Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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admin
169
luzia_research_agent.py
Executable file
169
luzia_research_agent.py
Executable file
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#!/usr/bin/env python3
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"""
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Luzia Research Agent Loop - Interactive Research Task Processing
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Continuously monitors incoming research tasks and routes them intelligently
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based on security, speed, and complexity filters.
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Can run in:
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- Interactive mode: Process individual tasks
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- Loop mode: Monitor queue continuously (default)
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"""
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import sys
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import json
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import time
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from pathlib import Path
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from datetime import datetime
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sys.path.insert(0, '/opt/server-agents/orchestrator/lib')
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from research_agent import LuziaResearchAgent, TaskFilter, ToolRouter
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def run_interactive():
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"""Interactive mode: process user input"""
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agent = LuziaResearchAgent()
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print("\n" + "=" * 70)
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print("🔬 LUZIA RESEARCH AGENT - INTERACTIVE MODE")
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print("=" * 70)
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print("\nEnter research tasks to analyze. Type 'exit' to quit.\n")
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while True:
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try:
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task = input("📋 Research task: ").strip()
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if task.lower() in ['exit', 'quit', 'q']:
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print("✅ Exiting research agent")
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break
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if not task:
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print("⚠️ Empty task, try again\n")
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continue
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# Process the task
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result = agent.process_research_task(task)
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print(f"\n{result['analysis']['routing_summary']}")
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if result['clarification']:
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print("❓ Clarification Questions:")
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for q in result['clarification']['questions']:
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print(f" {q}")
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print()
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except KeyboardInterrupt:
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print("\n✅ Exiting research agent")
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break
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except Exception as e:
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print(f"❌ Error: {e}\n")
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def run_continuous():
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"""Continuous loop mode: monitor and process tasks"""
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agent = LuziaResearchAgent()
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queue_file = Path("/opt/server-agents/state/research-queue.json")
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agent.log("🔬 Luzia Research Agent started (continuous mode)")
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while True:
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try:
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# Check if queue file exists
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if not queue_file.exists():
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time.sleep(10)
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continue
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# Read queue
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with open(queue_file) as f:
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queue_data = json.load(f)
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pending = queue_data.get('pending', [])
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if not pending:
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time.sleep(10)
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continue
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# Process first pending task
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task_entry = pending[0]
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task_id = task_entry.get('id', str(uuid.uuid4()))
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task = task_entry.get('task', '')
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agent.log(f"📥 Processing task {task_id}: {task[:50]}...")
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# Analyze and route
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result = agent.process_research_task(task)
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# Move to processed
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queue_data['pending'].pop(0)
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queue_data['processed'] = queue_data.get('processed', [])
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queue_data['processed'].append({
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'id': task_id,
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'task': task,
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'timestamp': datetime.now().isoformat(),
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'analysis': result['analysis'],
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'status': result['status'],
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})
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# Write back
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with open(queue_file, 'w') as f:
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json.dump(queue_data, f, indent=2)
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agent.log(f"✅ Processed: {task[:50]}... → {result['analysis']['recommended_tool']}")
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# Brief sleep between tasks
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time.sleep(2)
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except KeyboardInterrupt:
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agent.log("🛑 Research agent stopped by user")
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break
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except Exception as e:
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agent.log(f"❌ Error in loop: {e}")
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time.sleep(5)
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def show_demo():
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"""Show demonstration of smart filtering"""
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agent = LuziaResearchAgent()
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demo_tasks = [
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("quick answer: what is REST?", "Simple question, no urgency"),
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("urgent critical vulnerability in auth system", "Critical security, needs immediate review"),
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("research distributed caching approaches and tradeoffs", "Complex decision, needs exploration"),
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("debug: zen-proxy max_tokens not working", "Error diagnosis, straightforward"),
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("design: REST vs GraphQL for new API", "Architecture decision, multiple perspectives"),
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]
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print("\n" + "=" * 80)
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print("🔬 LUZIA RESEARCH AGENT - SMART FILTER DEMONSTRATION")
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print("=" * 80)
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for task, context in demo_tasks:
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print(f"\n📋 Task: {task}")
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print(f" Context: {context}")
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print(" " + "-" * 70)
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analysis = agent.analyze_task(task)
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print(f" Security: {analysis['security']}")
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print(f" Speed: {analysis['speed']}")
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print(f" Complexity: {analysis['complexity']}")
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print(f" → Tool: {analysis['recommended_tool']}")
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print(f" Reason: {analysis['reasoning']}")
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if __name__ == "__main__":
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if len(sys.argv) > 1:
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if sys.argv[1] == "--demo":
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show_demo()
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elif sys.argv[1] == "--interactive":
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run_interactive()
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elif sys.argv[1] == "--continuous":
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run_continuous()
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else:
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print("Usage:")
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print(" luzia_research_agent.py --demo Show demonstration")
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print(" luzia_research_agent.py --interactive Interactive mode")
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print(" luzia_research_agent.py --continuous Continuous queue monitoring")
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else:
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# Default: interactive mode
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run_interactive()
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