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🚀 CODITECT Cloud IDE - Complete Integration Guide

theia + MCP + CODITECT + FoundationDB + Socket.IO

TABLE OF CONTENTS

  1. System Overview
  2. Architecture Deep Dive
  3. Component Descriptions
  4. Setup Guide (Docker Compose)
  5. Setup Guide (Kubernetes + NPM)
  6. MCP Server Development
  7. CODITECT Agent Development
  8. Workflow Configuration
  9. Monitoring & Operations
  10. Production Deployment Checklist

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1. SYSTEM OVERVIEW

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What is CODITECT?​

CODITECT is a cloud-based AI-powered development environment that combines:

theia IDE - Browser-based VS Code-like interface MCP Servers - Model Context Protocol for llm integrations CODITECT Monitor - File watching and change management CODITECT Command - Multi-agent orchestration system FoundationDB - Fast, distributed state storage Socket.IO - Real-time bidirectional communication

Think of it as: GitHub Codespaces + Cursor + Claude + Multi-agent orchestration

Key Capabilities​

✅ Browser-Based IDE - Full development environment in the browser ✅ Multi-llm Integration - OpenAI, Anthropic, Google via unified MCP interface ✅ Agentic Workflows - AI agents that can code, test, review, document ✅ Real-Time Collaboration - Live file syncing and agent status updates ✅ Automatic Versioning - Every file change tracked and stored ✅ Distributed Execution - Agents run across Kubernetes pods ✅ Extensible - Add custom agents, skills, tools, and workflows

Use Cases​

👨‍💻 Solo Developer - AI pair programming with multiple llms 👥 Team Development - Collaborative coding with shared agents 🏢 Enterprise - Standardized dev environments with compliance 🎓 Education - Teaching platform with auto-grading agents 🔬 Research - Experiment tracking and reproducible environments

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2. ARCHITECTURE DEEP DIVE

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Data Flow: User Action → llm Response​

  1. User edits file in theia IDE ├─> Browser sends change via WebSocket └─> Socket.IO Gateway receives event

  2. Socket.IO broadcasts to all subscribers ├─> CODITECT Monitor (in theia pod) receives notification ├─> Watching agents receive notification └─> Other users' browsers receive notification (if shared workspace)

  3. CODITECT Monitor processes change ├─> Detects file modification via inotify ├─> Generates diff from previous version ├─> Stores in FoundationDB (transactional) ├─> Publishes to Redis (for real-time sync) └─> Triggers configured workflows

  4. Workflow executes (example: code review) ├─> CODITECT Command starts workflow ├─> Code Analyzer Agent → calls MCP Analysis Server │ └─> MCP Server uses tools (AST parsing, complexity analysis) ├─> Security Scanner Agent → calls MCP Security Server │ └─> MCP Server scans for vulnerabilities └─> Claude Review Agent → calls MCP Anthropic Server └─> MCP Server sends prompt to Claude API with context

  5. Claude streams response ├─> MCP Anthropic Server receives chunks from Claude ├─> Forwards to CODITECT Command ├─> CODITECT Command publishes to Redis ├─> Socket.IO Gateway sends to user's browser └─> theia IDE renders in status bar/panel

  6. Results stored and displayed ├─> Review results saved to FoundationDB ├─> User sees real-time feedback in IDE └─> Report available in workspace history

Component Communication Matrix​

┌─────────────┬─────────┬────────┬───────┬─────┬─────┬─────────┐ │ Component │ theia │ Socket │ MCP │ FDB │ Redis│ Monitor │ ├─────────────┼─────────┼────────┼───────┼─────┼─────┼─────────┤ │ theia │ - │ WS │ HTTP │ ✓ │ ✓ │ Internal│ │ Socket.IO │ WS │ - │ - │ ✓ │ ✓ │ - │ │ MCP Servers │ HTTP │ - │ - │ ✓ │ ✓ │ - │ │ FoundationDB│ ✓ │ ✓ │ ✓ │ - │ - │ ✓ │ │ Redis │ ✓ │ ✓ │ ✓ │ - │ - │ ✓ │ │ CODITECT Mon│ Internal│ WS │ - │ ✓ │ ✓ │ - │ │ CODITECT Cmd│ Internal│ WS │ HTTP │ ✓ │ ✓ │ Internal│ └─────────────┴─────────┴────────┴───────┴─────┴─────┴─────────┘

FoundationDB Schema Design​

# User workspaces
workspace:{workspace_id} = {
    owner_id, name, created_at, settings
}

# File contents (versioned)
file:{workspace_id}:{path}:{version} = {
    content, size, mime_type, timestamp
}

# File metadata
file-meta:{workspace_id}:{path} = {
    current_version, versions[], last_modified, permissions
}

# Change log (time-series)
change:{workspace_id}:{timestamp}:{change_id} = {
    type, path, diff, user_id, agent_id
}

# Agent states
agent:{agent_id}:state = {
    status, current_task, progress, last_active
}

# Workflow definitions
workflow:{workflow_id} = {
    name, trigger, steps[], enabled
}

# Execution logs
execution:{workflow_id}:{timestamp}:{run_id} = {
    status, steps[], results[], duration, errors[]
}

# MCP configurations
mcp:{server_name}:config = {
    url, tools[], resources[], prompts[]
}

# User sessions
session:{user_id}:{session_id} = {
    workspace_id, connected_at, pod_name, socket_id
}

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3. COMPONENT DESCRIPTIONS

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theia IDE Backend​

Technology: Node.js, TypeScript, Eclipse theia framework Responsibilities:

  • Serve Monaco editor to browser
  • Handle file system operations
  • Manage terminal sessions (xterm.js)
  • Git integration
  • Extension host for plugins
  • WebSocket server for frontend

Key Files:

theia-backend/
├── src/
│   ├── server.ts           # Main theia server
│   ├── filesystem.ts       # File operations
│   ├── terminal.ts         # terminal management
│   └── extensions/         # theia plugins
├── coditect/
│   ├── monitor.py          # File watcher
│   ├── command.py          # Agent orchestrator
│   ├── agents/             # Agent implementations
│   ├── skills/             # Reusable capabilities
│   └── workflows/          # Workflow definitions
└── package.json

CODITECT Monitor​

Technology: Python, watchdog (file watching), asyncio Responsibilities:

  • Watch all files in /workspace recursively
  • Detect changes (create, modify, delete, rename)
  • Generate diffs using Git-like algorithms
  • Store changes in FoundationDB with timestamps
  • Broadcast events via Socket.IO
  • Collect metrics (file access patterns, edit frequency)
  • Alert on suspicious activity

Event Types:

{
    'event': 'file-created',
    'path': '/workspace/src/app.py',
    'timestamp': 1234567890.123,
    'user_id': 'user-123',
    'size': 1024
}

{
    'event': 'file-modified',
    'path': '/workspace/src/app.py',
    'timestamp': 1234567891.456,
    'user_id': 'user-123',
    'diff': '+def new_function():\n+    pass\n',
    'lines_added': 2,
    'lines_removed': 0
}

{
    'event': 'file-deleted',
    'path': '/workspace/test.txt',
    'timestamp': 1234567892.789,
    'user_id': 'user-123'
}

CODITECT Command​

Technology: Python, asyncio, custom orchestration framework Responsibilities:

  • Load agent definitions from config
  • Execute workflows triggered by events
  • Coordinate multiple agents (parallel, sequential, conditional)
  • Manage agent lifecycle (start, pause, resume, stop)
  • Handle errors and retries
  • Track execution metrics
  • Store results in FoundationDB

Workflow Execution Model:

class Workflow:
    def __init__(self, workflow_id, steps):
        self.workflow_id = workflow_id
        self.steps = steps
        self.state = WorkflowState()
    
    async def execute(self):
        for step in self.steps:
            if step.type == 'agent':
                agent = self.load_agent(step.agent_name)
                result = await agent.execute(
                    action=step.action,
                    params=step.params,
                    context=self.state.context
                )
                self.state.add_result(step.name, result)
            
            elif step.type == 'condition':
                if self.evaluate_condition(step.condition):
                    await self.execute_branch(step.if_branch)
                else:
                    await self.execute_branch(step.else_branch)
            
            elif step.type == 'parallel':
                results = await asyncio.gather(
                    *[self.execute_step(s) for s in step.steps]
                )
                self.state.add_results(step.name, results)
        
        return self.state

MCP Servers​

Technology: Node.js (MCP SDK), Python (FastMCP) Responsibilities:

  • Expose tools, resources, prompts to llms
  • Handle tool calls from agents
  • Return structured responses
  • Manage rate limiting
  • Cache responses where appropriate

Example MCP Server (Analysis):

import { Server } from '@modelcontextprotocol/sdk/server';

const server = new Server({
    name: 'code-analysis-mcp',
    version: '1.0.0'
});

// Define tools
server.setRequestHandler('tools/list', async () => ({
    tools: [
        {
            name: 'analyze_complexity',
            description: 'Calculate code complexity metrics',
            inputSchema: {
                type: 'object',
                properties: {
                    file_path: { type: 'string' },
                    metrics: { 
                        type: 'array',
                        items: { 
                            type: 'string',
                            enum: ['cyclomatic', 'cognitive', 'halstead']
                        }
                    }
                },
                required: ['file_path']
            }
        },
        {
            name: 'find_code_patterns',
            description: 'Search for code patterns or anti-patterns',
            inputSchema: {
                type: 'object',
                properties: {
                    directory: { type: 'string' },
                    pattern_type: { 
                        type: 'string',
                        enum: ['singleton', 'factory', 'observer', 'god-class']
                    }
                },
                required: ['directory', 'pattern_type']
            }
        }
    ]
}));

// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
    const { name, arguments: args } = request.params;
    
    switch (name) {
        case 'analyze_complexity':
            const code = fs.readFileSync(args.file_path, 'utf8');
            const ast = parse(code);
            const metrics = calculateMetrics(ast, args.metrics);
            return {
                content: [{
                    type: 'text',
                    text: JSON.stringify(metrics, null, 2)
                }]
            };
        
        case 'find_code_patterns':
            const patterns = await findPatterns(
                args.directory,
                args.pattern_type
            );
            return {
                content: [{
                    type: 'text',
                    text: JSON.stringify(patterns, null, 2)
                }]
            };
    }
});

Socket.IO Gateway​

Technology: Node.js, Socket.IO, Redis adapter Responsibilities:

  • WebSocket connections from browsers
  • Real-time event broadcasting
  • Room management (workspace rooms)
  • Session affinity tracking
  • Message routing between services
  • Connection state management

Event Routing:

io.on('connection', (socket) => {
    // User joins their workspace
    socket.on('join-workspace', async (workspaceId) => {
        socket.join(`workspace:${workspaceId}`);
        
        // Load workspace state from FoundationDB
        const state = await getworkspaceState(workspaceId);
        socket.emit('workspace-state', state);
        
        // Notify others in workspace
        socket.to(`workspace:${workspaceId}`).emit('user-joined', {
            userId: socket.userId,
            username: socket.username
        });
    });
    
    // File change from theia
    socket.on('file-changed', async (data) => {
        // Store in FoundationDB
        await saveFileChange(data);
        
        // Broadcast to workspace
        io.to(`workspace:${data.workspaceId}`)
          .emit('file-changed', data);
        
        // Notify monitoring agents
        io.to(`agents:watching:${data.path}`)
          .emit('file-changed', data);
    });
    
    // Agent status update
    socket.on('agent-status', (data) => {
        io.to(`workspace:${data.workspaceId}`)
          .emit('agent-status', data);
    });
    
    // llm streaming
    socket.on('llm-stream-chunk', (data) => {
        socket.to(`workspace:${data.workspaceId}`)
              .emit('llm-chunk', data);
    });
});

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4. SETUP GUIDE (DOCKER COMPOSE)

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Prerequisites​

  • Docker & Docker Compose
  • Domain name with DNS access
  • API keys for llm providers
  • VPS with:
    • 8GB RAM minimum (16GB recommended)
    • 4 CPU cores minimum
    • 50GB disk space

Step 1: Project Structure​

mkdir coditect-cloud-ide
cd coditect-cloud-ide

# Create directory structure
mkdir -p theia-coditect/src
mkdir -p theia-coditect/coditect/{agents,skills,workflows}
mkdir -p socketio-gateway
mkdir -p mcp-servers/{analysis,security,git,anthropic,openai}
mkdir -p coditect-config
mkdir -p grafana/{dashboards,datasources}
mkdir -p prometheus

Step 2: Environment Variables​

cat > .env <<EOF
# NPM Database
NPM_DB_PASSWORD=npm_secure_password_$(openssl rand -hex 16)
NPM_DB_ROOT_PASSWORD=root_secure_password_$(openssl rand -hex 16)

# llm API Keys
ANTHROPIC_API_KEY=sk-ant-your-key-here
OPENAI_API_KEY=sk-your-key-here
GOOGLE_API_KEY=AIza-your-key-here

# Git Configuration
GIT_USER_NAME=CODITECT
GIT_USER_EMAIL=coditect@example.com

# Monitoring
GRAFANA_PASSWORD=secure_grafana_password
EOF

chmod 600 .env

Step 3: Copy Configuration Files​

Copy the docker-compose-theia-coditect.yml to docker-compose.yml

Step 4: Build Docker Images​

theia + CODITECT Dockerfile:

# theia-coditect/Dockerfile
FROM node:18-bookworm

# Install system dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    git \
    curl \
    wget \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Install FoundationDB client
RUN curl -L https://github.com/apple/foundationdb/releases/download/7.1.27/foundationdb-clients_7.1.27-1_amd64.deb \
    -o fdb-clients.deb && \
    dpkg -i fdb-clients.deb && \
    rm fdb-clients.deb

# Install theia
WORKDIR /theia
COPY package.json ./
RUN yarn install --production && \
    yarn theia build

# Install CODITECT dependencies
WORKDIR /coditect
COPY coditect/requirements.txt ./
RUN pip3 install --no-cache-dir -r requirements.txt

# Copy application code
COPY src/ /theia/src/
COPY coditect/ /coditect/

# Create workspace directory
RUN mkdir -p /workspace

EXPOSE 3030

# Start script runs both theia and CODITECT
COPY start.sh /
RUN chmod +x /start.sh

CMD ["/start.sh"]

Start Script:

# theia-coditect/start.sh
#!/bin/bash
set -e

# Start CODITECT Monitor in background
python3 /coditect/monitor.py &
MONITOR_PID=$!

# Start CODITECT Command in background
python3 /coditect/command.py &
COMMAND_PID=$!

# Start theia IDE (foreground)
node /theia/src-gen/backend/main.js --hostname=0.0.0.0 --port=3030 &
THEIA_PID=$!

# Wait for any process to exit
wait -n

# Exit with status of process that exited first
exit $?

Step 5: Start Services​

# Start core services
docker-compose up -d

# Check status
docker-compose ps

# View logs
docker-compose logs -f

# Wait for services to be healthy
docker-compose ps | grep healthy

Step 6: Configure NPM​

  1. Access NPM Admin UI: http://YOUR_SERVER_IP:81

  2. Login with default credentials:

  3. Change password immediately!

  4. Add Proxy Host for theia IDE:

    • Domain: ide.example.com
    • Forward to: theia-ide:3030
    • Enable WebSockets Support
    • Request SSL Certificate

  5. Add Proxy Host for Socket.IO:

    • Domain: ws.example.com
    • Forward to: socketio-gateway:3000
    • Enable WebSockets Support (CRITICAL!)
    • Add custom config (see docker-compose.yml)
    • Request SSL Certificate

Step 7: Test Access​

# Test theia IDE
curl https://ide.example.com

# Test Socket.IO
curl https://ws.example.com/health

# Test MCP servers
docker-compose exec theia-ide curl http://mcp-analysis:8001/health

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5. SETUP GUIDE (KUBERNETES + NPM)

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See kubernetes-theia-coditect.yaml for complete configuration.

Key Differences from Docker Compose​

Multi-User Support:

  • Each user gets their own theia pod (StatefulSet)
  • workspace controller manages pod lifecycle
  • Auto-shutdown idle workspaces

Scalability:

  • MCP servers scale independently
  • Socket.IO gateway with HPA
  • FoundationDB cluster mode

Isolation:

  • Network policies between components
  • Resource quotas per user
  • RBAC for workspace controller

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6. MCP SERVER DEVELOPMENT

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Creating a Custom MCP Server​

Example: Database Query MCP Server

// mcp-servers/database/server.js
import { Server } from '@modelcontextprotocol/sdk/server';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio';
import { Pool } from 'pg';

const db = new Pool({
    connectionString: process.env.DATABASE_URL
});

const server = new Server({
    name: 'database-query-mcp',
    version: '1.0.0'
}, {
    capabilities: {
        tools: {},
        resources: {}
    }
});

// Define tools
server.setRequestHandler('tools/list', async () => ({
    tools: [
        {
            name: 'execute_query',
            description: 'Execute a SELECT query on the database',
            inputSchema: {
                type: 'object',
                properties: {
                    query: { 
                        type: 'string',
                        description: 'SQL SELECT query to execute'
                    },
                    limit: {
                        type: 'integer',
                        default: 100,
                        maximum: 1000
                    }
                },
                required: ['query']
            }
        },
        {
            name: 'get_schema',
            description: 'Get database schema information',
            inputSchema: {
                type: 'object',
                properties: {
                    table_name: {
                        type: 'string',
                        description: 'Specific table name (optional)'
                    }
                }
            }
        }
    ]
}));

// Handle tool calls
server.setRequestHandler('tools/call', async (request) => {
    const { name, arguments: args } = request.params;
    
    try {
        switch (name) {
            case 'execute_query':
                // Validate query is SELECT only
                if (!args.query.trim().toUpperCase().startsWith('SELECT')) {
                    throw new Error('Only SELECT queries are allowed');
                }
                
                const result = await db.query(
                    args.query,
                    { rowMode: 'array', limit: args.limit }
                );
                
                return {
                    content: [{
                        type: 'text',
                        text: JSON.stringify({
                            rows: result.rows,
                            rowCount: result.rowCount,
                            fields: result.fields.map(f => f.name)
                        }, null, 2)
                    }]
                };
            
            case 'get_schema':
                const schema = await db.query(`
                    SELECT 
                        table_name,
                        column_name,
                        data_type,
                        is_nullable
                    FROM information_schema.columns
                    WHERE table_schema = 'public'
                    ${args.table_name ? "AND table_name = $1" : ""}
                    ORDER BY table_name, ordinal_position
                `, args.table_name ? [args.table_name] : []);
                
                return {
                    content: [{
                        type: 'text',
                        text: JSON.stringify(schema.rows, null, 2)
                    }]
                };
        }
    } catch (error) {
        return {
            content: [{
                type: 'text',
                text: `Error: ${error.message}`
            }],
            isError: true
        };
    }
});

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Testing MCP Server​

# Test with MCP Inspector
npx @modelcontextprotocol/inspector node server.js

# Test tool listing
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | \
  node server.js

# Test tool call
echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_schema","arguments":{}}}' | \
  node server.js

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7. CODITECT AGENT DEVELOPMENT

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Creating a Custom Agent​

Example: Automated Test Generator

# coditect/agents/test_generator.py
from coditect.agent import Agent, AgentResult
from coditect.mcp import MCPClient
import asyncio

class TestGeneratorAgent(Agent):
    """Generates unit tests for Python functions"""
    
    def __init__(self, config):
        super().__init__(config)
        self.mcp_client = MCPClient(config['mcp_server_url'])
        self.model = config.get('model', 'claude-sonnet-4')
    
    async def execute(self, action: str, params: dict) -> AgentResult:
        """Execute agent action"""
        if action == 'generate_tests':
            return await self.generate_tests(params['file_path'])
        elif action == 'run_tests':
            return await self.run_tests(params['test_file'])
        else:
            raise ValueError(f"Unknown action: {action}")
    
    async def generate_tests(self, file_path: str) -> AgentResult:
        """Generate unit tests for a Python file"""
        
        # Read the source file
        with open(file_path, 'r') as f:
            source_code = f.read()
        
        # Use MCP to analyze the code
        analysis = await self.mcp_client.call_tool(
            'analyze_functions',
            {'file_path': file_path}
        )
        
        # Build prompt for llm
        prompt = f"""Generate comprehensive unit tests for the following Python code.

Source code:
```python
{source_code}

Code analysis: {analysis}

Requirements:

  • Use pytest framework
  • Cover happy path and edge cases
  • Include docstrings
  • Mock external dependencies
  • Aim for 80%+ coverage

Generate only the test code, no explanations."""

    # Call llm via MCP
    response = await self.mcp_client.call_llm(
        model=self.model,
        prompt=prompt,
        max_tokens=4000
    )
    
    # Extract test code from response
    test_code = self.extract_code_block(response)
    
    # Save test file
    test_file_path = file_path.replace('.py', '_test.py')
    test_file_path = test_file_path.replace('/src/', '/tests/')
    
    with open(test_file_path, 'w') as f:
        f.write(test_code)
    
    # Run tests to verify they work
    test_result = await self.run_tests(test_file_path)
    
    return AgentResult(
        success=test_result.success,
        data={
            'test_file': test_file_path,
            'test_code': test_code,
            'test_result': test_result.data
        },
        message=f"Generated tests at {test_file_path}"
    )

async def run_tests(self, test_file: str) -> AgentResult:
    """Run pytest on a test file"""
    
    process = await asyncio.create_subprocess_exec(
        'pytest', test_file, '-v', '--tb=short',
        stdout=asyncio.subprocess.PIPE,
        stderr=asyncio.subprocess.PIPE
    )
    
    stdout, stderr = await process.communicate()
    
    return AgentResult(
        success=process.returncode == 0,
        data={
            'stdout': stdout.decode(),
            'stderr': stderr.decode(),
            'exit_code': process.returncode
        },
        message="Tests passed" if process.returncode == 0 else "Tests failed"
    )

def extract_code_block(self, text: str) -> str:
    """Extract code from markdown code block"""
    import re
    match = re.search(r'```python\n(.*?)\n```', text, re.DOTALL)
    if match:
        return match.group(1)
    return text

## Register Agent in Configuration

```yaml
# coditect-config/agents.yml
agents:
  - name: test_generator
    type: custom
    class: coditect.agents.test_generator.TestGeneratorAgent
    mcp_server: http://mcp-anthropic:8004
    model: claude-sonnet-4
    config:
      max_retries: 3
      timeout: 120

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8. WORKFLOW CONFIGURATION

═══════════════════════════════════════════════════════════

Example Workflows​

Auto-Documentation Workflow​

# coditect-config/workflows.yml
workflows:
  auto_document:
    name: "Automatic Documentation Generation"
    description: "Generates documentation when code is committed"
    
    trigger:
      type: git_commit
      filter:
        branches: ["main", "develop"]
        paths: ["src/**/*.py", "src/**/*.js"]
    
    steps:
      # Step 1: Analyze code structure
      - name: analyze
        agent: code_analyzer
        action: analyze_module
        params:
          path: "${trigger.file_path}"
        
      # Step 2: Generate docstrings
      - name: generate_docstrings
        agent: documentation_generator
        action: generate_docstrings
        params:
          file: "${trigger.file_path}"
          analysis: "${steps.analyze.result}"
      
      # Step 3: Generate README
      - name: generate_readme
        agent: documentation_generator
        action: generate_readme
        params:
          directory: "${trigger.directory}"
          modules: "${steps.analyze.modules}"
      
      # Step 4: Commit documentation
      - name: commit
        agent: git_agent
        action: commit
        params:
          message: "docs: Auto-generate documentation"
          files: ["${steps.generate_docstrings.file}", "${steps.generate_readme.file}"]

CI/CD Pipeline Workflow​

workflows:
  ci_pipeline:
    name: "Continuous Integration Pipeline"
    description: "Runs tests, linting, and security checks on PR"
    
    trigger:
      type: pull_request
      filter:
        action: opened
    
    steps:
      # Parallel execution of checks
      - name: parallel_checks
        type: parallel
        steps:
          # Linting
          - name: lint
            agent: code_quality_agent
            action: run_linter
            params:
              files: "${trigger.changed_files}"
          
          # Unit tests
          - name: test
            agent: test_runner_agent
            action: run_tests
            params:
              test_suite: "unit"
          
          # Security scan
          - name: security
            agent: security_scanner
            action: scan_dependencies
            params:
              manifest: "package.json"
      
      # Review results
      - name: review_results
        agent: review_agent
        action: analyze_results
        params:
          results:
            lint: "${steps.parallel_checks.lint.result}"
            test: "${steps.parallel_checks.test.result}"
            security: "${steps.parallel_checks.security.result}"
      
      # Post comment on PR
      - name: comment
        agent: github_agent
        action: post_comment
        params:
          pr_number: "${trigger.pr_number}"
          comment: "${steps.review_results.summary}"
      
      # Conditional: Auto-approve if all checks pass
      - name: auto_approve
        type: condition
        condition: "${steps.review_results.all_passed}"
        if_true:
          - name: approve
            agent: github_agent
            action: approve_pr
            params:
              pr_number: "${trigger.pr_number}"

═══════════════════════════════════════════════════════════

9. MONITORING & OPERATIONS

═══════════════════════════════════════════════════════════

Health Checks​

# Check all services
docker-compose ps

# Check FoundationDB
docker-compose exec foundationdb fdbcli --exec "status"

# Check Redis
docker-compose exec redis redis-cli ping

# Check theia IDE
curl http://localhost:3030/health

# Check Socket.IO Gateway
curl http://localhost:3000/health

# Check MCP servers
for port in 8001 8002 8003 8004; do
    curl http://localhost:${port}/health
done

Logs​

# View all logs
docker-compose logs -f

# View specific service
docker-compose logs -f theia-ide

# View CODITECT Monitor logs
docker-compose exec theia-ide tail -f /var/log/coditect/monitor.log

# View CODITECT Command logs
docker-compose exec theia-ide tail -f /var/log/coditect/command.log

# Search logs
docker-compose logs | grep ERROR

Metrics​

# Redis metrics
docker-compose exec redis redis-cli info stats

# FoundationDB metrics
docker-compose exec foundationdb fdbcli --exec "status json"

# Container resource usage
docker stats

═══════════════════════════════════════════════════════════

10. PRODUCTION DEPLOYMENT CHECKLIST

═══════════════════════════════════════════════════════════

Security​

□ Change all default passwords □ Use secrets management (Vault, AWS Secrets Manager) □ Enable HTTPS everywhere (Let's Encrypt) □ Configure firewall rules (only 80, 443, 22) □ Set up VPN for admin access □ Enable audit logging □ Regular security updates □ RBAC policies configured

Reliability​

□ Automated backups (FoundationDB, workspaces) □ Backup retention policy (30 days) □ Disaster recovery plan documented □ Multi-zone deployment (if using cloud) □ Health checks configured □ Auto-restart policies □ Resource limits set

Performance​

□ SSD storage for FoundationDB □ Redis persistence enabled □ CDN for static assets □ Compression enabled □ HTTP/2 everywhere □ Connection pooling □ Caching strategy

Monitoring​

□ Prometheus + Grafana dashboards □ Alerts configured:

  • Pod crashes
  • High resource usage
  • FoundationDB issues
  • MCP server errors
  • llm API failures □ Log aggregation (ELK/Loki) □ APM (Application Performance Monitoring) □ Uptime monitoring

Cost Optimization​

□ Auto-shutdown idle workspaces □ Spot instances for non-critical services □ Resource quotas per user □ llm API usage tracking □ Storage lifecycle policies

Documentation​

□ Architecture diagram □ Runbooks for common issues □ API documentation □ User guides □ Incident response procedures

═══════════════════════════════════════════════════════════

CONCLUSION

═══════════════════════════════════════════════════════════

You now have a complete cloud-based AI development environment with:

✅ Browser-based IDE (theia) ✅ Multi-llm integration (MCP) ✅ Agentic workflows (CODITECT) ✅ Real-time collaboration (Socket.IO) ✅ Fast distributed storage (FoundationDB) ✅ Scalable infrastructure (Kubernetes)

Next steps:

  1. Deploy to Docker Compose for testing
  2. Create custom MCP servers for your needs
  3. Develop agents for your workflows
  4. Graduate to Kubernetes for production scale
  5. Add monitoring and observability

The future of development is AI-assisted, collaborative, and cloud-native! 🚀