LogAgent: AI-Powered Log Analysis

Efficiently debug production issues with advanced anomaly detection and root cause analysis

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LogAgent Dashboard

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

INFODEBUGWARNERROR0255075100

Error Rate Over Time

04 PM05 PM06 PM07 PM08 PM09 PM10 PM11 PM12 AM01 AM02 AM03 AM09182736

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()