Show HN: Gonzo – A Go-based TUI for log analysis (OpenTelemetry/OTLP support)

Gonzo - The Go based TUI for log analysis A powerful, real-time log analysis terminal UI inspired by k9s. Analyze log streams with beautiful charts, AI-powered insights, and advanced filtering - all from your terminal. See it in action Main Dashboard Stats and Info Everyone loves a heatmap ✨ Features 🎯 Real-Time Analysis Live streaming - Process logs as they arrive from stdin, files, or network - Process logs as they arrive from stdin, files, or network OTLP native - First-class support for OpenTelemetry log format - First-class support for OpenTelemetry log format OTLP receiver - Built-in gRPC server to receive logs via OpenTelemetry protocol - Built-in gRPC server to receive logs via OpenTelemetry protocol Format detection - Automatically detects JSON, logfmt, and plain text - Automatically detects JSON, logfmt, and plain text Severity tracking - Color-coded severity levels with distribution charts 📈 Interactive Dashboard k9s-inspired layout - Familiar 2x2 grid interface - Familiar 2x2 grid interface Real-time charts - Word frequency, attributes, severity distribution, and time series - Word frequency, attributes, severity distribution, and time series Keyboard + mouse navigation - Vim-style shortcuts plus click-to-navigate and scroll wheel support - Vim-style shortcuts plus click-to-navigate and scroll wheel support Smart log viewer - Auto-scroll with intelligent pause/resume behavior - Auto-scroll with intelligent pause/resume behavior Global pause control - Spacebar pauses entire dashboard while buffering logs - Spacebar pauses entire dashboard while buffering logs Modal details - Deep dive into individual log entries with expandable views - Deep dive into individual log entries with expandable views Log Counts analysis - Detailed modal with heatmap visualization, pattern analysis by severity, and service distribution - Detailed modal with heatmap visualization, pattern analysis by severity, and service distribution AI analysis - Get intelligent insights about log patterns and anomalies with configurable models 🔍 Advanced Filtering Regex support - Filter logs with regular expressions - Filter logs with regular expressions Attribute search - Find logs by specific attribute values - Find logs by specific attribute values Severity filtering - Focus on errors, warnings, or specific log levels - Focus on errors, warnings, or specific log levels Interactive selection - Click or keyboard navigate to explore logs 🤖 AI-Powered Insights Pattern detection - Automatically identify recurring issues - Automatically identify recurring issues Anomaly analysis - Spot unusual patterns in your logs - Spot unusual patterns in your logs Root cause suggestions - Get AI-powered debugging assistance - Get AI-powered debugging assistance Configurable models - Choose from GPT-4, GPT-3.5, or any custom model - Choose from GPT-4, GPT-3.5, or any custom model Multiple providers - Works with OpenAI, LM Studio, Ollama, or any OpenAI-compatible API - Works with OpenAI, LM Studio, Ollama, or any OpenAI-compatible API Local AI support - Run completely offline with local models 🚀 Quick Start Installation Using Go go install github.com/control-theory/gonzo/cmd/gonzo@latest Using Homebrew (macOS/Linux) brew tap control-theory/gonzo brew install gonzo Download Binary Download the latest release for your platform from the releases page. Build from Source git clone https://github.com/control-theory/gonzo.git cd gonzo make build 📖 Usage Basic Usage # Read logs directly from files gonzo -f application.log # Read from multiple files gonzo -f application.log -f error.log -f debug.log # Use glob patterns to read multiple files gonzo -f " /var/log/*.log " gonzo -f " /var/log/app/*.log " -f " /var/log/nginx/*.log " # Follow log files in real-time (like tail -f) gonzo -f /var/log/app.log --follow gonzo -f " /var/log/*.log " --follow # Analyze logs from stdin (traditional way) cat application.log | gonzo # Stream logs from kubectl kubectl logs -f deployment/my-app | gonzo # Follow system logs tail -f /var/log/syslog | gonzo # Analyze Docker container logs docker logs -f my-container 2>&1 | gonzo # With AI analysis (requires API key) export OPENAI_API_KEY=sk-your-key-here gonzo -f application.log --ai-model= " gpt-4 " OTLP Network Receiver Gonzo can receive logs directly via OpenTelemetry Protocol (OTLP) over both gRPC and HTTP: # Start Gonzo as an OTLP receiver (both gRPC on port 4317 and HTTP on port 4318) gonzo --otlp-enabled # Use custom ports gonzo --otlp-enabled --otlp-grpc-port=5317 --otlp-http-port=5318 # gRPC endpoint: localhost:4317 # HTTP endpoint: http://localhost:4318/v1/logs Example: OpenTelemetry Collector Configuration Using gRPC: exporters : otlp/gonzo_grpc : endpoint : localhost:4317 tls : insecure : true service : pipelines : logs : receivers : [your_receivers] processors : [your_processors] exporters : [otlp/gonzo_grpc] Using HTTP: exporters : otlphttp/gonzo_http : endpoint : http://localhost:4318/v1/logs service : pipelines : logs : receivers : [your_receivers] processors : [your_processors] exporters : [otlphttp/gonzo_http] Example: Python Application Using gRPC: from opentelemetry . exporter . otlp . proto . grpc . _log_exporter import OTLPLogExporter exporter = OTLPLogExporter ( endpoint = "localhost:4317" , insecure = True ) Using HTTP: from opentelemetry . exporter . otlp . proto . http . _log_exporter import OTLPLogExporter exporter = OTLPLogExporter ( endpoint = "http://localhost:4318/v1/logs" , ) See examples/send_otlp_logs.py for a complete example. With AI Analysis # Auto-select best available model (recommended) - file input export OPENAI_API_KEY=sk-your-key-here gonzo -f logs.json # Or specify a particular model - file input export OPENAI_API_KEY=sk-your-key-here gonzo -f logs.json --ai-model= " gpt-4 " # Follow logs with AI analysis export OPENAI_API_KEY=sk-your-key-here gonzo -f " /var/log/app.log " --follow --ai-model= " gpt-4 " # Using local LM Studio (auto-selects first available) export OPENAI_API_KEY= " local-key " export OPENAI_API_BASE= " http://localhost:1234/v1 " gonzo -f logs.json # Using Ollama (auto-selects best model like gpt-oss:20b) export OPENAI_API_KEY= " ollama " export OPENAI_API_BASE= " http://localhost:11434 " gonzo -f logs.json --follow # Traditional stdin approach still works export OPENAI_API_KEY=sk-your-key-here cat logs.json | gonzo --ai-model= " gpt-4 " Keyboard Shortcuts Navigation Key/Mouse Action Tab / Shift+Tab Navigate between panels Mouse Click Click on any section to switch to it ↑ / ↓ or k / j Move selection up/down Mouse Wheel Scroll up/down to navigate selections ← / → or h / l Horizontal navigation Enter View log details or open analysis modal (Counts section) ESC Close modal/cancel Actions Key Action Space Pause/unpause entire dashboard / Enter filter mode (regex supported) s Search and highlight text in logs c Toggle Host/Service columns in log view r Reset all data (manual reset) u / U Cycle update intervals (forward/backward) i AI analysis (in detail view) m Switch AI model (shows available models) ? / h Show help q / Ctrl+C Quit Log Viewer Navigation Key Action Home Jump to top of log buffer (stops auto-scroll) End Jump to latest logs (resumes auto-scroll) PgUp / PgDn Navigate by pages (10 entries at a time) ↑ / ↓ or k / j Navigate entries with smart auto-scroll AI Chat (in log detail modal) Key Action c Start chat with AI about current log Tab Switch between log details and chat pane m Switch AI model (works in modal too) Log Counts Analysis Modal Press Enter on the Counts section to open a comprehensive analysis modal featuring: 🔥 Real-Time Heatmap Visualization Time-series heatmap showing severity levels vs. time (1-minute resolution) showing severity levels vs. time (1-minute resolution) 60-minute rolling window with automatic scaling per severity level with automatic scaling per severity level Color-coded intensity using ASCII characters (░▒▓█) with gradient effects using ASCII characters (░▒▓█) with gradient effects Precise alignment with time headers showing minutes ago (60, 50, 40, ..., 10, 0) with time headers showing minutes ago (60, 50, 40, ..., 10, 0) Receive time architecture - visualization based on when logs were received for reliable display 🔍 Pattern Analysis by Severity Top 3 patterns per severity using drain3 pattern extraction algorithm using drain3 pattern extraction algorithm Severity-specific tracking with dedicated drain3 instances for each level with dedicated drain3 instances for each level Real-time pattern detection as logs arrive and are processed as logs arrive and are processed Accurate pattern counts maintained separately for each severity level 🏢 Service Distribution Analysis Top 3 services per severity showing which services generate each log level showing which services generate each log level Service name extraction from common attributes (service.name, service, app, etc.) from common attributes (service.name, service, app, etc.) Real-time updates as new logs are processed and analyzed as new logs are processed and analyzed Fallback to host information when service names are not available ⌨️ Modal Navigation Scrollable content using mouse wheel or arrow keys using mouse wheel or arrow keys ESC to close and return to main dashboard and return to main dashboard Full-width display maximizing screen real estate for data visualization maximizing screen real estate for data visualization Real-time updates - data refreshes automatically as new logs arrive The modal uses the same receive time architecture as the main dashboard, ensuring consistent and reliable visualization regardless of log timestamp accuracy or clock skew issues. ⚙️ Configuration Command Line Options gonzo [flags] gonzo [command] Commands: version Print version information help Help about any command completion Generate shell autocompletion Flags: -f, --file stringArray Files or file globs to read logs from (can specify multiple) --follow Follow log files like ' tail -f ' (watch for new lines in real-time) -u, --update-interval duration Dashboard update interval (default: 1s) -b, --log-buffer int Maximum log entries to keep (default: 1000) -m, --memory-size int Maximum frequency entries (default: 10000) --ai-model string AI model for analysis (auto-selects best available if not specified) -t, --test-mode Run without TTY for testing -v, --version Print version information --config string Config file (default: $HOME /.config/gonzo/config.yml) -h, --help Show help message Configuration File Create ~/.config/gonzo/config.yml for persistent settings: # File input configuration files : - " /var/log/app.log " - " /var/log/error.log " - " /var/log/*.log " # Glob patterns supported follow : true # Enable follow mode (like tail -f) # Update frequency for dashboard refresh update-interval : 2s # Buffer sizes log-buffer : 2000 memory-size : 15000 # Development/testing test-mode : false # AI configuration ai-model : " gpt-4 " See examples/config.yaml for a complete configuration example with detailed comments. AI Configuration Gonzo supports multiple AI providers for intelligent log analysis. Configure using command line flags and environment variables. You can switch between available models at runtime using the m key. OpenAI # Set your API key export OPENAI_API_KEY= " sk-your-actual-key-here " # Auto-select best available model (recommended) cat logs.json | gonzo # Or specify a particular model cat logs.json | gonzo --ai-model= " gpt-4 " LM Studio (Local AI) # 1. Start LM Studio server with a model loaded # 2. Set environment variables (IMPORTANT: include /v1 in URL) export OPENAI_API_KEY= " local-key " export OPENAI_API_BASE= " http://localhost:1234/v1 " # Auto-select first available model (recommended) cat logs.json | gonzo # Or specify the exact model name from LM Studio cat logs.json | gonzo --ai-model= " openai/gpt-oss-120b " Ollama (Local AI) # 1. Start Ollama: ollama serve # 2. Pull a model: ollama pull gpt-oss:20b # 3. Set environment variables (note: no /v1 suffix needed) export OPENAI_API_KEY= " ollama " export OPENAI_API_BASE= " http://localhost:11434 " # Auto-select best model (prefers gpt-oss, llama3, mistral, etc.) cat logs.json | gonzo # Or specify a particular model cat logs.json | gonzo --ai-model= " gpt-oss:20b " cat logs.json | gonzo --ai-model= " llama3 " Custom OpenAI-Compatible APIs # For any OpenAI-compatible API endpoint export OPENAI_API_KEY= " your-api-key " export OPENAI_API_BASE= " https://api.your-provider.com/v1 " cat logs.json | gonzo --ai-model= " your-model-name " Runtime Model Switching Once Gonzo is running, you can switch between available AI models without restarting: Press m anywhere in the interface to open the model selection modal Navigate with arrow keys, page up/down, or mouse wheel Select a model with Enter Cancel with Escape The model selection modal shows: All available models from your configured AI provider Current active model (highlighted in green) Dynamic sizing based on terminal height Scroll indicators when there are many models Note: Model switching requires the AI service to be properly configured and running. The modal will only appear if models are available from your AI provider. Auto Model Selection When you don't specify the --ai-model flag, Gonzo automatically selects the best available model: Selection Priority: OpenAI: Prefers gpt-4 → gpt-3.5-turbo → first available Ollama: Prefers gpt-oss:20b → llama3 → mistral → codellama → first available LM Studio: Uses first available model from the server Other providers: Uses first available model Benefits: ✅ No need to know model names beforehand ✅ Works immediately with any AI provider ✅ Intelligent defaults for better performance ✅ Still allows manual model selection with m key Example: Instead of gonzo --ai-model="llama3" , simply run gonzo and it will auto-select llama3 if available. Troubleshooting AI Setup LM Studio Issues: ✅ Ensure server is running and model is loaded ✅ Use full model name: --ai-model="openai/model-name" ✅ Include /v1 in base URL: http://localhost:1234/v1 in base URL: ✅ Check available models: curl http://localhost:1234/v1/models Ollama Issues: ✅ Start server: ollama serve ✅ Verify model: ollama list ✅ Test API: curl http://localhost:11434/api/tags ✅ Use correct URL: http://localhost:11434 (no /v1 suffix) (no suffix) ✅ Model names include tags: gpt-oss:20b , llama3:8b OpenAI Issues: ✅ Verify API key is valid and has credits ✅ Check model availability (gpt-4 requires API access) Environment Variables Variable Description OPENAI_API_KEY API key for AI analysis (required for AI features) OPENAI_API_BASE Custom API endpoint (default: https://api.openai.com/v1) GONZO_FILES Comma-separated list of files/globs to read (equivalent to -f flags) GONZO_FOLLOW Enable follow mode (true/false) GONZO_UPDATE_INTERVAL Override update interval GONZO_LOG_BUFFER Override log buffer size GONZO_MEMORY_SIZE Override memory size GONZO_AI_MODEL Override default AI model GONZO_TEST_MODE Enable test mode NO_COLOR Disable colored output Shell Completion Enable shell completion for better CLI experience: # Bash source <( gonzo completion bash ) # Zsh source <( gonzo completion zsh ) # Fish gonzo completion fish | source # PowerShell gonzo completion powershell | Out-String | Invoke-Expression For permanent setup, save the completion script to your shell's completion directory. 🏗️ Architecture Gonzo is built with: Bubble Tea - Terminal UI framework - Terminal UI framework Lipgloss - Styling and layout - Styling and layout Bubbles - TUI components - TUI components Cobra - CLI framework - CLI framework Viper - Configuration management - Configuration management OpenTelemetry - Native OTLP support - Native OTLP support Large amounts of ☕️ The architecture follows a clean separation: cmd/gonzo/ # Main application entry internal/ ├── tui/ # Terminal UI implementation ├── analyzer/ # Log analysis engine ├── memory/ # Frequency tracking ├── otlplog/ # OTLP format handling └── ai/ # AI integration 🧪 Development Prerequisites Go 1.21 or higher Make (optional, for convenience) Building # Quick build make build # Run tests make test # Build for all platforms make cross-build # Development mode (format, vet, test, build) make dev Testing # Run unit tests make test # Run with race detection make test-race # Integration tests make test-integration # Test with sample data make demo 🤝 Contributing We love contributions! Please see CONTRIBUTING.md for details. Fork the repository Create your feature branch ( git checkout -b feature/amazing-feature ) Commit your changes ( git commit -m 'Add amazing feature' ) Push to the branch ( git push origin feature/amazing-feature ) Open a Pull Request 📝 License This project is licensed under the MIT License - see the LICENSE file for details. 🙏 Acknowledgments Inspired by k9s for the amazing TUI patterns Built with Charm libraries for beautiful terminal UIs OpenTelemetry community for the OTLP specifications 📚 Documentation Usage Guide - Detailed usage instructions Contributing Guide - How to contribute Changelog - Version history 🐛 Reporting Issues Found a bug? Please open an issue with: Your OS and Go version Steps to reproduce Expected vs actual behavior Log samples (sanitized if needed) ⭐ Star History If you find this project useful, please consider giving it a star! It helps others discover the tool. Made with ❤️ by ControlTheory and the Gonzo community