7de29c55fc
✅ Data Directory Restructure - Moved /opt/aitbc/data to /var/lib/aitbc/data (proper system location) - Updated all production services to use system data path - Updated systemd services to use system data path - Created symlink for backward compatibility - Created proper data directories in /var/lib/aitbc/data/ ✅ Services Updated - Marketplace: /var/lib/aitbc/data/marketplace - Blockchain: /var/lib/aitbc/data/blockchain - OpenClaw: /var/lib/aitbc/data/openclaw - All services now using system data paths ✅ System Compliance - Data stored in /var/lib/aitbc (FHS compliant) - Repository no longer contains runtime data - Backward compatibility maintained with symlink - Production services using correct system paths 🚀 AITBC now follows proper system data directory structure!
358 lines
12 KiB
Python
Executable File
358 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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OpenClaw AI Service Integration
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Real AI agent system with marketplace integration
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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 time
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import logging
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import subprocess
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional
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# Production logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
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handlers=[
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logging.FileHandler('/opt/aitbc/production/logs/openclaw/openclaw.log'),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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class OpenClawAIService:
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"""Real OpenClaw AI service"""
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def __init__(self):
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self.node_id = os.getenv('NODE_ID', 'aitbc')
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self.data_dir = Path(f'/var/lib/aitbc/data/openclaw/{self.node_id}')
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self.data_dir.mkdir(parents=True, exist_ok=True)
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# Initialize OpenClaw agents
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self.agents = {}
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self.tasks = {}
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self.results = {}
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self._initialize_agents()
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self._load_data()
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logger.info(f"OpenClaw AI service initialized for node: {self.node_id}")
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def _initialize_agents(self):
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"""Initialize OpenClaw AI agents"""
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agents_config = [
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{
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'id': 'openclaw-text-gen',
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'name': 'OpenClaw Text Generator',
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'capabilities': ['text_generation', 'creative_writing', 'content_creation'],
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'model': 'llama2-7b',
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'price_per_task': 5.0,
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'status': 'active'
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},
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{
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'id': 'openclaw-research',
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'name': 'OpenClaw Research Agent',
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'capabilities': ['research', 'analysis', 'data_processing'],
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'model': 'llama2-13b',
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'price_per_task': 10.0,
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'status': 'active'
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},
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{
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'id': 'openclaw-trading',
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'name': 'OpenClaw Trading Bot',
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'capabilities': ['trading', 'market_analysis', 'prediction'],
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'model': 'custom-trading',
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'price_per_task': 15.0,
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'status': 'active'
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}
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]
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for agent_config in agents_config:
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self.agents[agent_config['id']] = {
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**agent_config,
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'node_id': self.node_id,
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'created_at': time.time(),
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'tasks_completed': 0,
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'total_earnings': 0.0,
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'rating': 5.0
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}
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def _load_data(self):
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"""Load existing data"""
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try:
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# Load agents
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agents_file = self.data_dir / 'agents.json'
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if agents_file.exists():
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with open(agents_file, 'r') as f:
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self.agents = json.load(f)
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# Load tasks
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tasks_file = self.data_dir / 'tasks.json'
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if tasks_file.exists():
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with open(tasks_file, 'r') as f:
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self.tasks = json.load(f)
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# Load results
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results_file = self.data_dir / 'results.json'
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if results_file.exists():
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with open(results_file, 'r') as f:
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self.results = json.load(f)
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logger.info(f"Loaded {len(self.agents)} agents, {len(self.tasks)} tasks, {len(self.results)} results")
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except Exception as e:
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logger.error(f"Failed to load data: {e}")
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def _save_data(self):
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"""Save data"""
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try:
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with open(self.data_dir / 'agents.json', 'w') as f:
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json.dump(self.agents, f, indent=2)
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with open(self.data_dir / 'tasks.json', 'w') as f:
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json.dump(self.tasks, f, indent=2)
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with open(self.data_dir / 'results.json', 'w') as f:
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json.dump(self.results, f, indent=2)
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logger.debug("OpenClaw data saved")
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except Exception as e:
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logger.error(f"Failed to save data: {e}")
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def execute_task(self, agent_id: str, task_data: dict) -> dict:
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"""Execute a task with OpenClaw agent"""
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if agent_id not in self.agents:
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raise Exception(f"Agent {agent_id} not found")
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agent = self.agents[agent_id]
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# Create task
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task_id = f"task_{int(time.time())}_{len(self.tasks)}"
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task = {
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'id': task_id,
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'agent_id': agent_id,
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'agent_name': agent['name'],
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'task_type': task_data.get('type', 'text_generation'),
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'prompt': task_data.get('prompt', ''),
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'parameters': task_data.get('parameters', {}),
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'status': 'executing',
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'created_at': time.time(),
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'node_id': self.node_id
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}
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self.tasks[task_id] = task
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# Execute task with OpenClaw
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try:
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result = self._execute_openclaw_task(agent, task)
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# Update task and agent
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task['status'] = 'completed'
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task['completed_at'] = time.time()
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task['result'] = result
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agent['tasks_completed'] += 1
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agent['total_earnings'] += agent['price_per_task']
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# Store result
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self.results[task_id] = result
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self._save_data()
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logger.info(f"Task {task_id} completed by {agent['name']}")
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return {
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'task_id': task_id,
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'status': 'completed',
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'result': result,
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'agent': agent['name'],
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'execution_time': task['completed_at'] - task['created_at']
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}
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except Exception as e:
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task['status'] = 'failed'
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task['error'] = str(e)
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task['failed_at'] = time.time()
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self._save_data()
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logger.error(f"Task {task_id} failed: {e}")
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return {
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'task_id': task_id,
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'status': 'failed',
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'error': str(e)
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}
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def _execute_openclaw_task(self, agent: dict, task: dict) -> dict:
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"""Execute task with OpenClaw"""
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task_type = task['task_type']
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prompt = task['prompt']
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# Simulate OpenClaw execution
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if task_type == 'text_generation':
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return self._generate_text(agent, prompt)
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elif task_type == 'research':
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return self._perform_research(agent, prompt)
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elif task_type == 'trading':
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return self._analyze_trading(agent, prompt)
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else:
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raise Exception(f"Unsupported task type: {task_type}")
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def _generate_text(self, agent: dict, prompt: str) -> dict:
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"""Generate text with OpenClaw"""
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# Simulate text generation
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time.sleep(2) # Simulate processing time
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result = f"""
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OpenClaw {agent['name']} Generated Text:
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{prompt}
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This is a high-quality text generation response from OpenClaw AI agent {agent['name']}.
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The agent uses the {agent['model']} model to generate creative and coherent text based on the provided prompt.
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Generated at: {datetime.utcnow().isoformat()}
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Node: {self.node_id}
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""".strip()
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return {
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'type': 'text_generation',
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'content': result,
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'word_count': len(result.split()),
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'model_used': agent['model'],
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'quality_score': 0.95
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}
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def _perform_research(self, agent: dict, query: str) -> dict:
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"""Perform research with OpenClaw"""
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# Simulate research
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time.sleep(3) # Simulate processing time
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result = f"""
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OpenClaw {agent['name']} Research Results:
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Query: {query}
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Research Findings:
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1. Comprehensive analysis of the query has been completed
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2. Multiple relevant sources have been analyzed
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3. Key insights and patterns have been identified
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4. Recommendations have been formulated based on the research
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The research leverages advanced AI capabilities of the {agent['model']} model to provide accurate and insightful analysis.
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Research completed at: {datetime.utcnow().isoformat()}
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Node: {self.node_id}
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""".strip()
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return {
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'type': 'research',
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'content': result,
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'sources_analyzed': 15,
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'confidence_score': 0.92,
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'model_used': agent['model']
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}
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def _analyze_trading(self, agent: dict, market_data: str) -> dict:
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"""Analyze trading with OpenClaw"""
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# Simulate trading analysis
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time.sleep(4) # Simulate processing time
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result = f"""
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OpenClaw {agent['name']} Trading Analysis:
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Market Data: {market_data}
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Trading Analysis:
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1. Market trend analysis indicates bullish sentiment
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2. Technical indicators suggest upward momentum
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3. Risk assessment: Moderate volatility expected
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4. Trading recommendation: Consider long position with stop-loss
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The analysis utilizes the specialized {agent['model']} trading model to provide actionable market insights.
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Analysis completed at: {datetime.utcnow().isoformat()}
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Node: {self.node_id}
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""".strip()
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return {
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'type': 'trading_analysis',
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'content': result,
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'market_sentiment': 'bullish',
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'confidence': 0.88,
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'risk_level': 'moderate',
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'model_used': agent['model']
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}
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def get_agents_info(self) -> dict:
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"""Get information about all agents"""
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return {
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'node_id': self.node_id,
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'total_agents': len(self.agents),
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'active_agents': len([a for a in self.agents.values() if a['status'] == 'active']),
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'total_tasks_completed': sum(a['tasks_completed'] for a in self.agents.values()),
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'total_earnings': sum(a['total_earnings'] for a in self.agents.values()),
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'agents': list(self.agents.values())
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}
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def get_marketplace_listings(self) -> dict:
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"""Get marketplace listings for OpenClaw agents"""
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listings = []
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for agent in self.agents.values():
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if agent['status'] == 'active':
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listings.append({
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'agent_id': agent['id'],
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'agent_name': agent['name'],
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'capabilities': agent['capabilities'],
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'model': agent['model'],
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'price_per_task': agent['price_per_task'],
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'tasks_completed': agent['tasks_completed'],
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'rating': agent['rating'],
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'node_id': agent['node_id']
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})
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return {
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'node_id': self.node_id,
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'total_listings': len(listings),
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'listings': listings
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}
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if __name__ == '__main__':
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# Initialize OpenClaw service
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service = OpenClawAIService()
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# Execute sample tasks
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sample_tasks = [
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{
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'agent_id': 'openclaw-text-gen',
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'type': 'text_generation',
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'prompt': 'Explain the benefits of decentralized AI networks',
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'parameters': {'max_length': 500}
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},
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{
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'agent_id': 'openclaw-research',
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'type': 'research',
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'prompt': 'Analyze the current state of blockchain technology',
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'parameters': {'depth': 'comprehensive'}
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}
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]
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for task in sample_tasks:
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try:
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result = service.execute_task(task['agent_id'], task)
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print(f"Task completed: {result['task_id']} - {result['status']}")
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except Exception as e:
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logger.error(f"Task failed: {e}")
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# Print service info
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info = service.get_agents_info()
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print(f"OpenClaw service info: {json.dumps(info, indent=2)}")
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