qwen-code/integration-tests/concurrent-runner

Qwen Concurrent Runner

A Python tool for executing multiple Qwen CLI tasks across different models concurrently using isolated git worktrees.

Overview

This tool enables you to:

• Run multiple tasks against multiple models in parallel
• Create isolated git worktrees for each execution
• Track execution status in real-time
• Capture and store all outputs (stdout, stderr, and OpenAI logs)
• Resume or analyze results after completion

Installation

# Install dependencies
pip install -r requirements.txt

Usage

python runner.py config.json

Configuration

Create a JSON configuration file (see config.example.json):

{
  "concurrency": 3,
  "yolo": true,
  "source_repo": ".",
  "worktree_base": "~/.qwen/worktrees",
  "outputs_dir": "./outputs",
  "results_file": "./results.json",
  "tasks": [
    {
      "id": "code-review",
      "name": "Security Code Review",
      "prompts": ["Review the codebase for security vulnerabilities."]
    }
  ],
  "models": ["claude-3-5-sonnet-20241022", "qwen3-coder-plus"]
}

Configuration Options

Option	Type	Default	Description
concurrency	int	4	Maximum parallel executions
yolo	bool	true	Auto-approve all actions
source_repo	string	.	Source git repository path
branch	string	null	Git branch to checkout (uses default if null)
worktree_base	string	~/.qwen/worktrees	Base directory for git worktrees
outputs_dir	string	./outputs	Directory for captured output
results_file	string	./results.json	JSON file for run tracking
tasks	array	[]	List of task definitions
models	array	[]	List of model identifiers

Task Definition

Each task has:

• id: Unique identifier
• name: Human-readable name
• prompts: Array of prompt strings (joined with newlines)

Output Structure

Each run creates an isolated output directory:

outputs/
├── {run_id}/
│   ├── stdout.txt        # CLI stdout
│   ├── stderr.txt        # CLI stderr
│   └── logs/             # OpenAI API logs
│       └── openai-*.json

results.json

{
  "updated_at": "2026-01-28T10:30:00",
  "runs": [
    {
      "run_id": "abc123",
      "task_id": "code-review",
      "task_name": "Security Code Review",
      "model": "qwen3-coder-plus",
      "status": "succeeded",
      "worktree_path": "~/.qwen/worktrees/run-abc123",
      "output_dir": "outputs/abc123",
      "logs_dir": "outputs/abc123/logs",
      "started_at": "2026-01-28T10:00:00",
      "ended_at": "2026-01-28T10:05:00",
      "exit_code": 0,
      "stdout_file": "outputs/abc123/stdout.txt",
      "stderr_file": "outputs/abc123/stderr.txt"
    }
  ]
}

Execution Flow

1. Generate Matrix: Create N×M run combinations (tasks × models)
2. Create Worktree: Git worktree add from source repo
3. Initialize: npm install && npm run build
4. Execute: Run qwen CLI with captured output (logs go to run-specific folder)
5. Cleanup: Remove git worktree (always executed)

Status Values

• queued: Waiting to start
• preparing: Creating git worktree
• initializing: Running npm install + build
• running: Executing qwen CLI
• succeeded: Completed successfully
• failed: Error occurred

Requirements

• Python 3.10+
• Git repository (for worktree operations)
• Node.js and npm (for build step)
• qwen CLI in PATH

Exit Codes

• 0: All runs succeeded
• 1: One or more runs failed
• 130: Interrupted by user (Ctrl+C)