The QueueRunner is designed for distributed task processing and parallel execution. It allows you to implement agent logic that can be executed across multiple workers or processes, making it ideal for scaling up task processing in production environments.

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Basic Usage

The basic usage pattern of the QueueRunner follows these steps:
  1. Initialize the queue runner
  2. Implement agent logic for Workflow stages
  3. Put tasks into a queue system
  4. Execute tasks from the queue
The following a basic example.
queue_agent.py
from encord.objects.ontology_labels_impl import LabelRowV2
from encord_agents.tasks import QueueRunner
from encord.workflow.stages.agent import AgentTask

# Step 1: Initialize the queue runner
# project_hash is required for QueueRunner
runner = QueueRunner(project_hash="<your_project_hash>")

# Step 2: Implement agent logic
@runner.stage("my_stage_name")  # or stage="<stage_uuid>"
def process_task(task: AgentTask, lr: LabelRowV2) -> str:
    # Your agent logic here
    lr.set_priority(0.5)
    return "next_stage"  # or return a pathway UUID

# Step 3 & 4: Queue and execute tasks
if __name__ == "__main__":
    # Get all tasks that need processing
    for stage in runner.get_agent_stages():
        # Your queue implementation
        task_queue = []
        
        # Populate queue with task specifications
        for task in stage.get_tasks():
            task_queue.append(task.model_dump_json())
        
        # Process tasks from queue
        while task_queue:
            task_spec = task_queue.pop()
            # The wrapped function handles all dependency injection
            result_json = process_task(task_spec)
            # result_json contains success/failure status and next pathway
To avoid mixing up the agent implementations, we recommend to use a dedicated queue runner for each agent.

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Integration with Queue Systems

The QueueRunner is designed to work with various queue systems. Here are examples with popular frameworks:

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Celery Integration

celery_agent.py
from celery import Celery
from encord_agents.tasks import QueueRunner

app = Celery('tasks', broker='redis://localhost:6379/0')
runner = QueueRunner(project_hash="<your_project_hash>")

@runner.stage("my_stage")
def process_task(task: AgentTask) -> str:
    # Your processing logic
    return "next_stage"

# Register with Celery
@app.task
def celery_task(task_spec: str) -> str:
    return process_task(task_spec)

# Populate queue
for stage in runner.get_agent_stages():
    for task in stage.get_tasks():
        celery_task.delay(task.model_dump_json())
For a more detailed example, see the Celery example.
modal_agent.py
import modal
from encord_agents.tasks import QueueRunner

stub = modal.Stub("agent-tasks")
runner = QueueRunner(project_hash="<your_project_hash>")

@runner.stage("my_stage")
def process_task(task: AgentTask) -> str:
    # Your processing logic
    return "next_stage"

@stub.function
def modal_task(task_spec: str) -> str:
    return process_task(task_spec)

@stub.local_entrypoint()
def main():
    for stage in runner.get_agent_stages():
        for task in stage.get_tasks():
            modal_task.remote(task.model_dump_json())
For a more detailed example, see the Modal example.

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Key Concepts

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Task Specification Format

The QueueRunner uses JSON strings to pass task specifications between processes: 
# Converting task to JSON spec
task_spec = task.model_dump_json()

# The wrapped agent function handles parsing and dependency injection
result = my_agent(task_spec)  # Returns TaskCompletionResult as JSON
A task specification is a string in the following format: 
{
  "uuid": "<task-uuid>",
  "created_at": "2025-02-19T15:23:09.629049",
  "updated_at": "2025-02-19T15:23:09.679423",
  "status": "NEW",
  "data_hash": "<data-hash>",
  "data_title": "<data-title>",
  "label_branch_name": "<label-branch-name>",
  "assignee": null
}

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Task Completion Results

Agent functions return a JSON-serialized TaskCompletionResult containing:
  • Task UUID
  • Stage UUID
  • Success status
  • Error message (if any)
  • Next pathway (if successful)
{
  "task_uuid": "<task-uuid>",
  "stage_uuid": "<stage-uuid>",
  "success": true,
  "pathway": "<returned-pathway-uuid-or-name>",
  "error": null
}

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Error Handling

The QueueRunner provides error handling at the task level: 
from encord_agents.tasks.models import TaskCompletionResult

# The result JSON can be parsed back into a TaskCompletionResult
result = TaskCompletionResult.model_validate_json(result_json)

if not result.error:
    print(f"Task {result.task_uuid} completed, next pathway: {result.pathway}")
else:
    print(f"Task {result.task_uuid} failed: {result.error}")

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Configuration

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Initialization

::: encord_agents.tasks.runner.QueueRunner.init options: show_if_no_docstring: false show_submodules: false

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Stage Configuration

The stage decorator accepts the same configuration options as the sequential Runner: Example: 
@runner.stage(
    stage="my_stage",
    label_row_metadata_include_args=LabelRowMetadataIncludeArgs(...),
    label_row_initialise_labels_args=LabelRowInitialiseLabelsArgs(...)
)
def my_agent(task: AgentTask) -> str:
    ...

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Comparison with Sequential Runner

The key differences between QueueRunner and the sequential Runner are:
FeatureRunnerQueueRunner
Execution ModelExecutes tasks sequentially in a single processDesigned for distributed execution across multiple processes
Project HashOptional at initializationRequired at initialization
Function WrappingExecutes your function directly with injected dependenciesAdditionally wraps your function to handle JSON task specifications
Execution ControlHandles task polling and executionYou control task distribution and execution through your queue system
ScalingNot suitable for scalingSuitable for scaling
Choose the QueueRunner when you need to:
  • Process tasks in parallel
  • Scale processing across multiple machines
  • Integrate with existing queue infrastructure
  • Handle high task volumes efficiently