multi-agent-ralph-loop

🥈Silver

Multi-Agent AI Orchestration with GLM-4.7, Claude, Codex, and Gemini. Quality gates, memory system, and 67 hooks. Automates complex workflows, ensures code quality, and integrates with CI/CD pipelines. Benefits operations teams by reducing manual oversight and improving process reliability.

123130Updated 1mo ago

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/alfredolopez80/multi-agent-ralph-loop.git

Works with:

Claude

Overview

About This Skill

How to Use

["Prepare your workflow configuration: Create an `AGENT_CONFIG_FILE` (JSON/YAML) defining roles, memory hooks, and quality gates. Use the template: `{ \"agents\": { \"glm-4.7\": { \"role\": \"orchestrator\", \"hooks\": [\"plan_approved\", \"deploy_triggered\"] }, ... }, \"quality_gates\": { \"test_coverage\": 80, \"security_scan\": \"critical\" } }`.","Initialize the multi-agent system: Run `ralph-loop --init --config AGENT_CONFIG_FILE.json`. This sets up the memory system, hooks, and agent roles. Verify initialization with `ralph-loop --status`.","Execute the workflow: Use `ralph-loop --task \"Deploy user_auth module\" --input \"api/handlers.py,tests/auth_tests.py\"`. Monitor progress in real-time via the CLI or dashboard (e.g., `ralph-loop --monitor`).","Validate and integrate: After completion, check the `quality_gates` in the output. If all pass, use `ralph-loop --deploy --pipeline \"devops-pipeline-123\"` to push changes to your CI/CD system. For failures, review the `memory_hooks` to debug issues.","Optimize iteratively: After each run, analyze the `Agent Collaboration Score` and `Error Rate`. Adjust hooks or roles in the config file to improve efficiency. Use `ralph-loop --analyze --metrics \"collaboration_score,error_rate\"` to generate insights."]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/alfredolopez80/multi-agent-ralph-loop

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Orchestrate a multi-agent workflow to [TASK] using GLM-4.7, Claude, Codex, and Gemini. Follow these steps: 1) Initialize agents with [ROLES] and [MEMORY_HOOKS]. 2) Execute the workflow with [INPUT_DATA] and apply [QUALITY_GATES]. 3) Monitor progress via [MONITORING_HOOKS] and log outputs to [LOG_FILE]. 4) Validate results against [CRITERIA] and trigger [AUTOMATION_HOOKS] if thresholds are met. 5) Integrate final output into [CI/CD_PIPELINE]. Use [AGENT_CONFIG_FILE] for setup.

Example Output

```json
{
  "workflow_id": "ralph-loop-20241015-1422",
  "status": "completed",
  "agents": {
    "glm-4.7": {
      "role": "orchestrator",
      "output": {"plan": "validated", "next_steps": ["code_review", "test_automation"]},
      "memory_hook": "plan_approved"
    },
    "claude": {
      "role": "code_reviewer",
      "output": {"issues": [{"severity": "high", "file": "api/handlers.py", "line": 42}], "suggestions": ["refactor_logic", "add_unit_tests"]},
      "memory_hook": "review_completed"
    },
    "codex": {
      "role": "code_fixer",
      "output": {"changes": [{"file": "api/handlers.py", "diff": "- old_logic\n+ new_logic"}], "status": "applied"},
      "memory_hook": "fixes_applied"
    },
    "gemini": {
      "role": "test_automator",
      "output": {"test_coverage": "92%", "failed_tests": ["test_edge_case_12"], "status": "partial_pass"},
      "memory_hook": "tests_executed"
    }
  },
  "quality_gates": {
    "code_review": "passed",
    "test_coverage": "85% (threshold: 80%)",
    "security_scan": "no_critical_issues"
  },
  "automation_hooks": {
    "deploy_to_staging": "triggered",
    "notify_team": "slack_channel_#devops"
  },
  "ci_cd_integration": {
    "pipeline": "devops-pipeline-123",
    "status": "deployed_to_staging",
    "logs": "https://ci.example.com/builds/123/logs"
  },
  "next_actions": [
    "Monitor staging deployment for 30 minutes",
    "Run post-deployment smoke tests",
    "Update documentation if changes were significant"
  ]
}
```

### Key Metrics:
- **Total Execution Time:** 12 minutes 45 seconds
- **Agent Collaboration Score:** 94% (based on handoff efficiency)
- **Error Rate:** 0% (no manual interventions required)
- **Memory System Usage:** 67 hooks activated (100% coverage for this workflow)

### Observations:
The multi-agent system efficiently handled the deployment of the new `user_auth` module. Codex resolved 3 critical code issues identified by Claude, while Gemini ensured 92% test coverage—exceeding the 80% threshold. The GLM-4.7 orchestrator dynamically adjusted priorities when the security scan flagged a minor vulnerability in `auth_utils.py`, triggering a re-review by Claude. All quality gates passed, and the CI/CD pipeline automatically deployed the changes to staging. The memory system logged every decision point, enabling full traceability for compliance audits.