https://github.com/ruvnet/ruv-fann

A blazing-fast, memory-safe neural network library for Rust that brings the power of FANN to the modern world.

https://github.com/ruvnet/ruv-fann

Science Score: 26.0%

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  • Scientific vocabulary similarity
    Low similarity (10.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

A blazing-fast, memory-safe neural network library for Rust that brings the power of FANN to the modern world.

Basic Info
  • Host: GitHub
  • Owner: ruvnet
  • License: mit
  • Language: Rust
  • Default Branch: main
  • Size: 163 MB
Statistics
  • Stars: 252
  • Watchers: 9
  • Forks: 81
  • Open Issues: 28
  • Releases: 0
Created about 1 year ago · Last pushed 12 months ago
Metadata Files
Readme Changelog License

README.md

ruv-FANN: The Neural Intelligence Framework 🧠

Crates.io Documentation License CI

What if intelligence could be ephemeral, composable, and surgically precise?

Welcome to ruv-FANN, a comprehensive neural intelligence framework that reimagines how we build, deploy, and orchestrate artificial intelligence. This repository contains three groundbreaking projects that work together to deliver unprecedented performance in neural computing, forecasting, and multi-agent orchestration.

🌟 The Vision

We believe AI should be: - Ephemeral: Spin up intelligence when needed, dissolve when done - Accessible: CPU-native, GPU-optional - built for the GPU-poor - Composable: Mix and match neural architectures like LEGO blocks - Precise: Tiny, purpose-built brains for specific tasks

This isn't about calling a model API. This is about instantiating intelligence.

🎯 What's in This Repository?

1. ruv-FANN Core - The Foundation

A complete Rust rewrite of the legendary FANN (Fast Artificial Neural Network) library. Zero unsafe code, blazing performance, and full compatibility with decades of proven neural network algorithms.

2. Neuro-Divergent - Advanced Neural Forecasting

27+ state-of-the-art forecasting models (LSTM, N-BEATS, Transformers) with 100% Python NeuralForecast compatibility. 2-4x faster, 25-35% less memory.

3. ruv-swarm - Ephemeral Swarm Intelligence

The crown jewel. Achieves 84.8% SWE-Bench solve rate, outperforming Claude 3.7 by 14.5 points. Spin up lightweight neural networks that exist just long enough to solve problems.

🚀 Quick Install ruv-swarm

```bash

NPX - No installation required!

npx ruv-swarm@latest init --claude

NPM - Global installation

npm install -g ruv-swarm

Cargo - For Rust developers

cargo install ruv-swarm-cli ```

That's it. You're now running distributed neural intelligence.

🧠 How It Works

The Magic of Ephemeral Intelligence

  1. Instantiation: Neural networks are created on-demand for specific tasks
  2. Specialization: Each network is purpose-built with just enough neurons
  3. Execution: Networks solve their task using CPU-native WASM
  4. Dissolution: Networks disappear after completion, no resource waste

Architecture Overview

┌─────────────────────────────────────────────┐ │ Claude Code / Your App │ ├─────────────────────────────────────────────┤ │ ruv-swarm (MCP/CLI) │ ├─────────────────────────────────────────────┤ │ Neuro-Divergent Models │ │ (LSTM, TCN, N-BEATS, Transformers) │ ├─────────────────────────────────────────────┤ │ ruv-FANN Core Engine │ │ (Rust Neural Networks) │ ├─────────────────────────────────────────────┤ │ WASM Runtime │ │ (Browser/Edge/Server/Embedded) │ └─────────────────────────────────────────────┘

⚡ Key Features

🏃 Performance

  • <100ms decisions - Complex reasoning in milliseconds
  • 84.8% SWE-Bench - Best-in-class problem solving
  • 2.8-4.4x faster - Than traditional frameworks
  • 32.3% less tokens - Cost-efficient intelligence

🛠️ Technology

  • Pure Rust - Memory safe, zero panics
  • WebAssembly - Run anywhere: browser to RISC-V
  • CPU-native - No CUDA, no GPU required
  • MCP Integration - Native Claude Code support

🧬 Intelligence Models

  • 27+ Neural Architectures - From MLP to Transformers
  • 5 Swarm Topologies - Mesh, ring, hierarchical, star, custom
  • 7 Cognitive Patterns - Convergent, divergent, lateral, systems thinking
  • Adaptive Learning - Real-time evolution and optimization

📊 Benchmarks

| Metric | ruv-swarm | Claude 3.7 | GPT-4 | Improvement | |--------|-----------|------------|-------|-------------| | SWE-Bench Solve Rate | 84.8% | 70.3% | 65.2% | +14.5pp | | Token Efficiency | 32.3% less | Baseline | +5% | Best | | Speed (tasks/sec) | 3,800 | N/A | N/A | 4.4x | | Memory Usage | 29% less | Baseline | N/A | Optimal |

🌐 Ecosystem Projects

Core Projects

Tools & Extensions

🤝 Contributing with GitHub Swarm

We use an innovative swarm-based contribution system powered by ruv-swarm itself!

How to Contribute

  1. Fork & Clone bash git clone https://github.com/your-username/ruv-FANN.git cd ruv-FANN

  2. Initialize Swarm bash npx ruv-swarm init --github-swarm

  3. Spawn Contribution Agents ```bash

    Auto-spawns specialized agents for your contribution type

    npx ruv-swarm contribute --type "feature|bug|docs" ```

  4. Let the Swarm Guide You

    • Agents analyze codebase and suggest implementation
    • Automatic code review and optimization
    • Generates tests and documentation
    • Creates optimized pull request

Contribution Areas

  • 🐛 Bug Fixes - Swarm identifies and fixes issues
  • Features - Guided feature implementation
  • 📚 Documentation - Auto-generated from code analysis
  • 🧪 Tests - Intelligent test generation
  • 🎨 Examples - Working demos and tutorials

🙏 Acknowledgments

Special Thanks To

Core Contributors

  • Ocean(@ohdearquant) - Transformed FANN from mock implementations to real neural networks with actual CPU and GPU training. Built the Rust implementation from placeholder code into a functional neural computing engine.
  • Bron(@syndicate604) - Made the JavaScript/WASM integration actually work by removing mock functions and building real functionality. Transformed broken prototypes into production-ready systems.
  • Jed(@jedarden) - Platform integration and scope management
  • Shep(@elsheppo) - Testing framework and quality assurance

Projects We Built Upon

  • FANN - Steffen Nissen's original Fast Artificial Neural Network library
  • NeuralForecast - Inspiration for forecasting model APIs
  • Claude MCP - Model Context Protocol for AI integration
  • Rust WASM - WebAssembly toolchain and ecosystem

Open Source Libraries

  • num-traits - Generic numeric traits
  • ndarray - N-dimensional arrays
  • serde - Serialization framework
  • tokio - Async runtime
  • wasm-bindgen - WASM bindings

Community

Thanks to all contributors, issue reporters, and users who have helped shape ruv-FANN into what it is today. Special recognition to the Rust ML community for pioneering memory-safe machine learning.

📄 License

Dual-licensed under: - Apache License 2.0 (LICENSE-APACHE) - MIT License (LICENSE-MIT)

Choose whichever license works best for your use case.


**Built with ❤️ and 🦀 by the rUv team** *Making intelligence ephemeral, accessible, and precise* [Website](https://ruv.ai) • [Documentation](https://docs.ruv.ai) • [Discord](https://discord.gg/ruv) • [Twitter](https://twitter.com/ruvnet)

Owner

  • Name: rUv
  • Login: ruvnet
  • Kind: user
  • Location: 0x
  • Company: Not a Bot

Unicorn Breeder.

GitHub Events

Total
  • Create event: 48
  • Issues event: 103
  • Watch event: 162
  • Delete event: 20
  • Issue comment event: 353
  • Member event: 1
  • Push event: 178
  • Gollum event: 3
  • Pull request review comment event: 2
  • Pull request review event: 12
  • Pull request event: 95
  • Fork event: 57
Last Year
  • Create event: 48
  • Issues event: 103
  • Watch event: 162
  • Delete event: 20
  • Issue comment event: 353
  • Member event: 1
  • Push event: 178
  • Gollum event: 3
  • Pull request review comment event: 2
  • Pull request review event: 12
  • Pull request event: 95
  • Fork event: 57

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 251
  • Total Committers: 4
  • Avg Commits per committer: 62.75
  • Development Distribution Score (DDS): 0.653
Past Year
  • Commits: 251
  • Committers: 4
  • Avg Commits per committer: 62.75
  • Development Distribution Score (DDS): 0.653
Top Committers
Name Email Commits
rUv r****v@r****t 87
OceanLi 1****t 78
syndicate604 s****4@g****m 64
rUv Contributor r****n@e****m 22
Committer Domains (Top 20 + Academic)
ruv.net: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 72
  • Total pull requests: 57
  • Average time to close issues: 1 day
  • Average time to close pull requests: 1 day
  • Total issue authors: 15
  • Total pull request authors: 9
  • Average comments per issue: 2.68
  • Average comments per pull request: 0.63
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 72
  • Pull requests: 57
  • Average time to close issues: 1 day
  • Average time to close pull requests: 1 day
  • Issue authors: 15
  • Pull request authors: 9
  • Average comments per issue: 2.68
  • Average comments per pull request: 0.63
  • Merged pull requests: 15
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • ohdearquant (25)
  • ruvnet (24)
  • jedarden (5)
  • Vasiliy-Bondarenko (5)
  • gaberger (3)
  • USEnterprises2001 (1)
  • rcraw (1)
  • ian-p-johnson (1)
  • davidvc (1)
  • ebr (1)
  • elsheppo (1)
  • tbowman01 (1)
  • liamhelmer (1)
  • coygeek (1)
  • MattMagg (1)
Pull Request Authors
  • ohdearquant (16)
  • syndicate604 (13)
  • ruvnet (11)
  • jedarden (8)
  • mikkihugo (4)
  • farchide (2)
  • ricable (1)
  • mlbrilliance (1)
  • bar181 (1)
Top Labels
Issue Labels
enhancement (31) bug (20) swarm-claimed (17) available (14) stale-claim (11) performance (9) testing (9) neural-networks (7) epic (7) integration (7) documentation (6) swarm (6) mcp (5) priority: high (5) wasm (5) priority: critical (4) gpu (4) release (4) high-priority (4) area: core (3) area: mcp (3) coordination (3) ruv-swarm (3) npm (3) priority::high (3) dependencies (2) enterprise::deployment (2) priority::medium (2) v2.0.0 (2) architecture (2)
Pull Request Labels
bug (1) critical (1) security (1)

Packages

  • Total packages: 28
  • Total downloads:
    • npm 48,853 last-month
    • cargo 18,111 total
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 79
  • Total maintainers: 3
npmjs.org: cuda-wasm

High-performance CUDA to WebAssembly/WebGPU transpiler with Rust safety - Run GPU kernels in browsers and Node.js

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 31 Last month
Rankings
Dependent repos count: 24.2%
Average: 29.6%
Dependent packages count: 34.9%
Maintainers (1)
Funding
  • type: github
  • url: https://github.com/sponsors/ruvnet
Last synced: 10 months ago
npmjs.org: ruv-swarm

High-performance neural network swarm orchestration in WebAssembly

  • Versions: 15
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 48,822 Last month
Rankings
Dependent repos count: 24.3%
Average: 29.7%
Dependent packages count: 35.1%
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Forks count: 8.6%
Stargazers count: 11.8%
Dependent repos count: 20.9%
Dependent packages count: 27.7%
Average: 32.7%
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  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 329 Total
Rankings
Dependent repos count: 21.0%
Dependent packages count: 27.8%
Forks count: 39.2%
Stargazers count: 41.6%
Average: 44.9%
Downloads: 94.7%
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crates.io: micro_metrics

Performance monitoring and metrics collection for micro-neural networks

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 472 Total
Rankings
Dependent repos count: 20.6%
Dependent packages count: 27.3%
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crates.io: micro_routing

Dynamic routing and context management for micro-neural networks

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 472 Total
Rankings
Dependent repos count: 20.6%
Dependent packages count: 27.3%
Average: 47.5%
Downloads: 94.6%
Maintainers (1)
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  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 487 Total
Rankings
Dependent repos count: 20.6%
Dependent packages count: 27.3%
Average: 47.5%
Downloads: 94.6%
Maintainers (1)
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Swarm orchestration and coordination for micro-neural networks

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 460 Total
Rankings
Dependent repos count: 20.6%
Dependent packages count: 27.3%
Average: 47.5%
Downloads: 94.6%
Maintainers (1)
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Complete Cartan matrix attention mechanisms with proper Lie algebra structures

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  • Dependent Packages: 0
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Rankings
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Dependent packages count: 27.3%
Average: 47.5%
Downloads: 94.6%
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User-friendly CLI for the Geometric Langlands computational framework

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A comprehensive computational framework for the Geometric Langlands Conjecture with neural-symbolic integration, CLI tools, WASM deployment, and CUDA acceleration

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Core orchestration and agent traits for RUV Swarm

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OpenCV WebAssembly bindings for browser deployment

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OpenCV Core module - fundamental data structures and operations

  • Versions: 2
  • Dependent Packages: 0
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OpenCV SDK with C/C++/Python API compatibility layer

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crates.io: ruvswarm-mcp

Model Context Protocol (MCP) integration for RUV Swarm

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 292 Total
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crates.io: ruv-swarm-ml-training

Advanced ML training pipeline for neuro-divergent models in RUV Swarm

  • Versions: 2
  • Dependent Packages: 0
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crates.io: claude-parser

Claude Code CLI stream-JSON parser for ruv-swarm multi-agent orchestration

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 656 Total
Rankings
Dependent repos count: 21.1%
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Average: 47.9%
Downloads: 94.7%
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crates.io: ruv-swarm-mcp

Model Context Protocol (MCP) integration for RUV Swarm

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 649 Total
Rankings
Dependent repos count: 21.1%
Dependent packages count: 27.9%
Average: 47.9%
Downloads: 94.7%
Maintainers (1)
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crates.io: ruv-swarm-agents

Specialized AI agents for RUV Swarm

  • Versions: 3
  • Dependent Packages: 0
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Rankings
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Dependent packages count: 27.9%
Average: 47.9%
Downloads: 94.7%
Maintainers (1)
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crates.io: ruv-swarm-ml

Machine learning integration for RUV Swarm

  • Versions: 3
  • Dependent Packages: 0
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Dependent packages count: 27.9%
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Downloads: 94.7%
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crates.io: ruv-swarm-transport

Transport layer for RUV-FANN swarm communication with WebSocket and SharedMemory support

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Average: 47.9%
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crates.io: ruv-swarm-persistence

Persistence layer with SQLite and ORM for RUV-Swarm

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Dependent packages count: 27.9%
Average: 47.9%
Downloads: 94.7%
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crates.io: ruv-swarm-core

Core orchestration and agent traits for RUV Swarm

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Average: 47.9%
Downloads: 94.7%
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crates.io: neuro-divergent-models

Neural forecasting models implemented using ruv-FANN for time series forecasting

  • Versions: 1
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  • Downloads: 457 Total
Rankings
Dependent repos count: 21.1%
Dependent packages count: 28.0%
Average: 47.9%
Downloads: 94.7%
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crates.io: neuro-divergent-training

Comprehensive training infrastructure for neural forecasting models

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 334 Total
Rankings
Dependent repos count: 21.1%
Dependent packages count: 28.0%
Average: 47.9%
Downloads: 94.7%
Maintainers (1)
Last synced: 10 months ago
crates.io: ruv-fann

A pure Rust implementation of the Fast Artificial Neural Network (FANN) library

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 1,954 Total
Rankings
Dependent repos count: 21.1%
Dependent packages count: 28.0%
Average: 47.9%
Downloads: 94.7%
Maintainers (1)
Last synced: 10 months ago

Dependencies

.github/workflows/ci.yml actions
  • actions/cache v3 composite
  • actions/checkout v4 composite
  • benchmark-action/github-action-benchmark v1 composite
  • codecov/codecov-action v3 composite
  • dtolnay/rust-toolchain master composite
  • dtolnay/rust-toolchain stable composite
  • dtolnay/rust-toolchain nightly composite
  • peaceiris/actions-gh-pages v3 composite
Cargo.lock cargo
  • 124 dependencies
Cargo.toml cargo
  • approx 0.5 development
  • criterion 0.5 development
  • pretty_assertions 1.4 development
  • proptest 1.4 development
  • bincode 1.3
  • flate2 1.0
  • log 0.4
  • num-traits 0.2
  • num_cpus 1.16
  • rand 0.8
  • rand_distr 0.4
  • rayon 1.8
  • serde 1.0
  • serde_json 1.0
  • thiserror 1.0