npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2026 – Pkg Stats / Ryan Hefner

mehaisi-codeswarm

v1.0.0

Published

Mehaisi CodeSwarm: State-of-the-art multi-agent AI orchestration system for code quality, security, and performance optimization. Powered by SONA self-learning and vector memory.

Readme


📖 Table of Contents


✨ Key Features

🧠 Self-Learning Intelligence (SONA)

  • Adaptive Routing: System learns from every interaction and improves over time.
  • Automatic Weight Optimization: Adjusts routing algorithm based on real outcomes.
  • Capability Discovery: Learns new agent capabilities from successful completions.
  • Privacy-First: Captures outcomes, not sensitive data.

🎯 Intelligent Agent Routing

  • Smart Agent Selection: Automatically picks the best agent for any task.
  • Multi-Factor Scoring: Combines capability matching, semantic similarity, and success history.
  • Confidence Scoring: Transparent reasoning with confidence percentages.

⚡ Parallel Execution

  • High Concurrency: Execute up to 3 agents simultaneously.
  • Smart Queue Management: Automatic backpressure and rate limiting.
  • Hard Safety Limits: Circuit breakers and 10-minute timeouts.

🔌 Multi-Provider Support

  • Ollama Cloud & Local: Seamless switching between cloud and local models.
  • Claude Code Integration: Leverage Claude's advanced reasoning capabilities.
  • Smart Credentials: Interactive setup with secure storage.

🧠 Why Mehaisi CodeSwarm?

Traditional code quality tools are static. Mehaisi CodeSwarm is dynamic:

  1. It Learns: Unlike static linters, Mehaisi CodeSwarm remembers what worked and what didn't.
  2. It Coordinates: Agents don't work in silos; they share findings through a central hub.
  3. It's Safe: Built-in Git-based rollbacks and test validation ensure your code never breaks.
  4. It's Fast: Parallel execution allows for rapid analysis and fixing of large repositories.

📐 Architecture

graph TD
    User([User Task]) --> Router{Intelligent Router}
    Router -->|Confidence Scored| A[Agent 1]
    Router -->|Confidence Scored| B[Agent 2]
    Router -->|Confidence Scored| C[Agent 3]
    
    subgraph "Execution Layer"
        A & B & C --> Hub[Coordination Hub]
    end
    
    Hub --> Memory[(Vector Memory)]
    Hub --> SONA[SONA Learning Engine]
    
    SONA -->|Optimize Weights| Router
    Memory -->|Semantic Search| Router
    
    Hub --> Results[Final Resolution]

🚀 Quick Start

# 1. Initialize in your project
codeswarm init

# 2. Setup credentials interactively
codeswarm credentials

# 3. Get intelligent agent recommendation
codeswarm recommend "Fix security vulnerabilities in my API"

# 4. Run investigation workflow
codeswarm workflow investigate

🛠️ Installation

Prerequisites

  • Node.js (v16+)
  • Ollama (for local/cloud models)
  • Claude Code (optional, for advanced reasoning)
  • Git

Install via NPM

npm install -g mehaisi-codeswarm

From Source

git clone https://github.com/O96a/codeswarm.git
cd codeswarm
npm install
npm link

⚙️ Configuration

Interactive Setup

Mehaisi CodeSwarm features an intelligent configuration manager. No need to manually edit JSON files.

codeswarm config --interactive

Credential Management

Securely manage your API keys for Ollama Cloud and Claude Code.

codeswarm credentials

🤖 Agent Types

Mehaisi CodeSwarm comes with 19 specialized agents:

| Category | Agents | | :--- | :--- | | Investigators | api-detective, ui-inspector, security-scanner, accessibility-auditor | | Fixers | api-connector, event-binder, responsive-engineer, refactor-master | | Builders | test-writer, performance-optimizer, documentation-writer | | QA | integration-validator, stress-tester, production-checker |


🧪 Testing

Mehaisi CodeSwarm is built with reliability in mind.

# Run the full test suite
npm test

Current Status: 258/276 tests passing (94%) - All critical paths verified ✅.


🛡️ Safety

  • Git-based Rollback: Every change happens in an isolated branch.
  • Human Approval: High-risk changes always require confirmation.
  • Test-Driven: Changes are only applied if tests pass.
  • Token Budgets: Prevent runaway costs and API usage.

🔮 Roadmap

  • [x] Phase A: Foundation & Multi-provider support.
  • [x] Phase B: Intelligence & Vector Memory.
  • [x] Phase C: SONA Self-Learning & Optimization.
  • [ ] Phase D: Web Dashboard & Real-time Monitoring.
  • [ ] Phase E: Agent Marketplace & Community Plugins.

🤝 Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

See CONTRIBUTING.md for more details.


📜 License

Distributed under the MIT License. See LICENSE for more information.