Welcome to aio-pika’s documentation!

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aio-pika is a wrapper for the aiormq for asyncio and humans.


  • Completely asynchronous API.

  • Object oriented API.

  • Transparent auto-reconnects with complete state recovery with connect_robust (e.g. declared queues or exchanges, consuming state and bindings).

  • Python 3.6+ compatible.

  • For python 3.5 users available aio-pika<7

  • Transparent publisher confirms support

  • Transactions support

  • Completely type-hints coverage.

AMQP URL parameters

URL is the supported way to configure connection. For customisation of connection behaviour you might pass the parameters in URL query-string like format.

This article describes a description for these parameters.

aiormq specific

  • name (str url encoded) - A string that will be visible in the RabbitMQ management console and in the server logs, convenient for diagnostics.

  • cafile (str) - Path to Certificate Authority file

  • capath (str) - Path to Certificate Authority directory

  • cadata (str url encoded) - URL encoded CA certificate content

  • keyfile (str) - Path to client ssl private key file

  • certfile (str) - Path to client ssl certificate file

  • no_verify_ssl - No verify server SSL certificates. 0 by default and means False other value means True.

  • heartbeat (int-like) - interval in seconds between AMQP heartbeat packets. 0 disables this feature.

aio_pika.connect function and aio_pika.Connection class specific

  • interleave (int-like) - controls address reordering when a host name resolves to multiple IP addresses. If 0 or unspecified, no reordering is done, and addresses are tried in the order returned by getaddrinfo(). If a positive integer is specified, the addresses are interleaved by address family, and the given integer is interpreted as “First Address Family Count” as defined in RFC 8305. The default is 0 if happy_eyeballs_delay is not specified, and 1 if it is.


    Really useful for RabbitMQ clusters with one DNS name with many A/AAAA records.


    This option is supported by asyncio.DefaultEventLoopPolicy and available since python 3.8.

  • happy_eyeballs_delay (float-like) - if given, enables Happy Eyeballs for this connection. It should be a floating-point number representing the amount of time in seconds to wait for a connection attempt to complete, before starting the next attempt in parallel. This is the “Connection Attempt Delay” as defined in RFC 8305. A sensible default value recommended by the RFC is 0.25 (250 milliseconds).


    Really useful for RabbitMQ clusters with one DNS name with many A/AAAA records.


    This option is supported by asyncio.DefaultEventLoopPolicy and available since python 3.8.

aio_pika.connect_robust function and aio_pika.RobustConnection class specific

For aio_pika.RobustConnection class is applicable all aio_pika.Connection related parameters like, name/interleave/happy_eyeballs_delay and some specific:

  • reconnect_interval (float-like) - is the period in seconds, not more often than the attempts to re-establish the connection will take place.

  • fail_fast (true/yes/y/enable/on/enabled/1 means True, otherwise False) - special behavior for the start connection attempt, if it fails, all other attempts stops and an exception will be thrown at the connection stage. Enabled by default, if you are sure you need to disable this feature, be ensures for the passed URL is reallt working. Otherwise, your program will go into endless reconnection attempts that can not be successed.

URL examples

  • amqp://username:password@hostname/vhost?name=connection%20name&heartbeat=60&happy_eyeballs_delay=0.25

  • amqps://username:password@hostname/vhost?reconnect_interval=5&fail_fast=1

  • amqps://username:password@hostname/vhost?cafile=/path/to/ca.pem

  • amqps://username:password@hostname/vhost?cafile=/path/to/ca.pem&keyfile=/path/to/key.pem&certfile=/path/to/sert.pem


Installation with pip:

pip install aio-pika

Installation from git:

# via pip
pip install https://github.com/mosquito/aio-pika/archive/master.zip

# manually
git clone https://github.com/mosquito/aio-pika.git
cd aio-pika
python setup.py install


Clone the project:

git clone https://github.com/mosquito/aio-pika.git
cd aio-pika

Create a new virtualenv for aio-pika:

virtualenv -p python3.5 env

Install all requirements for aio-pika:

env/bin/pip install -e '.[develop]'

Table Of Contents

Thanks for contributing

See also


aiormq is a pure python AMQP client library. It is under the hood of aio-pika and might to be used when you really loving works with the protocol low level. Following examples demonstrates the user API.

Simple consumer:

import asyncio
import aiormq

async def on_message(message):
    on_message doesn't necessarily have to be defined as async.
    Here it is to show that it's possible.
    print(f" [x] Received message {message!r}")
    print(f"Message body is: {message.body!r}")
    print("Before sleep!")
    await asyncio.sleep(5)   # Represents async I/O operations
    print("After sleep!")

async def main():
    # Perform connection
    connection = await aiormq.connect("amqp://guest:guest@localhost/")

    # Creating a channel
    channel = await connection.channel()

    # Declaring queue
    declare_ok = await channel.queue_declare('helo')
    consume_ok = await channel.basic_consume(
        declare_ok.queue, on_message, no_ack=True

loop = asyncio.get_event_loop()

Simple publisher:

import asyncio
from typing import Optional

import aiormq
from aiormq.abc import DeliveredMessage

MESSAGE: Optional[DeliveredMessage] = None

async def main():
    global MESSAGE
    body = b'Hello World!'

    # Perform connection
    connection = await aiormq.connect("amqp://guest:guest@localhost//")

    # Creating a channel
    channel = await connection.channel()
    declare_ok = await channel.queue_declare("hello", auto_delete=True)

    # Sending the message
    await channel.basic_publish(body, routing_key='hello')
    print(f" [x] Sent {body}")

    MESSAGE = await channel.basic_get(declare_ok.queue)
    print(f" [x] Received message from {declare_ok.queue!r}")

loop = asyncio.get_event_loop()

assert MESSAGE is not None
assert MESSAGE.routing_key == "hello"
assert MESSAGE.body == b'Hello World!'

The patio and the patio-rabbitmq

PATIO is an acronym for Python Asynchronous Tasks for AsyncIO - an easily extensible library, for distributed task execution, like celery, only targeting asyncio as the main design approach.

patio-rabbitmq provides you with the ability to use RPC over RabbitMQ services with extremely simple implementation:

from patio import Registry, ThreadPoolExecutor
from patio_rabbitmq import RabbitMQBroker

rpc = Registry(project="patio-rabbitmq", auto_naming=False)

def sum(*args):
    return sum(args)

async def main():
    async with ThreadPoolExecutor(rpc, max_workers=16) as executor:
        async with RabbitMQBroker(
            executor, amqp_url="amqp://guest:guest@localhost/",
        ) as broker:
            await broker.join()

And the caller side might be written like this:

import asyncio
from patio import NullExecutor, Registry
from patio_rabbitmq import RabbitMQBroker

async def main():
    async with NullExecutor(Registry(project="patio-rabbitmq")) as executor:
        async with RabbitMQBroker(
            executor, amqp_url="amqp://guest:guest@localhost/",
        ) as broker:
            print(await asyncio.gather(
                    broker.call("mul", i, i, timeout=1) for i in range(10)


FastStream is a powerful and easy-to-use Python library for building asynchronous services that interact with event streams..

If you need no deep dive into RabbitMQ details, you can use more high-level FastStream interfaces:

from faststream import FastStream
from faststream.rabbit import RabbitBroker

broker = RabbitBroker("amqp://guest:guest@localhost:5672/")
app = FastStream(broker)

async def user_created(user_id: int):
    assert isinstance(user_id, int)
    return f"user-{user_id}: created"

async def pub_smth():
    assert (
        await broker.publish(1, "user", rpc=True)
    ) ==  "user-1: created"

Also, FastStream validates messages by pydantic, generates your project AsyncAPI spec, supports In-Memory testing, RPC calls, and more.

In fact, it is a high-level wrapper on top of aio-pika, so you can use both of these libraries’ advantages at the same time.


Socket.IO is a transport protocol that enables real-time bidirectional event-based communication between clients (typically, though not always, web browsers) and a server. This package provides Python implementations of both, each with standard and asyncio variants.

Also this package is suitable for building messaging services over RabbitMQ via aio-pika adapter:

import socketio
from aiohttp import web

sio = socketio.AsyncServer(client_manager=socketio.AsyncAioPikaManager())
app = web.Application()

async def chat_message(sid, data):
    print("message ", data)

if __name__ == '__main__':

And a client is able to call chat_message the following way:

import asyncio
import socketio

sio = socketio.AsyncClient()

async def main():
    await sio.connect('http://localhost:8080')
    await sio.emit('chat_message', {'response': 'my response'})

if __name__ == '__main__':

The taskiq and the taskiq-aio-pika

Taskiq is an asynchronous distributed task queue for python. The project takes inspiration from big projects such as Celery and Dramatiq. But taskiq can send and run both the sync and async functions.

The library provides you with aio-pika broker for running tasks too.

from taskiq_aio_pika import AioPikaBroker

broker = AioPikaBroker()

async def test() -> None:

async def main():
    await broker.startup()
    await test.kiq()


With over 25 million downloads, Rasa Open Source is the most popular open source framework for building chat and voice-based AI assistants.

With Rasa, you can build contextual assistants on:

  • Facebook Messenger

  • Slack

  • Google Hangouts

  • Webex Teams

  • Microsoft Bot Framework

  • Rocket.Chat

  • Mattermost

  • Telegram

  • Twilio

Your own custom conversational channels or voice assistants as:

  • Alexa Skills

  • Google Home Actions

Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a scalable way.

And it also uses aio-pika to interact with RabbitMQ deep inside!


This software follows Semantic Versioning