ChaosPilot – AI-Powered Log Analysis Platform

Modern, full-stack AI platform for automated log analysis, incident classification, and intelligent remediation.

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🚀 Quick Start | 📖 Setup Guide | 🔧 ADK Integration | 🧪 Test Integration

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🎯 What is ChaosPilot?

ChaosPilot is an intelligent log analysis platform that uses AI agents to automatically analyze error, warning, and critical logs, detect patterns, classify incidents, and recommend fixes.
We manage the chaos of production errors through intelligent log analysis.

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🏆 Features

• 🔍 AI-Powered Analysis: LLMs analyze logs for patterns and anomalies
• 📊 Incident Classification: Auto-classifies by severity, impact, urgency
• 📋 Smart Response Planning: Generates actionable response plans
• 🛠️ Automated Fix Recommendations: Suggests and executes safe fixes
• ⚡ Safe Auto-Fixing: Automated fixes with rollback capability
• 📢 Smart Alerting: Intelligent notifications and escalations
• 📈 Real-Time Dashboard: Live metrics, workflow visualization, and insights

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🏗️ Architecture

Layer	Tech/Tools
Frontend	Angular, TypeScript, TailwindCSS, RxJS
Backend	Python, FastAPI, Google ADK, async/await
Auth	Supabase (user/session management)
Data/AI	Google ADK agents, BigQuery, LLMs
DevOps	Docker, GCP, uv, hatch

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🚀 Quick Start

Prerequisites

• Google Cloud CLI
• Python 3.9+
• Uv - 🚀 A single tool to replace pip, pip-tools, pipx, poetry, pyenv, twine, virtualenv, and more.

  

• Node.js 16+
• MCP Toolbox for Databases

Setup

1. Clone the repository

   git clone https://github.com/pmutua/ChaosPilot
   cd ChaosPilot

2. Create and activate a Python virtual environment

   • Create virtual environment

     uv venv
     

   • Activate virtual environment

     # Linux/macOS:
     
     source .venv/bin/activate
     
     # Windows 
     
     .venv\Scripts\activate
     
     

3. Install dependencies

   uv sync

4. Rename .env.template to .env and add relevant environment variables:

   APP_NAME="agent_manager"
   VERSION=0.0.0
   MODEL=""
   
   # If you are using Google  Gemini use this
   GOOGLE_GENAI_USE_VERTEXAI=FALSE
   GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE
   
   # If you are using Azure Open AI use this
   AZURE_API_KEY="add api key"
   AZURE_API_BASE=https://example.openai.azure.com/
   AZURE_API_VERSION="2025-05-05-preview"

5. Set up Google Cloud, BigQuery, and MCP Toolbox
   (See Setup Guide for full details.)

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🖥️ Running the Application

Start All Services

# Start MCP Toolbox
cd mcp-toolbox

toolbox --tools-file="tools.yaml"

# Start ADK API Server (with CORS)

cd ../agent_manager

adk api_server  --allow_origins="*"


# or in root directory run: 

adk api_server agent_manager --allow_origins="*"

# Start Frontend
cd ../web
npm install
npm start

Debugging with Agent Development Kit UI

Simply run the following command:

adk web

# And navigate to port 8000. Now you can interact with the Agents and debug.

NOTE: Make sure that you select "agent_manager" on "Select Agent" option.

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📚 Documentation

• Setup & Deployment Guide

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📊 Usage Examples

Analyze Error Logs

1. Go to the Log Analysis page
2. Select the "Log Analyzer" agent
3. Provide error logs or describe the issue
4. Get AI-powered analysis with confidence scores

Get Fix Recommendations

1. Use the "Fix Recommender" agent
2. Share the analyzed logs and issues
3. Get specific fix suggestions with implementation steps

Automated Incident Response

1. System detects critical issues
2. AI agents generate response plans
3. Safe automated fixes are applied
4. Teams are notified with detailed reports

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🚀 Production Deployment

See Setup & Deployment Guide for GCP/Cloud Run instructions.

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🔧 Troubleshooting

• CORS Errors: Use adk api_server app --allow_origins="*"
• Service Account Issues: Run the IAM role assignment scripts
• Billing Errors: Ensure GCP billing is enabled
• Agent Not Found: Verify you're running from the correct directory

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🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

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📄 License

MIT License – see the LICENSE file for details.

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🆘 Support

• Create an issue in the repository
• Check the documentation
• Review the troubleshooting guide

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ChaosPilot – Managing the chaos of production errors through AI-powered log analysis.