OpenBug

Product Introduction

1. Definition: OpenBug is an open-source, AI-powered Command Line Interface (CLI) tool designed for real-time debugging of running applications. It falls under the technical categories of AI-assisted development tools, observability platforms, and CLI debugging utilities.
2. Core Value Proposition: OpenBug exists to automate complex debugging workflows by transforming bug reports into actionable code fixes. It eliminates manual log correlation and code tracing by using AI agents to investigate runtime logs, analyze code context, correlate issues across microservices, and generate fix diffs directly, while building a shared, git-based knowledge repository.

Main Features

1. Real-Time Log Capture & AI Analysis:

   • How it works: Execute any command (e.g., debug npm run dev, debug python app.py) in a terminal. OpenBug automatically captures stdout/stderr logs and streams them securely to its local WebSocket cluster (ws://127.0.0.1:4466). The AI agent analyzes these runtime logs in real-time.
   • Technology: Uses a local WebSocket server for log streaming and an Agent Graph architecture for processing. Logs are only sent when needed for AI queries.

2. Natural Language Code Search & Context Retrieval:

   • How it works: Developers ask questions like "Where is JWT validation handled?" or "Show auth endpoint code." The AI searches the local codebase using semantic understanding, retrieves relevant snippets, and provides context-aware answers without requiring code uploads.
   • Technology: Leverages local code indexing and semantic search algorithms. Only specific snippets referenced in queries are sent to the AI server.

3. Multi-Service Debugging & Correlation:

   • How it works: Run debug in separate terminals for frontend, backend, or other services. All connect to the same local cluster. The AI agent correlates logs and errors across all connected services, enabling diagnosis of complex, distributed issues (e.g., auth failures spanning UI and API).
   • Technology: Centralized local cluster management (OPENBUG_CLUSTER_URL) allowing cross-service context sharing for the AI.

4. Automated Fix Generation & Runbook Curation:

   • How it works: After diagnosing an issue, OpenBug generates a Git diff (diff) representing the fix. Every validated fix is automatically committed to a shared git runbook, building institutional knowledge.
   • Technology: AI-powered code generation produces patch files. Integrates with Git version control for persistent knowledge storage.

Problems Solved

1. Pain Point: Debugging Context Switching - Developers waste time manually switching between terminal logs, code editors, and documentation to trace errors. OpenBug unifies logs, code search, and AI analysis in one CLI/UI.
2. Pain Point: Distributed System Debugging - Diagnosing failures in microservices requires correlating logs across multiple services, a tedious and error-prone manual process. OpenBug provides a unified, AI-powered view.
3. Target Audience:
   • Full-Stack & Backend Engineers debugging complex applications.
   • Site Reliability Engineers (SREs) investigating production-like issues locally.
   • Engineering Managers seeking to reduce MTTR (Mean Time To Resolution) and build team knowledge.
   • Developers Onboarding to unfamiliar, large codebases needing efficient code navigation.
4. Use Cases:
   • Diagnosing runtime errors during local development or testing (debug npm test).
   • Understanding failure chains in Dockerized or microservice environments (debug docker-compose up).
   • Quickly locating implementation details in legacy or complex codebases via natural language.
   • Creating reproducible fixes and documented solutions for recurring bugs via the git runbook.

Unique Advantages

1. Differentiation vs. AI Code Assistants (Copilot/Cursor): Competitors focus on static code generation/completion. OpenBug uniquely integrates real-time runtime logs and live multi-service context, enabling dynamic debugging of actual application behavior, not just static code patterns.
2. Differentiation vs. Traditional Logging/Monitoring: Tools like Splunk or ELK require complex setup, centralized infrastructure, and lack deep code integration. OpenBug offers lightweight, local-first debugging with direct code context linking and AI-driven insights without external services.
3. Key Innovation: The Agent Graph architecture combined with the local WebSocket cluster enables seamless correlation of live logs and code context across services, executed entirely within the developer's environment. The git-based runbook turns individual fixes into persistent, searchable team knowledge.

Frequently Asked Questions (FAQ)

1. Is my code uploaded to OpenBug's servers?
   No. Your codebase remains entirely local. Only specific code snippets explicitly referenced in your natural language queries to the AI agent are sent to the server for analysis. Logs are only streamed when actively needed for debugging a query.

2. Can OpenBug debug applications written in any language?
   Yes. Since OpenBug operates by capturing stdout/stderr logs and searching local files, it is language-agnostic. It works with any command producing terminal output (debug npm run dev, debug python app.py, debug java -jar app.jar).

3. How does OpenBug handle privacy and security for logs?
   Logs are streamed only when the AI agent is actively processing a user query related to them. All communication uses authenticated WebSocket connections (via your API key). You control exactly which commands run under debug. Sensitive data should follow standard log redaction practices.

4. Can I use OpenBug with my existing OpenAI API key?
   The hosted version (app.openbug.ai) uses its own infrastructure. However, OpenBug is open-source (MIT license) and supports self-hosting. You can clone the server repo, configure it with your OpenAI API key (or compatible provider), and point the CLI to your self-hosted instance using WEB_SOCKET_URL and API_BASE_URL environment variables.

5. How does OpenBug compare to just using grep and tail -f?
   While grep and tail are powerful, they require manual pattern definition and lack semantic understanding. OpenBug adds AI-powered log analysis, natural language code search across the entire project, cross-service correlation, and automated fix generation, significantly accelerating the debugging process for complex issues.