turing-db/turingdb

A high performance in-memory columnar graph database built for critical industries

Website • Documentation • Quickstart • Discord • Examples

What is TuringDB?

TuringDB is a high-performance, in-memory, column-oriented graph database engine, built in C++ for analytical, AI-driven, and read-intensive workloads.

With version-controlled storage, zero-locking execution, and integrated vector search for GraphRAG and embeddings, it gives you low-latency queries, snapshot isolation, and seamless integration with modern AI pipelines.

It is designed around three properties: fast multi-hop traversals, native Git-style versioning, and minimal memory footprint.

Quickstart

Get from zero to your first query in under a minute.

1. Install

pip install turingdb

# uv (create a project first, then turingdb is on your $PATH)
uv add turingdb

# curl install script
curl https://install.turingdb.ai | bash

# Docker (other methods are preferred, Docker has some performance overhead)
docker run -it turingdbai/turingdb:nightly turingdb

# nix, available on the nixpkgs unstable channel (x86 Linux and AArch64 macOS)
nix run nixpkgs/nixos-unstable#turingdb

2. Run your first query, no server needed

TuringDB runs in-process, right inside your Python program, with no separate server to start. Ideal if you want to embed it directly in your application:

from turingdb import TuringDB

# Embedded mode, no server to start
client = TuringDB(type="embedded", data_dir="~/.turing")

client.create_graph("social")
client.set_graph("social")

# Writes go through a versioned change: create, commit, submit
change = client.new_change()
client.checkout(change=change)
client.query("""
    CREATE (a:Person {name: 'Alice', age: 30})
    CREATE (b:Person {name: 'Bob', age: 25})
    CREATE (a)-[:KNOWS {since: 2020}]->(b)
""")
client.query("COMMIT")
client.query("CHANGE SUBMIT")
client.checkout()

# Reads come back as a pandas DataFrame
df = client.query("MATCH (a:Person)-[:KNOWS]->(b) RETURN a.name, b.name")
print(df)

That is it. You have created a graph, written a versioned change, and queried it.

3. Or run it as a server

Prefer a long-running server (binary protocol on port 6666)?

turingdb            # foreground / interactive
turingdb -demon     # background (daemon); stop with `turingdb stop`
turingdb -ui        # launch with the graph visualizer

Then point the client at it:

client = TuringDB(host="localhost", port=6666)

See the full Quickstart and Python SDK guide for more.

Visualize your graph

TuringDB ships with a WebGL-accelerated graph visualizer for exploring large graphs in the browser. Launch it by starting TuringDB with the -ui flag:

turingdb -ui

Once started, open the visualizer at http://127.0.0.1:8080 (or the port set via -ui-port).

Options

For example, to run TuringDB as a background daemon with the UI on a custom port:

turingdb -demon -ui -ui-port 9090

_{Example: Europe power grid generation and distribution network modeled as a graph in TuringDB}

Core Features

1. Real-time speed on deep queries

Deep multi-hop traversals stay in the millisecond range even on very large graphs, so a fraud check or cohort lookup returns now, not in minutes.

• Millisecond query latency at scale, commonly 35x to 480x faster than Neo4j and Memgraph (benchmarks), powered by a columnar storage architecture
• Zero-lock execution: reads and writes never compete, so analytics never stall behind ingestion

2. Git-style versioning for traceability and audit

Every change is an immutable commit you can branch, merge, roll back, and time-travel through at full speed, making auditability native for finance, compliance, insurance, and supply chain.

• Fully ACID with snapshot isolation: transactions are atomic and consistent, reads see a stable snapshot without locking, and commits are durably written to disk, thanks to immutable DataParts
• Versioning and time-travel queries: reproducible results and tamper-evident history for audit and compliance

3. Memory efficiency and flexible deployment

A compact in-memory representation runs multi-million node graphs on modest hardware, so you can deploy in the cloud, at the edge, or embedded in local hardware.

• Runs on small, constrained machines with no dedicated cluster
• Lower infrastructure cost than typical graph databases

Read the documentation for the full architecture and design considerations.

Benchmarks

TuringDB is commonly 35x to 480x faster than Neo4j and Memgraph on label scans and multi-hop traversals.

Benchmarked on the public Reactome knowledge graph: 2,978,202 nodes, 11,537,843 relationships, 108 node labels, 88 relationship types. Specs: Intel Xeon Gold 5412U (48 cores), 251.4 GB RAM, SSD, Ubuntu 24.04.3 LTS.

All results are cold runs, with no indexing or tuning on either side. TuringDB is queried over HTTP; Neo4j and Memgraph over Bolt.

Run these yourself with turing-bench, or see the detailed benchmarks for full methodology.

TuringDB for Agents

Agentic systems reason over graphs, and they need answers in the loop, not after a stall. TuringDB fits the way agents actually work:

• Real-time multi-hop retrieval. Agents traverse deep relationships to plan and act. Millisecond latency means an agent retrieves the context it needs without waiting, keeping the reasoning loop tight.
• Time-travel for explainability. Because every change is versioned, you or your agents can query the exact state of the graph as it was during a specific action, letting you reconstruct and audit why an agent did what it did, step by step.
• Built-in vector search for GraphRAG. Run native kNN over your embeddings and chain the results straight into Cypher traversals, so semantic retrieval and multi-hop graph reasoning happen in a single query.
• Rich context. Nodes and edges carry unlimited properties, including large text, making the graph a natural store for agent memory and GraphRAG.

Industry-specific examples of TuringDB

How TuringDB is used across critical industries:

• Bioinformatics and Life Sciences - deep, multi-scale biological networks explored in real time
• Financial Services - real-time fraud and risk detection with a full audit trail via versioning
• Compliance, Insurance, and Legal - entity and claims networks with reproducible, provable history
• Supply Chain and Logistics - multi-tier dependency tracing and root cause analysis on large graphs
• Defense - drone coordination, operational intelligence, and graphs embedded in constrained hardware
• AI and Machine Learning - GraphRAG, knowledge graphs, agent memory, and GNN training
• Digital Twins and Analytics - large-scale, multi-modal digital twins and accelerated simulations

Querying

TuringDB speaks a Cypher dialect, so here it is in the interactive shell:

// Create nodes
CREATE (alice:Person {name: 'Alice', age: 30})
CREATE (bob:Person {name: 'Bob', age: 25})
CREATE (computers:Interest {name: 'Computers'})

// Create relationships
MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
CREATE (a)-[:KNOWS {since: 2020}]->(b)

MATCH (a:Person {name: 'Alice'}), (i:Interest {name: 'Computers'})
CREATE (a)-[:INTERESTED_IN]->(i)

// Query the graph
MATCH (a:Person)-->(b)
WHERE a.age > 25
RETURN a.name, b.name

Time-travel queries

Versioning lets you ask what the graph looked like at any past point, which is the basis for audit and agent explainability:

# Query a historical version of the graph at full speed
client.checkout(change=previous_change)
df = client.query("MATCH (a:Account)-[:SENT]->(b:Account) RETURN a.id, b.id")
client.checkout()  # back to the latest state

Importing data

Load existing graphs into TuringDB from common formats (import guide):

• JSONL - build a graph from line-delimited JSON (Neo4j APOC-compatible) with LOAD JSONL
• CSV - stream rows with LOAD CSV to drive CREATE or RETURN
• GML - import a full graph natively with LOAD GML
• Neo4j - migrate an existing database via APOC JSON export, loaded as JSONL
• Parquet - columnar Parquet, including embeddings for vector search

Example Notebooks

Real-world, end-to-end notebooks across domains, from fraud detection to biological graph exploration, showing off TuringDB's speed and versioning.

Finance and Fraud

• Paysim Financial Fraud Detection
• Crypto Orbitaal Fraud Detection

Transport and Supply Chain

• London Transport (TfL)
• Supply Chain, ETO Chip Explorer

Healthcare and Life Sciences

• Reactome Biological Pathways
• Healthcare Knowledge Graph

Browse them all at turing-db/turingdb-examples

_{Visualizing a massive mesh graph with OpenGL-accelerated graph visualization}

Building from Source

TuringDB runs natively on x86-64 and ARM 64-bit platforms. SSD or NVMe storage is recommended.

Requirements

Linux:

• Ubuntu Jammy 22.04 LTS or later
• GCC >= 11
• CMake >= 3.10

macOS:

• LLVM Clang >= 15
• CMake >= 3.10

Build

# 1. Clone with submodules
git clone --recursive https://github.com/turing-db/turingdb.git
cd turingdb
./pull.sh

# 2. Install dependencies (run once)
#    Builds/installs: Curl, OpenSSL, Boost,
#    OpenBLAS, AWS SDK for C++, and the Faiss vector-search library
./dependencies.sh

# 3. Build
mkdir -p build && cd build
cmake -G Ninja ..
ninja -j8
ninja install

# 4. Add turingdb binaries to your current shell's $PATH
source setup.sh

Community and Support

• Discord: chat with users and the team
• Issue Tracker: report bugs or request features
• Email: [email protected]
• LinkedIn: follow for updates

Contributing

We welcome contributions. Check the open issues to get started, and reach out at [email protected] if you would like to get involved.

License

TuringDB Community Edition is licensed under the Business Source License (BSL). See LICENSE for details.

_{Built for the graph database community. If TuringDB is useful to you, please star the repo.}