# rootcoz

> Automatically diagnose CI/CD test failures with AI and get actionable root cause analysis in seconds

## Getting Started

- [Quickstart](quickstart.md): Install rootcoz via Docker Compose, configure your AI provider and CI system, register your first user, and run your first failure analysis in under five minutes
- [Setting Up the CLI](cli-setup.md): Install the rootcoz CLI tool, configure server connections in config.toml, authenticate with API keys, and run your first analysis from the command line

## User Guides

- [Analyzing Test Failures](analyzing-failures.md): Submit Jenkins jobs, JUnit XML files, or raw failure lists for AI-powered root cause analysis via the web UI, API, or CLI
- [Reviewing Analysis Results](reviewing-results.md): Navigate the report page, understand AI classifications (Product Bug, Test Bug, Infra Bug), review failures, override classifications, and track review progress
- [Chatting with AI About Failures](chatting-with-ai.md): Use the interactive chat feature to ask follow-up questions about analyzed jobs, explore root causes deeper, and get code fix suggestions
- [Creating Bug Reports from Analysis](creating-issues.md): Preview and create GitHub issues or Jira bugs directly from failure analysis results, with AI-generated titles, descriptions, and duplicate detection
- [Configuring Integrations](configuring-integrations.md): Set up Jenkins, Jira, GitHub, and Report Portal integrations including authentication, SSL settings, and per-request overrides
- [Using Multi-AI Peer Analysis](peer-analysis.md): Configure multiple AI providers to debate and reach consensus on failure classifications, improving accuracy through peer review rounds
- [Managing Users and Roles](managing-users.md): Register users, assign roles (viewer, reviewer, operator, admin), approve pending registrations, configure allow lists, and rotate API keys
- [Tracking Failure History and Reports](tracking-history.md): Use the failure history page to find recurring test failures, view per-test timelines, and generate analytics reports on classification trends and team metrics
- [Integrating with pytest](pytest-integration.md): Add the --analyze-with-ai flag to your pytest runs to automatically enrich JUnit XML reports with AI root cause analysis

## Recipes

- [Deployment Recipes](deployment-recipes.md): Common deployment patterns including Docker Compose production setup, OpenShift deployment, reverse proxy with trusted headers, and dev mode with hot reload
- [CLI Recipes](cli-recipes.md): Copy-paste CLI commands for common workflows: batch analysis, bulk classification overrides, filtered dashboards, exporting reports, and admin operations

## Reference

- [REST API Reference](api-reference.md): Complete reference for all API endpoints including analysis submission, results retrieval, comments, classifications, chat, admin operations, and SSE streams
- [CLI Command Reference](cli-reference.md): Full reference for all rootcoz CLI commands and subcommands: analyze, results, history, comments, classifications, chat, reports, config, auth, and admin
- [Environment Variables and Configuration](environment-variables.md): Complete list of all environment variables organized by category: Jenkins, AI providers, Jira, GitHub, Report Portal, authentication, server settings, and Web Push
