AI Logs Watcher

An AI-powered log monitoring and alerting system that uses artificial intelligence to analyze log entries and send SMS notifications via Twilio when critical issues are detected.

Features Status

✅ Available

• 🤖 AI-Powered Analysis: Uses AI to intelligently analyze log entries and detect critical issues
• ☁️ Cloud tools: Server that exposes cloud tools and manage github repository exploration
• 📱 SMS Alerts: Automatic SMS notifications via Twilio for critical and warning conditions
• ⚡ Real-time Monitoring: Configurable polling intervals for continuous log analysis
• 🎯 Smart Detection: Advanced keyword heuristics for detecting critical system failures
• 📊 Flexible Log Input: Support for single logs, multi-line strings, or arrays of log entries
• 🔧 Highly Configurable: Customizable AI models, polling intervals, and alert thresholds
• 📞 Voice Interactive Calling: AI-powered voice calls for critical alerts with interactive responses
• 📝 Enhanced Logger: Drop-in console replacement with automatic AI feeding and optional file writing

Future

• 📋 Phone Contacts Management: Manage multiple contact lists with priority levels and escalation chains
• 🔄 Alert Fallbacks: Automatic fallback mechanisms when primary contacts are unavailable
• 📈 Analytics Dashboard: Real-time monitoring dashboard with alert history and trends

Installation

npm install @shadokan87/ai-logs-watcher

Quick Start

import { Ailogw } from "./Ailogw/Ailogw";

const ailogwatch = new Ailogw({
    name: "vercel",
    twilio: {
        numberFrom: "+17153645914"
    },
    events: {
        async alert({ options, logs, diagnostic, sendSms }) {
            switch (diagnostic.raw.status) {
                case "warning":
                case "normal": {
                    await sendSms("+33727926138", diagnostic.formatted);
                    return;
                }
                case "critical": {
                    await sendSms("+33727926138", diagnostic.formatted);
                    return;
                }
            }
        }
    }
});

// Feed logs to be analyzed
ailogwatch.feedLog("2024-09-16 ERROR Database connection failed");

Configuration

Environment Variables

Set up your environment variables for Twilio and AI service:

# Required for Twilio integration
AILOGW_TWILIO_ACCOUNT_SID="<your_twilio_account_sid>"
AILOGW_TWILIO_AUTH_TOKEN="<your_twilio_auth_token>"
AILOGW_NUMBER_TO="<destination_phone_number>"      # The phone number to send SMS alerts to
AILOGW_NUMBER_FROM="<twilio_phone_number>"         # Your Twilio phone number (sender)
# Required for eleven labs agents and voice
ELEVENLABS_API_KEY="<your_elevenlabs_api_key>"
# Required for llm analysis
BLACKBOX_API_KEY="<your_blackbox_api_key>"

# Optional: Experimental Render real time logs
RENDER_API_KEY=<your_render_api_key>
RT_RESOURCE="<your_render_resource>"
RT_OWNER_ID="<your_workspace_id>"

# Optional: disable verbose mode
AILOGW_LOG=<boolean>

Usage Examples

Basic Log Monitoring

const watcher = new Ailogw({
    name: "production-api",
    twilio: {
            numberFrom: "+17153645914"
    },
    events: {
        alert: async ({ options, logs, diagnostic, sendSms }) => {
            if (diagnostic.raw.status === "critical") {
                await sendSms("+1234567890", diagnostic.formatted);
            }
        }
    }
});

// Single log entry
watcher.feedLog("2024-09-16 10:30:25 ERROR Database connection failed");

// Multiple log entries
watcher.feedLog([
    "2024-09-16 10:30:25 INFO Server started",
    "2024-09-16 10:30:26 WARN High memory usage",
    "2024-09-16 10:30:27 CRITICAL System overload"
]);

Custom Polling Configuration

const watcher = new Ailogw({
    name: "database-logs",
    polling: {
        delay: "5:minutes",          // Check every 5 minutes
        tailAmount: 20               // Analyze last 20 log entries
    },
    events: {
        alert: async ({ options, logs, diagnostic, sendSms }) => {
            console.log(`Alert Status: ${diagnostic.raw.status}`);
            if (diagnostic.raw.topErrors) {
                console.log("Critical errors found:", diagnostic.raw.topErrors);
            }
        }
    }
});

Advanced AI Configuration

const watcher = new Ailogw({
    name: "advanced-monitoring",
    clientOptions: {
        baseURL: "https://api.blackbox.ai",
        apiKey: "sk-your-api-key"
    },
    modelSettings: {
        model: "gpt-4",
        temperature: 0.1
        // and any other openai compatible options
    },
    events: {
        alert: async ({ options, logs, diagnostic, sendSms }) => {
            // Custom alert logic
        }
    }
});

Log Status Detection

The AI analyzes logs and categorizes them into three severity levels:

• 🟢 Normal: Standard operation, no issues detected
• 🟡 Warning: Potential issues that should be monitored
• 🔴 Critical: Severe problems requiring immediate attention

Critical Detection Keywords

The system automatically detects critical issues based on keywords such as:

• FATAL, PANIC, EMERGENCY, SEVERE
• OOM, OUT OF MEMORY, SEGFAULT
• CRASH, KERNEL PANIC, DATA LOSS
• SECURITY BREACH, DISK FAILURE
• SERVICE UNAVAILABLE, DEADLOCK And any other words related to a critical issue

Error Handling

The system includes built-in error handling for:

• Invalid config e.g credentials
• Invalid time units in polling configuration
• AI service connectivity issues
• SMS delivery failures

API Reference

Class Methods

feedLog(log: string | string[])

Feeds log entries to the watcher for analysis.

activatePolling()

Starts or resumes the log polling process.

pausePolling()

Pauses the log polling process.

get options()

Returns the current configuration options.

createLogger(options?: CreateLoggerOptions)

Creates an enhanced logger that automatically feeds all log entries to the Ailogw instance for AI analysis while maintaining full console compatibility.

Features:

• 🔄 Automatic Log Feeding: Every log call automatically feeds to AI analysis
• 📁 File Writing: Optional file destinations for each log level
• 🕒 Timestamping: All logs are automatically timestamped
• ⚡ Console Compatible: Drop-in replacement for console object
• 🛡️ Error Handling: Graceful handling of file writing errors

Parameters:

interface CreateLoggerOptions {
  destination?: {
    log?: string;     // File path for general logs
    error?: string;   // File path for error logs
    warn?: string;    // File path for warning logs
    info?: string;    // File path for info logs
    debug?: string;   // File path for debug logs
  }
}

Examples:

// Basic usage - automatic AI analysis only
const logger = ailogw.createLogger();
logger.info("Server started successfully");
logger.error("Database connection failed");

// With file destinations - logs written to files AND analyzed by AI
const logger = ailogw.createLogger({
  destination: {
    info: "./logs/info.log",
    error: "./logs/errors.log",
    warn: "./logs/warnings.log",
    debug: "./logs/debug.log"
  }
});

// All standard console methods work
logger.log("General message");
logger.info("Information message");  
logger.warn("Warning message");
logger.error("Error occurred");
logger.debug("Debug information");

// Supports multiple arguments like console
logger.error("Database error:", error.message, { userId: 123, timestamp: Date.now() });

Log Format: All logs are formatted as: YYYY-MM-DDTHH:mm:ss.sssZ [LEVEL] your message here

Example output:

2024-09-30T14:30:25.123Z INFO Server started successfully
2024-09-30T14:30:26.456Z ERROR Database connection failed

Constructor Options

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project license will be available soon.

Support

For support and questions, please open an issue on the GitHub repository.