logontg 1.0.1

Python SDK for LogonTG - Simple logging with uptime monitoring

LogonTG Python SDK

Simple logging client with uptime monitoring capabilities for LogonTG. Send logs directly to Telegram with smart error batching and LLM-powered analysis.

Features

• 🚀 Simple Logging - Send logs to Telegram in one line
• 📊 Four Log Levels - info, error, warning, debug
• 🔍 Uptime Monitoring - Automatic error detection and batching (Pro only)
• 🤖 AI Analysis - LLM-powered error analysis and insights (Pro only)
• ⚡ Lightweight - Minimal dependencies, maximum performance

Installation

pip install logontg

Quick Start

from logontg import logontg

# Initialize client
logger = logontg(api_key="your-api-key")

# Send logs
await logger.log("Application started")
await logger.error("Something went wrong!")
await logger.warn("This is a warning")
await logger.debug("Debug information")

# Synchronous versions also available
logger.log_sync("Application started")
logger.error_sync("Database connection failed")

Constructor Options

logger = logontg(
    api_key="your-api-key",          # Required: Your LogonTG API key
    uptime=False,                    # Optional: Enable uptime monitoring (Pro only)
    base_url="http://sruve.com/api", # Optional: API base URL
    debug=True                       # Optional: Enable debug logging
)

Logging Methods

Async Methods (Recommended)

await logger.log("Info message")     # Info level
await logger.error("Error message")  # Error level  
await logger.warn("Warning message") # Warning level
await logger.debug("Debug message")  # Debug level

Sync Methods

logger.log_sync("Info message")     # Info level
logger.error_sync("Error message")  # Error level
logger.warn_sync("Warning message") # Warning level
logger.debug_sync("Debug message")  # Debug level

Uptime Monitoring (Pro Feature)

Enable automatic error detection and AI-powered analysis:

# Enable uptime monitoring
logger = logontg(
    api_key="your-api-key",
    uptime=True  # Requires Pro subscription
)

# Errors are automatically detected and batched
# LLM analysis is sent to Telegram when thresholds are met

Uptime Features:

• Error Batching - Groups similar errors over 2-minute windows
• Smart Thresholds - Alerts after 3+ similar errors
• LLM Analysis - AI-powered error insights and solutions
• Stack Trace Capture - Detailed error context

Manual Control:

# Enable/disable uptime monitoring at runtime
logger.set_uptime_monitoring(True)
logger.set_uptime_monitoring(False)

Message Types

All logging methods accept any data type:

# Strings
await logger.log("Simple string message")

# Dictionaries
await logger.error({
    "error": "Database connection failed",
    "host": "localhost",
    "port": 5432,
    "retry_count": 3
})

# Lists
await logger.debug(["step1", "step2", "step3"])

# Any JSON-serializable data
await logger.warn({"users": [1, 2, 3], "active": True})

Error Handling

try:
    await logger.log("Test message")
except Exception as e:
    if "Rate limit exceeded" in str(e):
        print("Upgrade your plan for higher limits")
    else:
        print(f"Logging failed: {e}")

Examples

Basic Application Logging

from logontg import logontg

logger = logontg("your-api-key")

# Application lifecycle
await logger.log("🚀 Application starting...")
await logger.log("✅ Database connected")
await logger.log("🌐 Server listening on port 8000")

# Error scenarios  
try:
    # Some operation
    result = risky_operation()
    await logger.log(f"✅ Operation completed: {result}")
except Exception as e:
    await logger.error(f"❌ Operation failed: {str(e)}")

Web Framework Integration

from flask import Flask
from logontg import logontg

app = Flask(__name__)
logger = logontg("your-api-key")

@app.route("/api/users")
def get_users():
    logger.log_sync("📋 Fetching users list")
    try:
        users = fetch_users()
        logger.log_sync(f"✅ Found {len(users)} users")
        return {"users": users}
    except Exception as e:
        logger.error_sync(f"❌ Failed to fetch users: {str(e)}")
        return {"error": "Internal server error"}, 500

Monitoring with Context

# Rich logging with context
await logger.log({
    "event": "user_registration",
    "user_id": 12345,
    "email": "[e-mail]", 
    "source": "web_form",
    "timestamp": "2024-01-15T10:30:00Z"
})

# Performance monitoring
import time
start = time.time()
# ... some operation ...
duration = time.time() - start

await logger.debug({
    "operation": "database_query",
    "duration_ms": duration * 1000,
    "query": "SELECT * FROM users",
    "rows_returned": 150
})

Environment Variables

You can also set your API key via environment variable:

export LOGONTG_API_KEY="your-api-key"

import os
from logontg import logontg

logger = logontg(os.getenv("LOGONTG_API_KEY"))

Requirements

• Python 3.7+
• requests library

License

MIT License - see LICENSE file for details.

Support

• 📧 Email: [e-mail]
• 🌐 Website: https://sruve.com
• 📖 Documentation: https://sruve.com/docs

Contributing

Contributions welcome! Please read our contributing guidelines and submit pull requests to our GitHub repository.

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LogonTG - Simple, powerful logging for modern applications.