What is RedRing?

RedRing is an evidence-driven developer diagnostics platform that helps developers identify and resolve environment-specific issues. The product is built around the idea that tools should inspect the local developer environment before producing recommendations.

Instead of guessing, RedRing gathers system context, runs stack-specific scanners, and then reasons over the evidence to provide grounded, practical guidance.

Why RedRing?

Most developer troubleshooting tools assume they already know your environment. RedRing takes a different approach by verifying the actual runtime state first, then making recommendations.

• Evidence-first diagnosis — collect and normalize environment facts before any conclusion.
• Developer-centric guidance — focus on actionable, explainable results.
• Explainable AI — AI helps describe findings, but the diagnosis is rooted in real evidence.
• Modular expansion — every supported technology is added as a separate scanner module.

Installation

The easiest way to install RedRing is with Python's package manager. The package is published on PyPI and installs with a single command:

This installs the `redring` CLI and its runtime dependencies. After installation, you can run the command directly from your terminal.

Quick Start

Start by initializing RedRing in your project directory. This generates the configuration and prepares the scan pipeline.

Once initialized, run a diagnosis against the current directory to inspect your environment and surface problems.

First Diagnosis

RedRing builds a context-aware report by collecting evidence from the local machine and then identifying the most likely root causes.

Example output includes detected environment issues, evidence summary, and recommended next steps in human-readable form.

Architecture

RedRing is built as a modular pipeline: CLI input leads to context collection, which drives scanner execution, then those results feed the diagnosis engine and AI reasoning layer.

Core components

• CLI layer — handles user commands and output formatting.
• Context builder — detects OS, runtime toolchains, and scan requirements.
• Scanner orchestrator — selects and runs scanners for the request.
• Scanner modules — collect structured evidence for each supported technology.
• Diagnosis engine — interprets evidence and identifies root causes.
• AI reasoning layer — turns evidence into clear explanations and remediation guidance.

Design principles

• Evidence-first diagnosis.
• Developer-centric guidance.
• Explainable AI.
• Modular expansion.
• Safety-oriented behavior.

Scanner Specification

A scanner is an isolated component that collects one specific piece of evidence from the local operating system. Scanners do not diagnose, explain, or modify the system.

Scanner responsibilities

• Collect one specific piece of information.
• Read system state only.
• Return structured evidence.
• Report failures safely.

Output format

Every scanner returns structured data with a clear status and evidence payload.

Scanner rules

• One responsibility — one scanner, one task.
• Read only — do not modify the OS.
• No AI — scanners only collect evidence.
• No printing — return structured results, not logs.
• Structured output — no untyped strings.

Discovery

Scanners are discovered through a registry rather than hardcoded technology checks. This makes RedRing more extensible and easier to maintain.

Roadmap

RedRing is guided by a phased release path that builds a foundation, expands support for developer ecosystems, and introduces extensibility and safe remediation workflows.

Phase 1 — Core foundation

• Project architecture and CLI foundations.
• Core scanner framework and early diagnostics.
• Initial Python ecosystem support.

Phase 2 — Web ecosystem

• Support for Node.js, npm, pnpm, yarn, and frontend build tools.
• Toolchain validation for modern JavaScript environments.

Phase 3 — Infrastructure

• Container and virtualization diagnostics for Docker, WSL, and VM environments.
• Database service discovery for PostgreSQL, MySQL, Redis, and MongoDB.

Phase 4 — Mobile toolchains

• Flutter, Android SDK, JDK, and mobile build environment validation.

Phase 5 — Extensibility

• Plugin architecture for third-party scanners and community modules.

Phase 6 — Intelligence & automated remediation

• Evidence-to-explanation engine for clearer root-cause analysis.
• Safe auto-fix workflows with reversible repairs.

Phase 7 — General availability

• Production-grade stability, cross-platform compatibility, and complete documentation.

Contributing

RedRing is open source and welcomes contributions that improve diagnosis accuracy, scanner coverage, documentation, and developer experience.

How to contribute

• Improve the CLI experience.
• Add new scanners for supported stacks.
• Refine diagnosis logic.
• Improve output clarity.
• Expand documentation and add tests.

Development setup

1. Clone the repository.
2. Create a Python virtual environment.
3. Install the project in editable mode.
4. Run tests and validation before submitting changes.

Contribution guidelines

• Write clear, readable Python code.
• Keep scanner logic separate from output and AI reasoning.
• Ensure new scanners return structured evidence and are easy to test.
• Prefer evidence-based diagnosis over heuristics.

Branch naming

• feature/docker-scanner
• bugfix/gpu-detection
• docs/roadmap-update

Commit messages

• feat: for new features
• fix: for bug fixes
• docs: for documentation changes
• refactor: for non-functional code changes
• test: for added or updated tests

Pull request checklist

• Describe the problem and your solution clearly.
• Add or update tests when appropriate.
• Update documentation for user-facing changes.
• Keep the code review focused and respectful.