deer-flow/backend/docs/task_tool_improvements.md

Task Tool Improvements

Overview

The task tool has been improved to eliminate wasteful LLM polling. Previously, when using background tasks, the LLM had to repeatedly call task_status to poll for completion, causing unnecessary API requests.

Changes Made

1. Removed run_in_background Parameter

The run_in_background parameter has been removed from the task tool. All subagent tasks now run asynchronously by default, but the tool handles completion automatically.

Before:

# LLM had to manage polling
task_id = task(
    subagent_type="bash",
    prompt="Run tests",
    description="Run tests",
    run_in_background=True
)
# Then LLM had to poll repeatedly:
while True:
    status = task_status(task_id)
    if completed:
        break

After:

# Tool blocks until complete, polling happens in backend
result = task(
    subagent_type="bash",
    prompt="Run tests",
    description="Run tests"
)
# Result is available immediately after the call returns

2. Backend Polling

The task_tool now:

• Starts the subagent task asynchronously
• Polls for completion in the backend (every 2 seconds)
• Blocks the tool call until completion
• Returns the final result directly

This means:

• ✅ LLM makes only ONE tool call
• ✅ No wasteful LLM polling requests
• ✅ Backend handles all status checking
• ✅ Timeout protection (5 minutes max)

3. Removed task_status from LLM Tools

The task_status_tool is no longer exposed to the LLM. It's kept in the codebase for potential internal/debugging use, but the LLM cannot call it.

4. Updated Documentation

• Updated SUBAGENT_SECTION in prompt.py to remove all references to background tasks and polling
• Simplified usage examples
• Made it clear that the tool automatically waits for completion

Implementation Details

Polling Logic

Located in packages/harness/deerflow/tools/builtins/task_tool.py:

# Start background execution
task_id = executor.execute_async(prompt)

# Poll for task completion in backend
while True:
    result = get_background_task_result(task_id)

    # Check if task completed or failed
    if result.status == SubagentStatus.COMPLETED:
        return f"[Subagent: {subagent_type}]\n\n{result.result}"
    elif result.status == SubagentStatus.FAILED:
        return f"[Subagent: {subagent_type}] Task failed: {result.error}"

    # Wait before next poll
    time.sleep(2)

    # Timeout protection (5 minutes)
    if poll_count > 150:
        return "Task timed out after 5 minutes"

Execution Timeout

In addition to polling timeout, subagent execution now has a built-in timeout mechanism:

Configuration (packages/harness/deerflow/subagents/config.py):

@dataclass
class SubagentConfig:
    # ...
    timeout_seconds: int = 300  # 5 minutes default

Thread Pool Architecture:

To avoid nested thread pools and resource waste, we use two dedicated thread pools:

1. Scheduler Pool (_scheduler_pool):

   • Max workers: 4
   • Purpose: Orchestrates background task execution
   • Runs run_task() function that manages task lifecycle

2. Execution Pool (_execution_pool):

   • Max workers: 8 (larger to avoid blocking)
   • Purpose: Actual subagent execution with timeout support
   • Runs execute() method that invokes the agent

How it works:

# In execute_async():
_scheduler_pool.submit(run_task)  # Submit orchestration task

# In run_task():
future = _execution_pool.submit(self.execute, task)  # Submit execution
exec_result = future.result(timeout=timeout_seconds)  # Wait with timeout

Benefits:

• ✅ Clean separation of concerns (scheduling vs execution)
• ✅ No nested thread pools
• ✅ Timeout enforcement at the right level
• ✅ Better resource utilization

Two-Level Timeout Protection:

1. Execution Timeout: Subagent execution itself has a 5-minute timeout (configurable in SubagentConfig)
2. Polling Timeout: Tool polling has a 5-minute timeout (30 polls × 10 seconds)

This ensures that even if subagent execution hangs, the system won't wait indefinitely.

Benefits

1. Reduced API Costs: No more repeated LLM requests for polling
2. Simpler UX: LLM doesn't need to manage polling logic
3. Better Reliability: Backend handles all status checking consistently
4. Timeout Protection: Two-level timeout prevents infinite waiting (execution + polling)

Testing

To verify the changes work correctly:

1. Start a subagent task that takes a few seconds
2. Verify the tool call blocks until completion
3. Verify the result is returned directly
4. Verify no task_status calls are made

Example test scenario:

# This should block for ~10 seconds then return result
result = task(
    subagent_type="bash",
    prompt="sleep 10 && echo 'Done'",
    description="Test task"
)
# result should contain "Done"

Migration Notes

For users/code that previously used run_in_background=True:

• Simply remove the parameter
• Remove any polling logic
• The tool will automatically wait for completion

No other changes needed - the API is backward compatible (minus the removed parameter).