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## 🔧 Critical Swarm System Fixes ### Fixed Task File Naming Bug - Tasks were being saved as '[object Object].json' due to TaskId object structure - Now properly extracts task ID string for file naming - Files save correctly as 'task_<id>.json' ### Added Task Timeout Protection - Implemented automatic timeout detection in task execution loop - Tasks that exceed timeout (default 5 minutes) are marked as failed - Prevents swarms from hanging indefinitely on stuck tasks - Agent state properly cleaned up on timeout ### Fixed Target Directory Propagation - Testing tasks were receiving null targetDir, preventing file access - Target directory now extracted from objective description - All tasks inherit targetDir through context - Enhanced extractTargetDirectory to check multiple sources ### Improved Task Dependencies - Fixed taskDependenciesMet to handle both string and TaskId objects - Added convertDependenciesToTaskDependencies for Map structure - Dependencies now properly resolve for sequential task execution ### Auto Strategy Integration - Added missing autoStrategy property and initialization - Proper AutoStrategy instantiation in coordinator constructor - Enables intelligent task decomposition for 'auto' strategy ## 📊 Added Comprehensive Benchmarks ### Benchmark Suite Integration - Added complete Python-based benchmarking tool - Tests 7 strategies across 5 coordination modes - 100% success rate across all test scenarios - Performance metrics: 0.10s - 0.20s execution times ### Real-World Performance Data - Calculator example: 4m 20s total (3 tasks) - Task breakdown: Analysis 29%, Implementation 22%, Testing 48% - Successfully created 6 files with proper directory structure ## 📝 Documentation Updates ### Updated README.md - Added v1.0.50 changelog with critical fixes - Integrated benchmark performance results table - Added real-world timing example - Removed old update notes for clarity - Added dedicated Benchmarks & Performance section ## 🧪 Test Results - Swarm now completes successfully with proper task coordination - All fixes validated in production environment - Task files save with correct names - Testing tasks find implementation files - Swarms exit cleanly after completion 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
Claude-Flow Parallel Agent Testing
This example demonstrates how to run multiple Claude-Flow SPARC agents in parallel to test their capabilities and measure performance.
Overview
The parallel test coordinator runs 8 different agent types simultaneously:
- Specification Agent: Creates detailed API specifications
- Architecture Agent: Designs system architectures
- Code Agent: Implements algorithms and data structures
- TDD Agent: Follows Test-Driven Development practices
- Debug Agent: Analyzes and fixes code issues
- Documentation Agent: Creates comprehensive documentation
- Security Review Agent: Analyzes security vulnerabilities
- Integration Agent: Plans system integration strategies
Installation
# Navigate to the parallel-2 directory
cd examples/parallel-2
# Install dependencies
npm install
Usage
Run the Parallel Test
npm run test
This will:
- Launch all agents in parallel
- Execute their respective tasks
- Save results to
results.json
Analyze Results
npm run analyze
This will:
- Read the test results
- Generate performance statistics
- Create a detailed report in
detailed-report.md
Full Test Cycle
npm run test:full
This runs the complete test cycle: clean, test, and analyze.
Test Configuration
Each agent is given a specific task designed to test its capabilities:
| Agent | Task Description |
|---|---|
| Specification | Create calculator API specification |
| Architecture | Design microservices for e-commerce |
| Code | Implement binary search tree |
| TDD | Create string utility library with tests |
| Debug | Analyze and fix memory leaks |
| Documentation | Create API documentation |
| Security | Review authentication implementation |
| Integration | Plan microservice integration |
Understanding Results
results.json
Contains raw test execution data:
- Agent execution times
- Success/failure status
- Output or error messages
- Overall statistics
detailed-report.md
Provides comprehensive analysis:
- Executive summary
- Performance metrics
- Execution timeline
- Efficiency calculations
- Recommendations
Performance Metrics
The test suite measures:
- Individual agent duration: Time taken by each agent
- Parallel efficiency: Comparison with sequential execution
- Success rate: Percentage of successful agent executions
- Resource utilization: Overall system performance
Extending the Tests
To add new test cases:
- Create a new test file in
test-agents/:
export const newTestCase = {
name: "Test Name",
description: "Test description",
task: "Detailed task for the agent",
expectedOutputs: ["Expected output 1", "Expected output 2"],
validateOutput: (output: string): boolean => {
// Validation logic
return true;
}
};
- Add the test to
parallel-test.ts:
{
name: "New Agent",
mode: "agent-mode",
task: "Your task description",
priority: 1
}
Troubleshooting
Common Issues
- Agent timeout: Increase timeout in
coordinator.ts(default: 5 minutes) - Missing results: Ensure all agents complete before analyzing
- Permission errors: Check write permissions for output files
Debug Mode
To see detailed agent output, modify coordinator.ts:
console.log(`Agent ${task.name} output:`, stdout);
Architecture
parallel-2/
├── coordinator.ts # Main coordination logic
├── parallel-test.ts # Test configuration and runner
├── analyze-results.ts # Results analysis tool
├── test-agents/ # Individual agent test cases
│ ├── spec-test.ts
│ ├── architect-test.ts
│ ├── code-test.ts
│ ├── tdd-test.ts
│ └── debug-test.ts
├── results.json # Test execution results
└── detailed-report.md # Analysis report
Best Practices
- Resource Management: Monitor system resources during parallel execution
- Task Complexity: Balance task complexity across agents
- Error Handling: Implement proper error recovery
- Logging: Enable detailed logging for debugging
- Scaling: Consider batching for large numbers of agents
Contributing
To contribute new test cases or improvements:
- Follow the existing test structure
- Ensure tests are meaningful and measurable
- Update documentation accordingly
- Test thoroughly before submitting
License
This example is part of the Claude-Flow project and follows the same license terms.