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🚀 CodeMode Unified - Now Production Ready!

I'm excited to share CodeMode Unified - a high-performance code execution platform for AI agents that I've been building! 🎉

🎯 What is it?

A dual-architecture service that lets AI agents (like Claude Code) execute JavaScript/TypeScript with full access to MCP tools, APIs, and knowledge systems.

Two ways to use it:

  1. MCP Server Mode - Direct integration with Claude Code
  2. HTTP Backend - REST API for any AI platform

⚡ Key Features

Multi-Runtime Execution

Choose the right runtime for your use case:

  • Bun ⭐ - Full async/await, fastest execution (50-100ms startup)
  • QuickJS - Ultra-lightweight, 5-10ms startup
  • Deno - Secure by default with explicit permissions
  • isolated-vm - V8 isolates for maximum control
  • E2B - Cloud sandboxes for untrusted code

🧠 MCP Tool Integration

Execute code with instant access to your entire MCP ecosystem:

  • AutoMem - AI memory with graph relationships
  • Context7 - Official library documentation
  • Sequential Thinking - Multi-step reasoning
  • HelpScout, WordPress, GitHub - All your MCP servers!

🔒 Production-Ready

  • ✅ 6/6 workflows passing (100% success rate)
  • ✅ 150-300ms execution time
  • ✅ 1000+ requests/second throughput
  • ✅ Sandboxed execution with memory/CPU limits
  • ✅ OAuth 2.1 + JWT authentication

💡 Real-World Example

// AI agent executes this code with full MCP access
const pokemon = await fetch('https://pokeapi.co/api/v2/pokemon/pikachu')
  .then(r => r.json());

// Store in AutoMem knowledge graph
const memory = await mcp.automem.store_memory({
  content: `Pokemon: ${pokemon.name} has ${pokemon.abilities.length} abilities`,
  tags: ['pokemon', 'api-test'],
  importance: 0.8,
  metadata: {
    height: pokemon.height,
    weight: pokemon.weight
  }
});

// Return structured results
return {
  pokemon: pokemon.name,
  memory_id: memory.memory_id,
  abilities: pokemon.abilities.map(a => a.ability.name)
};

Result: Agent can fetch external APIs, store knowledge, and return structured data - all in one execution!

🎬 Quick Start

For Claude Code users:

// Add to .mcp.json
{
  "mcpServers": {
    "codemode-unified": {
      "command": "node",
      "args": ["/path/to/codemode-unified/dist/mcp-server.js"],
      "env": {
        "MCP_CONFIG_PATH": "/path/to/your/.mcp.json"
      }
    }
  }
}

For HTTP API:

npm install
npm run build
npm start  # Runs on http://localhost:3001

🏗️ Architecture Highlights

Dual Service Design

┌─────────────────────────────────────────┐
│         CodeMode Unified                │
├──────────────┬──────────────────────────┤
│  MCP Server  │      HTTP Backend        │
│   (stdio)    │      (port 3001)         │
└──────┬───────┴──────────┬───────────────┘
       │                  │
       └────────┬─────────┘
                ▼
       ┌────────────────┐
       │    Executor    │
       ├────────────────┤
       │ Multi-Runtime  │
       │   • Bun ⭐     │
       │   • QuickJS    │
       │   • Deno       │
       └────────┬───────┘
                │
       ┌────────┴────────┐
       │                 │
       ▼                 ▼
   MCP Tools      Standard APIs
   AutoMem        fetch()
   Context7       console
   Sequential     Date, JSON

📊 Performance Benchmarks

Metric Performance
Startup (Bun) 50-100ms
Startup (QuickJS) 5-10ms
Throughput 1000+ req/sec
Memory/sandbox 5-50MB
Success Rate 100% (6/6 workflows)

🐛 Recently Fixed

Big shoutout to my testing partner for finding these critical bugs:

  1. ✅ Return statement wrapping - Multi-line returns work correctly
  2. ✅ MCP response parsing - Handles both JSON and formatted text
  3. ✅ Config loading - Proper mcpServers→servers mapping
  4. ✅ Process cleanup - No more zombie processes

🎯 Use Cases

✅ API Aggregation Fetch from multiple APIs in parallel, aggregate results

✅ Knowledge Graph Building Store entities and relationships in AutoMem

✅ Research Workflows Use Context7 for docs, Sequential Thinking for analysis

✅ Customer Data Analysis Access HelpScout, analyze patterns, generate insights

✅ Content Operations WordPress integration for content management automation

📚 Resources

  • GitHub: danieliser/codemode-unified
  • Documentation: Full architecture, runtime comparison, MCP setup guide
  • Examples: Pokemon showcase, API aggregation, knowledge graphs
  • License: MIT (open source!)

🙏 Built With

💬 Let's Connect!

Would love to hear your thoughts, feedback, or use cases! Feel free to:

  • Open issues or discussions on GitHub
  • Contribute improvements or runtime implementations
  • Share how you're using it in your AI workflows

Built with ❤️ for the AI agent ecosystem 🤖

Screenshots & Demos

📸 Suggested Screenshots:

  1. Terminal Output - Pokemon workflow execution with colored output
  2. Architecture Diagram - The ASCII art from README
  3. Performance Metrics - Execution times and throughput graph
  4. MCP Config - Sample .mcp.json configuration
  5. Code Sample - The Pokemon example with syntax highlighting
  6. Test Results - 6/6 workflows passing output

🎥 Demo Ideas:

  1. Live execution - Run Pokemon workflow via MCP tool
  2. Performance comparison - Show different runtime speeds
  3. Knowledge graph - Visualize AutoMem associations being created
  4. API aggregation - Parallel Promise.all execution
  5. Error handling - Show graceful failure and recovery

Ready to supercharge your AI agents with code execution? Give it a try! 🚀