Cleora.Ai

Cleora.ai is an open-source Data & Analytics tool that provides a Rust-powered graph embedding engine. It generates deterministic, production-grade embeddings from CPU-only operations, offering blazing fast performance without GPUs.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Cleora.ai?

Cleora.ai is a Rust-powered graph embedding engine designed for generating deterministic, production-grade embeddings. It leverages sparse Markov propagation with interleaved whitening, operating exclusively on CPU without requiring GPUs. This tool is significantly faster and uses less memory than many alternatives, making it ideal for large-scale datasets and complex graph structures like heterogeneous hypergraphs. Cleora eliminates the need for random walk sampling and skip-gram training, ensuring perfect reproducibility. It offers a Python API and includes 7 alternative algorithms for comparison, along with a rich evaluation suite and hyperparameter tuning capabilities, all within a compact 5MB installation.

Best used for

Ideal for data scientists who need to generate highly accurate and deterministic graph embeddings for large-scale datasets. Especially valuable for powering recommendation systems, detecting fraud, analyzing social networks, and drug discovery, where speed, memory efficiency, and reproducibility are critical without relying on GPUs.

Common actions

generate graph embeddings

analyze graph data

power recommendation engines

detect fraud

data science embeddingsgraph embeddings creationenterprise embeddings generation

Capabilities

Key features

Rust-powered core
Deterministic embeddings
CPU-only operation
Heterogeneous hypergraph support
Sparse Markov propagation
7 alternative algorithms
Rich evaluation suite

Target Audience

data scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Not publicly disclosed. Check cleora.ai for current pricing.

FAQs

What makes Cleora.ai faster and more memory-efficient than other graph embedding libraries?

Cleora.ai uses a Rust-powered core and a unique sparse Markov propagation algorithm that computes exact walk distributions via a single sparse matrix power, eliminating random walk sampling and GPU requirements. This approach results in significantly faster processing and lower memory consumption compared to libraries like GraphSAGE or NetMF.

Can Cleora.ai handle complex graph structures like heterogeneous hypergraphs?

Yes, Cleora.ai natively handles heterogeneous hypergraphs, including multi-type nodes and edges, bipartite graphs, and hypergraphs. It accepts TSV input with typed columns, removing the need for complex graph preprocessing before generating embeddings.

Is Cleora.ai suitable for production environments and does it offer reproducible results?

Cleora.ai is designed for production-grade use, offering deterministic embeddings where the same input always yields the same output. This ensures perfect reproducibility, which is critical for research and reliable ML pipelines, as demonstrated by its use in major companies like Zomato and Dailymotion.

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