oib/aitbc
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update database paths and fix foreign key references across coordinator API

Browse Source 
- Change SQLite database path from `/home/oib/windsurf/aitbc/data/` to `/opt/data/`
- Fix foreign key references to use correct table names (users, wallets, gpu_registry)
- Replace governance router with new governance and community routers
- Add multi-modal RL router to main application
- Simplify DEPLOYMENT_READINESS_REPORT.md to focus on production deployment status
- Update governance router with decentralized DAO voting
This commit is contained in:
oib
2026-02-26 19:32:06 +01:00
parent 1e2ea0bb9d
commit 7bb2905cca
89 changed files with 38245 additions and 1260 deletions
Download Patch File Download Diff File Expand all files Collapse all files

340
tests/openclaw_marketplace/README.md Normal file
View File

@@ -0,0 +1,340 @@
# OpenClaw Agent Marketplace Test Suite
Comprehensive test suite for the OpenClaw Agent Marketplace implementation covering Phase 8-10 of the AITBC roadmap.
## 🎯 Test Coverage
### Phase 8: Global AI Power Marketplace Expansion (Weeks 1-6)
#### 8.1 Multi-Region Marketplace Deployment (Weeks 1-2)
- **File**: `test_multi_region_deployment.py`
- **Coverage**:
- Geographic load balancing for marketplace transactions
- Edge computing nodes for AI power trading globally
- Multi-region redundancy and failover mechanisms
- Global marketplace monitoring and analytics
- Performance targets: <100ms response time, 99.9% uptime
#### 8.2 Blockchain Smart Contract Integration (Weeks 3-4)
- **File**: `test_blockchain_integration.py`
- **Coverage**:
- AI power rental smart contracts
- Payment processing contracts
- Escrow services for transactions
- Performance verification contracts
- Dispute resolution mechanisms
- Dynamic pricing contracts
#### 8.3 OpenClaw Agent Economics Enhancement (Weeks 5-6)
- **File**: `test_agent_economics.py`
- **Coverage**:
- Advanced agent reputation and trust systems
- Performance-based reward mechanisms
- Agent-to-agent AI power trading protocols
- Marketplace analytics and economic insights
- Agent certification and partnership programs
### Phase 9: Advanced Agent Capabilities & Performance (Weeks 7-12)
#### 9.1 Enhanced OpenClaw Agent Performance (Weeks 7-9)
- **File**: `test_advanced_agent_capabilities.py`
- **Coverage**:
- Advanced meta-learning for faster skill acquisition
- Self-optimizing agent resource management
- Multi-modal agent fusion for enhanced capabilities
- Advanced reinforcement learning for marketplace strategies
- Agent creativity and specialized AI capability development
#### 9.2 Marketplace Performance Optimization (Weeks 10-12)
- **File**: `test_performance_optimization.py`
- **Coverage**:
- GPU acceleration and resource utilization optimization
- Distributed agent processing frameworks
- Advanced caching and optimization for marketplace data
- Real-time marketplace performance monitoring
- Adaptive resource scaling for marketplace demand
### Phase 10: OpenClaw Agent Community & Governance (Weeks 13-18)
#### 10.1 Agent Community Development (Weeks 13-15)
- **File**: `test_agent_governance.py`
- **Coverage**:
- Comprehensive OpenClaw agent development tools and SDKs
- Agent innovation labs and research programs
- Marketplace for third-party agent solutions
- Agent community support and collaboration platforms
#### 10.2 Decentralized Agent Governance (Weeks 16-18)
- **Coverage**:
- Token-based voting and governance mechanisms
- Decentralized autonomous organization (DAO) for agent ecosystem
- Community proposal and voting systems
- Governance analytics and transparency reporting
- Agent certification and partnership programs
## 🚀 Quick Start
### Prerequisites
- Python 3.13+
- pytest with plugins:
```bash
pip install pytest pytest-asyncio pytest-json-report httpx requests numpy psutil
```
### Running Tests
#### Run All Test Suites
```bash
cd tests/openclaw_marketplace
python run_all_tests.py
```
#### Run Individual Test Suites
```bash
# Framework tests
pytest test_framework.py -v
# Multi-region deployment tests
pytest test_multi_region_deployment.py -v
# Blockchain integration tests
pytest test_blockchain_integration.py -v
# Agent economics tests
pytest test_agent_economics.py -v
# Advanced agent capabilities tests
pytest test_advanced_agent_capabilities.py -v
# Performance optimization tests
pytest test_performance_optimization.py -v
# Governance tests
pytest test_agent_governance.py -v
```
#### Run Specific Test Classes
```bash
# Test only marketplace health
pytest test_multi_region_deployment.py::TestRegionHealth -v
# Test only smart contracts
pytest test_blockchain_integration.py::TestAIPowerRentalContract -v
# Test only agent reputation
pytest test_agent_economics.py::TestAgentReputationSystem -v
```
## 📊 Test Metrics and Targets
### Performance Targets
- **Response Time**: <50ms for marketplace operations
- **Throughput**: >1000 requests/second
- **GPU Utilization**: >90% efficiency
- **Cache Hit Rate**: >85%
- **Uptime**: 99.9% availability globally
### Economic Targets
- **AITBC Trading Volume**: 10,000+ daily
- **Agent Participation**: 5,000+ active agents
- **AI Power Transactions**: 1,000+ daily rentals
- **Transaction Speed**: <30 seconds settlement
- **Payment Reliability**: 99.9% success rate
### Governance Targets
- **Proposal Success Rate**: >60% approval threshold
- **Voter Participation**: >40% quorum
- **Trust System Accuracy**: >95%
- **Transparency Rating**: >80%
## 🛠️ CLI Tools
The enhanced marketplace CLI provides comprehensive operations:
### Agent Operations
```bash
# Register agent
aitbc marketplace agents register --agent-id agent001 --agent-type compute_provider --capabilities "gpu_computing,ai_inference"
# List agents
aitbc marketplace agents list --agent-type compute_provider --reputation-min 0.8
# List AI resource
aitbc marketplace agents list-resource --resource-id gpu001 --resource-type nvidia_a100 --price-per-hour 2.5
# Rent AI resource
aitbc marketplace agents rent --resource-id gpu001 --consumer-id consumer001 --duration 4
# Check agent reputation
aitbc marketplace agents reputation --agent-id agent001
# Check agent balance
aitbc marketplace agents balance --agent-id agent001
```
### Governance Operations
```bash
# Create proposal
aitbc marketplace governance create-proposal --title "Reduce Fees" --proposal-type parameter_change --params '{"transaction_fee": 0.02}'
# Vote on proposal
aitbc marketplace governance vote --proposal-id prop001 --vote for --reasoning "Good for ecosystem"
# List proposals
aitbc marketplace governance list-proposals --status active
```
### Blockchain Operations
```bash
# Execute smart contract
aitbc marketplace agents execute-contract --contract-type ai_power_rental --params '{"resourceId": "gpu001", "duration": 4}'
# Process payment
aitbc marketplace agents pay --from-agent consumer001 --to-agent provider001 --amount 10.0
```
### Testing Operations
```bash
# Run load test
aitbc marketplace test load --concurrent-users 50 --rps 100 --duration 60
# Check health
aitbc marketplace test health
```
## 📈 Test Reports
### JSON Reports
Test results are automatically saved in JSON format:
- `test_results.json` - Comprehensive test run results
- Individual suite reports in `/tmp/test_report.json`
### Report Structure
```json
{
"test_run_summary": {
"start_time": "2026-02-26T12:00:00",
"end_time": "2026-02-26T12:05:00",
"total_duration": 300.0,
"total_suites": 7,
"passed_suites": 7,
"failed_suites": 0,
"success_rate": 100.0
},
"suite_results": {
"framework": { ... },
"multi_region": { ... },
...
},
"recommendations": [ ... ]
}
```
## 🔧 Configuration
### Environment Variables
```bash
# Marketplace configuration
export AITBC_COORDINATOR_URL="http://127.0.0.1:18000"
export AITBC_API_KEY="your-api-key"
# Test configuration
export PYTEST_JSON_REPORT_FILE="/tmp/test_report.json"
export AITBC_TEST_TIMEOUT=30
```
### Test Configuration
Tests can be configured via pytest configuration:
```ini
[tool:pytest]
testpaths = .
python_files = test_*.py
python_classes = Test*
python_functions = test_*
addopts = -v --tb=short --json-report --json-report-file=/tmp/test_report.json
asyncio_mode = auto
```
## 🐛 Troubleshooting
### Common Issues
#### Test Failures
1. **Connection Errors**: Check marketplace service is running
2. **Timeout Errors**: Increase `AITBC_TEST_TIMEOUT`
3. **Authentication Errors**: Verify API key configuration
#### Performance Issues
1. **Slow Tests**: Check system resources and GPU availability
2. **Memory Issues**: Reduce concurrent test users
3. **Network Issues**: Verify localhost connectivity
#### Debug Mode
Run tests with additional debugging:
```bash
pytest test_framework.py -v -s --tb=long --log-cli-level=DEBUG
```
## 📝 Test Development
### Adding New Tests
1. Create test class inheriting from appropriate base
2. Use async/await for async operations
3. Follow naming convention: `test_*`
4. Add comprehensive assertions
5. Include error handling
### Test Structure
```python
class TestNewFeature:
@pytest.mark.asyncio
async def test_new_functionality(self, test_fixture):
# Arrange
setup_data = {...}
# Act
result = await test_function(setup_data)
# Assert
assert result.success is True
assert result.data is not None
```
## 🎯 Success Criteria
### Phase 8 Success
- ✅ Multi-region deployment with <100ms latency
- ✅ Smart contract execution with <30s settlement
- ✅ Agent economics with 99.9% payment reliability
### Phase 9 Success
- ✅ Advanced agent capabilities with meta-learning
- ✅ Performance optimization with >90% GPU utilization
- ✅ Marketplace throughput >1000 req/s
### Phase 10 Success
- ✅ Community tools with comprehensive SDKs
- ✅ Governance systems with token-based voting
- ✅ DAO formation with transparent operations
## 📞 Support
For test-related issues:
1. Check test reports for detailed error information
2. Review logs for specific failure patterns
3. Verify environment configuration
4. Consult individual test documentation
## 🚀 Next Steps
After successful test completion:
1. Deploy to staging environment
2. Run integration tests with real blockchain
3. Conduct security audit
4. Performance testing under production load
5. Deploy to production with monitoring
---
**Note**: This test suite is designed for the OpenClaw Agent Marketplace implementation and covers all aspects of Phase 8-10 of the AITBC roadmap. Ensure all prerequisites are met before running tests.

223
tests/openclaw_marketplace/run_all_tests.py Normal file
View File

@@ -0,0 +1,223 @@
#!/usr/bin/env python3
"""
Comprehensive OpenClaw Agent Marketplace Test Runner
Executes all test suites for Phase 8-10 implementation
"""
import pytest
import sys
import os
import time
import json
from pathlib import Path
from datetime import datetime
from typing import Dict, List, Any
# Add the tests directory to Python path
test_dir = Path(__file__).parent
sys.path.insert(0, str(test_dir))
class OpenClawTestRunner:
"""Comprehensive test runner for OpenClaw Agent Marketplace"""
def __init__(self):
self.test_suites = {
"framework": "test_framework.py",
"multi_region": "test_multi_region_deployment.py",
"blockchain": "test_blockchain_integration.py",
"economics": "test_agent_economics.py",
"capabilities": "test_advanced_agent_capabilities.py",
"performance": "test_performance_optimization.py",
"governance": "test_agent_governance.py"
}
self.results = {}
self.start_time = datetime.now()
def run_test_suite(self, suite_name: str, test_file: str) -> Dict[str, Any]:
"""Run a specific test suite"""
print(f"\n{'='*60}")
print(f"Running {suite_name.upper()} Test Suite")
print(f"{'='*60}")
start_time = time.time()
# Configure pytest arguments
pytest_args = [
str(test_dir / test_file),
"-v",
"--tb=short",
"--json-report",
"--json-report-file=/tmp/test_report.json",
"-x" # Stop on first failure for debugging
]
# Run pytest and capture results
exit_code = pytest.main(pytest_args)
end_time = time.time()
duration = end_time - start_time
# Load JSON report if available
report_file = "/tmp/test_report.json"
test_results = {}
if os.path.exists(report_file):
try:
with open(report_file, 'r') as f:
test_results = json.load(f)
except Exception as e:
print(f"Warning: Could not load test report: {e}")
suite_result = {
"suite_name": suite_name,
"exit_code": exit_code,
"duration": duration,
"timestamp": datetime.now().isoformat(),
"test_results": test_results,
"success": exit_code == 0
}
# Print summary
if exit_code == 0:
print(f"✅ {suite_name.upper()} tests PASSED ({duration:.2f}s)")
else:
print(f"❌ {suite_name.upper()} tests FAILED ({duration:.2f}s)")
if test_results.get("summary"):
summary = test_results["summary"]
print(f" Tests: {summary.get('total', 0)}")
print(f" Passed: {summary.get('passed', 0)}")
print(f" Failed: {summary.get('failed', 0)}")
print(f" Skipped: {summary.get('skipped', 0)}")
return suite_result
def run_all_tests(self) -> Dict[str, Any]:
"""Run all test suites"""
print(f"\n🚀 Starting OpenClaw Agent Marketplace Test Suite")
print(f"📅 Started at: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}")
print(f"📁 Test directory: {test_dir}")
total_suites = len(self.test_suites)
passed_suites = 0
for suite_name, test_file in self.test_suites.items():
result = self.run_test_suite(suite_name, test_file)
self.results[suite_name] = result
if result["success"]:
passed_suites += 1
end_time = datetime.now()
total_duration = (end_time - self.start_time).total_seconds()
# Generate final report
final_report = {
"test_run_summary": {
"start_time": self.start_time.isoformat(),
"end_time": end_time.isoformat(),
"total_duration": total_duration,
"total_suites": total_suites,
"passed_suites": passed_suites,
"failed_suites": total_suites - passed_suites,
"success_rate": (passed_suites / total_suites) * 100
},
"suite_results": self.results,
"recommendations": self._generate_recommendations()
}
# Print final summary
self._print_final_summary(final_report)
# Save detailed report
report_file = test_dir / "test_results.json"
with open(report_file, 'w') as f:
json.dump(final_report, f, indent=2)
print(f"\n📄 Detailed report saved to: {report_file}")
return final_report
def _generate_recommendations(self) -> List[str]:
"""Generate recommendations based on test results"""
recommendations = []
failed_suites = [name for name, result in self.results.items() if not result["success"]]
if failed_suites:
recommendations.append(f"🔧 Fix failing test suites: {', '.join(failed_suites)}")
# Check for specific patterns
for suite_name, result in self.results.items():
if not result["success"]:
if suite_name == "framework":
recommendations.append("🏗️ Review test framework setup and configuration")
elif suite_name == "multi_region":
recommendations.append("🌍 Check multi-region deployment configuration")
elif suite_name == "blockchain":
recommendations.append("⛓️ Verify blockchain integration and smart contracts")
elif suite_name == "economics":
recommendations.append("💰 Review agent economics and payment systems")
elif suite_name == "capabilities":
recommendations.append("🤖 Check advanced agent capabilities and AI models")
elif suite_name == "performance":
recommendations.append("⚡ Optimize marketplace performance and resource usage")
elif suite_name == "governance":
recommendations.append("🏛️ Review governance systems and DAO functionality")
if not failed_suites:
recommendations.append("🎉 All tests passed! Ready for production deployment")
recommendations.append("📈 Consider running performance tests under load")
recommendations.append("🔍 Conduct security audit before production")
return recommendations
def _print_final_summary(self, report: Dict[str, Any]):
"""Print final test summary"""
summary = report["test_run_summary"]
print(f"\n{'='*80}")
print(f"🏁 OPENCLAW MARKETPLACE TEST SUITE COMPLETED")
print(f"{'='*80}")
print(f"📊 Total Duration: {summary['total_duration']:.2f} seconds")
print(f"📈 Success Rate: {summary['success_rate']:.1f}%")
print(f"✅ Passed Suites: {summary['passed_suites']}/{summary['total_suites']}")
print(f"❌ Failed Suites: {summary['failed_suites']}/{summary['total_suites']}")
if summary['failed_suites'] == 0:
print(f"\n🎉 ALL TESTS PASSED! 🎉")
print(f"🚀 OpenClaw Agent Marketplace is ready for deployment!")
else:
print(f"\n⚠️ {summary['failed_suites']} test suite(s) failed")
print(f"🔧 Please review and fix issues before deployment")
print(f"\n📋 RECOMMENDATIONS:")
for i, rec in enumerate(report["recommendations"], 1):
print(f" {i}. {rec}")
print(f"\n{'='*80}")
def main():
"""Main entry point"""
runner = OpenClawTestRunner()
try:
results = runner.run_all_tests()
# Exit with appropriate code
if results["test_run_summary"]["failed_suites"] == 0:
print(f"\n✅ All tests completed successfully!")
sys.exit(0)
else:
print(f"\n❌ Some tests failed. Check the report for details.")
sys.exit(1)
except KeyboardInterrupt:
print(f"\n⏹️ Test run interrupted by user")
sys.exit(130)
except Exception as e:
print(f"\n💥 Unexpected error: {e}")
sys.exit(1)
if __name__ == "__main__":
main()

965
tests/openclaw_marketplace/test_advanced_agent_capabilities.py Normal file
View File

@@ -0,0 +1,965 @@
#!/usr/bin/env python3
"""
Advanced Agent Capabilities Tests
Phase 9.1: Enhanced OpenClaw Agent Performance (Weeks 7-9)
"""
import pytest
import asyncio
import time
import json
import requests
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import logging
from enum import Enum
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class LearningAlgorithm(Enum):
"""Machine learning algorithms for agents"""
Q_LEARNING = "q_learning"
DEEP_Q_NETWORK = "deep_q_network"
ACTOR_CRITIC = "actor_critic"
PPO = "ppo"
REINFORCE = "reinforce"
SARSA = "sarsa"
class AgentCapability(Enum):
"""Advanced agent capabilities"""
META_LEARNING = "meta_learning"
SELF_OPTIMIZATION = "self_optimization"
MULTIMODAL_FUSION = "multimodal_fusion"
REINFORCEMENT_LEARNING = "reinforcement_learning"
CREATIVITY = "creativity"
SPECIALIZATION = "specialization"
@dataclass
class AgentSkill:
"""Agent skill definition"""
skill_id: str
skill_name: str
skill_type: str
proficiency_level: float
learning_rate: float
acquisition_date: datetime
last_used: datetime
usage_count: int
@dataclass
class LearningEnvironment:
"""Learning environment configuration"""
environment_id: str
environment_type: str
state_space: Dict[str, Any]
action_space: Dict[str, Any]
reward_function: str
constraints: List[str]
@dataclass
class ResourceAllocation:
"""Resource allocation for agents"""
agent_id: str
cpu_cores: int
memory_gb: float
gpu_memory_gb: float
network_bandwidth_mbps: float
storage_gb: float
allocation_strategy: str
class AdvancedAgentCapabilitiesTests:
"""Test suite for advanced agent capabilities"""
def __init__(self, agent_service_url: str = "http://127.0.0.1:8005"):
self.agent_service_url = agent_service_url
self.agents = self._setup_agents()
self.skills = self._setup_skills()
self.learning_environments = self._setup_learning_environments()
self.session = requests.Session()
self.session.timeout = 30
def _setup_agents(self) -> List[Dict[str, Any]]:
"""Setup advanced agents for testing"""
return [
{
"agent_id": "advanced_agent_001",
"agent_type": "meta_learning_agent",
"capabilities": [
AgentCapability.META_LEARNING,
AgentCapability.SELF_OPTIMIZATION,
AgentCapability.MULTIMODAL_FUSION
],
"learning_algorithms": [
LearningAlgorithm.DEEP_Q_NETWORK,
LearningAlgorithm.ACTOR_CRITIC,
LearningAlgorithm.PPO
],
"performance_metrics": {
"learning_speed": 0.85,
"adaptation_rate": 0.92,
"problem_solving": 0.88,
"creativity_score": 0.76
},
"resource_needs": {
"min_cpu_cores": 8,
"min_memory_gb": 16,
"min_gpu_memory_gb": 8,
"preferred_gpu_type": "nvidia_a100"
}
},
{
"agent_id": "creative_agent_001",
"agent_type": "creative_specialist",
"capabilities": [
AgentCapability.CREATIVITY,
AgentCapability.SPECIALIZATION,
AgentCapability.MULTIMODAL_FUSION
],
"learning_algorithms": [
LearningAlgorithm.REINFORCE,
LearningAlgorithm.ACTOR_CRITIC
],
"performance_metrics": {
"creativity_score": 0.94,
"innovation_rate": 0.87,
"specialization_depth": 0.91,
"cross_domain_application": 0.82
},
"resource_needs": {
"min_cpu_cores": 12,
"min_memory_gb": 32,
"min_gpu_memory_gb": 16,
"preferred_gpu_type": "nvidia_h100"
}
},
{
"agent_id": "optimization_agent_001",
"agent_type": "resource_optimizer",
"capabilities": [
AgentCapability.SELF_OPTIMIZATION,
AgentCapability.REINFORCEMENT_LEARNING
],
"learning_algorithms": [
LearningAlgorithm.Q_LEARNING,
LearningAlgorithm.PPO,
LearningAlgorithm.SARSA
],
"performance_metrics": {
"optimization_efficiency": 0.96,
"resource_utilization": 0.89,
"cost_reduction": 0.84,
"adaptation_speed": 0.91
},
"resource_needs": {
"min_cpu_cores": 6,
"min_memory_gb": 12,
"min_gpu_memory_gb": 4,
"preferred_gpu_type": "nvidia_a100"
}
}
]
def _setup_skills(self) -> List[AgentSkill]:
"""Setup agent skills for testing"""
return [
AgentSkill(
skill_id="multimodal_processing_001",
skill_name="Advanced Multi-Modal Processing",
skill_type="technical",
proficiency_level=0.92,
learning_rate=0.15,
acquisition_date=datetime.now() - timedelta(days=30),
last_used=datetime.now() - timedelta(hours=2),
usage_count=145
),
AgentSkill(
skill_id="market_analysis_001",
skill_name="Market Trend Analysis",
skill_type="analytical",
proficiency_level=0.87,
learning_rate=0.12,
acquisition_date=datetime.now() - timedelta(days=45),
last_used=datetime.now() - timedelta(hours=6),
usage_count=89
),
AgentSkill(
skill_id="creative_problem_solving_001",
skill_name="Creative Problem Solving",
skill_type="creative",
proficiency_level=0.79,
learning_rate=0.18,
acquisition_date=datetime.now() - timedelta(days=20),
last_used=datetime.now() - timedelta(hours=1),
usage_count=34
)
]
def _setup_learning_environments(self) -> List[LearningEnvironment]:
"""Setup learning environments for testing"""
return [
LearningEnvironment(
environment_id="marketplace_optimization_001",
environment_type="reinforcement_learning",
state_space={
"market_conditions": 10,
"agent_performance": 5,
"resource_availability": 8
},
action_space={
"pricing_adjustments": 5,
"resource_allocation": 7,
"strategy_selection": 4
},
reward_function="profit_maximization_with_constraints",
constraints=["fair_trading", "resource_limits", "market_stability"]
),
LearningEnvironment(
environment_id="skill_acquisition_001",
environment_type="meta_learning",
state_space={
"current_skills": 20,
"learning_progress": 15,
"performance_history": 50
},
action_space={
"skill_selection": 25,
"learning_strategy": 6,
"resource_allocation": 8
},
reward_function="skill_acquisition_efficiency",
constraints=["cognitive_load", "time_constraints", "resource_budget"]
)
]
async def test_meta_learning_capability(self, agent_id: str, learning_tasks: List[str]) -> Dict[str, Any]:
"""Test advanced meta-learning for faster skill acquisition"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test meta-learning setup
meta_learning_payload = {
"agent_id": agent_id,
"learning_tasks": learning_tasks,
"meta_learning_algorithm": "MAML", # Model-Agnostic Meta-Learning
"adaptation_steps": 5,
"meta_batch_size": 32,
"inner_learning_rate": 0.01,
"outer_learning_rate": 0.001
}
response = self.session.post(
f"{self.agent_service_url}/v1/meta-learning/setup",
json=meta_learning_payload,
timeout=20
)
if response.status_code == 200:
setup_result = response.json()
# Test meta-learning training
training_payload = {
"agent_id": agent_id,
"training_episodes": 100,
"task_distribution": "uniform",
"adaptation_evaluation": True
}
training_response = self.session.post(
f"{self.agent_service_url}/v1/meta-learning/train",
json=training_payload,
timeout=60
)
if training_response.status_code == 200:
training_result = training_response.json()
return {
"agent_id": agent_id,
"learning_tasks": learning_tasks,
"setup_result": setup_result,
"training_result": training_result,
"adaptation_speed": training_result.get("adaptation_speed"),
"meta_learning_efficiency": training_result.get("efficiency"),
"skill_acquisition_rate": training_result.get("skill_acquisition_rate"),
"success": True
}
else:
return {
"agent_id": agent_id,
"setup_result": setup_result,
"training_error": f"Training failed with status {training_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"Meta-learning setup failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_self_optimizing_resource_management(self, agent_id: str, initial_allocation: ResourceAllocation) -> Dict[str, Any]:
"""Test self-optimizing agent resource management"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test resource optimization setup
optimization_payload = {
"agent_id": agent_id,
"initial_allocation": asdict(initial_allocation),
"optimization_objectives": [
"minimize_cost",
"maximize_performance",
"balance_utilization"
],
"optimization_algorithm": "reinforcement_learning",
"optimization_horizon": "24h",
"constraints": {
"max_cost_per_hour": 10.0,
"min_performance_threshold": 0.85,
"max_resource_waste": 0.15
}
}
response = self.session.post(
f"{self.agent_service_url}/v1/resource-optimization/setup",
json=optimization_payload,
timeout=15
)
if response.status_code == 200:
setup_result = response.json()
# Test optimization execution
execution_payload = {
"agent_id": agent_id,
"optimization_period_hours": 24,
"performance_monitoring": True,
"auto_adjustment": True
}
execution_response = self.session.post(
f"{self.agent_service_url}/v1/resource-optimization/execute",
json=execution_payload,
timeout=30
)
if execution_response.status_code == 200:
execution_result = execution_response.json()
return {
"agent_id": agent_id,
"initial_allocation": asdict(initial_allocation),
"optimized_allocation": execution_result.get("optimized_allocation"),
"cost_savings": execution_result.get("cost_savings"),
"performance_improvement": execution_result.get("performance_improvement"),
"resource_utilization": execution_result.get("resource_utilization"),
"optimization_efficiency": execution_result.get("efficiency"),
"success": True
}
else:
return {
"agent_id": agent_id,
"setup_result": setup_result,
"execution_error": f"Optimization execution failed with status {execution_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"Resource optimization setup failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_multimodal_agent_fusion(self, agent_id: str, modalities: List[str]) -> Dict[str, Any]:
"""Test multi-modal agent fusion for enhanced capabilities"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test multimodal fusion setup
fusion_payload = {
"agent_id": agent_id,
"input_modalities": modalities,
"fusion_architecture": "cross_modal_attention",
"fusion_strategy": "adaptive_weighting",
"output_modalities": ["unified_representation"],
"performance_targets": {
"fusion_accuracy": 0.90,
"processing_speed": 0.5, # seconds
"memory_efficiency": 0.85
}
}
response = self.session.post(
f"{self.agent_service_url}/v1/multimodal-fusion/setup",
json=fusion_payload,
timeout=20
)
if response.status_code == 200:
setup_result = response.json()
# Test fusion processing
processing_payload = {
"agent_id": agent_id,
"test_inputs": {
"text": "Analyze market trends for AI compute resources",
"image": "market_chart.png",
"audio": "market_analysis.wav",
"tabular": "price_data.csv"
},
"fusion_evaluation": True
}
processing_response = self.session.post(
f"{self.agent_service_url}/v1/multimodal-fusion/process",
json=processing_payload,
timeout=25
)
if processing_response.status_code == 200:
processing_result = processing_response.json()
return {
"agent_id": agent_id,
"input_modalities": modalities,
"fusion_result": processing_result,
"fusion_accuracy": processing_result.get("accuracy"),
"processing_time": processing_result.get("processing_time"),
"memory_usage": processing_result.get("memory_usage"),
"cross_modal_attention_weights": processing_result.get("attention_weights"),
"enhanced_capabilities": processing_result.get("enhanced_capabilities"),
"success": True
}
else:
return {
"agent_id": agent_id,
"setup_result": setup_result,
"processing_error": f"Fusion processing failed with status {processing_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"Multimodal fusion setup failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_advanced_reinforcement_learning(self, agent_id: str, environment_id: str) -> Dict[str, Any]:
"""Test advanced reinforcement learning for marketplace strategies"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
environment = next((e for e in self.learning_environments if e.environment_id == environment_id), None)
if not environment:
return {"error": f"Environment {environment_id} not found"}
# Test RL training setup
rl_payload = {
"agent_id": agent_id,
"environment_id": environment_id,
"algorithm": "PPO", # Proximal Policy Optimization
"hyperparameters": {
"learning_rate": 0.0003,
"batch_size": 64,
"gamma": 0.99,
"lambda": 0.95,
"clip_epsilon": 0.2,
"entropy_coefficient": 0.01
},
"training_episodes": 1000,
"evaluation_frequency": 100,
"convergence_threshold": 0.001
}
response = self.session.post(
f"{self.agent_service_url}/v1/reinforcement-learning/train",
json=rl_payload,
timeout=120) # 2 minutes for training
if response.status_code == 200:
training_result = response.json()
# Test policy evaluation
evaluation_payload = {
"agent_id": agent_id,
"environment_id": environment_id,
"evaluation_episodes": 100,
"deterministic_evaluation": True
}
evaluation_response = self.session.post(
f"{self.agent_service_url}/v1/reinforcement-learning/evaluate",
json=evaluation_payload,
timeout=30
)
if evaluation_response.status_code == 200:
evaluation_result = evaluation_response.json()
return {
"agent_id": agent_id,
"environment_id": environment_id,
"training_result": training_result,
"evaluation_result": evaluation_result,
"convergence_episode": training_result.get("convergence_episode"),
"final_performance": evaluation_result.get("average_reward"),
"policy_stability": evaluation_result.get("policy_stability"),
"learning_curve": training_result.get("learning_curve"),
"success": True
}
else:
return {
"agent_id": agent_id,
"training_result": training_result,
"evaluation_error": f"Policy evaluation failed with status {evaluation_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"RL training failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_agent_creativity_development(self, agent_id: str, creative_challenges: List[str]) -> Dict[str, Any]:
"""Test agent creativity and specialized AI capability development"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test creativity development setup
creativity_payload = {
"agent_id": agent_id,
"creative_challenges": creative_challenges,
"creativity_metrics": [
"novelty",
"usefulness",
"surprise",
"elegance",
"feasibility"
],
"development_method": "generative_adversarial_learning",
"inspiration_sources": [
"market_data",
"scientific_papers",
"art_patterns",
"natural_systems"
]
}
response = self.session.post(
f"{self.agent_service_url}/v1/creativity/develop",
json=creativity_payload,
timeout=45
)
if response.status_code == 200:
development_result = response.json()
# Test creative problem solving
problem_solving_payload = {
"agent_id": agent_id,
"problem_statement": "Design an innovative pricing strategy for AI compute resources that maximizes both provider earnings and consumer access",
"creativity_constraints": {
"market_viability": True,
"technical_feasibility": True,
"ethical_considerations": True
},
"solution_evaluation": True
}
solving_response = self.session.post(
f"{self.agent_service_url}/v1/creativity/solve",
json=problem_solving_payload,
timeout=30
)
if solving_response.status_code == 200:
solving_result = solving_response.json()
return {
"agent_id": agent_id,
"creative_challenges": creative_challenges,
"development_result": development_result,
"problem_solving_result": solving_result,
"creativity_score": solving_result.get("creativity_score"),
"innovation_level": solving_result.get("innovation_level"),
"practical_applicability": solving_result.get("practical_applicability"),
"novel_solutions": solving_result.get("solutions"),
"success": True
}
else:
return {
"agent_id": agent_id,
"development_result": development_result,
"solving_error": f"Creative problem solving failed with status {solving_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"Creativity development failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_agent_specialization_development(self, agent_id: str, specialization_domain: str) -> Dict[str, Any]:
"""Test agent specialization in specific domains"""
try:
agent = next((a for a in self.agents if a["agent_id"] == agent_id), None)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test specialization development
specialization_payload = {
"agent_id": agent_id,
"specialization_domain": specialization_domain,
"training_data_sources": [
"domain_expert_knowledge",
"best_practices",
"case_studies",
"simulation_data"
],
"specialization_depth": "expert",
"cross_domain_transfer": True,
"performance_targets": {
"domain_accuracy": 0.95,
"expertise_level": 0.90,
"adaptation_speed": 0.85
}
}
response = self.session.post(
f"{self.agent_service_url}/v1/specialization/develop",
json=specialization_payload,
timeout=60
)
if response.status_code == 200:
development_result = response.json()
# Test specialization performance
performance_payload = {
"agent_id": agent_id,
"specialization_domain": specialization_domain,
"test_scenarios": 20,
"difficulty_levels": ["basic", "intermediate", "advanced", "expert"],
"performance_benchmark": True
}
performance_response = self.session.post(
f"{self.agent_service_url}/v1/specialization/evaluate",
json=performance_payload,
timeout=30
)
if performance_response.status_code == 200:
performance_result = performance_response.json()
return {
"agent_id": agent_id,
"specialization_domain": specialization_domain,
"development_result": development_result,
"performance_result": performance_result,
"specialization_score": performance_result.get("specialization_score"),
"expertise_level": performance_result.get("expertise_level"),
"cross_domain_transferability": performance_result.get("cross_domain_transfer"),
"specialized_skills": performance_result.get("acquired_skills"),
"success": True
}
else:
return {
"agent_id": agent_id,
"development_result": development_result,
"performance_error": f"Specialization evaluation failed with status {performance_response.status_code}",
"success": False
}
else:
return {
"agent_id": agent_id,
"error": f"Specialization development failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
# Test Fixtures
@pytest.fixture
async def advanced_agent_tests():
"""Create advanced agent capabilities test instance"""
return AdvancedAgentCapabilitiesTests()
@pytest.fixture
def sample_resource_allocation():
"""Sample resource allocation for testing"""
return ResourceAllocation(
agent_id="advanced_agent_001",
cpu_cores=8,
memory_gb=16.0,
gpu_memory_gb=8.0,
network_bandwidth_mbps=1000,
storage_gb=500,
allocation_strategy="balanced"
)
@pytest.fixture
def sample_learning_tasks():
"""Sample learning tasks for testing"""
return [
"market_price_prediction",
"resource_demand_forecasting",
"trading_strategy_optimization",
"risk_assessment",
"portfolio_management"
]
@pytest.fixture
def sample_modalities():
"""Sample modalities for multimodal fusion testing"""
return ["text", "image", "audio", "tabular", "graph"]
@pytest.fixture
def sample_creative_challenges():
"""Sample creative challenges for testing"""
return [
"design_novel_marketplace_mechanism",
"create_efficient_resource_allocation_algorithm",
"develop_innovative_pricing_strategy",
"solve_cold_start_problem_for_new_agents"
]
# Test Classes
class TestMetaLearningCapabilities:
"""Test advanced meta-learning capabilities"""
@pytest.mark.asyncio
async def test_meta_learning_setup(self, advanced_agent_tests, sample_learning_tasks):
"""Test meta-learning setup and configuration"""
result = await advanced_agent_tests.test_meta_learning_capability(
"advanced_agent_001",
sample_learning_tasks
)
assert result.get("success", False), "Meta-learning setup failed"
assert "setup_result" in result, "No setup result provided"
assert "training_result" in result, "No training result provided"
assert result.get("adaptation_speed", 0) > 0, "No adaptation speed measured"
@pytest.mark.asyncio
async def test_skill_acquisition_acceleration(self, advanced_agent_tests):
"""Test accelerated skill acquisition through meta-learning"""
result = await advanced_agent_tests.test_meta_learning_capability(
"advanced_agent_001",
["quick_skill_acquisition_test"]
)
assert result.get("success", False), "Skill acquisition test failed"
assert result.get("skill_acquisition_rate", 0) > 0.5, "Skill acquisition rate too low"
assert result.get("meta_learning_efficiency", 0) > 0.7, "Meta-learning efficiency too low"
class TestSelfOptimization:
"""Test self-optimizing resource management"""
@pytest.mark.asyncio
async def test_resource_optimization(self, advanced_agent_tests, sample_resource_allocation):
"""Test self-optimizing resource management"""
result = await advanced_agent_tests.test_self_optimizing_resource_management(
"optimization_agent_001",
sample_resource_allocation
)
assert result.get("success", False), "Resource optimization test failed"
assert "optimized_allocation" in result, "No optimized allocation provided"
assert result.get("cost_savings", 0) > 0, "No cost savings achieved"
assert result.get("performance_improvement", 0) > 0, "No performance improvement achieved"
@pytest.mark.asyncio
async def test_adaptive_resource_scaling(self, advanced_agent_tests):
"""Test adaptive resource scaling based on workload"""
dynamic_allocation = ResourceAllocation(
agent_id="optimization_agent_001",
cpu_cores=4,
memory_gb=8.0,
gpu_memory_gb=4.0,
network_bandwidth_mbps=500,
storage_gb=250,
allocation_strategy="dynamic"
)
result = await advanced_agent_tests.test_self_optimizing_resource_management(
"optimization_agent_001",
dynamic_allocation
)
assert result.get("success", False), "Adaptive scaling test failed"
assert result.get("resource_utilization", 0) > 0.8, "Resource utilization too low"
class TestMultimodalFusion:
"""Test multi-modal agent fusion capabilities"""
@pytest.mark.asyncio
async def test_multimodal_fusion_setup(self, advanced_agent_tests, sample_modalities):
"""Test multi-modal fusion setup and processing"""
result = await advanced_agent_tests.test_multimodal_agent_fusion(
"advanced_agent_001",
sample_modalities
)
assert result.get("success", False), "Multimodal fusion test failed"
assert "fusion_result" in result, "No fusion result provided"
assert result.get("fusion_accuracy", 0) > 0.85, "Fusion accuracy too low"
assert result.get("processing_time", 10) < 1.0, "Processing time too slow"
@pytest.mark.asyncio
async def test_cross_modal_attention(self, advanced_agent_tests):
"""Test cross-modal attention mechanisms"""
result = await advanced_agent_tests.test_multimodal_agent_fusion(
"advanced_agent_001",
["text", "image", "audio"]
)
assert result.get("success", False), "Cross-modal attention test failed"
assert "cross_modal_attention_weights" in result, "No attention weights provided"
assert len(result.get("enhanced_capabilities", [])) > 0, "No enhanced capabilities detected"
class TestAdvancedReinforcementLearning:
"""Test advanced reinforcement learning for marketplace strategies"""
@pytest.mark.asyncio
async def test_ppo_training(self, advanced_agent_tests):
"""Test PPO reinforcement learning training"""
result = await advanced_agent_tests.test_advanced_reinforcement_learning(
"advanced_agent_001",
"marketplace_optimization_001"
)
assert result.get("success", False), "PPO training test failed"
assert "training_result" in result, "No training result provided"
assert "evaluation_result" in result, "No evaluation result provided"
assert result.get("final_performance", 0) > 0, "No positive final performance"
assert result.get("convergence_episode", 1000) < 1000, "Training did not converge efficiently"
@pytest.mark.asyncio
async def test_policy_stability(self, advanced_agent_tests):
"""Test policy stability and consistency"""
result = await advanced_agent_tests.test_advanced_reinforcement_learning(
"advanced_agent_001",
"marketplace_optimization_001"
)
assert result.get("success", False), "Policy stability test failed"
assert result.get("policy_stability", 0) > 0.8, "Policy stability too low"
assert "learning_curve" in result, "No learning curve provided"
class TestAgentCreativity:
"""Test agent creativity and innovation capabilities"""
@pytest.mark.asyncio
async def test_creativity_development(self, advanced_agent_tests, sample_creative_challenges):
"""Test creativity development and enhancement"""
result = await advanced_agent_tests.test_agent_creativity_development(
"creative_agent_001",
sample_creative_challenges
)
assert result.get("success", False), "Creativity development test failed"
assert "development_result" in result, "No creativity development result"
assert "problem_solving_result" in result, "No creative problem solving result"
assert result.get("creativity_score", 0) > 0.7, "Creativity score too low"
assert result.get("innovation_level", 0) > 0.6, "Innovation level too low"
@pytest.mark.asyncio
async def test_novel_solution_generation(self, advanced_agent_tests):
"""Test generation of novel solutions"""
result = await advanced_agent_tests.test_agent_creativity_development(
"creative_agent_001",
["generate_novel_solution_test"]
)
assert result.get("success", False), "Novel solution generation test failed"
assert len(result.get("novel_solutions", [])) > 0, "No novel solutions generated"
assert result.get("practical_applicability", 0) > 0.5, "Solutions not practically applicable"
class TestAgentSpecialization:
"""Test agent specialization in specific domains"""
@pytest.mark.asyncio
async def test_domain_specialization(self, advanced_agent_tests):
"""Test agent specialization in specific domains"""
result = await advanced_agent_tests.test_agent_specialization_development(
"creative_agent_001",
"marketplace_design"
)
assert result.get("success", False), "Domain specialization test failed"
assert "development_result" in result, "No specialization development result"
assert "performance_result" in result, "No specialization performance result"
assert result.get("specialization_score", 0) > 0.8, "Specialization score too low"
assert result.get("expertise_level", 0) > 0.7, "Expertise level too low"
@pytest.mark.asyncio
async def test_cross_domain_transfer(self, advanced_agent_tests):
"""Test cross-domain knowledge transfer"""
result = await advanced_agent_tests.test_agent_specialization_development(
"advanced_agent_001",
"multi_domain_optimization"
)
assert result.get("success", False), "Cross-domain transfer test failed"
assert result.get("cross_domain_transferability", 0) > 0.6, "Cross-domain transferability too low"
assert len(result.get("specialized_skills", [])) > 0, "No specialized skills acquired"
if __name__ == "__main__":
pytest.main([__file__, "-v", "--tb=short"])

809
tests/openclaw_marketplace/test_agent_economics.py Normal file
View File

@@ -0,0 +1,809 @@
#!/usr/bin/env python3
"""
Agent Economics Enhancement Tests
Phase 8.3: OpenClaw Agent Economics Enhancement (Weeks 5-6)
"""
import pytest
import asyncio
import time
import json
import requests
import statistics
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import logging
from enum import Enum
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class AgentType(Enum):
"""Agent types in the marketplace"""
COMPUTE_PROVIDER = "compute_provider"
COMPUTE_CONSUMER = "compute_consumer"
POWER_TRADER = "power_trader"
MARKET_MAKER = "market_maker"
ARBITRAGE_AGENT = "arbitrage_agent"
class ReputationLevel(Enum):
"""Reputation levels for agents"""
BRONZE = 0.0
SILVER = 0.6
GOLD = 0.8
PLATINUM = 0.9
DIAMOND = 0.95
@dataclass
class AgentEconomics:
"""Agent economics data"""
agent_id: str
agent_type: AgentType
aitbc_balance: float
total_earned: float
total_spent: float
reputation_score: float
reputation_level: ReputationLevel
successful_transactions: int
failed_transactions: int
total_transactions: int
average_rating: float
certifications: List[str] = field(default_factory=list)
partnerships: List[str] = field(default_factory=list)
@dataclass
class Transaction:
"""Transaction record"""
transaction_id: str
from_agent: str
to_agent: str
amount: float
transaction_type: str
timestamp: datetime
status: str
reputation_impact: float
@dataclass
class RewardMechanism:
"""Reward mechanism configuration"""
mechanism_id: str
mechanism_type: str
performance_threshold: float
reward_rate: float
bonus_conditions: Dict[str, Any]
@dataclass
class TradingProtocol:
"""Agent-to-agent trading protocol"""
protocol_id: str
protocol_type: str
participants: List[str]
terms: Dict[str, Any]
settlement_conditions: List[str]
class AgentEconomicsTests:
"""Test suite for agent economics enhancement"""
def __init__(self, marketplace_url: str = "http://127.0.0.1:18000"):
self.marketplace_url = marketplace_url
self.agents = self._setup_agents()
self.transactions = []
self.reward_mechanisms = self._setup_reward_mechanisms()
self.trading_protocols = self._setup_trading_protocols()
self.session = requests.Session()
self.session.timeout = 30
def _setup_agents(self) -> List[AgentEconomics]:
"""Setup test agents with economics data"""
agents = []
# High-reputation provider
agents.append(AgentEconomics(
agent_id="provider_diamond_001",
agent_type=AgentType.COMPUTE_PROVIDER,
aitbc_balance=2500.0,
total_earned=15000.0,
total_spent=2000.0,
reputation_score=0.97,
reputation_level=ReputationLevel.DIAMOND,
successful_transactions=145,
failed_transactions=3,
total_transactions=148,
average_rating=4.9,
certifications=["gpu_expert", "ml_specialist", "reliable_provider"],
partnerships=["enterprise_client_a", "research_lab_b"]
))
# Medium-reputation provider
agents.append(AgentEconomics(
agent_id="provider_gold_001",
agent_type=AgentType.COMPUTE_PROVIDER,
aitbc_balance=800.0,
total_earned=3500.0,
total_spent=1200.0,
reputation_score=0.85,
reputation_level=ReputationLevel.GOLD,
successful_transactions=67,
failed_transactions=8,
total_transactions=75,
average_rating=4.3,
certifications=["gpu_provider"],
partnerships=["startup_c"]
))
# Consumer agent
agents.append(AgentEconomics(
agent_id="consumer_silver_001",
agent_type=AgentType.COMPUTE_CONSUMER,
aitbc_balance=300.0,
total_earned=0.0,
total_spent=1800.0,
reputation_score=0.72,
reputation_level=ReputationLevel.SILVER,
successful_transactions=23,
failed_transactions=2,
total_transactions=25,
average_rating=4.1,
certifications=["verified_consumer"],
partnerships=[]
))
# Power trader
agents.append(AgentEconomics(
agent_id="trader_platinum_001",
agent_type=AgentType.POWER_TRADER,
aitbc_balance=1200.0,
total_earned=8500.0,
total_spent=6000.0,
reputation_score=0.92,
reputation_level=ReputationLevel.PLATINUM,
successful_transactions=89,
failed_transactions=5,
total_transactions=94,
average_rating=4.7,
certifications=["certified_trader", "market_analyst"],
partnerships=["exchange_a", "liquidity_provider_b"]
))
# Arbitrage agent
agents.append(AgentEconomics(
agent_id="arbitrage_gold_001",
agent_type=AgentType.ARBITRAGE_AGENT,
aitbc_balance=600.0,
total_earned=4200.0,
total_spent=2800.0,
reputation_score=0.88,
reputation_level=ReputationLevel.GOLD,
successful_transactions=56,
failed_transactions=4,
total_transactions=60,
average_rating=4.5,
certifications=["arbitrage_specialist"],
partnerships=["market_maker_c"]
))
return agents
def _setup_reward_mechanisms(self) -> List[RewardMechanism]:
"""Setup reward mechanisms for testing"""
return [
RewardMechanism(
mechanism_id="performance_bonus_001",
mechanism_type="performance_based",
performance_threshold=0.90,
reward_rate=0.10, # 10% bonus
bonus_conditions={
"min_transactions": 10,
"avg_rating_min": 4.5,
"uptime_min": 0.95
}
),
RewardMechanism(
mechanism_id="volume_discount_001",
mechanism_type="volume_based",
performance_threshold=1000.0, # 1000 AITBC volume
reward_rate=0.05, # 5% discount
bonus_conditions={
"monthly_volume_min": 1000.0,
"consistent_trading": True
}
),
RewardMechanism(
mechanism_id="referral_program_001",
mechanism_type="referral_based",
performance_threshold=0.80,
reward_rate=0.15, # 15% referral bonus
bonus_conditions={
"referrals_min": 3,
"referral_performance_min": 0.85
}
)
]
def _setup_trading_protocols(self) -> List[TradingProtocol]:
"""Setup agent-to-agent trading protocols"""
return [
TradingProtocol(
protocol_id="direct_p2p_001",
protocol_type="direct_peer_to_peer",
participants=["provider_diamond_001", "consumer_silver_001"],
terms={
"price_per_hour": 3.5,
"min_duration_hours": 2,
"payment_terms": "prepaid",
"performance_sla": 0.95
},
settlement_conditions=["performance_met", "payment_confirmed"]
),
TradingProtocol(
protocol_id="arbitrage_opportunity_001",
protocol_type="arbitrage",
participants=["arbitrage_gold_001", "trader_platinum_001"],
terms={
"price_difference_threshold": 0.5,
"max_trade_size": 100.0,
"settlement_time": "immediate"
},
settlement_conditions=["profit_made", "risk_managed"]
)
]
def _get_agent_by_id(self, agent_id: str) -> Optional[AgentEconomics]:
"""Get agent by ID"""
return next((agent for agent in self.agents if agent.agent_id == agent_id), None)
async def test_agent_reputation_system(self, agent_id: str) -> Dict[str, Any]:
"""Test agent reputation system"""
try:
agent = self._get_agent_by_id(agent_id)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test reputation calculation
reputation_payload = {
"agent_id": agent_id,
"transaction_history": {
"successful": agent.successful_transactions,
"failed": agent.failed_transactions,
"total": agent.total_transactions
},
"performance_metrics": {
"average_rating": agent.average_rating,
"uptime": 0.97,
"response_time_avg": 0.08
},
"certifications": agent.certifications,
"partnerships": agent.partnerships
}
response = self.session.post(
f"{self.marketplace_url}/v1/agents/reputation/calculate",
json=reputation_payload,
timeout=15
)
if response.status_code == 200:
result = response.json()
return {
"agent_id": agent_id,
"current_reputation": agent.reputation_score,
"calculated_reputation": result.get("reputation_score"),
"reputation_level": result.get("reputation_level"),
"reputation_factors": result.get("factors"),
"accuracy": abs(agent.reputation_score - result.get("reputation_score", 0)) < 0.05,
"success": True
}
else:
return {
"agent_id": agent_id,
"error": f"Reputation calculation failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_performance_based_rewards(self, agent_id: str, performance_metrics: Dict[str, Any]) -> Dict[str, Any]:
"""Test performance-based reward mechanisms"""
try:
agent = self._get_agent_by_id(agent_id)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test performance reward calculation
reward_payload = {
"agent_id": agent_id,
"performance_metrics": performance_metrics,
"reward_mechanism": "performance_bonus_001",
"calculation_period": "monthly"
}
response = self.session.post(
f"{self.marketplace_url}/v1/rewards/calculate",
json=reward_payload,
timeout=15
)
if response.status_code == 200:
result = response.json()
return {
"agent_id": agent_id,
"performance_metrics": performance_metrics,
"reward_amount": result.get("reward_amount"),
"reward_rate": result.get("reward_rate"),
"bonus_conditions_met": result.get("bonus_conditions_met"),
"reward_breakdown": result.get("breakdown"),
"success": True
}
else:
return {
"agent_id": agent_id,
"error": f"Reward calculation failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"error": str(e),
"success": False
}
async def test_agent_to_agent_trading(self, protocol_id: str) -> Dict[str, Any]:
"""Test agent-to-agent AI power trading protocols"""
try:
protocol = next((p for p in self.trading_protocols if p.protocol_id == protocol_id), None)
if not protocol:
return {"error": f"Protocol {protocol_id} not found"}
# Test trading protocol execution
trading_payload = {
"protocol_id": protocol_id,
"participants": protocol.participants,
"terms": protocol.terms,
"execution_type": "immediate"
}
response = self.session.post(
f"{self.marketplace_url}/v1/trading/execute",
json=trading_payload,
timeout=20
)
if response.status_code == 200:
result = response.json()
# Record transaction
transaction = Transaction(
transaction_id=result.get("transaction_id"),
from_agent=protocol.participants[0],
to_agent=protocol.participants[1],
amount=protocol.terms.get("price_per_hour", 0) * protocol.terms.get("min_duration_hours", 1),
transaction_type=protocol.protocol_type,
timestamp=datetime.now(),
status="completed",
reputation_impact=result.get("reputation_impact", 0.01)
)
self.transactions.append(transaction)
return {
"protocol_id": protocol_id,
"transaction_id": transaction.transaction_id,
"participants": protocol.participants,
"trading_terms": protocol.terms,
"execution_result": result,
"reputation_impact": transaction.reputation_impact,
"success": True
}
else:
return {
"protocol_id": protocol_id,
"error": f"Trading execution failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"protocol_id": protocol_id,
"error": str(e),
"success": False
}
async def test_marketplace_analytics(self, time_range: str = "monthly") -> Dict[str, Any]:
"""Test marketplace analytics and economic insights"""
try:
analytics_payload = {
"time_range": time_range,
"metrics": [
"trading_volume",
"agent_participation",
"price_trends",
"reputation_distribution",
"earnings_analysis"
]
}
response = self.session.post(
f"{self.marketplace_url}/v1/analytics/marketplace",
json=analytics_payload,
timeout=15
)
if response.status_code == 200:
result = response.json()
return {
"time_range": time_range,
"trading_volume": result.get("trading_volume"),
"agent_participation": result.get("agent_participation"),
"price_trends": result.get("price_trends"),
"reputation_distribution": result.get("reputation_distribution"),
"earnings_analysis": result.get("earnings_analysis"),
"economic_insights": result.get("insights"),
"success": True
}
else:
return {
"time_range": time_range,
"error": f"Analytics failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"time_range": time_range,
"error": str(e),
"success": False
}
async def test_agent_certification(self, agent_id: str, certification_type: str) -> Dict[str, Any]:
"""Test agent certification and partnership programs"""
try:
agent = self._get_agent_by_id(agent_id)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test certification process
certification_payload = {
"agent_id": agent_id,
"certification_type": certification_type,
"current_certifications": agent.certifications,
"performance_history": {
"successful_transactions": agent.successful_transactions,
"average_rating": agent.average_rating,
"reputation_score": agent.reputation_score
}
}
response = self.session.post(
f"{self.marketplace_url}/v1/certifications/evaluate",
json=certification_payload,
timeout=15
)
if response.status_code == 200:
result = response.json()
return {
"agent_id": agent_id,
"certification_type": certification_type,
"certification_granted": result.get("granted", False),
"certification_level": result.get("level"),
"valid_until": result.get("valid_until"),
"requirements_met": result.get("requirements_met"),
"benefits": result.get("benefits"),
"success": True
}
else:
return {
"agent_id": agent_id,
"certification_type": certification_type,
"error": f"Certification failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"certification_type": certification_type,
"error": str(e),
"success": False
}
async def test_earnings_analysis(self, agent_id: str, period: str = "monthly") -> Dict[str, Any]:
"""Test agent earnings analysis and projections"""
try:
agent = self._get_agent_by_id(agent_id)
if not agent:
return {"error": f"Agent {agent_id} not found"}
# Test earnings analysis
earnings_payload = {
"agent_id": agent_id,
"analysis_period": period,
"historical_data": {
"total_earned": agent.total_earned,
"total_spent": agent.total_spent,
"transaction_count": agent.total_transactions,
"average_transaction_value": (agent.total_earned + agent.total_spent) / max(agent.total_transactions, 1)
}
}
response = self.session.post(
f"{self.marketplace_url}/v1/analytics/earnings",
json=earnings_payload,
timeout=15
)
if response.status_code == 200:
result = response.json()
return {
"agent_id": agent_id,
"analysis_period": period,
"current_earnings": agent.total_earned,
"earnings_trend": result.get("trend"),
"projected_earnings": result.get("projected"),
"earnings_breakdown": result.get("breakdown"),
"optimization_suggestions": result.get("suggestions"),
"success": True
}
else:
return {
"agent_id": agent_id,
"analysis_period": period,
"error": f"Earnings analysis failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"agent_id": agent_id,
"analysis_period": period,
"error": str(e),
"success": False
}
async def test_trust_system_accuracy(self) -> Dict[str, Any]:
"""Test trust system accuracy and reliability"""
try:
# Test trust system across all agents
trust_results = []
for agent in self.agents:
trust_payload = {
"agent_id": agent.agent_id,
"reputation_score": agent.reputation_score,
"transaction_history": {
"successful": agent.successful_transactions,
"failed": agent.failed_transactions,
"total": agent.total_transactions
},
"certifications": agent.certifications,
"partnerships": agent.partnerships
}
response = self.session.post(
f"{self.marketplace_url}/v1/trust/evaluate",
json=trust_payload,
timeout=10
)
if response.status_code == 200:
result = response.json()
trust_results.append({
"agent_id": agent.agent_id,
"actual_reputation": agent.reputation_score,
"predicted_trust": result.get("trust_score"),
"accuracy": abs(agent.reputation_score - result.get("trust_score", 0)),
"confidence": result.get("confidence", 0)
})
if trust_results:
avg_accuracy = statistics.mean([r["accuracy"] for r in trust_results])
avg_confidence = statistics.mean([r["confidence"] for r in trust_results])
return {
"total_agents_tested": len(trust_results),
"average_accuracy": avg_accuracy,
"target_accuracy": 0.95, # 95% accuracy target
"meets_target": avg_accuracy <= 0.05, # Within 5% error margin
"average_confidence": avg_confidence,
"trust_results": trust_results,
"success": True
}
else:
return {
"error": "No trust results available",
"success": False
}
except Exception as e:
return {"error": str(e), "success": False}
# Test Fixtures
@pytest.fixture
async def agent_economics_tests():
"""Create agent economics test instance"""
return AgentEconomicsTests()
@pytest.fixture
def sample_performance_metrics():
"""Sample performance metrics for testing"""
return {
"uptime": 0.98,
"response_time_avg": 0.07,
"task_completion_rate": 0.96,
"gpu_utilization_avg": 0.89,
"customer_satisfaction": 4.8,
"monthly_volume": 1500.0
}
# Test Classes
class TestAgentReputationSystem:
"""Test agent reputation and trust systems"""
@pytest.mark.asyncio
async def test_reputation_calculation_accuracy(self, agent_economics_tests):
"""Test reputation calculation accuracy"""
test_agents = ["provider_diamond_001", "provider_gold_001", "trader_platinum_001"]
for agent_id in test_agents:
result = await agent_economics_tests.test_agent_reputation_system(agent_id)
assert result.get("success", False), f"Reputation calculation failed for {agent_id}"
assert result.get("accuracy", False), f"Reputation calculation inaccurate for {agent_id}"
assert "reputation_level" in result, f"No reputation level for {agent_id}"
@pytest.mark.asyncio
async def test_trust_system_reliability(self, agent_economics_tests):
"""Test trust system reliability across all agents"""
result = await agent_economics_tests.test_trust_system_accuracy()
assert result.get("success", False), "Trust system accuracy test failed"
assert result.get("meets_target", False), "Trust system does not meet accuracy target"
assert result.get("average_accuracy", 1.0) <= 0.05, "Trust system accuracy too low"
assert result.get("average_confidence", 0) >= 0.8, "Trust system confidence too low"
class TestRewardMechanisms:
"""Test performance-based reward mechanisms"""
@pytest.mark.asyncio
async def test_performance_based_rewards(self, agent_economics_tests, sample_performance_metrics):
"""Test performance-based reward calculation"""
test_agents = ["provider_diamond_001", "trader_platinum_001"]
for agent_id in test_agents:
result = await agent_economics_tests.test_performance_based_rewards(
agent_id,
sample_performance_metrics
)
assert result.get("success", False), f"Reward calculation failed for {agent_id}"
assert "reward_amount" in result, f"No reward amount for {agent_id}"
assert result.get("reward_amount", 0) >= 0, f"Negative reward for {agent_id}"
assert "bonus_conditions_met" in result, f"No bonus conditions for {agent_id}"
@pytest.mark.asyncio
async def test_volume_based_rewards(self, agent_economics_tests):
"""Test volume-based reward mechanisms"""
high_volume_metrics = {
"monthly_volume": 2500.0,
"consistent_trading": True,
"transaction_count": 150
}
result = await agent_economics_tests.test_performance_based_rewards(
"trader_platinum_001",
high_volume_metrics
)
assert result.get("success", False), "Volume-based reward test failed"
assert result.get("reward_amount", 0) > 0, "No volume reward calculated"
class TestAgentToAgentTrading:
"""Test agent-to-agent AI power trading protocols"""
@pytest.mark.asyncio
async def test_direct_p2p_trading(self, agent_economics_tests):
"""Test direct peer-to-peer trading protocol"""
result = await agent_economics_tests.test_agent_to_agent_trading("direct_p2p_001")
assert result.get("success", False), "Direct P2P trading failed"
assert "transaction_id" in result, "No transaction ID generated"
assert result.get("reputation_impact", 0) > 0, "No reputation impact calculated"
@pytest.mark.asyncio
async def test_arbitrage_trading(self, agent_economics_tests):
"""Test arbitrage trading protocol"""
result = await agent_economics_tests.test_agent_to_agent_trading("arbitrage_opportunity_001")
assert result.get("success", False), "Arbitrage trading failed"
assert "transaction_id" in result, "No transaction ID for arbitrage"
assert result.get("participants", []) == 2, "Incorrect number of participants"
class TestMarketplaceAnalytics:
"""Test marketplace analytics and economic insights"""
@pytest.mark.asyncio
async def test_monthly_analytics(self, agent_economics_tests):
"""Test monthly marketplace analytics"""
result = await agent_economics_tests.test_marketplace_analytics("monthly")
assert result.get("success", False), "Monthly analytics test failed"
assert "trading_volume" in result, "No trading volume data"
assert "agent_participation" in result, "No agent participation data"
assert "price_trends" in result, "No price trends data"
assert "earnings_analysis" in result, "No earnings analysis data"
@pytest.mark.asyncio
async def test_weekly_analytics(self, agent_economics_tests):
"""Test weekly marketplace analytics"""
result = await agent_economics_tests.test_marketplace_analytics("weekly")
assert result.get("success", False), "Weekly analytics test failed"
assert "economic_insights" in result, "No economic insights provided"
class TestAgentCertification:
"""Test agent certification and partnership programs"""
@pytest.mark.asyncio
async def test_gpu_expert_certification(self, agent_economics_tests):
"""Test GPU expert certification"""
result = await agent_economics_tests.test_agent_certification(
"provider_diamond_001",
"gpu_expert"
)
assert result.get("success", False), "GPU expert certification test failed"
assert "certification_granted" in result, "No certification result"
assert "certification_level" in result, "No certification level"
@pytest.mark.asyncio
async def test_market_analyst_certification(self, agent_economics_tests):
"""Test market analyst certification"""
result = await agent_economics_tests.test_agent_certification(
"trader_platinum_001",
"market_analyst"
)
assert result.get("success", False), "Market analyst certification test failed"
assert result.get("certification_granted", False), "Certification not granted"
class TestEarningsAnalysis:
"""Test agent earnings analysis and projections"""
@pytest.mark.asyncio
async def test_monthly_earnings_analysis(self, agent_economics_tests):
"""Test monthly earnings analysis"""
result = await agent_economics_tests.test_earnings_analysis(
"provider_diamond_001",
"monthly"
)
assert result.get("success", False), "Monthly earnings analysis failed"
assert "earnings_trend" in result, "No earnings trend provided"
assert "projected_earnings" in result, "No earnings projection provided"
assert "optimization_suggestions" in result, "No optimization suggestions"
@pytest.mark.asyncio
async def test_earnings_projections(self, agent_economics_tests):
"""Test earnings projections for different agent types"""
test_agents = ["provider_diamond_001", "trader_platinum_001", "arbitrage_gold_001"]
for agent_id in test_agents:
result = await agent_economics_tests.test_earnings_analysis(agent_id, "monthly")
assert result.get("success", False), f"Earnings analysis failed for {agent_id}"
assert result.get("projected_earnings", 0) > 0, f"No positive earnings projection for {agent_id}"
if __name__ == "__main__":
pytest.main([__file__, "-v", "--tb=short"])

1101
tests/openclaw_marketplace/test_agent_governance.py Normal file
View File

File diff suppressed because it is too large Load Diff

902
tests/openclaw_marketplace/test_blockchain_integration.py Normal file
View File

@@ -0,0 +1,902 @@
#!/usr/bin/env python3
"""
Blockchain Smart Contract Integration Tests
Phase 8.2: Blockchain Smart Contract Integration (Weeks 3-4)
"""
import pytest
import asyncio
import time
import json
import requests
import hashlib
import secrets
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
from datetime import datetime, timedelta
import logging
from enum import Enum
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ContractType(Enum):
"""Smart contract types"""
AI_POWER_RENTAL = "ai_power_rental"
PAYMENT_PROCESSING = "payment_processing"
ESCROW_SERVICE = "escrow_service"
PERFORMANCE_VERIFICATION = "performance_verification"
DISPUTE_RESOLUTION = "dispute_resolution"
DYNAMIC_PRICING = "dynamic_pricing"
@dataclass
class SmartContract:
"""Smart contract configuration"""
contract_address: str
contract_type: ContractType
abi: Dict[str, Any]
bytecode: str
deployed: bool = False
gas_limit: int = 1000000
@dataclass
class Transaction:
"""Blockchain transaction"""
tx_hash: str
from_address: str
to_address: str
value: float
gas_used: int
gas_price: float
status: str
timestamp: datetime
block_number: int
@dataclass
class ContractExecution:
"""Contract execution result"""
contract_address: str
function_name: str
parameters: Dict[str, Any]
result: Dict[str, Any]
gas_used: int
execution_time: float
success: bool
class BlockchainIntegrationTests:
"""Test suite for blockchain smart contract integration"""
def __init__(self, blockchain_url: str = "http://127.0.0.1:8545"):
self.blockchain_url = blockchain_url
self.contracts = self._setup_contracts()
self.transactions = []
self.session = requests.Session()
self.session.timeout = 30
def _setup_contracts(self) -> Dict[ContractType, SmartContract]:
"""Setup smart contracts for testing"""
contracts = {}
# AI Power Rental Contract
contracts[ContractType.AI_POWER_RENTAL] = SmartContract(
contract_address="0x1234567890123456789012345678901234567890",
contract_type=ContractType.AI_POWER_RENTAL,
abi={
"name": "AIPowerRental",
"functions": [
"rentResource(resourceId, consumerId, durationHours)",
"completeRental(rentalId, performanceMetrics)",
"cancelRental(rentalId, reason)",
"getRentalStatus(rentalId)"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=800000
)
# Payment Processing Contract
contracts[ContractType.PAYMENT_PROCESSING] = SmartContract(
contract_address="0x2345678901234567890123456789012345678901",
contract_type=ContractType.PAYMENT_PROCESSING,
abi={
"name": "PaymentProcessing",
"functions": [
"processPayment(fromAgent, toAgent, amount, paymentType)",
"validatePayment(paymentId)",
"refundPayment(paymentId, reason)",
"getPaymentStatus(paymentId)"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=500000
)
# Escrow Service Contract
contracts[ContractType.ESCROW_SERVICE] = SmartContract(
contract_address="0x3456789012345678901234567890123456789012",
contract_type=ContractType.ESCROW_SERVICE,
abi={
"name": "EscrowService",
"functions": [
"createEscrow(payer, payee, amount, conditions)",
"releaseEscrow(escrowId)",
"disputeEscrow(escrowId, reason)",
"getEscrowStatus(escrowId)"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=600000
)
# Performance Verification Contract
contracts[ContractType.PERFORMANCE_VERIFICATION] = SmartContract(
contract_address="0x4567890123456789012345678901234567890123",
contract_type=ContractType.PERFORMANCE_VERIFICATION,
abi={
"name": "PerformanceVerification",
"functions": [
"submitPerformanceReport(rentalId, metrics)",
"verifyPerformance(rentalId)",
"calculatePerformanceScore(rentalId)",
"getPerformanceReport(rentalId)"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=400000
)
# Dispute Resolution Contract
contracts[ContractType.DISPUTE_RESOLUTION] = SmartContract(
contract_address="0x5678901234567890123456789012345678901234",
contract_type=ContractType.DISPUTE_RESOLUTION,
abi={
"name": "DisputeResolution",
"functions": [
"createDispute(disputer, disputee, reason, evidence)",
"voteOnDispute(disputeId, vote, reason)",
"resolveDispute(disputeId, resolution)",
"getDisputeStatus(disputeId)"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=700000
)
# Dynamic Pricing Contract
contracts[ContractType.DYNAMIC_PRICING] = SmartContract(
contract_address="0x6789012345678901234567890123456789012345",
contract_type=ContractType.DYNAMIC_PRICING,
abi={
"name": "DynamicPricing",
"functions": [
"updatePricing(resourceType, basePrice, demandFactor)",
"calculateOptimalPrice(resourceType, supply, demand)",
"getPricingHistory(resourceType, timeRange)",
"adjustPricingForMarketConditions()"
]
},
bytecode="0x608060405234801561001057600080fd5b50...",
gas_limit=300000
)
return contracts
def _generate_transaction_hash(self) -> str:
"""Generate a mock transaction hash"""
return "0x" + secrets.token_hex(32)
def _generate_address(self) -> str:
"""Generate a mock blockchain address"""
return "0x" + secrets.token_hex(20)
async def test_contract_deployment(self, contract_type: ContractType) -> Dict[str, Any]:
"""Test smart contract deployment"""
try:
contract = self.contracts[contract_type]
# Simulate contract deployment
deployment_payload = {
"contract_bytecode": contract.bytecode,
"abi": contract.abi,
"gas_limit": contract.gas_limit,
"sender": self._generate_address()
}
start_time = time.time()
response = self.session.post(
f"{self.blockchain_url}/v1/contracts/deploy",
json=deployment_payload,
timeout=20
)
end_time = time.time()
if response.status_code == 200:
result = response.json()
contract.deployed = True
return {
"contract_type": contract_type.value,
"contract_address": result.get("contract_address"),
"deployment_time": (end_time - start_time),
"gas_used": result.get("gas_used", contract.gas_limit),
"success": True,
"block_number": result.get("block_number")
}
else:
return {
"contract_type": contract_type.value,
"error": f"Deployment failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"contract_type": contract_type.value,
"error": str(e),
"success": False
}
async def test_contract_execution(self, contract_type: ContractType, function_name: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""Test smart contract function execution"""
try:
contract = self.contracts[contract_type]
if not contract.deployed:
return {
"contract_type": contract_type.value,
"function_name": function_name,
"error": "Contract not deployed",
"success": False
}
execution_payload = {
"contract_address": contract.contract_address,
"function_name": function_name,
"parameters": parameters,
"gas_limit": contract.gas_limit,
"sender": self._generate_address()
}
start_time = time.time()
response = self.session.post(
f"{self.blockchain_url}/v1/contracts/execute",
json=execution_payload,
timeout=15
)
end_time = time.time()
if response.status_code == 200:
result = response.json()
# Record transaction
transaction = Transaction(
tx_hash=self._generate_transaction_hash(),
from_address=execution_payload["sender"],
to_address=contract.contract_address,
value=parameters.get("value", 0),
gas_used=result.get("gas_used", 0),
gas_price=result.get("gas_price", 0),
status="confirmed",
timestamp=datetime.now(),
block_number=result.get("block_number", 0)
)
self.transactions.append(transaction)
return {
"contract_type": contract_type.value,
"function_name": function_name,
"execution_time": (end_time - start_time),
"gas_used": transaction.gas_used,
"transaction_hash": transaction.tx_hash,
"result": result.get("return_value"),
"success": True
}
else:
return {
"contract_type": contract_type.value,
"function_name": function_name,
"error": f"Execution failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"contract_type": contract_type.value,
"function_name": function_name,
"error": str(e),
"success": False
}
async def test_ai_power_rental_contract(self) -> Dict[str, Any]:
"""Test AI power rental contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.AI_POWER_RENTAL)
if not deployment_result["success"]:
return deployment_result
# Test resource rental
rental_params = {
"resourceId": "gpu_resource_001",
"consumerId": "agent_consumer_001",
"durationHours": 4,
"maxPricePerHour": 5.0,
"value": 20.0 # Total payment
}
rental_result = await self.test_contract_execution(
ContractType.AI_POWER_RENTAL,
"rentResource",
rental_params
)
if rental_result["success"]:
# Test rental completion
completion_params = {
"rentalId": rental_result["result"].get("rentalId"),
"performanceMetrics": {
"actualComputeHours": 3.8,
"performanceScore": 0.95,
"gpuUtilization": 0.87
}
}
completion_result = await self.test_contract_execution(
ContractType.AI_POWER_RENTAL,
"completeRental",
completion_params
)
return {
"deployment": deployment_result,
"rental": rental_result,
"completion": completion_result,
"overall_success": all([
deployment_result["success"],
rental_result["success"],
completion_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"rental": rental_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_payment_processing_contract(self) -> Dict[str, Any]:
"""Test payment processing contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.PAYMENT_PROCESSING)
if not deployment_result["success"]:
return deployment_result
# Test payment processing
payment_params = {
"fromAgent": "agent_consumer_001",
"toAgent": "agent_provider_001",
"amount": 25.0,
"paymentType": "ai_power_rental",
"value": 25.0
}
payment_result = await self.test_contract_execution(
ContractType.PAYMENT_PROCESSING,
"processPayment",
payment_params
)
if payment_result["success"]:
# Test payment validation
validation_params = {
"paymentId": payment_result["result"].get("paymentId")
}
validation_result = await self.test_contract_execution(
ContractType.PAYMENT_PROCESSING,
"validatePayment",
validation_params
)
return {
"deployment": deployment_result,
"payment": payment_result,
"validation": validation_result,
"overall_success": all([
deployment_result["success"],
payment_result["success"],
validation_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"payment": payment_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_escrow_service_contract(self) -> Dict[str, Any]:
"""Test escrow service contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.ESCROW_SERVICE)
if not deployment_result["success"]:
return deployment_result
# Test escrow creation
escrow_params = {
"payer": "agent_consumer_001",
"payee": "agent_provider_001",
"amount": 50.0,
"conditions": {
"resourceDelivered": True,
"performanceMet": True,
"timeframeMet": True
},
"value": 50.0
}
escrow_result = await self.test_contract_execution(
ContractType.ESCROW_SERVICE,
"createEscrow",
escrow_params
)
if escrow_result["success"]:
# Test escrow release
release_params = {
"escrowId": escrow_result["result"].get("escrowId")
}
release_result = await self.test_contract_execution(
ContractType.ESCROW_SERVICE,
"releaseEscrow",
release_params
)
return {
"deployment": deployment_result,
"creation": escrow_result,
"release": release_result,
"overall_success": all([
deployment_result["success"],
escrow_result["success"],
release_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"creation": escrow_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_performance_verification_contract(self) -> Dict[str, Any]:
"""Test performance verification contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.PERFORMANCE_VERIFICATION)
if not deployment_result["success"]:
return deployment_result
# Test performance report submission
report_params = {
"rentalId": "rental_001",
"metrics": {
"computeHoursDelivered": 3.5,
"averageGPUUtilization": 0.89,
"taskCompletionRate": 0.97,
"errorRate": 0.02,
"responseTimeAvg": 0.08
}
}
report_result = await self.test_contract_execution(
ContractType.PERFORMANCE_VERIFICATION,
"submitPerformanceReport",
report_params
)
if report_result["success"]:
# Test performance verification
verification_params = {
"rentalId": "rental_001"
}
verification_result = await self.test_contract_execution(
ContractType.PERFORMANCE_VERIFICATION,
"verifyPerformance",
verification_params
)
return {
"deployment": deployment_result,
"report_submission": report_result,
"verification": verification_result,
"overall_success": all([
deployment_result["success"],
report_result["success"],
verification_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"report_submission": report_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_dispute_resolution_contract(self) -> Dict[str, Any]:
"""Test dispute resolution contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.DISPUTE_RESOLUTION)
if not deployment_result["success"]:
return deployment_result
# Test dispute creation
dispute_params = {
"disputer": "agent_consumer_001",
"disputee": "agent_provider_001",
"reason": "Performance below agreed SLA",
"evidence": {
"performanceMetrics": {"actualScore": 0.75, "promisedScore": 0.90},
"logs": ["timestamp1: GPU utilization below threshold"],
"screenshots": ["performance_dashboard.png"]
}
}
dispute_result = await self.test_contract_execution(
ContractType.DISPUTE_RESOLUTION,
"createDispute",
dispute_params
)
if dispute_result["success"]:
# Test voting on dispute
vote_params = {
"disputeId": dispute_result["result"].get("disputeId"),
"vote": "favor_disputer",
"reason": "Evidence supports performance claim"
}
vote_result = await self.test_contract_execution(
ContractType.DISPUTE_RESOLUTION,
"voteOnDispute",
vote_params
)
return {
"deployment": deployment_result,
"dispute_creation": dispute_result,
"voting": vote_result,
"overall_success": all([
deployment_result["success"],
dispute_result["success"],
vote_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"dispute_creation": dispute_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_dynamic_pricing_contract(self) -> Dict[str, Any]:
"""Test dynamic pricing contract functionality"""
try:
# Deploy contract
deployment_result = await self.test_contract_deployment(ContractType.DYNAMIC_PRICING)
if not deployment_result["success"]:
return deployment_result
# Test pricing update
pricing_params = {
"resourceType": "nvidia_a100",
"basePrice": 2.5,
"demandFactor": 1.2,
"supplyFactor": 0.8
}
update_result = await self.test_contract_execution(
ContractType.DYNAMIC_PRICING,
"updatePricing",
pricing_params
)
if update_result["success"]:
# Test optimal price calculation
calculation_params = {
"resourceType": "nvidia_a100",
"supply": 15,
"demand": 25,
"marketConditions": {
"competitorPricing": [2.3, 2.7, 2.9],
"seasonalFactor": 1.1,
"geographicPremium": 0.15
}
}
calculation_result = await self.test_contract_execution(
ContractType.DYNAMIC_PRICING,
"calculateOptimalPrice",
calculation_params
)
return {
"deployment": deployment_result,
"pricing_update": update_result,
"price_calculation": calculation_result,
"overall_success": all([
deployment_result["success"],
update_result["success"],
calculation_result["success"]
])
}
else:
return {
"deployment": deployment_result,
"pricing_update": update_result,
"overall_success": False
}
except Exception as e:
return {"error": str(e), "overall_success": False}
async def test_transaction_speed(self) -> Dict[str, Any]:
"""Test blockchain transaction speed"""
try:
transaction_times = []
# Test multiple transactions
for i in range(10):
start_time = time.time()
# Simple contract execution
result = await self.test_contract_execution(
ContractType.PAYMENT_PROCESSING,
"processPayment",
{
"fromAgent": f"agent_{i}",
"toAgent": f"provider_{i}",
"amount": 1.0,
"paymentType": "test",
"value": 1.0
}
)
end_time = time.time()
if result["success"]:
transaction_times.append((end_time - start_time) * 1000) # Convert to ms
if transaction_times:
avg_time = sum(transaction_times) / len(transaction_times)
min_time = min(transaction_times)
max_time = max(transaction_times)
return {
"transaction_count": len(transaction_times),
"average_time_ms": avg_time,
"min_time_ms": min_time,
"max_time_ms": max_time,
"target_time_ms": 30000, # 30 seconds target
"within_target": avg_time <= 30000,
"success": True
}
else:
return {
"error": "No successful transactions",
"success": False
}
except Exception as e:
return {"error": str(e), "success": False}
async def test_payment_reliability(self) -> Dict[str, Any]:
"""Test AITBC payment processing reliability"""
try:
payment_results = []
# Test multiple payments
for i in range(20):
result = await self.test_contract_execution(
ContractType.PAYMENT_PROCESSING,
"processPayment",
{
"fromAgent": f"consumer_{i}",
"toAgent": f"provider_{i}",
"amount": 5.0,
"paymentType": "ai_power_rental",
"value": 5.0
}
)
payment_results.append(result["success"])
successful_payments = sum(payment_results)
total_payments = len(payment_results)
success_rate = (successful_payments / total_payments) * 100
return {
"total_payments": total_payments,
"successful_payments": successful_payments,
"success_rate_percent": success_rate,
"target_success_rate": 99.9,
"meets_target": success_rate >= 99.9,
"success": True
}
except Exception as e:
return {"error": str(e), "success": False}
# Test Fixtures
@pytest.fixture
async def blockchain_tests():
"""Create blockchain integration test instance"""
return BlockchainIntegrationTests()
# Test Classes
class TestContractDeployment:
"""Test smart contract deployment"""
@pytest.mark.asyncio
async def test_all_contracts_deployment(self, blockchain_tests):
"""Test deployment of all smart contracts"""
deployment_results = {}
for contract_type in ContractType:
result = await blockchain_tests.test_contract_deployment(contract_type)
deployment_results[contract_type.value] = result
# Assert all contracts deployed successfully
failed_deployments = [
contract for contract, result in deployment_results.items()
if not result.get("success", False)
]
assert len(failed_deployments) == 0, f"Failed deployments: {failed_deployments}"
# Assert deployment times are reasonable
slow_deployments = [
contract for contract, result in deployment_results.items()
if result.get("deployment_time", 0) > 10.0 # 10 seconds max
]
assert len(slow_deployments) == 0, f"Slow deployments: {slow_deployments}"
class TestAIPowerRentalContract:
"""Test AI power rental contract functionality"""
@pytest.mark.asyncio
async def test_complete_rental_workflow(self, blockchain_tests):
"""Test complete AI power rental workflow"""
result = await blockchain_tests.test_ai_power_rental_contract()
assert result.get("overall_success", False), "AI power rental workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["rental"]["success"], "Resource rental failed"
assert result["completion"]["success"], "Rental completion failed"
# Check transaction hash is generated
assert "transaction_hash" in result["rental"], "No transaction hash for rental"
assert "transaction_hash" in result["completion"], "No transaction hash for completion"
class TestPaymentProcessingContract:
"""Test payment processing contract functionality"""
@pytest.mark.asyncio
async def test_complete_payment_workflow(self, blockchain_tests):
"""Test complete payment processing workflow"""
result = await blockchain_tests.test_payment_processing_contract()
assert result.get("overall_success", False), "Payment processing workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["payment"]["success"], "Payment processing failed"
assert result["validation"]["success"], "Payment validation failed"
# Check payment ID is generated
assert "paymentId" in result["payment"]["result"], "No payment ID generated"
class TestEscrowServiceContract:
"""Test escrow service contract functionality"""
@pytest.mark.asyncio
async def test_complete_escrow_workflow(self, blockchain_tests):
"""Test complete escrow service workflow"""
result = await blockchain_tests.test_escrow_service_contract()
assert result.get("overall_success", False), "Escrow service workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["creation"]["success"], "Escrow creation failed"
assert result["release"]["success"], "Escrow release failed"
# Check escrow ID is generated
assert "escrowId" in result["creation"]["result"], "No escrow ID generated"
class TestPerformanceVerificationContract:
"""Test performance verification contract functionality"""
@pytest.mark.asyncio
async def test_performance_verification_workflow(self, blockchain_tests):
"""Test performance verification workflow"""
result = await blockchain_tests.test_performance_verification_contract()
assert result.get("overall_success", False), "Performance verification workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["report_submission"]["success"], "Performance report submission failed"
assert result["verification"]["success"], "Performance verification failed"
class TestDisputeResolutionContract:
"""Test dispute resolution contract functionality"""
@pytest.mark.asyncio
async def test_dispute_resolution_workflow(self, blockchain_tests):
"""Test dispute resolution workflow"""
result = await blockchain_tests.test_dispute_resolution_contract()
assert result.get("overall_success", False), "Dispute resolution workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["dispute_creation"]["success"], "Dispute creation failed"
assert result["voting"]["success"], "Dispute voting failed"
# Check dispute ID is generated
assert "disputeId" in result["dispute_creation"]["result"], "No dispute ID generated"
class TestDynamicPricingContract:
"""Test dynamic pricing contract functionality"""
@pytest.mark.asyncio
async def test_dynamic_pricing_workflow(self, blockchain_tests):
"""Test dynamic pricing workflow"""
result = await blockchain_tests.test_dynamic_pricing_contract()
assert result.get("overall_success", False), "Dynamic pricing workflow failed"
assert result["deployment"]["success"], "Contract deployment failed"
assert result["pricing_update"]["success"], "Pricing update failed"
assert result["price_calculation"]["success"], "Price calculation failed"
# Check optimal price is calculated
assert "optimalPrice" in result["price_calculation"]["result"], "No optimal price calculated"
class TestBlockchainPerformance:
"""Test blockchain performance metrics"""
@pytest.mark.asyncio
async def test_transaction_speed(self, blockchain_tests):
"""Test blockchain transaction speed"""
result = await blockchain_tests.test_transaction_speed()
assert result.get("success", False), "Transaction speed test failed"
assert result.get("within_target", False), "Transaction speed below target"
assert result.get("average_time_ms", 100000) <= 30000, "Average transaction time too high"
@pytest.mark.asyncio
async def test_payment_reliability(self, blockchain_tests):
"""Test AITBC payment processing reliability"""
result = await blockchain_tests.test_payment_reliability()
assert result.get("success", False), "Payment reliability test failed"
assert result.get("meets_target", False), "Payment reliability below target"
assert result.get("success_rate_percent", 0) >= 99.9, "Payment success rate too low"
if __name__ == "__main__":
pytest.main([__file__, "-v", "--tb=short"])

544
tests/openclaw_marketplace/test_framework.py Normal file
View File

@@ -0,0 +1,544 @@
#!/usr/bin/env python3
"""
Comprehensive Test Framework for OpenClaw Agent Marketplace
Tests for Phase 8-10: Global AI Power Marketplace Expansion
"""
import pytest
import asyncio
import time
import json
import requests
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class MarketplaceConfig:
"""Configuration for marketplace testing"""
primary_marketplace: str = "http://127.0.0.1:18000"
secondary_marketplace: str = "http://127.0.0.1:18001"
gpu_service: str = "http://127.0.0.1:8002"
test_timeout: int = 30
max_retries: int = 3
@dataclass
class AgentInfo:
"""Agent information for testing"""
agent_id: str
agent_type: str
capabilities: List[str]
reputation_score: float
aitbc_balance: float
region: str
@dataclass
class AIResource:
"""AI resource for marketplace trading"""
resource_id: str
resource_type: str
compute_power: float
gpu_memory: int
price_per_hour: float
availability: bool
provider_id: str
class OpenClawMarketplaceTestFramework:
"""Comprehensive test framework for OpenClaw Agent Marketplace"""
def __init__(self, config: MarketplaceConfig):
self.config = config
self.agents: List[AgentInfo] = []
self.resources: List[AIResource] = []
self.session = requests.Session()
self.session.timeout = config.test_timeout
async def setup_test_environment(self):
"""Setup test environment with agents and resources"""
logger.info("Setting up OpenClaw Marketplace test environment...")
# Create test agents
self.agents = [
AgentInfo(
agent_id="agent_provider_001",
agent_type="compute_provider",
capabilities=["gpu_computing", "multimodal_processing", "reinforcement_learning"],
reputation_score=0.95,
aitbc_balance=1000.0,
region="us-east-1"
),
AgentInfo(
agent_id="agent_consumer_001",
agent_type="compute_consumer",
capabilities=["ai_inference", "model_training", "data_processing"],
reputation_score=0.88,
aitbc_balance=500.0,
region="us-west-2"
),
AgentInfo(
agent_id="agent_trader_001",
agent_type="power_trader",
capabilities=["resource_optimization", "price_arbitrage", "market_analysis"],
reputation_score=0.92,
aitbc_balance=750.0,
region="eu-central-1"
)
]
# Create test AI resources
self.resources = [
AIResource(
resource_id="gpu_resource_001",
resource_type="nvidia_a100",
compute_power=312.0,
gpu_memory=40,
price_per_hour=2.5,
availability=True,
provider_id="agent_provider_001"
),
AIResource(
resource_id="gpu_resource_002",
resource_type="nvidia_h100",
compute_power=670.0,
gpu_memory=80,
price_per_hour=5.0,
availability=True,
provider_id="agent_provider_001"
),
AIResource(
resource_id="edge_resource_001",
resource_type="edge_gpu",
compute_power=50.0,
gpu_memory=8,
price_per_hour=0.8,
availability=True,
provider_id="agent_provider_001"
)
]
logger.info(f"Created {len(self.agents)} test agents and {len(self.resources)} test resources")
async def cleanup_test_environment(self):
"""Cleanup test environment"""
logger.info("Cleaning up test environment...")
self.agents.clear()
self.resources.clear()
async def test_marketplace_health(self, marketplace_url: str) -> bool:
"""Test marketplace health endpoint"""
try:
response = self.session.get(f"{marketplace_url}/health", timeout=10)
return response.status_code == 200
except Exception as e:
logger.error(f"Marketplace health check failed: {e}")
return False
async def test_agent_registration(self, agent: AgentInfo, marketplace_url: str) -> bool:
"""Test agent registration"""
try:
payload = {
"agent_id": agent.agent_id,
"agent_type": agent.agent_type,
"capabilities": agent.capabilities,
"region": agent.region,
"initial_reputation": agent.reputation_score
}
response = self.session.post(
f"{marketplace_url}/v1/agents/register",
json=payload,
timeout=10
)
return response.status_code == 201
except Exception as e:
logger.error(f"Agent registration failed: {e}")
return False
async def test_resource_listing(self, resource: AIResource, marketplace_url: str) -> bool:
"""Test AI resource listing"""
try:
payload = {
"resource_id": resource.resource_id,
"resource_type": resource.resource_type,
"compute_power": resource.compute_power,
"gpu_memory": resource.gpu_memory,
"price_per_hour": resource.price_per_hour,
"availability": resource.availability,
"provider_id": resource.provider_id
}
response = self.session.post(
f"{marketplace_url}/v1/marketplace/list",
json=payload,
timeout=10
)
return response.status_code == 201
except Exception as e:
logger.error(f"Resource listing failed: {e}")
return False
async def test_ai_power_rental(self, resource_id: str, consumer_id: str, duration_hours: int, marketplace_url: str) -> Dict[str, Any]:
"""Test AI power rental transaction"""
try:
payload = {
"resource_id": resource_id,
"consumer_id": consumer_id,
"duration_hours": duration_hours,
"max_price_per_hour": 10.0,
"requirements": {
"min_compute_power": 50.0,
"min_gpu_memory": 8,
"gpu_required": True
}
}
response = self.session.post(
f"{marketplace_url}/v1/marketplace/rent",
json=payload,
timeout=15
)
if response.status_code == 201:
return response.json()
else:
return {"error": f"Rental failed with status {response.status_code}"}
except Exception as e:
logger.error(f"AI power rental failed: {e}")
return {"error": str(e)}
async def test_smart_contract_execution(self, contract_type: str, params: Dict[str, Any], marketplace_url: str) -> Dict[str, Any]:
"""Test smart contract execution"""
try:
payload = {
"contract_type": contract_type,
"parameters": params,
"gas_limit": 1000000,
"value": params.get("value", 0)
}
response = self.session.post(
f"{marketplace_url}/v1/blockchain/contracts/execute",
json=payload,
timeout=20
)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Contract execution failed with status {response.status_code}"}
except Exception as e:
logger.error(f"Smart contract execution failed: {e}")
return {"error": str(e)}
async def test_performance_metrics(self, marketplace_url: str) -> Dict[str, Any]:
"""Test marketplace performance metrics"""
try:
response = self.session.get(f"{marketplace_url}/v1/metrics/performance", timeout=10)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Performance metrics failed with status {response.status_code}"}
except Exception as e:
logger.error(f"Performance metrics failed: {e}")
return {"error": str(e)}
async def test_geographic_load_balancing(self, consumer_region: str, marketplace_urls: List[str]) -> Dict[str, Any]:
"""Test geographic load balancing"""
results = {}
for url in marketplace_urls:
try:
start_time = time.time()
response = self.session.get(f"{url}/v1/marketplace/nearest", timeout=10)
end_time = time.time()
results[url] = {
"response_time": (end_time - start_time) * 1000, # Convert to ms
"status_code": response.status_code,
"success": response.status_code == 200
}
except Exception as e:
results[url] = {
"error": str(e),
"success": False
}
return results
async def test_agent_reputation_system(self, agent_id: str, marketplace_url: str) -> Dict[str, Any]:
"""Test agent reputation system"""
try:
response = self.session.get(f"{marketplace_url}/v1/agents/{agent_id}/reputation", timeout=10)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Reputation check failed with status {response.status_code}"}
except Exception as e:
logger.error(f"Agent reputation check failed: {e}")
return {"error": str(e)}
async def test_payment_processing(self, from_agent: str, to_agent: str, amount: float, marketplace_url: str) -> Dict[str, Any]:
"""Test AITBC payment processing"""
try:
payload = {
"from_agent": from_agent,
"to_agent": to_agent,
"amount": amount,
"currency": "AITBC",
"payment_type": "ai_power_rental"
}
response = self.session.post(
f"{marketplace_url}/v1/payments/process",
json=payload,
timeout=15
)
if response.status_code == 200:
return response.json()
else:
return {"error": f"Payment processing failed with status {response.status_code}"}
except Exception as e:
logger.error(f"Payment processing failed: {e}")
return {"error": str(e)}
# Test Fixtures
@pytest.fixture
async def marketplace_framework():
"""Create marketplace test framework"""
config = MarketplaceConfig()
framework = OpenClawMarketplaceTestFramework(config)
await framework.setup_test_environment()
yield framework
await framework.cleanup_test_environment()
@pytest.fixture
def sample_agent():
"""Sample agent for testing"""
return AgentInfo(
agent_id="test_agent_001",
agent_type="compute_provider",
capabilities=["gpu_computing", "ai_inference"],
reputation_score=0.90,
aitbc_balance=100.0,
region="us-east-1"
)
@pytest.fixture
def sample_resource():
"""Sample AI resource for testing"""
return AIResource(
resource_id="test_resource_001",
resource_type="nvidia_a100",
compute_power=312.0,
gpu_memory=40,
price_per_hour=2.5,
availability=True,
provider_id="test_provider_001"
)
# Test Classes
class TestMarketplaceHealth:
"""Test marketplace health and connectivity"""
@pytest.mark.asyncio
async def test_primary_marketplace_health(self, marketplace_framework):
"""Test primary marketplace health"""
result = await marketplace_framework.test_marketplace_health(marketplace_framework.config.primary_marketplace)
assert result is True, "Primary marketplace should be healthy"
@pytest.mark.asyncio
async def test_secondary_marketplace_health(self, marketplace_framework):
"""Test secondary marketplace health"""
result = await marketplace_framework.test_marketplace_health(marketplace_framework.config.secondary_marketplace)
assert result is True, "Secondary marketplace should be healthy"
class TestAgentRegistration:
"""Test agent registration and management"""
@pytest.mark.asyncio
async def test_agent_registration_success(self, marketplace_framework, sample_agent):
"""Test successful agent registration"""
result = await marketplace_framework.test_agent_registration(
sample_agent,
marketplace_framework.config.primary_marketplace
)
assert result is True, "Agent registration should succeed"
@pytest.mark.asyncio
async def test_agent_reputation_tracking(self, marketplace_framework, sample_agent):
"""Test agent reputation tracking"""
# First register the agent
await marketplace_framework.test_agent_registration(
sample_agent,
marketplace_framework.config.primary_marketplace
)
# Then check reputation
reputation = await marketplace_framework.test_agent_reputation_system(
sample_agent.agent_id,
marketplace_framework.config.primary_marketplace
)
assert "reputation_score" in reputation, "Reputation score should be tracked"
assert reputation["reputation_score"] >= 0.0, "Reputation score should be valid"
class TestResourceTrading:
"""Test AI resource trading and marketplace operations"""
@pytest.mark.asyncio
async def test_resource_listing_success(self, marketplace_framework, sample_resource):
"""Test successful resource listing"""
result = await marketplace_framework.test_resource_listing(
sample_resource,
marketplace_framework.config.primary_marketplace
)
assert result is True, "Resource listing should succeed"
@pytest.mark.asyncio
async def test_ai_power_rental_success(self, marketplace_framework, sample_resource):
"""Test successful AI power rental"""
# First list the resource
await marketplace_framework.test_resource_listing(
sample_resource,
marketplace_framework.config.primary_marketplace
)
# Then rent the resource
rental_result = await marketplace_framework.test_ai_power_rental(
sample_resource.resource_id,
"test_consumer_001",
2, # 2 hours
marketplace_framework.config.primary_marketplace
)
assert "rental_id" in rental_result, "Rental should create a rental ID"
assert rental_result.get("status") == "confirmed", "Rental should be confirmed"
class TestSmartContracts:
"""Test blockchain smart contract integration"""
@pytest.mark.asyncio
async def test_ai_power_rental_contract(self, marketplace_framework):
"""Test AI power rental smart contract"""
params = {
"resource_id": "test_resource_001",
"consumer_id": "test_consumer_001",
"provider_id": "test_provider_001",
"duration_hours": 2,
"price_per_hour": 2.5,
"value": 5.0 # Total payment in AITBC
}
result = await marketplace_framework.test_smart_contract_execution(
"ai_power_rental",
params,
marketplace_framework.config.primary_marketplace
)
assert "transaction_hash" in result, "Contract execution should return transaction hash"
assert result.get("status") == "success", "Contract execution should succeed"
@pytest.mark.asyncio
async def test_payment_processing_contract(self, marketplace_framework):
"""Test payment processing smart contract"""
params = {
"from_agent": "test_consumer_001",
"to_agent": "test_provider_001",
"amount": 5.0,
"payment_type": "ai_power_rental",
"value": 5.0
}
result = await marketplace_framework.test_smart_contract_execution(
"payment_processing",
params,
marketplace_framework.config.primary_marketplace
)
assert "transaction_hash" in result, "Payment contract should return transaction hash"
assert result.get("status") == "success", "Payment contract should succeed"
class TestPerformanceOptimization:
"""Test marketplace performance and optimization"""
@pytest.mark.asyncio
async def test_performance_metrics_collection(self, marketplace_framework):
"""Test performance metrics collection"""
metrics = await marketplace_framework.test_performance_metrics(
marketplace_framework.config.primary_marketplace
)
assert "response_time" in metrics, "Response time should be tracked"
assert "throughput" in metrics, "Throughput should be tracked"
assert "gpu_utilization" in metrics, "GPU utilization should be tracked"
@pytest.mark.asyncio
async def test_geographic_load_balancing(self, marketplace_framework):
"""Test geographic load balancing"""
marketplace_urls = [
marketplace_framework.config.primary_marketplace,
marketplace_framework.config.secondary_marketplace
]
results = await marketplace_framework.test_geographic_load_balancing(
"us-east-1",
marketplace_urls
)
for url, result in results.items():
assert result.get("success", False), f"Load balancing should work for {url}"
assert result.get("response_time", 1000) < 1000, f"Response time should be < 1000ms for {url}"
class TestAgentEconomics:
"""Test agent economics and payment systems"""
@pytest.mark.asyncio
async def test_aitbc_payment_processing(self, marketplace_framework):
"""Test AITBC payment processing"""
result = await marketplace_framework.test_payment_processing(
"test_consumer_001",
"test_provider_001",
5.0,
marketplace_framework.config.primary_marketplace
)
assert "payment_id" in result, "Payment should create a payment ID"
assert result.get("status") == "completed", "Payment should be completed"
@pytest.mark.asyncio
async def test_agent_balance_tracking(self, marketplace_framework, sample_agent):
"""Test agent balance tracking"""
# Register agent first
await marketplace_framework.test_agent_registration(
sample_agent,
marketplace_framework.config.primary_marketplace
)
# Check balance
response = marketplace_framework.session.get(
f"{marketplace_framework.config.primary_marketplace}/v1/agents/{sample_agent.agent_id}/balance"
)
if response.status_code == 200:
balance_data = response.json()
assert "aitbc_balance" in balance_data, "AITBC balance should be tracked"
assert balance_data["aitbc_balance"] >= 0.0, "Balance should be non-negative"
if __name__ == "__main__":
# Run tests
pytest.main([__file__, "-v", "--tb=short"])

542
tests/openclaw_marketplace/test_multi_region_deployment.py Normal file
View File

@@ -0,0 +1,542 @@
#!/usr/bin/env python3
"""
Multi-Region Marketplace Deployment Tests
Phase 8.1: Multi-Region Marketplace Deployment (Weeks 1-2)
"""
import pytest
import asyncio
import time
import json
import requests
import aiohttp
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
import logging
from concurrent.futures import ThreadPoolExecutor
import statistics
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class RegionConfig:
"""Configuration for a geographic region"""
region_id: str
region_name: str
marketplace_url: str
edge_nodes: List[str]
latency_targets: Dict[str, float]
expected_response_time: float
@dataclass
class EdgeNode:
"""Edge computing node configuration"""
node_id: str
region_id: str
node_url: str
gpu_available: bool
compute_capacity: float
network_latency: float
class MultiRegionMarketplaceTests:
"""Test suite for multi-region marketplace deployment"""
def __init__(self):
self.regions = self._setup_regions()
self.edge_nodes = self._setup_edge_nodes()
self.session = requests.Session()
self.session.timeout = 30
def _setup_regions(self) -> List[RegionConfig]:
"""Setup geographic regions for testing"""
return [
RegionConfig(
region_id="us-east-1",
region_name="US East (N. Virginia)",
marketplace_url="http://127.0.0.1:18000",
edge_nodes=["edge-use1-001", "edge-use1-002"],
latency_targets={"local": 50, "regional": 100, "global": 200},
expected_response_time=50.0
),
RegionConfig(
region_id="us-west-2",
region_name="US West (Oregon)",
marketplace_url="http://127.0.0.1:18001",
edge_nodes=["edge-usw2-001", "edge-usw2-002"],
latency_targets={"local": 50, "regional": 100, "global": 200},
expected_response_time=50.0
),
RegionConfig(
region_id="eu-central-1",
region_name="EU Central (Frankfurt)",
marketplace_url="http://127.0.0.1:18002",
edge_nodes=["edge-euc1-001", "edge-euc1-002"],
latency_targets={"local": 50, "regional": 100, "global": 200},
expected_response_time=50.0
),
RegionConfig(
region_id="ap-southeast-1",
region_name="Asia Pacific (Singapore)",
marketplace_url="http://127.0.0.1:18003",
edge_nodes=["edge-apse1-001", "edge-apse1-002"],
latency_targets={"local": 50, "regional": 100, "global": 200},
expected_response_time=50.0
)
]
def _setup_edge_nodes(self) -> List[EdgeNode]:
"""Setup edge computing nodes"""
nodes = []
for region in self.regions:
for node_id in region.edge_nodes:
nodes.append(EdgeNode(
node_id=node_id,
region_id=region.region_id,
node_url=f"http://127.0.0.1:800{node_id[-1]}",
gpu_available=True,
compute_capacity=100.0,
network_latency=10.0
))
return nodes
async def test_region_health_check(self, region: RegionConfig) -> Dict[str, Any]:
"""Test health check for a specific region"""
try:
start_time = time.time()
response = self.session.get(f"{region.marketplace_url}/health", timeout=10)
end_time = time.time()
return {
"region_id": region.region_id,
"status_code": response.status_code,
"response_time": (end_time - start_time) * 1000,
"healthy": response.status_code == 200,
"within_target": (end_time - start_time) * 1000 <= region.expected_response_time
}
except Exception as e:
return {
"region_id": region.region_id,
"error": str(e),
"healthy": False,
"within_target": False
}
async def test_edge_node_connectivity(self, edge_node: EdgeNode) -> Dict[str, Any]:
"""Test connectivity to edge computing nodes"""
try:
start_time = time.time()
response = self.session.get(f"{edge_node.node_url}/health", timeout=10)
end_time = time.time()
return {
"node_id": edge_node.node_id,
"region_id": edge_node.region_id,
"status_code": response.status_code,
"response_time": (end_time - start_time) * 1000,
"gpu_available": edge_node.gpu_available,
"compute_capacity": edge_node.compute_capacity,
"connected": response.status_code == 200
}
except Exception as e:
return {
"node_id": edge_node.node_id,
"region_id": edge_node.region_id,
"error": str(e),
"connected": False
}
async def test_geographic_load_balancing(self, consumer_region: str, resource_requirements: Dict[str, Any]) -> Dict[str, Any]:
"""Test geographic load balancing for resource requests"""
try:
# Find the consumer's region
consumer_region_config = next((r for r in self.regions if r.region_id == consumer_region), None)
if not consumer_region_config:
return {"error": f"Region {consumer_region} not found"}
# Test resource request with geographic optimization
payload = {
"consumer_region": consumer_region,
"resource_requirements": resource_requirements,
"optimization_strategy": "geographic_latency",
"max_acceptable_latency": 200.0
}
start_time = time.time()
response = self.session.post(
f"{consumer_region_config.marketplace_url}/v1/marketplace/optimal-resource",
json=payload,
timeout=15
)
end_time = time.time()
if response.status_code == 200:
result = response.json()
return {
"consumer_region": consumer_region,
"recommended_region": result.get("optimal_region"),
"recommended_node": result.get("optimal_edge_node"),
"estimated_latency": result.get("estimated_latency"),
"response_time": (end_time - start_time) * 1000,
"success": True
}
else:
return {
"consumer_region": consumer_region,
"error": f"Load balancing failed with status {response.status_code}",
"success": False
}
except Exception as e:
return {
"consumer_region": consumer_region,
"error": str(e),
"success": False
}
async def test_cross_region_resource_discovery(self, source_region: str, target_regions: List[str]) -> Dict[str, Any]:
"""Test resource discovery across multiple regions"""
try:
source_config = next((r for r in self.regions if r.region_id == source_region), None)
if not source_config:
return {"error": f"Source region {source_region} not found"}
results = {}
for target_region in target_regions:
target_config = next((r for r in self.regions if r.region_id == target_region), None)
if target_config:
try:
start_time = time.time()
response = self.session.get(
f"{source_config.marketplace_url}/v1/marketplace/resources/{target_region}",
timeout=10
)
end_time = time.time()
results[target_region] = {
"status_code": response.status_code,
"response_time": (end_time - start_time) * 1000,
"resources_found": len(response.json()) if response.status_code == 200 else 0,
"success": response.status_code == 200
}
except Exception as e:
results[target_region] = {
"error": str(e),
"success": False
}
return {
"source_region": source_region,
"target_regions": results,
"total_regions_queried": len(target_regions),
"successful_queries": sum(1 for r in results.values() if r.get("success", False))
}
except Exception as e:
return {"error": str(e)}
async def test_global_marketplace_synchronization(self) -> Dict[str, Any]:
"""Test synchronization across all marketplace regions"""
try:
sync_results = {}
# Test resource listing synchronization
resource_counts = {}
for region in self.regions:
try:
response = self.session.get(f"{region.marketplace_url}/v1/marketplace/resources", timeout=10)
if response.status_code == 200:
resources = response.json()
resource_counts[region.region_id] = len(resources)
else:
resource_counts[region.region_id] = 0
except Exception:
resource_counts[region.region_id] = 0
# Test pricing synchronization
pricing_data = {}
for region in self.regions:
try:
response = self.session.get(f"{region.marketplace_url}/v1/marketplace/pricing", timeout=10)
if response.status_code == 200:
pricing_data[region.region_id] = response.json()
else:
pricing_data[region.region_id] = {}
except Exception:
pricing_data[region.region_id] = {}
# Calculate synchronization metrics
resource_variance = statistics.pstdev(resource_counts.values()) if len(resource_counts) > 1 else 0
return {
"resource_counts": resource_counts,
"resource_variance": resource_variance,
"pricing_data": pricing_data,
"total_regions": len(self.regions),
"synchronized": resource_variance < 5.0 # Allow small variance
}
except Exception as e:
return {"error": str(e)}
async def test_failover_and_redundancy(self, primary_region: str, backup_regions: List[str]) -> Dict[str, Any]:
"""Test failover and redundancy mechanisms"""
try:
primary_config = next((r for r in self.regions if r.region_id == primary_region), None)
if not primary_config:
return {"error": f"Primary region {primary_region} not found"}
# Test normal operation
normal_response = self.session.get(f"{primary_config.marketplace_url}/v1/marketplace/status", timeout=10)
normal_status = normal_response.status_code == 200
# Simulate primary region failure (test backup regions)
backup_results = {}
for backup_region in backup_regions:
backup_config = next((r for r in self.regions if r.region_id == backup_region), None)
if backup_config:
try:
response = self.session.get(f"{backup_config.marketplace_url}/v1/marketplace/status", timeout=10)
backup_results[backup_region] = {
"available": response.status_code == 200,
"response_time": time.time()
}
except Exception as e:
backup_results[backup_region] = {
"available": False,
"error": str(e)
}
available_backups = [r for r, data in backup_results.items() if data.get("available", False)]
return {
"primary_region": primary_region,
"primary_normal_status": normal_status,
"backup_regions": backup_results,
"available_backups": available_backups,
"redundancy_level": len(available_backups) / len(backup_regions),
"failover_ready": len(available_backups) > 0
}
except Exception as e:
return {"error": str(e)}
async def test_latency_optimization(self, consumer_region: str, target_latency: float) -> Dict[str, Any]:
"""Test latency optimization for cross-region requests"""
try:
consumer_config = next((r for r in self.regions if r.region_id == consumer_region), None)
if not consumer_config:
return {"error": f"Consumer region {consumer_region} not found"}
# Test latency to all regions
latency_results = {}
for region in self.regions:
start_time = time.time()
try:
response = self.session.get(f"{region.marketplace_url}/v1/marketplace/ping", timeout=10)
end_time = time.time()
latency_results[region.region_id] = {
"latency_ms": (end_time - start_time) * 1000,
"within_target": (end_time - start_time) * 1000 <= target_latency,
"status_code": response.status_code
}
except Exception as e:
latency_results[region.region_id] = {
"error": str(e),
"within_target": False
}
# Find optimal regions
optimal_regions = [
region for region, data in latency_results.items()
if data.get("within_target", False)
]
return {
"consumer_region": consumer_region,
"target_latency_ms": target_latency,
"latency_results": latency_results,
"optimal_regions": optimal_regions,
"latency_optimization_available": len(optimal_regions) > 0
}
except Exception as e:
return {"error": str(e)}
# Test Fixtures
@pytest.fixture
def multi_region_tests():
"""Create multi-region test instance"""
return MultiRegionMarketplaceTests()
@pytest.fixture
def sample_resource_requirements():
"""Sample resource requirements for testing"""
return {
"compute_power_min": 50.0,
"gpu_memory_min": 8,
"gpu_required": True,
"duration_hours": 2,
"max_price_per_hour": 5.0
}
# Test Classes
class TestRegionHealth:
"""Test region health and connectivity"""
@pytest.mark.asyncio
async def test_all_regions_health(self, multi_region_tests):
"""Test health of all configured regions"""
health_results = []
for region in multi_region_tests.regions:
result = await multi_region_tests.test_region_health_check(region)
health_results.append(result)
# Assert all regions are healthy
unhealthy_regions = [r for r in health_results if not r.get("healthy", False)]
assert len(unhealthy_regions) == 0, f"Unhealthy regions: {unhealthy_regions}"
# Assert response times are within targets
slow_regions = [r for r in health_results if not r.get("within_target", False)]
assert len(slow_regions) == 0, f"Slow regions: {slow_regions}"
@pytest.mark.asyncio
async def test_edge_node_connectivity(self, multi_region_tests):
"""Test connectivity to all edge nodes"""
connectivity_results = []
for edge_node in multi_region_tests.edge_nodes:
result = await multi_region_tests.test_edge_node_connectivity(edge_node)
connectivity_results.append(result)
# Assert all edge nodes are connected
disconnected_nodes = [n for n in connectivity_results if not n.get("connected", False)]
assert len(disconnected_nodes) == 0, f"Disconnected edge nodes: {disconnected_nodes}"
class TestGeographicLoadBalancing:
"""Test geographic load balancing functionality"""
@pytest.mark.asyncio
async def test_geographic_optimization(self, multi_region_tests, sample_resource_requirements):
"""Test geographic optimization for resource requests"""
test_regions = ["us-east-1", "us-west-2", "eu-central-1"]
for region in test_regions:
result = await multi_region_tests.test_geographic_load_balancing(
region,
sample_resource_requirements
)
assert result.get("success", False), f"Load balancing failed for region {region}"
assert "recommended_region" in result, f"No recommendation for region {region}"
assert "estimated_latency" in result, f"No latency estimate for region {region}"
assert result["estimated_latency"] <= 200.0, f"Latency too high for region {region}"
@pytest.mark.asyncio
async def test_cross_region_discovery(self, multi_region_tests):
"""Test resource discovery across regions"""
source_region = "us-east-1"
target_regions = ["us-west-2", "eu-central-1", "ap-southeast-1"]
result = await multi_region_tests.test_cross_region_resource_discovery(
source_region,
target_regions
)
assert result.get("successful_queries", 0) > 0, "No successful cross-region queries"
assert result.get("total_regions_queried", 0) == len(target_regions), "Not all regions queried"
class TestGlobalSynchronization:
"""Test global marketplace synchronization"""
@pytest.mark.asyncio
async def test_resource_synchronization(self, multi_region_tests):
"""Test resource synchronization across regions"""
result = await multi_region_tests.test_global_marketplace_synchronization()
assert result.get("synchronized", False), "Marketplace regions are not synchronized"
assert result.get("total_regions", 0) > 0, "No regions configured"
assert result.get("resource_variance", 100) < 5.0, "Resource variance too high"
@pytest.mark.asyncio
async def test_pricing_consistency(self, multi_region_tests):
"""Test pricing consistency across regions"""
result = await multi_region_tests.test_global_marketplace_synchronization()
pricing_data = result.get("pricing_data", {})
assert len(pricing_data) > 0, "No pricing data available"
# Check that pricing is consistent across regions
# (This is a simplified check - in reality, pricing might vary by region)
for region, prices in pricing_data.items():
assert isinstance(prices, dict), f"Invalid pricing data for region {region}"
class TestFailoverAndRedundancy:
"""Test failover and redundancy mechanisms"""
@pytest.mark.asyncio
async def test_regional_failover(self, multi_region_tests):
"""Test regional failover capabilities"""
primary_region = "us-east-1"
backup_regions = ["us-west-2", "eu-central-1"]
result = await multi_region_tests.test_failover_and_redundancy(
primary_region,
backup_regions
)
assert result.get("failover_ready", False), "Failover not ready"
assert result.get("redundancy_level", 0) > 0.5, "Insufficient redundancy"
assert len(result.get("available_backups", [])) > 0, "No available backup regions"
@pytest.mark.asyncio
async def test_latency_optimization(self, multi_region_tests):
"""Test latency optimization across regions"""
consumer_region = "us-east-1"
target_latency = 100.0 # 100ms target
result = await multi_region_tests.test_latency_optimization(
consumer_region,
target_latency
)
assert result.get("latency_optimization_available", False), "Latency optimization not available"
assert len(result.get("optimal_regions", [])) > 0, "No optimal regions found"
class TestPerformanceMetrics:
"""Test performance metrics collection"""
@pytest.mark.asyncio
async def test_global_performance_tracking(self, multi_region_tests):
"""Test global performance tracking"""
performance_data = {}
for region in multi_region_tests.regions:
try:
response = multi_region_tests.session.get(
f"{region.marketplace_url}/v1/metrics/performance",
timeout=10
)
if response.status_code == 200:
performance_data[region.region_id] = response.json()
else:
performance_data[region.region_id] = {"error": f"Status {response.status_code}"}
except Exception as e:
performance_data[region.region_id] = {"error": str(e)}
# Assert we have performance data from all regions
successful_regions = [r for r, data in performance_data.items() if "error" not in data]
assert len(successful_regions) > 0, "No performance data available"
# Check that performance metrics include expected fields
for region, metrics in successful_regions:
assert "response_time" in metrics, f"Missing response time for {region}"
assert "throughput" in metrics, f"Missing throughput for {region}"
if __name__ == "__main__":
pytest.main([__file__, "-v", "--tb=short"])

1003
tests/openclaw_marketplace/test_performance_optimization.py Normal file
View File

File diff suppressed because it is too large Load Diff