FastWorker

No Redis. No RabbitMQ. Just Python.

Background tasks in 30 seconds. Zero infrastructure.

pip install fastworker

Why FastWorker?

	FastWorker	Celery + Redis	RabbitMQ	AWS SQS
External dependencies	0	2+ (broker + backend)	1+ (RabbitMQ)	SQS + IAM
Setup time	30 seconds	30+ minutes	30+ minutes	15+ minutes
Built-in dashboard	Yes	No (needs Flower)	No (needs RabbitMQ UI)	No (needs CloudWatch)
Worker discovery	Automatic	Manual config	Manual config	None
Cron/periodic tasks	Built-in	needs celery-beat	needs scheduler	needs CloudWatch Events
FastAPI integration	Native (FastWorker(app))	Manual	Manual	Manual
Lines of config	1	15+	20+	10+

Celery — 10+ lines of config:

# celery_app.py
from celery import Celery

app = Celery("tasks", broker="redis://localhost:6379/0", backend="redis://localhost:6379/1")
app.conf.update(
    task_serializer="json",
    result_serializer="json",
    accept_content=["json"],
    timezone="UTC",
    enable_utc=True,
    task_track_started=True,
    task_acks_late=True,
    worker_prefetch_multiplier=1,
)

redis-server &                        # broker
celery -A celery_app worker &         # worker
celery -A celery_app beat &           # scheduler (for periodic tasks)
flower -A celery_app &                # monitoring (optional)

FastWorker — 1 line:

# mytasks.py
from fastworker import task

@task
def add(x: int, y: int) -> int:
    return x + y

fastworker control-plane --task-modules mytasks

Features

• Zero Infrastructure — No Redis, RabbitMQ, or message broker. Pure Python.
• FastAPI Native — FastWorker(app) auto-wires lifecycle, feels like a built-in feature.
• Periodic & Cron Tasks — @task(repeat_interval=60) or @task(cron="0 */6 * * *"). Built-in, no beat scheduler.
• Built-in Dashboard — Real-time web UI with dark mode. Workers, queues, task history. No extra setup.
• Automatic Worker Discovery — Workers and clients find each other on the network. Zero config.
• Priority Queues — CRITICAL, HIGH, NORMAL, LOW. Tasks routed by urgency.
• Result Caching — LRU cache with configurable TTL and size limits.
• Task Callbacks — Real-time notifications when tasks complete.
• OpenTelemetry — Optional distributed tracing and metrics.

FastWorker is designed for moderate-scale Python applications (1K-10K tasks/min). For extreme scale or complex workflows, see Limitations & Scope.

Quick Start

1. Install

pip install fastworker

2. Define Tasks

# mytasks.py
from fastworker import task

@task
def add(x: int, y: int) -> int:
    return x + y

@task(repeat_interval=300)
def refresh_cache():
    return {"cache": "refreshed"}

3. Run

fastworker control-plane --task-modules mytasks

The dashboard opens at http://127.0.0.1:8080.

Submit tasks:

fastworker submit --task-name add --args 5 3
fastworker submit --task-name add --args 10 20 --non-blocking

FastAPI Integration (v0.3.0)

from fastapi import FastAPI
from fastworker.integration.fastapi import FastWorker

app = FastAPI()
fw = FastWorker(app)  # done — lifecycle, discovery, everything


@task
def send_welcome_email(user_id: int, email: str) -> str:
    return f"Welcome email sent to {email}"


@app.post("/users/{user_id}/welcome")
async def welcome_user(user_id: int, email: str):
    task_id = await fw.delay("send_welcome_email", user_id, email)
    return {"task_id": task_id, "status": "queued"}


@app.get("/health")
async def health():
    return {"status": "healthy", "workers_online": fw.worker_count}

# Terminal 1
fastworker control-plane --task-modules app

# Terminal 2
uvicorn app:app --reload

Full FastAPI docs →

Periodic & Cron Tasks (v0.3.0)

@task(repeat_interval=60)        # every 60 seconds
def heartbeat():
    ...

@task(cron="*/5 * * * *")        # every 5 minutes
def sync_data():
    ...

@task(cron="0 9 * * 1-5")        # weekdays at 9am
def morning_report():
    ...

@task(repeat_interval=30, repeat_count=100)  # exactly 100 times
def limited_job():
    ...

Periodic tasks docs →

Project Structure — Grow From One File

FastWorker scales with your project:

# Level 1: Single file
mytasks.py

# Level 2: Package
tasks/
├── __init__.py
├── emails.py
└── reports.py

# Level 3: Organized
app/
├── tasks/
│   ├── background.py
│   └── scheduled.py
├── services/
└── models/

# Level 4: FastAPI
app/
├── api/
├── tasks/
├── main.py      # FastWorker(app)
└── ...

Project structure guide →

Client Usage

from fastworker import Client

client = Client()
await client.start()

# Non-blocking — returns task ID immediately
task_id = await client.delay("add", 5, 3)

# Blocking — waits for result
result = await client.submit_task("add", args=(5, 3))
print(result.result)  # 8

# Batch submit
task_ids = await client.submit_batch([
    {"task_name": "add", "args": (1, 2)},
    {"task_name": "add", "args": (3, 4)},
])

# With callback
task_id = await client.delay_with_callback(
    "process_data", "tcp://127.0.0.1:6000", data,
    callback_data={"source": "api"},
)

# Query status
result = await client.get_task_result(task_id)

client.stop()

CLI Reference

# Control plane (with dashboard)
fastworker control-plane --task-modules mytasks

# Subworker (for scaling)
fastworker subworker --worker-id w1 --control-plane-address tcp://127.0.0.1:5555 --task-modules mytasks

# Submit tasks
fastworker submit --task-name add --args 5 3
fastworker submit --task-name add --args 5 3 --non-blocking
fastworker submit --task-name add --args 5 3 --priority critical
fastworker submit --task-name report --args '"Q1"' --countdown 60

# List tasks
fastworker list --task-modules mytasks
fastworker list --task-modules mytasks --list-periodic
fastworker list --task-modules mytasks --tree

# Task management
fastworker status --task-id <uuid>
fastworker cancel --task-id <uuid>

Dashboard

Start the control plane and open http://127.0.0.1:8080.

• Real-time worker status and load metrics
• Queue sizes by priority
• Task history with status and timing
• Cache utilization stats
• Dark mode
• Auto-refresh

fastworker control-plane --gui-host 0.0.0.0 --gui-port 9000 --task-modules mytasks
fastworker control-plane --no-gui --task-modules mytasks  # disable dashboard

Extending FastWorker

Clear extension points for custom behavior:

• Task Hooks — @task(before=..., after=...) for per-task middleware
• Event Bus — Subscribe to task.success, task.failure, worker.inactive events
• Custom Serializers — Implement your own serialization format
• Result Backends — Redis, S3, PostgreSQL persistence

Extending FastWorker →

Development

git clone https://github.com/neul-labs/fastworker.git
cd fastworker

uv sync
uv run pytest
uv run ruff check .

Requirements

• Python 3.12+
• pynng >= 0.8.1
• pydantic >= 2.0.0

Documentation

• Index
• Why FastWorker?
• Project Structure
• Architecture
• FastAPI Integration
• Periodic & Cron Tasks
• Management GUI
• Extending FastWorker
• Internals
• Benchmarks
• API Reference
• Telemetry
• Limitations & Scope

License

MIT — see LICENSE.