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The unified temporal-graph database for AI & digital twins

Time-series, graph, geo, vector and full-text search — unified in one self-hosted binary, with a built-in MCP server and on-device embeddings. No glue code, no sidecars.

  • Free Community edition
  • Standalone 4.6 MB binary
  • Made in the EU 🇪🇺
curl -fsSL https://get.greycat.io/install.sh | bash -s stable
REC ingest · digital-twin/global-net 15/01/2017 08:02:30.21256
0throughput 0nodes 0relations 0ts values
Luxembourg · origin AI analyzing... timeline →

At a glance

Database system

Efficient storing and retrieval of relational, temporal and geographic data as a graph.

One language, disk to API

Model, store, and serve your data with a single object model — no ORM, no mapping layer.

Data Analytics Stack

Integrated tools and libraries to identify and visualize crucial insights of your data

Install GreyCat for free

Install Documentation Introduction

Our community version is completely free

Capabilities

Everything in one binary

GreyCat unifies five data systems — time-series, graph, geospatial, vector and full-text search — into a single ~4.6 MB engine with a built-in MCP server. Replace your polyglot stack with one self-hosted binary.

Time-series

Native temporal storage with point-in-time (as-of) queries and server-side sampling — no separate time-series database.

Graph

Traverse typed relationships with dot-notation — no JOINs and no separate query language to learn.

Geospatial

Native geo indexing and geometry, co-located with the rest of your data in the same store.

Vector search

A built-in vector index plus on-device embeddings — semantic search with no external embedding API.

Full-text search

BM25, hybrid and fuzzy search built in — no Elasticsearch sidecar to operate.

MCP server

A built-in Model Context Protocol server — your data and logic are AI-agent-ready out of the box.

Track record

Proven in production at scale

45B

meter readings / year on a national electricity-grid digital twin

1M

grid assets & 330k delivery points modelled live

1.27M

searchable paragraphs in an enterprise legal-search platform

8 → 1

separate systems replaced by a single GreyCat binary

Consolidate

Replace your stack today

Most teams bolt together a time-series database, a graph database, a search cluster, a vector store, an embedding API and an MCP gateway — then pay to operate, sync and secure all of them. GreyCat does the same job in one self-hosted binary.

Compare GreyCat vs the polyglot stack

Your stack today

PostgreSQL TimescaleDB / InfluxDB Neo4j Elasticsearch Pinecone / pgvector Embedding API MCP gateway
One GreyCat binary

Use cases

Built for AI & digital-twin workloads

One engine behind your most demanding temporal, graph and semantic workloads.

Go deeper

How GreyCat stacks up

An honest, side-by-side look at GreyCat next to the specialists — including the few places each incumbent still wins.

Compare side by side

Free to start, transparent editions

The Community edition is free forever. See what's included across Community, Pro and Enterprise.

View pricing & editions

Developer experience

A Programmable Database

All the benefits of a graph database without needing to learn a new fringe syntax.

No Query

No need for complex queries, traverse a graph as you would a simple object with dot notation.

No Mapping

A single Data Model from Disk to Api.

Any Scale

Datasets do not impose the infrastructure anymore, Hardware defines the speed.

No Tabular

No complex join or filters, leverage Object-Oriented traversal of the graph.

What-If, Many Worlds coming soon

Branch your data into parallel "what-if" worlds, simulate scenarios, and merge the results.

Stateful programming

Reduce the cost and time of processing by keeping the state and resume from where you left on the next iteration.

Script or serve

Use GreyCat as a stateful scripting solution or serve webapps (backend & frontend) from one executable.

AI-ready

Two annotations → a live API and an MCP tool

Write a function in GreyCat's language. Add @expose and @tag and it is instantly a typed REST endpoint and a Model Context Protocol tool your AI agents can call — no glue code, no separate gateway.

On the backend

Write a function & add two annotations…

@expose
@tag("openapi", "mcp")
fn add(a: int, b: int): int { return a + b; }
  • @expose — turns the function into a typed REST endpoint
  • @tag("openapi", "mcp") — adds it to the OpenAPI spec and the built-in MCP server
  • Same function, same RBAC — no controllers, DTOs or gateway to maintain

From the frontend & AI agents

Call it over HTTP like any REST endpoint…

curl -s https://your-server/add -d '[40, 2]'
# => 42

…or let an AI agent discover and run it over MCP:

  • initialize — the agent connects to GreyCat's built-in MCP server
  • tools/listadd appears automatically with its typed schema
  • tools/call — the agent invokes it, under the same RBAC as every caller

Case studies

GreyCat In Production

Kopr - Digital Twin

Kopr is a full-fledged AI Twin of the Luxembourg electricity grid. This digital counterpart of the physical grid and processes can be trained in near real-time – with the ever-increasing amount of available data – to serve as operational decision helper. Kopr aggregates, visualizes, analyzes, and learns data from various systems, e.g., GIS, SAP, metering infrastructures, real-time sensors, and much more. Kopr is built on top of our GreyCat technology that allows us to scale to millions of grid elements and to billions of metering measurement points per year.

Kopr digital twin of an electricity grid, built on GreyCat

Predictive - Industry 4.0

To create a full scale digital twin of the factories, we used our core technology GreyCat to devise a suitable data structure to store and later analyze and learn from the production data with its context. Where possible, the existing connectivity of the production lines (PLCs) have been exploited to collect their data. In other places, made to measure sensors have been deployed (electricity, air pressure, temperature, vibration, etc.) to derive production indicators and detect anomalies.

Each production line (and each of its station/sensor/actuator) is profiled and monitored independently in live, enabling fine grained analysis and predictions of each of its composing elements. Learning from past experiences, algorithms are able to estimate the yields and provide insights on the evolution of the OEE. Using learned model, simulations can be run to estimate the impact of production plannings modification.

Predictive Industry 4.0 factory analytics, built on GreyCat
The GreyCat team at DataThings

Our story

The history of GreyCat

From a theoretical idea into a fully fledged programming language managing & simulating an entire country's energy grid

Learn more about our history

Want to learn more about GreyCat?

Our Blog Posts
Read the GreyCat blog

Get started

Start building in a unified tech stack

Install the free Community edition in seconds, or talk to us about Pro and Enterprise.

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