name: long-running-agent description: Build autonomous, long-running AI agents that parse PRDs/specifications into structured task lists and execute them autonomously with state persistence, error recovery, and cross-session resumption. Works with any agent framework (Cursor, OpenCode, etc.). license: MIT compatibility: Requires file system access, JSON processing, and ability to execute tasks over extended periods. Compatible with any AI agent that supports file operations and persistent state management.

Long Running Agent

Build resilient autonomous agents that can parse PRDs/specifications, generate structured task lists, and execute tasks autonomously over extended periods with state persistence and automatic recovery.

This skill provides agent-agnostic patterns that work with any AI agent framework including Cursor, OpenCode, Claude, and others.

Core Architecture

A long-running agent consists of seven core systems that work with any agent framework:

1. PRD/Spec Processing - Parse requirements documents into structured, executable task lists
2. Task Execution Engine - Autonomous task processing with dependency management
3. API Rotation & Management - Intelligent API key rotation, rate limiting, and load balancing
4. State Management - File-based persistence for workflow states and task tracking
5. Error Handling - Classification, recovery strategies, and graceful degradation
6. Cross-Session Persistence - Resume work across interruptions and restarts
7. Learning & Memory - Pattern recognition and improvement over time

These patterns are framework-agnostic and can be implemented with any AI agent that has file system access.

Implementation Workflow

Step 1: Set Up Project Structure

Create the basic directory structure for persistent state management:

def setup_project_structure(project_name: str):
    """Create directory structure for long-running agent."""
    directories = [
        f"tasks/{project_name}",
        f"results/{project_name}", 
        f"memories/{project_name}",
        f"logs/{project_name}"
    ]
    
    for directory in directories:
        os.makedirs(directory, exist_ok=True)
        print(f"✅ Created: {directory}")

Step 2: Implement PRD Processing

Parse requirements documents into structured, executable task lists:

def parse_prd_to_tasks(prd_content: str, project_name: str) -> Dict:
    """Parse PRD into structured task list with dependencies."""
    # See references/prd-processing.md for full implementation
    
    tasks = {
        "project_name": project_name,
        "created_at": datetime.now().isoformat(),
        "total_tasks": 0,
        "completed_tasks": 0,
        "tasks": []
    }
    
    # Extract sections, analyze dependencies, categorize tasks
    # Returns structured JSON with full task metadata
    return tasks

Full Implementation: See references/prd-processing.md

Step 3: Set Up API Rotation and Management

Configure intelligent API rotation for external service calls:

def setup_api_rotation(api_configs: List[Dict]):
    """Setup API rotation with multiple endpoints."""
    # See references/api-rotation.md for full implementation
    
    global api_manager
    api_manager = APIRotationManager()
    
    for config in api_configs:
        api_manager.add_endpoint(
            name=config["name"],
            base_url=config["base_url"], 
            api_key=config["api_key"],
            rate_limit=config.get("rate_limit", 60),
            quota_limit=config.get("quota_limit", 1000)
        )
    
    print(f"🔄 API rotation configured with {len(api_configs)} endpoints")

Full Implementation: See references/api-rotation.md

Step 4: Implement State Management

Create persistent state management for cross-session continuity:

def save_task_list(task_list: Dict, file_path: str = None):
    """Save task list to persistent storage."""
    # See references/state-management.md for full implementation
    
    if not file_path:
        file_path = f"tasks/{task_list['project_name']}/current_tasks.json"
    
    os.makedirs(os.path.dirname(file_path), exist_ok=True)
    with open(file_path, 'w') as f:
        json.dump(task_list, f, indent=2)

def load_task_list(project_name: str = None, file_path: str = None) -> Dict:
    """Load task list from persistent storage."""
    # Implementation details in references/state-management.md
    pass

Full Implementation: See references/state-management.md

Step 5: Implement Task Execution Engine

Execute tasks autonomously with dependency management:

def execute_next_task(project_name: str) -> Dict:
    """Execute the next available task with dependency checking."""
    # See references/task-execution.md for full implementation
    
    task_list = load_task_list(project_name)
    
    # Find next executable task (dependencies met, status pending)
    next_task = find_next_executable_task(task_list)
    
    if not next_task:
        return {"status": "no_tasks_available"}
    
    # Execute task by category with API rotation support
    result = execute_task_by_category(next_task)
    
    # Update task status and save state
    update_task_status(next_task["id"], "completed" if result["success"] else "failed")
    
    return result

Full Implementation: See references/task-execution.md

Step 6: Set Up Learning and Memory System

Implement pattern recognition and continuous improvement:

def save_execution_pattern(task: Dict, execution_result: Dict, pattern_file: str = "memories/patterns.json"):
    """Save successful execution patterns for learning."""
    # See references/learning-system.md for full implementation
    
    pattern = {
        "task_category": task["category"],
        "task_type": task.get("type", "general"),
        "execution_approach": execution_result.get("approach"),
        "success_factors": execution_result.get("success_factors", []),
        "timestamp": datetime.now().isoformat()
    }
    
    # Save pattern for future reference
    patterns = load_json_file(pattern_file, [])
    patterns.append(pattern)
    save_json_file(pattern_file, patterns)

Full Implementation: See references/learning-system.md

Step 7: Agent Integration

Integrate with your specific AI agent framework:

# For any agent framework (Cursor, OpenCode, Claude, etc.)
def run_long_running_agent(prd_content: str, project_name: str):
    """Main entry point for long-running agent workflow."""
    
    # 1. Setup
    setup_project_structure(project_name)
    setup_api_rotation(load_api_config())
    
    # 2. Parse PRD
    task_list = parse_prd_to_tasks(prd_content, project_name)
    save_task_list(task_list)
    
    # 3. Execute tasks
    while has_pending_tasks(project_name):
        result = execute_next_task(project_name)
        
        if result["status"] == "no_tasks_available":
            break
            
        # Learn from execution
        if result.get("success"):
            save_execution_pattern(result["task"], result)
    
    # 4. Generate summary
    return generate_project_summary(project_name)

Agent Framework Integration

For Cursor, OpenCode, and other AI Agents:

1. Load this skill when starting a new project or resuming work
2. Call run_long_running_agent() with your PRD content
3. Monitor progress through the generated task files
4. Resume anytime by calling execute_next_task()

Example Workflow:

# Start new project
prd = "Your PRD content here..."
summary = run_long_running_agent(prd, "ecommerce-platform")

# Resume existing project  
result = execute_next_task("ecommerce-platform")

# Check status
status = get_project_status("ecommerce-platform")

Key Patterns Summary

| Pattern | Purpose | Implementation | |---------|---------|----------------| | PRD Parsing | Convert specs to structured tasks | parse_prd_to_tasks() function with regex parsing | | API Rotation | Intelligent API key rotation and load balancing | APIRotationManager with weighted selection | | Rate Limiting | Prevent API quota exhaustion | Per-endpoint usage tracking and throttling | | Task State Management | Track progress across sessions | JSON file-based persistence in tasks/ directory | | Autonomous Execution | Self-directed task processing | execute_next_task() with dependency checking | | Cross-Session Persistence | Resume work after interruption | File-based state management | | Dependency Management | Ensure proper task ordering | Dependency analysis and validation | | Progress Tracking | Monitor and update status | update_task_status() with counters | | Parallel Execution | Handle independent tasks concurrently | ThreadPoolExecutor with file locking | | Error Recovery | Handle failures gracefully | Try-catch with error logging and retry logic | | Learning System | Improve from execution patterns | Pattern and solution storage in memories/ | | Agent Agnostic | Work with any AI agent | Standard Python functions, no framework dependencies |

File Structure

project-name/
├── tasks/project-name/
│   ├── current_tasks.json    # Current task list and status
│   └── task_history.json     # Completed task history
├── results/project-name/
│   ├── task_001/            # Individual task outputs
│   └── task_002/
├── memories/project-name/
│   ├── patterns.json        # Learned execution patterns
│   └── solutions.json       # Error solutions
└── logs/project-name/
    └── execution.log        # Detailed execution logs

Reference Files

For detailed implementations, see:

• prd-processing.md - PRD parsing patterns, task extraction, structured generation
• api-rotation.md - API rotation, rate limiting, load balancing, error handling
• task-execution.md - Autonomous task processing, dependency management, status tracking
• state-management.md - File-based persistence, cross-session continuity, data integrity
• agent-integration.md - Integration patterns for different AI agents (Cursor, OpenCode, etc.)
• parallel-execution.md - Concurrent task processing, thread safety, coordination patterns
• error-handling.md - Error classification, recovery strategies, graceful degradation
• learning-system.md - Pattern recognition, continuous improvement, memory management

Quick Start

1. Parse your PRD: tasks = parse_prd_to_tasks(prd_content, "my-project")
2. Start execution: run_long_running_agent(prd_content, "my-project")
3. Monitor progress: Check files in tasks/my-project/
4. Resume anytime: execute_next_task("my-project")

Agent Instructions

This skill works with any AI agent that can:

• Read and write files
• Execute Python functions
• Maintain state across conversations
• Handle JSON data structures

Simply load this skill and call the main functions with your PRD content to begin autonomous task execution with full persistence and recovery capabilities.