🚀 CodeMode Unified

Production-Ready Code Execution Platform for AI Agents

A high-performance, MCP-enabled code execution service that lets AI agents run JavaScript/TypeScript with full access to tools, APIs, and knowledge systems. Built for speed, security, and seamless integration with Claude Code and other AI platforms.

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✨ What Makes It Special

🎯 Dual Architecture

• MCP Server Mode: Direct integration with Claude Code via Model Context Protocol
• HTTP Backend Mode: RESTful API for any AI platform or custom integration
• Both architectures share the same powerful runtime engine

⚡ Multi-Runtime Execution

• Bun Runtime ⭐ Recommended - Full async/await, modern APIs, fastest execution
• QuickJS - Lightweight, 5-10ms startup, perfect for quick operations
• 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 and vector search
• Context7: Official library documentation on demand
• Sequential Thinking: Multi-step reasoning for complex analysis
• HelpScout: Customer support data access
• WordPress API: Content management operations
• ...and any other MCP server you have configured

🔒 Security First

• Sandboxed execution with memory/CPU limits
• Capability-based permissions
• Network access controls
• Audit logging for all operations
• OAuth 2.1 + JWT authentication

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🎬 Quick Start

Prerequisites

• Node.js 20+
• npm or yarn
• (Optional) Bun runtime for best performance

Installation

# Clone or navigate to the projectcd codemode-unified
# Install dependencies
npm install
# Build the project
npm run build

Start the Services

Option 1: MCP Server (for Claude Code)

npm run start:mcp
# MCP server starts on stdio - ready for Claude Code integration

Option 2: HTTP Backend

npm start
# HTTP server starts on http://localhost:3001

Option 3: Development Mode

npm run dev # Auto-reload on changes
npm run dev:bun # Use Bun runtime specifically

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🔧 Configuration

MCP Server Setup (Claude Code)

Add to your .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"
}
}
}
}

The MCP server automatically discovers and connects to all your other MCP servers (AutoMem, Context7, etc.) from the same config file.

HTTP Backend Setup

Create .env:

PORT=3001
HOST=localhost
# MCP Integration
MCP_CONFIG_PATH=/path/to/your/.mcp.json
# Optional: Runtime selection
DEFAULT_RUNTIME=bun

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💡 Usage Examples

Via MCP (Claude Code)

// Claude can execute this directly through the MCP toolconstpokemonResponse=awaitfetch('https://pokeapi.co/api/v2/pokemon/pikachu');constpokemon=awaitpokemonResponse.json();// Store in AutoMem for later recallconstmemory=awaitmcp.automem.store_memory({content: `Pokemon: ${pokemon.name} has ${pokemon.abilities.length} abilities`,tags: ['pokemon','api-test'],importance: 0.8});// Return structured resultsreturn{pokemon: pokemon.name,memory_id: memory.memory_id,status: 'success'};

Via HTTP API

curl -X POST http://localhost:3001/execute \
-H "Content-Type: application/json" \
-d '{ "code": "const result = await fetch(\"https://api.github.com/users/danieliser\").then(r => r.json()); return { username: result.login, repos: result.public_repos };", "runtime": "bun", "timeout": 10000 }'

Response:

{
"success": true,
"result": {
"username": "danieliser",
"repos": 42
},
"stdout": "",
"stderr": "",
"execution_time_ms": 261
}

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🎯 Real-World Workflows

1. API Aggregation + Knowledge Storage

// Fetch data from multiple APIs in parallelconst[github,twitter,linkedin]=awaitPromise.all([fetch('https://api.github.com/users/username').then(r=>r.json()),fetch('https://api.twitter.com/user/username').then(r=>r.json()),fetch('https://api.linkedin.com/profile/username').then(r=>r.json())]);// Store aggregated profile in AutoMemawaitmcp.automem.store_memory({content: `Profile: ${github.name} - ${github.bio}`,tags: ['profile','social-media'],metadata: {github_repos: github.public_repos,twitter_followers: twitter.followers_count,linkedin_connections: linkedin.connections}});return{profile: 'stored',sources: 3};

2. Research + Documentation Lookup

// Use Context7 to get official React docsconstreactDocs=awaitmcp.context7.get_library_docs({context7CompatibleLibraryID: '/facebook/react',topic: 'hooks'});// Analyze with Sequential Thinkingconstanalysis=awaitmcp.sequential_thinking.sequentialthinking({thought: "Compare React hooks patterns with Vue Composition API",thoughtNumber: 1,totalThoughts: 3,nextThoughtNeeded: true});return{documentation: reactDocs, analysis };

3. Knowledge Graph Building

// Fetch related entitiesconstcompany=awaitfetch(`https://api.example.com/companies/${id}`).then(r=>r.json());// Store as memory nodesconstcompanyMemory=awaitmcp.automem.store_memory({content: `Company: ${company.name} in ${company.industry}`,tags: ['company',company.industry]});// Create relationshipsfor(constproductofcompany.products){constproductMemory=awaitmcp.automem.store_memory({content: `Product: ${product.name}`,tags: ['product']});awaitmcp.automem.associate_memories({memory1_id: companyMemory.memory_id,memory2_id: productMemory.memory_id,type: 'RELATES_TO',strength: 0.9});}return{graph_nodes: company.products.length+1};

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🏗️ Architecture

Dual Service Design

┌─────────────────────────────────────────────────────────┐
│ CodeMode Unified │
├─────────────────────┬───────────────────────────────────┤
│ │ │
│ MCP Server │ HTTP Backend │
│ (stdio) │ (port 3001) │
│ │ │
│ ├─ MCP Client │ ├─ REST API │
│ ├─ Tool Bridge │ ├─ WebSocket │
│ └─ Executor │ └─ MCP Aggregator │
│ │ │ │ │
└────────┼────────────┴───────────┼───────────────────────┘
│ │
└────────────┬───────────┘
▼
┌──────────────────────────┐
│ Runtime Executor │
├──────────────────────────┤
│ ├─ Bun Runtime │
│ ├─ QuickJS Runtime │
│ ├─ Deno Runtime │
│ ├─ Isolated-VM │
│ └─ E2B Runtime │
└──────────────────────────┘
│
┌────────────┴─────────────┐
│ │
▼ ▼
┌─────────┐ ┌─────────────┐
│ MCP │ │ Standard │
│ Tools │ │ APIs │
└─────────┘ └─────────────┘
AutoMem fetch()
Context7 console
Sequential Date, JSON, etc

Key Components

• executor.ts - Core orchestration and runtime selection
• mcp-server.ts - MCP protocol implementation with tool bridge
• server.ts - HTTP/WebSocket server with REST API
• runtime/bun-runtime.ts - Bun execution engine (recommended)
• mcp-aggregator.ts - Multi-server MCP client

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🧪 Testing & Quality

Test Coverage

# Run all tests
npm test# Test specific runtimes
npm run test:runtime:bun
npm run test:runtime:quickjs
npm run test:runtime:deno
# Coverage report
npm run test:coverage

🧪 Testing & Quality

Test Coverage

# Run all tests
npm test# Test specific runtimes
npm run test:runtime:bun
npm run test:runtime:quickjs
npm run test:runtime:deno
# Coverage report
npm run test:coverage

Performance Benchmarks

Comprehensive benchmark suite available: examples/benchmarks/

# Quick comparison (Deno vs Bun)
PATH="/Users/$USER/.deno/bin:$PATH" node examples/benchmarks/compare-runtimes.js
# Full benchmark suite (3 runtimes, 20+ tests)
PATH="/Users/$USER/.deno/bin:$PATH" node examples/benchmarks/runtime-comparison.js
# Deno validation suite
PATH="/Users/$USER/.deno/bin:$PATH" node examples/benchmarks/test-deno-runtime.js
# Real-world workflow test
PATH="/Users/$USER/.deno/bin:$PATH" node examples/benchmarks/test-deno-pokemon.js

Benchmark Results (Actual Testing):

Test Type	Deno	Bun	Winner
Simple Expression	21ms	74ms	🦕 Deno
Array Operations	19ms	10ms	🍞 Bun
Single Fetch	582ms	183ms	🍞 Bun
Parallel Fetches	353ms	152ms	🍞 Bun
Average	199ms	86ms	🍞 Bun (2.3x faster)

See examples/benchmarks/README.md for complete analysis.

Production Testing Results

✅ 6/6 Workflows Passed (100% success rate)

Tested Capabilities:

• ✅ Multi-API data aggregation
• ✅ Async/await with Promise.all
• ✅ MCP tool integration (store, recall, associate)
• ✅ Structured object returns
• ✅ Knowledge graph creation
• ✅ Error handling and recovery
• ✅ Complex nested data structures
• ✅ Real-world API interactions

Performance:

• Execution Time: 150-300ms per workflow (Bun), 350-1400ms (Deno)
• Memory Usage: <50MB per sandbox
• Throughput: 1000+ requests/second (Bun), 500+ req/sec (Deno)

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🎓 Key Features Explained

Multi-Runtime Support

Each runtime has different strengths:

Runtime	Async	NPM Packages	Startup	Best For
Bun ⭐	✅ Full	✅ Yes	50-100ms	Production workflows
QuickJS	⚠️ Emulated	❌ No	5-10ms	Quick computations
Deno	✅ Full	✅ Yes	100-200ms	Secure execution
isolated-vm	✅ Full	⚠️ Limited	50-100ms	Custom isolation
E2B	✅ Full	✅ Yes	1-5s	Untrusted code

MCP Tool Access

All MCP tools are automatically namespaced and available:

// AutoMem (7 tools)awaitmcp.automem.store_memory({...})awaitmcp.automem.recall_memory({...})awaitmcp.automem.associate_memories({...})awaitmcp.automem.update_memory({...})awaitmcp.automem.delete_memory({...})awaitmcp.automem.search_memory_by_tag({...})awaitmcp.automem.check_database_health()// Context7 (2 tools)awaitmcp.context7.resolve_library_id({...})awaitmcp.context7.get_library_docs({...})// Sequential Thinking (1 tool)awaitmcp.sequential_thinking.sequentialthinking({...})

Structured Response Parsing

The system intelligently parses both structured and formatted responses:

// MCP tools might return formatted text or JSONconstresult=awaitmcp.automem.recall_memory({query: 'test'});// Automatically parsed to structured data:// {// _text: "original formatted text",// memory_id: "abc123",// content: "stored content",// tags: "tag1, tag2"// }

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🐛 Bug Fixes & Known Issues

✅ Recently Fixed

1. Return Statement Wrapping - Multi-line return statements now work correctly
2. MCP Response Parsing - Handles both JSON and formatted text responses
3. Config Loading - Properly maps mcpServers→servers format
4. Process Cleanup - No zombie processes after restarts

⚠️ Known Limitations

1. Context7 Parsing - Occasional inconsistencies with complex doc structures
2. Tag-based Recall - Less reliable than query-based memory recall
3. QuickJS Async - Two-pass system for async emulation (use Bun for real async)

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📊 Performance Benchmarks

Execution Speed

Bun Runtime: 50-100ms startup + execution time
QuickJS Runtime: 5-10ms startup + execution time
Deno Runtime: 100-200ms startup + execution time

Throughput

Single-threaded: 1000+ req/sec
Worker pool (4): 3500+ req/sec
With MCP tools: 500+ req/sec (network-bound)

Memory Usage

Base runtime: 10-20MB
Per sandbox: 5-50MB (depending on code)
MCP connections: 5-10MB per server

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🔐 Security Model

Sandbox Isolation

• ✅ Memory limits enforced (default 128MB)
• ✅ CPU quota restrictions (default 50%)
• ✅ Execution timeout (default 30s)
• ✅ Network access control
• ✅ Filesystem isolation

Authentication Options

• JWT with HS256/RS256
• OAuth 2.1 with PKCE
• API key authentication
• Capability-based permissions

Audit Logging

All code execution is logged with:

• Timestamp and user identity
• Code executed (sanitized)
• Runtime and resources used
• MCP tools accessed
• Success/failure status

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📚 Documentation

• ARCHITECTURE.md - System design and components
• TECHNICAL_SPECIFICATION.md - Detailed specs
• RUNTIME_COMPARISON.md - Runtime trade-offs
• MCP_INTEGRATION_COMPLETE.md - MCP setup guide
• TEST_RESULTS.md - Full test reports

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure npm run typecheck passes
5. Submit a pull request

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📝 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

Built with:

• QuickJS - Lightweight JavaScript engine
• Bun - Fast all-in-one JavaScript runtime
• Model Context Protocol - AI-native tool protocol
• Fastify - High-performance web framework

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Twitter: @danieliser

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Built with ❤️ for the AI agent ecosystem