Todolist: Implement Core Functionality of the command_queue Module

[yaohaowei0914]

Description

1. Feature Overview

• Developed the command_queue module to provide a comprehensive task queuing solution for Trae Agent.
• Supports command queuing and ordered execution in multi-task environments, ensuring tasks are processed sequentially.
• Implements a persistent queue management mechanism to prevent task loss.
• Provides real-time queue status monitoring and management capabilities.

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2. Main Changes

Core Module Files

• Added trae_agent/utils/command_queue.py — Core implementation of the CommandQueue class
• Updated trae_agent/cli.py — Integrated CLI commands for queue management
• Added docs/command_queue.md — Complete usage documentation for the feature

Key Features Implemented

• CommandQueue Base Class**: Full lifecycle management of command queues
• Thread-Safe Operations: Safe interfaces including add_command, cancel_command, and get_commands
• State Management: Supports five states: PENDING, RUNNING, COMPLETED, FAILED, CANCELLED
• Persistence: JSON-based file storage for durable queue data
• Process Locking Mechanism: Prevents conflicts from multiple instances processing the queue simultaneously

CLI Command Extensions

• trae-cli add-queue — Add a new task to the queue
• trae-cli queue-status — View current queue status and list of commands
• trae-cli clear-all — Clear all commands from the queue
• trae-cli run --queue — Start the queue processor

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3. Technical Implementation

Architecture Design
CommandQueue (Core Class)
├── Queue Management (add_command, get_commands, cancel_command)
├── Status Monitoring (get_queue_status, _is_processor_running)
├── Persistence (_save_queue, _load_queue)
└── Processor (process_queue)

Key Technical Features

• Asynchronous Processing: Utilizes async/await pattern for non-blocking task execution
• File Locking Mechanism: Uses lock files to prevent concurrent access across processes
• Status Enumeration: Leverages CommandStatus enum to ensure state consistency
• Error Handling: Comprehensive exception catching and error logging
• Data Merging Strategy: Intelligent merging of queue data to maintain consistency during reloads

Data Structure
@DataClass
class QueuedCommand:
id: str # Unique identifier
task: str # Task description
working_dir: str # Working directory
options: Dict[str, Any] # CLI option parameters
status: CommandStatus # Command status
created_at: float # Timestamp of creation
error_message: str = "" # Error message (optional)

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4. Implementation Rationale

Business Value

• Improved User Experience: Users can submit tasks in batches without waiting for individual completion
• Enhanced System Stability: Prevents system blocking caused by long-running operations
• Increased Reliability: Persistent storage ensures no task is lost upon system restart
• Easier Management: Full visibility and control over queue state via monitoring and management tools

Technical Advantages

• Thread Safety: Safe operation in multi-process environments using file locks and atomic operations
• Extensibility: Modular design allows easy addition of new features (e.g., priority queues, retries)
• Fault Tolerance: Robust error handling with recovery support and detailed logging
• Performance Optimization: Asynchronous processing improves responsiveness and resource utilization

Development Efficiency

• Standardized API: Unified interface reduces complexity and learning curve
• Comprehensive Documentation: Detailed guides and API references included
• Seamless CLI Integration: New functionality integrates smoothly into existing toolchain
• Developer-Friendly Debugging: Rich logging and real-time status output aid troubleshooting

Compatibility

• Fully backward compatible with existing CLI commands
• New features exposed through dedicated subcommands, leaving original workflows unaffected
• Configuration file format remains unchanged and compatible