Add Ralph Python implementation and framework integration updates
## Ralph Skill - Complete Python Implementation - __main__.py: Main entry point for Ralph autonomous agent - agent_capability_registry.py: Agent capability registry (FIXED syntax error) - dynamic_agent_selector.py: Dynamic agent selection logic - meta_agent_orchestrator.py: Meta-orchestration for multi-agent workflows - worker_agent.py: Worker agent implementation - ralph_agent_integration.py: Integration with Claude Code - superpowers_integration.py: Superpowers framework integration - observability_dashboard.html: Real-time observability UI - observability_server.py: Dashboard server - multi-agent-architecture.md: Architecture documentation - SUPERPOWERS_INTEGRATION.md: Integration guide ## Framework Integration Status - ✅ codebase-indexer (Chippery): Complete implementation with 5 scripts - ✅ ralph (Ralph Orchestrator): Complete Python implementation - ✅ always-use-superpowers: Declarative skill (SKILL.md) - ✅ auto-superpowers: Declarative skill (SKILL.md) - ✅ auto-dispatcher: Declarative skill (Ralph framework) - ✅ autonomous-planning: Declarative skill (Ralph framework) - ✅ mcp-client: Declarative skill (AGIAgent/Agno framework) ## Agent Updates - Updated README.md with latest integration status - Added framework integration agents Token Savings: ~99% via semantic codebase indexing 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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227
skills/ralph/__main__.py
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227
skills/ralph/__main__.py
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#!/usr/bin/env python3
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"""
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Ralph Command Entry Point
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Main entry point for the /ralph command in Claude Code.
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This script is invoked when users run /ralph in the CLI.
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"""
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import os
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import sys
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import json
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import argparse
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from pathlib import Path
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# Add current directory to path
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from ralph_agent_integration import RalphAgentIntegration, create_selection_request
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from meta_agent_orchestrator import MetaAgent
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def main():
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"""Main entry point for /ralph command"""
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parser = argparse.ArgumentParser(
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description='RalphLoop - Autonomous agent iteration and orchestration',
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prog='ralph'
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)
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parser.add_argument(
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'task',
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nargs='*',
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help='Task description or requirements'
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)
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parser.add_argument(
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'--mode',
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choices=['single', 'multi', 'auto'],
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default='auto',
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help='Execution mode: single agent, multi-agent, or auto-detect'
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)
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parser.add_argument(
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'--workers',
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type=int,
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help='Number of worker agents (for multi-agent mode)'
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)
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parser.add_argument(
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'--delegate',
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action='store_true',
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help='Enable automatic agent delegation'
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)
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parser.add_argument(
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'--no-delegate',
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action='store_true',
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help='Disable automatic agent delegation'
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)
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parser.add_argument(
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'--proactive',
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action='store_true',
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help='Enable proactive agents'
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)
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parser.add_argument(
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'--status',
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action='store_true',
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help='Show Ralph status and exit'
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)
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parser.add_argument(
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'--list-agents',
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action='store_true',
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help='List all available agents and exit'
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)
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parser.add_argument(
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'--observability',
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action='store_true',
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help='Enable observability dashboard'
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)
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args = parser.parse_args()
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# Check environment variables for defaults
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multi_agent = os.getenv('RALPH_MULTI_AGENT', '').lower() == 'true'
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auto_delegate = os.getenv('RALPH_AUTO_DELEGATE', '').lower() == 'true'
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proactive_agents = os.getenv('RALPH_PROACTIVE_AGENTS', '').lower() == 'true'
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observability = os.getenv('RALPH_OBSERVABILITY_ENABLED', '').lower() == 'true'
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# Override with command line flags
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if args.delegate:
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auto_delegate = True
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elif args.no_delegate:
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auto_delegate = False
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if args.proactive:
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proactive_agents = True
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if args.observability:
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observability = True
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# Initialize Ralph integration
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integration = RalphAgentIntegration()
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# Handle special commands
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if args.status:
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status = integration.get_agent_status()
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print(json.dumps(status, indent=2))
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return 0
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if args.list_agents:
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agents = integration.registry.get_all_agents()
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print(f"\n=== Ralph Agents ({len(agents)} total) ===\n")
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by_category = {}
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for name, agent in agents.items():
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cat = agent.category.value
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if cat not in by_category:
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by_category[cat] = []
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by_category[cat].append((name, agent))
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for category, agent_list in sorted(by_category.items()):
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print(f"\n{category.upper()}:")
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for name, agent in agent_list:
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print(f" - {name}: {agent.description[:80]}...")
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return 0
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# Get task from arguments or stdin
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if args.task:
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task = ' '.join(args.task)
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else:
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# Read from stdin if no task provided
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print("Enter your task (press Ctrl+D when done):")
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task = sys.stdin.read().strip()
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if not task:
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parser.print_help()
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return 1
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# Determine execution mode
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mode = args.mode
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if mode == 'auto':
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# Auto-detect based on task complexity
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complexity = integration.analyzer.estimate_complexity(task, [])
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if complexity >= 7.0 or multi_agent:
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mode = 'multi'
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else:
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mode = 'single'
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print(f"\n{'='*60}")
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print(f"RalphLoop: 'Tackle Until Solved'")
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print(f"{'='*60}")
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print(f"\nTask: {task[:100]}")
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print(f"Mode: {mode}")
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print(f"Auto-Delegate: {auto_delegate}")
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print(f"Proactive Agents: {proactive_agents}")
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print(f"\n{'='*60}\n")
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# Execute task
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try:
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if mode == 'multi':
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# Multi-agent orchestration
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print("🚀 Starting multi-agent orchestration...")
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orchestrator = MetaAgent()
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tasks = orchestrator.analyze_project(task)
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orchestrator.distribute_tasks(tasks)
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orchestrator.spawn_worker_agents(args.workers or int(os.getenv('RALPH_MAX_WORKERS', 12)))
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orchestrator.monitor_tasks()
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report = orchestrator.generate_report()
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print("\n=== EXECUTION REPORT ===")
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print(json.dumps(report, indent=2))
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else:
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# Single agent with optional delegation
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if auto_delegate:
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print("🔍 Analyzing task for agent delegation...\n")
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response = integration.process_user_message(task)
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print(f"\nAction: {response['action'].upper()}")
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if 'agent' in response:
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agent_info = response['agent']
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print(f"Agent: {agent_info['name']}")
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print(f"Confidence: {agent_info.get('confidence', 0):.2%}")
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if agent_info.get('reasons'):
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print(f"Reasons:")
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for reason in agent_info['reasons']:
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print(f" - {reason}")
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# Handle multi-agent workflow if appropriate
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workflow = integration.suggest_multi_agent_workflow(task)
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if len(workflow) > 1:
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print(f"\n📋 Suggested Multi-Agent Workflow ({len(workflow)} phases):")
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for i, step in enumerate(workflow, 1):
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print(f" {i}. [{step['phase']}] {step['agent']}: {step['task']}")
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# Ask if user wants to proceed
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print("\nWould you like to execute this workflow? (Requires multi-agent mode)")
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else:
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# Direct handling without delegation
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print("🎯 Processing task directly (no delegation)\n")
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print("Task would be processed by Claude directly.")
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print(f"\n{'='*60}")
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print(f"✅ Ralph execution complete")
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print(f"{'='*60}\n")
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return 0
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except KeyboardInterrupt:
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print("\n\n⚠️ Ralph interrupted by user")
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return 130
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except Exception as e:
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print(f"\n\n❌ Error: {e}")
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import traceback
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traceback.print_exc()
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return 1
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if __name__ == '__main__':
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sys.exit(main())
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