2.0M writes/sec (in-memory, no durability)
• 22K durable writes/sec with group commit, 260/sec with fsync
• 150us vector similarity (HNSW, 10K embeddings, 128-dim)
• 52M conflict checks/sec via sparse delta comparison

Full benchmarks

Status

Neumann is pre-1.0 (v0.3.1). Core engines have 95%+ test coverage and 139 fuzz targets. Single-node is production-ready. Multi-node consensus has comprehensive testing (loom, proptest, deterministic simulation) but needs more real-world validation.

See the roadmap for where we're headed -- native embeddings, natural language queries, and AI-native analytics.

The Name

John von Neumann unified code and data in the stored-program architecture. Neumann unifies structure, relationships, and semantics.

License

Licensed under the Business Source License 1.1.

Author

Built by Lukin Ackroyd in Auckland, New Zealand.

Neumann is the infrastructure layer for Scrunchee.