I built FastWorker after getting tired of deploying Celery + Redis for simple background tasks in FastAPI apps. Every time I needed to offload work from API requests, I had to manage 4-6 separate services. For small projects, this felt like overkill.

FastWorker is a brokerless task queue requiring only Python processes. No Redis, no RabbitMQ – just 2-3 Python services instead of 4-6+.

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Quick example:

# tasks.py

from fastworker import task

@task

def send_email(to: str, subject: str):

    return {"sent": True}

# FastAPI app

from fastworker import Client

client = Client()

@app.post("/send/") async def send_notification(email: str):

    task_id = await client.delay("send_email", email, "Welcome!")

    return {"task_id": task_id}

Start workers:

fastworker control-plane --task-modules tasks

fastworker subworker --task-modules tasks # optional

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Architecture: Uses NNG messaging for direct peer-to-peer communication. Control plane coordinates task distribution via priority heap and tracks worker load. Workers auto-discover via discovery socket. Results cached in-memory with LRU/TTL.

Designed for: Moderate-scale Python apps (1K-10K tasks/min) doing background processing – image resizing, report generation, emails, webhooks. Great for FastAPI/Flask/Django.

NOT for: Extreme scale (100K+ tasks/min), multi-language stacks, or systems requiring persistent task storage. For those, use Celery/RabbitMQ/Kafka.

Try it:

pip install fastworker

Repo: https://github.com/neul-labs/fastworker

FastAPI integration docs: https://github.com/neul-labs/fastworker/blob/main/docs/fasta...

Would love feedback on whether this fills a useful niche or if the limitations make it too narrow.