beanie

📢 Important Update 📢

We are excited to announce that Beanie is transitioning from solo development to a team-based approach! This move will help us enhance the project with new features and more collaborative development.

At this moment we are establishing a board of members that will decide all the future steps of the project. We are looking for contributors and maintainers to join the board.

Join Us

If you are interested in contributing or want to stay updated, please join our Discord channel. We're looking forward to your ideas and contributions!

Join our Discord

Let’s make Beanie better, together!

Overview

Beanie - is an asynchronous Python object-document mapper (ODM) for MongoDB. Data models are based on Pydantic.

When using Beanie each database collection has a corresponding Document that is used to interact with that collection. In addition to retrieving data, Beanie allows you to add, update, or delete documents from the collection as well.

Beanie saves you time by removing boilerplate code, and it helps you focus on the parts of your app that actually matter.

Data and schema migrations are supported by Beanie out of the box.

There is a synchronous version of Beanie ODM - Bunnet

Installation

PIP

pip install beanie

Poetry

poetry add beanie

For more installation options (eg: aws, gcp, srv ...) you can look in the getting started

Example

import asyncio
from typing import Optional

from motor.motor_asyncio import AsyncIOMotorClient
from pydantic import BaseModel

from beanie import Document, Indexed, init_beanie


class Category(BaseModel):
    name: str
    description: str


class Product(Document):
    name: str                          # You can use normal types just like in pydantic
    description: Optional[str] = None
    price: Indexed(float)              # You can also specify that a field should correspond to an index
    category: Category                 # You can include pydantic models as well


# This is an asynchronous example, so we will access it from an async function
async def example():
    # Beanie uses Motor async client under the hood 
    client = AsyncIOMotorClient("mongodb://user:pass@host:27017")

    # Initialize beanie with the Product document class
    await init_beanie(database=client.db_name, document_models=[Product])

    chocolate = Category(name="Chocolate", description="A preparation of roasted and ground cacao seeds.")
    # Beanie documents work just like pydantic models
    tonybar = Product(name="Tony's", price=5.95, category=chocolate)
    # And can be inserted into the database
    await tonybar.insert() 
    
    # You can find documents with pythonic syntax
    product = await Product.find_one(Product.price < 10)
    
    # And update them
    await product.set({Product.name:"Gold bar"})


if __name__ == "__main__":
    asyncio.run(example())

Release files for beanie 1.29.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for beanie 1.29.0
File	Size	Uploaded
beanie-1.29.0.tar.gz	174.9 kB	Jan 6, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for beanie 1.29.0
File	Interpreter	ABI	Platform	Reset
beanie-1.29.0-py3-none-any.whl 86.5 kB Jan 6, 2025	Python 3	none	any	Details

Total release size: 261.4 kB

Release files / beanie-1.29.0.tar.gz

Download URL	beanie-1.29.0.tar.gz
Size	174.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`f078620c3515a8222d40a3a8ab195b1d8f1153010f009ed5d205b73371c43869`
BLAKE2b-256 checksum How to use checksums	`dfa1e77d6957d17138c821ed3b7fb34a9271e33d9809eaf05c07a250cd443613`
Upload date	Jan 6, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`python-requests/2.25.1`

Release files / beanie-1.29.0-py3-none-any.whl

Download URL	beanie-1.29.0-py3-none-any.whl
Size	86.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`aeb53e6648ceccf70eb35c35233e45406fe4de4c9887075581c01b968bfec2c7`
BLAKE2b-256 checksum How to use checksums	`0428f6038727827ed06659a57b3c70e6f4a339f57f92ce910f868ac928843a30`
Upload date	Jan 6, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`python-requests/2.25.1`