Multi-Agent Ralph Wiggum

关于

Multi-Agent Ralph Wiggum is a sophisticated orchestration system designed for AI-assisted coding, coordinating models like Claude, OpenAI Codex, Gemini, and MiniMax through iterative refinement loops. It tackles the challenge of inconsistent AI output by employing multi-model validation, quality gates, and adversarial consensus checks across an 8-step workflow. This system ensures quality and consistency, automatically preserving context across sessions, optimizing token usage, and reducing API costs while incorporating self-improvement capabilities.

主要功能

• LLM-TLDR token optimization providing 95% savings and 155x faster queries
• Enhanced Codex CLI security hardening with zero insecure auto-approvals and 100% sandbox isolation
• Multi-Agent Orchestration with 14 specialized agents and an 8-step workflow
• Robust Quality Assurance through 9-language quality gates and adversarial validation
• 19 GitHub stars
• Automatic Context Preservation using a ledger/handoff system to maintain session state

使用案例

• Developing high-quality, validated code through iterative AI refinement
• Generating structured product requirements documents (PRDs) and user stories for complex features
• Automating code security audits, bug hunting, and PR reviews with AI