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Ruv Swarm MCP server

by ruvnet·io.github.ruvnet/ruv-swarm·v1.0.19·381 stars
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Neural network swarm orchestration with WebAssembly acceleration and MCP integration

C62/100grade C
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Safety scan
C62/100

full report

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381 stars

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If you have run it, two minutes of your experience saves the next person an afternoon.

Ruv Swarm tools (25, 4 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • agent_list

    List all active agents in the swarm

  • agent_metrics

    Get performance metrics for agents

  • agent_spawn

    Spawn a new agent in the swarm

  • benchmark_runwrite action

    Execute performance benchmarks

  • daa_agent_adapt

    Trigger agent adaptation based on feedback

  • daa_agent_createwrite action

    Create an autonomous agent with DAA capabilities

  • daa_cognitive_pattern

    Analyze or change cognitive patterns for agents

  • daa_init

    Initialize DAA (Decentralized Autonomous Agents) service

  • daa_knowledge_share

    Share knowledge between autonomous agents

  • daa_learning_status

    Get learning progress and status for DAA agents

  • daa_meta_learning

    Enable meta-learning capabilities across domains

  • daa_performance_metrics

    Get comprehensive DAA performance metrics

  • daa_workflow_createwrite action

    Create an autonomous workflow with DAA coordination

  • daa_workflow_executewrite action

    Execute a DAA workflow with autonomous agents

  • features_detect

    Detect runtime features and capabilities

  • memory_usage

    Get current memory usage statistics

  • neural_patterns

    Get cognitive pattern information

  • neural_status

    Get neural agent status and performance metrics

  • neural_train

    Train neural agents with sample tasks

  • swarm_init

    Initialize a new swarm with specified topology

  • swarm_monitor

    Monitor swarm activity in real-time

  • swarm_status

    Get current swarm status and agent information

  • task_orchestrate

    Orchestrate a task across the swarm

  • task_results

    Retrieve results from completed tasks

  • task_status

    Check progress of running tasks

Public scan report

scanner v0.1.10 · 2026-10-01 · same rubric, same numbers if you re-run it

1 high1 medium
  • Code scan78 source files scanned8/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitylocal package, no credentials required12/15
  • Maintenancelast push 54 days ago12/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Findings (2)

  • highShell command built from a string (injection risk)exec.shell-concat
    src/github-coordinator/gh-cli-coordinator.js: …s.config.labelPrefix}${swarmId}`; execSync(`gh issue edit ${issueNumber} --repo ${this.config.owner}/${this.config.repo} --add-label "${label}"`, { stdio: 'ignore' }); /…
  • mediumeval / new Function usedexec.eval
    wasm/ruv_swarm_wasm.js: …ction(arg0, arg1) { const ret = new Function(getStringFromWasm0(arg0, arg1)); …
Overall 62/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

Install Ruv Swarm in Claude Code, Cursor or VS Code

Runs npx -y ruv-swarm on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add ruv-swarm -- npx -y ruv-swarm
Add to Cursor

What the publisher says

From the Ruv Swarm repository's README, as published. We do not edit it. Read it on GitHub

ruv-FANN: The Neural Intelligence Framework 🧠

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

# 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

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Ruv Swarm: common questions

Is Ruv Swarm MCP server safe?
With care: it is graded C, so read the findings first (62/100). Read the Ruv Swarm safety report
How do I install Ruv Swarm?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Ruv Swarm need an API key?
No secret keys are declared. It reads 3 settings from the environment.
Is Ruv Swarm maintained?
The last commit was 65 days ago (2026-08-09). The latest release is v1.0.19.

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