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.
