What are the main requirements to run NFT Log Analyzer?

Key requirements include Python 3.11+, Ollama (with `deepseek-r1:14b`), `ripgrep`, and at least 16GB RAM (32GB recommended) on macOS Ventura 13+.

What types of log files can it analyze?

It's designed to efficiently scan large log files (500MB+) in seconds using `ripgrep`. It then parses and deduplicates unique error patterns from streaming log data.

How does NFT Log Analyzer ensure data privacy?

It ensures data privacy by leveraging local LLMs via Ollama, so all log analysis and processing occurs 100% on your machine. No raw log content ever leaves your system.

What is NFT Log Analyzer?

NFT Log Analyzer is an AI-powered tool for log analysis. It uses local LLMs (Ollama) to identify error patterns, root causes, and automatically files structured GitHub Issues, ensuring full data privacy.

How does it help with GitHub Issues?

It automatically composes structured GitHub Issues with root causes and suggested fixes. These are then filed directly to your repositories with built-in duplicate detection, streamlining bug reporting.

NFT Log Analyzer

Name: NFT Log Analyzer
Author: mashish

bymashish

0•

Developer Tools

Analytics & Monitoring

Deployment & DevOps

Performs AI-powered log analysis and automatically files structured GitHub Issues, leveraging local LLMs via Ollama to ensure data privacy.

The NFT Log Analyzer is an AI-powered solution designed for efficient and private analysis of application logs. It processes large log files (500MB+) rapidly using `ripgrep`, identifies and deduplicates unique error patterns, and then leverages local LLMs (Ollama with deepseek-r1:14b) orchestrating CrewAI agents to perform in-depth analysis. This process culminates in the automatic composition and filing of structured GitHub Issues, complete with root cause identification and suggested fixes, all while ensuring zero raw log data leaves your machine. It integrates seamlessly with MCP clients like Claude Desktop and Cursor, providing both natural language and programmatic interaction.

Key Features

01Efficiently scan large log files (500MB+) in seconds using ripgrep.

020 GitHub stars

03File GitHub Issues to repositories, with built-in duplicate detection and 100% local processing.

04Perform AI-driven log analysis using local LLMs (Ollama) via CrewAI agents.

05Parse and deduplicate unique error patterns from streaming log data.

06Automatically compose structured GitHub Issues with root causes and suggested fixes.

Use Cases

01Providing AI-powered root cause analysis and suggested fixes for complex log events.

02Integrating log monitoring and issue management directly into AI coding assistants like Claude Desktop or Cursor.

03Automating the identification and structured reporting of application errors into GitHub Issues.