Features Overview

Sherlock AI provides comprehensive monitoring and analysis capabilities for Python applications. Each feature is designed to work independently or in combination with others for complete observability.

Core Features

Performance Monitoring

Track execution times of functions and code blocks with the @log_performance decorator and PerformanceTimer context manager.

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Memory Monitoring

Monitor Python memory usage with detailed heap analysis and tracemalloc integration using @monitor_memory.

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Resource Monitoring

Track comprehensive system resources including CPU, memory, I/O, and network usage with @monitor_resources.

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Error Analysis

AI-powered error analysis with automatic probable cause detection and MongoDB storage using @sherlock_error_handler.

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Code Analysis

Automatically detect and refactor hardcoded values in your code using @hardcoded_value_detector.

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Auto-Instrumentation

Zero-code setup for popular frameworks like FastAPI, automatically instrumenting routes with monitoring decorators.

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Feature Comparison

Feature	Decorator	Context Manager	Async Support	Storage Options
Performance Monitoring	✓	✓	✓	Log files
Memory Monitoring	✓	✓	✓	Log files
Resource Monitoring	✓	✓	✓	Log files
Error Analysis	✓	✗	✓	MongoDB, API
Code Analysis	✓	✗	✓	File system
Auto-Instrumentation	✓	✗	✓	All supported

Combined Usage

Features can be stacked for comprehensive monitoring:

from sherlock_ai import log_performance, monitor_memory, monitor_resources
from sherlock_ai.monitoring import sherlock_error_handler
from sherlock_ai import hardcoded_value_detector

@log_performance
@monitor_memory(trace_malloc=True)
@monitor_resources(include_io=True)
@sherlock_error_handler
@hardcoded_value_detector
def comprehensive_function():
    # This function will be monitored for:
    # - Execution time (performance)
    # - Memory usage (memory)
    # - System resources (CPU, I/O, etc.)
    # - Error analysis (AI-powered)
    # - Hardcoded value detection
    data = process_large_dataset()
    save_to_database(data)
    return len(data)

Next Steps

Explore each feature in detail to understand how to use them effectively in your application:

1. Start with Performance Monitoring for basic tracking
2. Add Memory Monitoring for memory-intensive operations
3. Use Resource Monitoring for comprehensive system analysis
4. Enable Error Analysis for AI-powered debugging
5. Try Code Analysis to improve code quality
6. Set up Auto-Instrumentation for zero-code monitoring