# Cleora.ai

According to OpenAITools.app telemetry verified on 2026-11-11, Cleora.ai (cleora.ai) is an AI platform indexed under Graph AI generating 0 monthly visits with a month-over-month traffic delta of +0 (0%). Its primary geographic market is Global (— traffic share), and it operates on a Freemium, Free Trial, Paid, Contact for Pricing pricing model. Top competing alternatives by monthly volume include Graphy (81.22K visits/mo), AI Graph Maker (38.33K visits/mo), LakeSail (3.52K visits/mo).

Rust-powered, parameter-free graph embedding engine that runs on one CPU core

## Key Telemetry & Specifications

| Metric | Value |
| :--- | :--- |
| Tool Name | Cleora.ai |
| Handle | cleora-ai |
| Status | active |
| Origin | open_source |
| Maintenance | active |
| Official Domain | cleora.ai |
| Website URL | https://cleora.ai |
| Categories | Graph AI, Open Source AI Models, AI Developer Tools, AI For Data Analytics |
| Pricing Model | Freemium, Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 0 |
| MoM Traffic Change | 0 |
| MoM Growth Rate | 0% |
| Primary Market | Global |
| Market Share | — |
| Direct Visits | 0 |
| Organic Search Visits | 0 |
| Social Visits | 0 |
| Referral Visits | 0 |
| Email Visits | 0 |
| Review Score | 4.6 |
| Saved Count | 1 |
| Supported Platforms | Website |
| Billing Platforms |  |
| Attributes | Features: Website; Pricing: Freemium; Pricing: Free Trial; Pricing: Paid; Pricing: Contact for Pricing |
| Tags | Version control, Git, Source code management, Collaboration, Open source, Repositories, CI/CD, GitHub Actions, Issues, Pull requests, Code review |

## Overview

Cleora is a graph embedding engine designed around a single mathematical insight: instead of sampling random walks and training a skip-gram model, you can capture the full distribution of walks by repeatedly multiplying an embedding matrix by a sparse Markov transition matrix. Each iteration replaces every node's embedding with the L2-normalized average of its neighbors, producing embeddings that are stable, reproducible, and remarkably fast on commodity hardware.

Practically, Cleora ships as a compact Rust extension installed with pip, with only numpy and scipy as dependencies. It natively supports heterogeneous hypergraphs through typed TSV columns, so multi-relational graphs, bipartite graphs, and knowledge triples can be fed in directly without preprocessing. Typical benchmarks from the project and its users report large speedups versus deep-learning baselines — Zomato, for example, reports embedding generation dropping from roughly 20 hours with GraphSAGE to under five minutes.

The project also bundles a wide evaluation and comparison suite, including alternative algorithms (ProNE, RandNE, HOPE, NetMF, GraRep, DeepWalk, Node2Vec) and metrics such as AUC, MRR, Hits@K, MAP@K and nDCG, so teams can benchmark embedding quality without adding heavy dependencies. Because embeddings are inductive and stable, new nodes can be embedded without retraining the whole graph — useful in production recommendation, fraud detection, and entity resolution pipelines.

## Pricing & Plans

Open-source and free to use (installable via pip). Any paid tiers or enterprise support are handled directly through the vendor; check the official site for current commercial options.

## Key Features & Capabilities

- **Deterministic embeddings:** Same input always produces the same output — no stochastic variation, which is critical for reproducible research and production ML pipelines.
- **No sampling, no GPU:** Eliminates random walks and skip-gram training entirely by computing exact walk distributions through sparse matrix powers on a single CPU core.
- **Heterogeneous hypergraph support:** Handles multi-type nodes and edges, bipartite graphs, and hypergraphs natively from a single typed TSV file — no graph preprocessing required.
- **Lightweight footprint:** Around 5 MB with only numpy and scipy as dependencies, versus hundreds of megabytes for PyTorch Geometric or DGL.
- **Built-in evaluation suite:** Includes alternative algorithms and metrics such as AUC, MRR, Hits@K, MAP@K and nDCG for comparing embedding quality out of the box.

## Primary Use Cases

- **Recommendation systems at scale (ML engineer):** Build customer, product or video embeddings for personalized recommendation and lookalike modeling without provisioning GPU infrastructure.
- **Fraud detection and entity resolution (Data scientist):** Encode transaction and relationship graphs to surface anomalous clusters and match duplicate entities across datasets.
- **Reproducible graph benchmarks (Researcher):** Produce deterministic embeddings with an integrated evaluation suite and built-in baseline algorithms for fair, repeatable comparisons.
- **Lightweight production pipeline (Platform team):** Add graph embeddings to an existing ML pipeline with a small dependency footprint and inductive embedding of new nodes.

## Strengths

- Deterministic, reproducible results across runs with no stochastic variation
- Runs on CPU only — no CUDA, no GPU drivers, no expensive hardware
- Very small instal size with minimal dependencies (numpy and scipy)
- Natively supports heterogeneous hypergraphs and inductive embedding

## Limitations

- Requires comfort with graph ML concepts and matrix-based propagation tuning
- Feature depth is narrower than full deep-learning graph frameworks with GPU training

## Frequently Asked Questions

### Do I need a GPU to use Cleora?
No. Cleora is CPU-only by design. Its sparse matrix operations are implemented in Rust with adaptive parallelism, and the project explicitly reports zero GPUs required ever.

### How is Cleora different from DeepWalk or Node2Vec?
Those libraries sample random walks and train a skip-gram model to approximate walk distributions. Cleora computes exact walk distributions through sparse matrix powers, so there is no sampling noise and results are deterministic.

### Can Cleora handle multi-type graphs?
Yes. It natively handles heterogeneous hypergraphs with typed, multi-relational edges supplied as a single TSV file, so bipartite projections or preprocessing are not required.

### How do I install Cleora?
Cleora is distributed as an open-source Python package installed with pip install pycleora, which pulls in the compiled Rust core.

### What is Cleora.ai's monthly traffic and growth trend?
Cleora.ai currently receives 0 monthly visits, with a month-over-month traffic change of +0 (0% growth rate). Its primary source market is Global (— share), including 0 direct visits/mo and 0 organic search visits/mo.

### Is Cleora.ai free? What is its pricing model?
Cleora.ai operates on a Freemium, Free Trial, Paid, Contact for Pricing pricing model. Open-source and free to use (installable via pip). Any paid tiers or enterprise support are handled directly through the vendor; check the official site for current commercial options. Supported billing and checkout platforms include: not listed.

### What are the best Cleora.ai alternatives in 2026?
Based on OpenAITools.app category traffic and feature fit, the top five alternatives to Cleora.ai are: 1. Graphy (81.22K visits/mo); 2. AI Graph Maker (38.33K visits/mo); 3. LakeSail (3.52K visits/mo); 4. ClearML (20.12K visits/mo); 5. Tinybird (85.44K visits/mo).

### Which platforms and core AI capabilities does Cleora.ai support?
Cleora.ai is classified under Graph AI, Open Source AI Models, AI Developer Tools, AI For Data Analytics, supports the following platforms: Website, and its core capability tags include: Version control, Git, Source code management, Collaboration, Open source, Repositories.

## Alternatives

| Tool Name | Monthly Visits | Pricing Model | Compare |
| :--- | :--- | :--- | :--- |
| [Graphy](https://openaitools.app/tool/graphy-for-data-storytelling) | 81,219 | Freemium | [Compare](https://openaitools.app/compare/cleora-ai-vs-graphy-for-data-storytelling) |
| [AI Graph Maker](https://openaitools.app/tool/ai-graph-maker) | 38,331 | Freemium | [Compare](https://openaitools.app/compare/ai-graph-maker-vs-cleora-ai) |
| [LakeSail](https://openaitools.app/tool/lakesail) | 3,516 | Freemium (open-source free; usage-based managed tier) | [Compare](https://openaitools.app/compare/cleora-ai-vs-lakesail) |
| [ClearML](https://openaitools.app/tool/clear-ml) | 20,123 | Contact for Pricing | [Compare](https://openaitools.app/compare/clear-ml-vs-cleora-ai) |
| [Tinybird](https://openaitools.app/tool/tinybird) | 85,443 | Freemium | [Compare](https://openaitools.app/compare/cleora-ai-vs-tinybird) |
| [ModelScope](https://openaitools.app/tool/modelscope-community) | 3,085,423 | Free | [Compare](https://openaitools.app/compare/cleora-ai-vs-modelscope-community) |
| [Rose AI](https://openaitools.app/tool/rose-ai) | 8,608 | Contact for Pricing | [Compare](https://openaitools.app/compare/cleora-ai-vs-rose-ai) |
| [CanIRun.ai](https://openaitools.app/tool/canirun-ai) | 284,892 | Free | [Compare](https://openaitools.app/compare/canirun-ai-vs-cleora-ai) |
| [Scoop Analytics](https://openaitools.app/tool/scoop-analytics) | 9,790 | Contact Sales | [Compare](https://openaitools.app/compare/cleora-ai-vs-scoop-analytics) |
| [Databrain](https://openaitools.app/tool/databrain) | 5,880 | Free Trial, Paid, Contact for Pricing | [Compare](https://openaitools.app/compare/cleora-ai-vs-databrain) |
| [OSS Insight](https://openaitools.app/tool/oss-insight) | 97,152 | Free | [Compare](https://openaitools.app/compare/cleora-ai-vs-oss-insight) |
| [Clusy](https://openaitools.app/tool/clusy) | 15,075 | Freemium | [Compare](https://openaitools.app/compare/cleora-ai-vs-clusy) |

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Source: https://openaitools.app/tool/cleora-ai