vibelogger 0.1.0

AI-Native Logging for LLM Agent Development

VibeCoding Logger

AI-Native Logging for LLM Agent Development - Multi-Language Implementation

VibeCoding Logger is a specialized logging library designed for AI-driven development where LLMs need rich, structured context to understand and debug code effectively. Unlike traditional human-readable logs, this creates "AI briefing packages" with comprehensive context, correlation tracking, and embedded human annotations.

🎯 Concept

In VibeCoding (AI-driven development), the quality of debugging depends on how much context you can provide to the LLM. Traditional logs are designed for humans, but LLMs need structured, machine-readable data with rich context to provide accurate analysis and solutions.

✨ Key Features

• 🤖 AI-Optimized: Structured JSON format optimized for LLM consumption
• 📦 Rich Context: Function arguments, stack traces, environment info
• 🔗 Correlation Tracking: Track request flows across operations
• 💬 Human Annotations: Embed AI instructions directly in logs (human_note, ai_todo)
• ⏰ Timestamped Files: Automatic file saving with timestamp-based naming
• 🔄 Log Rotation: Prevent large files with automatic rotation
• 🧵 Thread Safe: Safe for concurrent/multi-threaded applications
• 🌍 UTC Timestamps: Consistent timezone handling
• 💾 Memory Management: Configurable memory limits to prevent OOM

🌍 Language Support

Language	Status	Package	Documentation
Python	✅ Stable	pip install vibelogger	Python Docs
TypeScript/Node.js	🚧 Need Contributors	Coming Soon	Contribute!
Go	📋 Planned	-	-
Rust	📋 Planned	-	-

🚀 Quick Start (Python)

Installation

pip install vibelogger

Vibe Usage

Just ask Claude Code or Google CLI to use this.

or paste this page for instruction.

Basic Usage

from vibelogger import create_file_logger

# Create logger with auto-save to timestamped file
logger = create_file_logger("my_project")

# Log with rich context for AI analysis
logger.info(
    operation="fetchUserProfile",
    message="Starting user profile fetch",
    context={"user_id": "123", "source": "api_endpoint"},
    human_note="AI-TODO: Check if user exists before fetching profile"
)

# Log exceptions with full context
try:
    result = risky_operation()
except Exception as e:
    logger.log_exception(
        operation="fetchUserProfile",
        exception=e,
        context={"user_id": "123"},
        ai_todo="Suggest proper error handling for this case"
    )

# Get logs formatted for AI analysis
ai_context = logger.get_logs_for_ai()
print(ai_context)  # Send this to your LLM for analysis

For complete Python documentation, see python/README.md.

📋 Advanced Usage

Custom Configuration

from vibelogger import create_logger, VibeLoggerConfig

config = VibeLoggerConfig(
    log_file="./logs/custom.log",
    max_file_size_mb=50,
    auto_save=True,
    keep_logs_in_memory=True,
    max_memory_logs=1000
)
logger = create_logger(config=config)

Environment-Based Configuration

from vibelogger import create_env_logger

# Set environment variables:
# VIBE_LOG_FILE=/path/to/logfile.log
# VIBE_MAX_FILE_SIZE_MB=25
# VIBE_AUTO_SAVE=true

logger = create_env_logger()

Memory-Efficient Logging

from vibelogger import VibeLoggerConfig, create_logger

# For long-running processes - disable memory storage
config = VibeLoggerConfig(
    log_file="./logs/production.log",
    keep_logs_in_memory=False,  # Don't store logs in memory
    auto_save=True
)
logger = create_logger(config=config)

🔧 AI Integration

The logger creates structured data that LLMs can immediately understand:

{
  "timestamp": "2025-07-07T08:36:42.123Z",
  "level": "ERROR", 
  "correlation_id": "req_abc123",
  "operation": "fetchUserProfile",
  "message": "User profile not found",
  "context": {
    "user_id": "user-123",
    "query": "SELECT * FROM users WHERE id = ?"
  },
  "environment": {
    "python_version": "3.11.0",
    "os": "Darwin"
  },
  "source": "/app/user_service.py:42 in get_user_profile()",
  "human_note": "AI-TODO: Check database connection",
  "ai_todo": "Analyze why user lookup is failing"
}

Key Fields for AI Analysis

• timestamp: ISO format with UTC timezone
• correlation_id: Links related operations across the request
• operation: What the code was trying to accomplish
• context: Function arguments, variables, state information
• environment: Runtime info for reproduction
• source: Exact file location and function name
• human_note: Natural language instructions for the AI
• ai_todo: Specific analysis requests

📁 Log File Organization

Logs are automatically organized with timestamps in your project folder:

./logs/
├── my_project/
│   ├── vibe_20250707_143052.log
│   ├── vibe_20250707_151230.log
│   └── vibe_20250707_163045.log.20250707_170000  # Rotated
└── other_project/
    └── vibe_20250707_144521.log

🛡️ Thread Safety

VibeCoding Logger is fully thread-safe:

import threading
from vibelogger import create_file_logger

logger = create_file_logger("multi_threaded_app")

def worker(worker_id):
    logger.info(
        operation="worker_task",
        message=f"Worker {worker_id} processing",
        context={"worker_id": worker_id}
    )

# Safe to use across multiple threads
threads = [threading.Thread(target=worker, args=(i,)) for i in range(10)]
for t in threads:
    t.start()

🎯 VibeCoding Workflow

1. Code with VibeCoding Logger: Add rich logging to your development process
2. Run Your Code: Logger captures detailed context automatically
3. Get AI Analysis: Use logger.get_logs_for_ai() to get formatted data
4. Send to LLM: Paste the structured logs into your LLM for analysis
5. Get Precise Solutions: LLM provides targeted fixes with full context

📚 Documentation

Core Documentation

• VibeCoding Concept & Theory - Understanding VibeCoding and AI-native logging
• Technical Specification - Detailed API and implementation spec
• Python Implementation - Python-specific documentation and examples

Examples

• Python: python/examples/ - Basic usage and framework integrations
• TypeScript: typescript/ - Coming soon (contributors needed!)

Framework Integrations

• Django: python/examples/integrations/django_integration.py
• FastAPI: python/examples/integrations/fastapi_integration.py
• Flask: python/examples/integrations/flask_integration.py
• Standard Logging: python/examples/integrations/standard_logging_example.py

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎉 Why VibeCoding Logger?

Traditional logging is designed for human debugging. But in the age of AI-assisted development, we need logs that AI can understand and act upon. VibeCoding Logger bridges this gap by providing:

• Context-Rich Data: Everything an LLM needs to understand the problem
• Structured Format: Machine-readable JSON instead of human-readable text
• AI Instructions: Direct communication with your AI assistant
• Correlation Tracking: Understanding of request flows and relationships

Transform your debugging from "guess and check" to "analyze and solve" with AI-native logging.

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Built for the VibeCoding era - where humans design and AI implements. 🚀