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README.md
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# Duty AI Ops
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AI-powered Kubernetes monitoring and diagnostics tool that automatically detects and diagnoses cluster issues.
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## Features
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- 🤖 **AI-Powered Diagnostics**: Uses LLM with function calling to analyze Kubernetes issues
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- 🔍 **Smart Event Detection**: Monitors pods and nodes for problems (CrashLoopBackOff, NotReady, high restarts, etc.)
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- 🧠 **Correlation Engine**: Detects patterns like mass pod failures and diagnoses the root cause (node issues) instead of individual symptoms
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- 📱 **Telegram Notifications**: Optional real-time alerts with automatic "resolved" status updates
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- ⚡ **Concurrent Processing**: Configurable parallel AI diagnosis requests with semaphore-based rate limiting
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- 🎯 **Resource-Aware**: Tracks pod CPU/memory requests and limits for better diagnostics
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## How It Works
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1. **Watches** Kubernetes cluster for pod/node events
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2. **Correlates** issues (e.g., if 5+ pods fail on same node within 60s, diagnose the node)
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3. **Analyzes** using AI with access to k8s API tools (get pod details, logs, node status)
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4. **Notifies** via Telegram with structured diagnostic reports
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5. **Tracks** issue resolution and updates notifications automatically
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## Installation
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```bash
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# Clone repository
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git clone https://github.com/yourusername/duty-ai-ops
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cd duty-ai-ops
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# Build
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cargo build --release
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# Copy and configure
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cp config.toml.example config.toml
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# Edit config.toml with your settings
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```
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## Configuration
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```toml
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# API Configuration (OpenAI-compatible)
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api_base = "http://localhost:11434/v1" # Ollama, vLLM, etc.
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api_key = "your-key"
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model = "qwen3-tools:latest"
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# Concurrency
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max_concurrent_diagnoses = 1
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# Telegram (optional)
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telegram_bot_token = "your-bot-token"
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telegram_chat_id = "your-chat-id"
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# System prompt (customize AI behavior)
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system_prompt = """..."""
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```
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## Usage
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```bash
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# Run with kubeconfig
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cargo run --release
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# Or use specific kubeconfig
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KUBECONFIG=/path/to/kubeconfig cargo run --release
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```
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## Requirements
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- Rust 1.70+
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- Kubernetes cluster access (via kubeconfig)
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- OpenAI-compatible LLM endpoint with function calling support
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- Tested with: Ollama (qwen3-tools, devstral-small-2)
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- Should work with: OpenAI, vLLM, etc.
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## Example Output
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```
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🔍 Node: worker-1
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📋 Problem: Node NotReady due to disk pressure
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🔎 Root Cause: Node has exceeded 85% disk usage on /var/lib/kubelet.
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12 pods evicted due to insufficient disk space.
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```
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## Architecture
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- **Event Handlers**: Process pod/node watch events
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- **Correlation Engine**: Detect patterns (mass failures, repeated issues)
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- **AI Diagnostics**: LLM with k8s tools analyzes and generates reports
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- **Telegram Notifier**: Sends/updates notifications
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## License
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MIT
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