AsyncMQ

⚡ Supercharge your async applications with tasks so fast, you’ll think you’re bending time itself. ⚡

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Welcome to AsyncMQ, the modern task queue that brings powerful, flexible, and lightning-fast background processing to your Python stack. Whether you're building microservices, handling high-throughput data pipelines, or just want reliable delayed jobs, AsyncMQ has your back.

🚀 Why Choose AsyncMQ?

1. Asyncio & AnyIO-Native No more wrestling with threads or callbacks—AsyncMQ is built on asyncio and anyio, so your tasks are non-blocking and integrate seamlessly into modern async frameworks like FastAPI and Esmerald.

2. Multi-Backend Flexibility From the blistering speed of Redis to the ACID guarantees of Postgres, or the schema-less power of MongoDB, AsyncMQ supports multiple backends out of the box—and you can write your own by implementing BaseBackend.

3. Advanced Scheduling

4. Delayed Jobs: Fire off tasks in the future with precise delays.

5. Repeatable & Cron: Create heartbeat jobs or cron-like schedules with a single line.

6. Scan & Scheduler: Smart polling intervals and cron logic keep your timings accurate without burning CPU cycles.

7. Robust Error Handling

8. Retries & Backoff: Exponential or custom backoff strategies to gracefully handle failures.

9. Dead Letter Queue (DLQ): Automatically route permanently failed tasks for later inspection and reprocessing.

10. Observability & Event Hooks Tap into lifecycle events (job:started, job:completed, job:failed, job:progress) to power real-time dashboards, metrics, or custom alerts.

11. Rate Limiting & Concurrency Control Fine-tune throughput with token-bucket rate limiting and capacity limiters. Scale your worker concurrency without overloading downstream systems.

12. Sandboxed Execution Run untrusted or CPU-bound tasks in isolated subprocesses with timeouts to protect your main workers from rogue code or infinite loops.

13. Flow/DAG Orchestration Create complex task graphs with dependencies using FlowProducer. Enqueue entire pipelines atomically or fall back to safe sequential logic.

14. CLI & Dev Experience A feature-rich CLI for managing queues, jobs, and workers—intuitive commands, JSON output support, and built-in help for every scenario.

15. Seamless ASGI Integration Out-of-the-box compatibility with FastAPI, Esmerald, or any ASGI application. Manage workers within your app’s lifecycle events.

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Comparison with Other Python Task Queues

Feature	AsyncMQ	Celery	RQ	Dramatiq	Huey
AsyncIO Native	✅	❌	❌	✅	❌
Pluggable Backends	✅ Redis, Postgres, Mongo, In-Mem	RabbitMQ only	Redis only	Redis only	Redis only
Rate Limiting	✅ built-in	❌	❌	✅ via addon	❌
Cron & Repeatable Jobs	✅ interval & cron expressions	✅	✅	✅	✅
Progress Reporting	✅ event hooks	✅ callbacks	❌	✅ hooks	❌
Dead Letter Queue	✅	✅	❌	✅	✅
Flow / DAG Support	✅ FlowProducer	✅ chords	❌	✅ extensions	❌
ASGI-Friendly	✅ FastAPI/Esmerald integration	❌	❌	❌	❌
CLI Management	✅ rich, JSON-friendly	✅	✅	✅	✅

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Where AsyncMQ Shines

• True AsyncIO & AnyIO integration: zero thread hacks, full non-blocking background tasks.
• Backend flexibility: swap Redis, Postgres, MongoDB, or In-Memory with a single setting.
• Built-in rate limiting & concurrency control: protect downstream services out-of-the-box.
• Event-driven hooks: subscribe to job:started, completed, failed, progress, etc., for metrics and alerts.
• ASGI integration: manage workers within FastAPI or Esmerald lifecycles—no extra wrappers needed.

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⚡ Core Features at a Glance

Category	Highlights
Task Definition	@task decorator, .enqueue(), progress callbacks, TTL, retries, dependencies, repeats
Queue API	add(), add_bulk(), add_repeatable(), pause(), resume(), clean(), stats
Worker Engine	process_job(), capacity & rate limiters, sandbox, event emitter, DLQ handling
Scheduling	delayed_job_scanner, repeatable_scheduler, cron vs interval support
Flow Orchestration	FlowProducer.add_flow(), atomic backend support, fallback mode for dependency wiring
Configuration	Central Settings dataclass, env var override, dynamic tuning of concurrency & intervals
Observability	LoggingConfig protocol, structured logs, event subscriptions, metrics integration
CLI Management	asyncmq queue, asyncmq job, asyncmq worker, asyncmq info commands

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🎬 Quickstart

1. Install

pip install asyncmq

2. Configure

Override the default settings from asyncmq.conf import Settings and create your own, then make it global.

export ASYNCMQ_SETTINGS_MODULE=myapp.settings.Settings

3. Define a Task

from asyncmq.tasks import task

@task(queue="emails", retries=2, ttl=120)
async def send_welcome(email: str):
    # Imagine real email-sending logic here
    print(f"📧 Sent welcome to {email}")

4. Enqueue & Run

import anyio
from asyncmq.queues import Queue

async def main():
    q = Queue("emails")
    await send_welcome.enqueue(q.backend, "alice@example.com", delay=10)
anyio.run(main)

5. Start Workers

asyncmq worker start emails --concurrency 5

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AsyncMQ is more than just a task queue, it’s the swiss army knife of async background processing. Dive into the Learn section to master every feature, or jump into the Features docs for quick reference.

Ready to bend time?

Get started today and experience async tasking at warp speed! 🎉