LogAgent
LogAgent: AI-Powered Log Analysis
Efficiently debug production issues with advanced anomaly detection and root cause analysis
About LogAgent
LogAgent is an open-source tool that leverages Large Language Models (LLMs) to analyze log files, interpret event flows, and identify subtle anomalies that traditional error detection methods might miss.
It's specifically designed to address complex logic and business flow issues in production environments, significantly reducing the time required for issue diagnosis and resolution.
Key Features
LLM-Based Analysis
Utilizes state-of-the-art language models for advanced pattern recognition in log data
Anomaly Detection
Identifies logic issues and business flow anomalies that don't trigger traditional error alerts
Large File Processing
Efficiently handles log files up to 256k tokens, suitable for extensive production logs
Context-Aware Insights
Provides detailed analysis considering the full context of the application's behavior
Open Source
Fully customizable and extensible to fit specific organizational needs
Root Cause Analysis
Accelerates issue resolution by pinpointing the underlying causes of production problems
Real-World Use Case Analysis
Scenario: AMER Region Order Fulfillment Issue
Unfulfilled orders for PROD-X in the AMER region, potentially due to inventory or fulfillment issues.
- Successful order fulfillment for ORD-APAC-001 and ORD-EMEA-001
- ORD-AMER-001 placed for 300 units of PROD-X in AMER region
- Inventory discrepancy detected: expected 300 units, actual 370 units of PROD-X
Log Level Distribution
Error Rate Over Time
LogAgent in Action
Sample Usage
import logagent
# Initialize LogAgent
agent = logagent.LogAgent()
# Load and analyze log file
analysis = agent.analyze("path/to/logfile.log")
# Get key insights
insights = analysis.get_insights()
# Print root cause
print("Root Cause:", analysis.get_root_cause())
# Get recommended actions
actions = analysis.get_recommended_actions()
# Visualize log level distribution
analysis.plot_log_level_distribution()