Comparing Python ODMs for MongoDB

This article was written by Fabio Cionini.

ORM and ODM: Relational vs. document

You might already be familiar with object-relational mapping (ORM) techniques that convert relational database data into objects suitable for use in a programming language. Libraries like Sequelize (for Node.js) or, sticking with Python, SQLAlchemy, allow us to easily interact with data structures that are very different from the object metaphor we use in modern software development. They save us from writing boilerplate code and help encapsulate business rules and data validation.

When working with a document-oriented, non-relational database like MongoDB — whose data structure more closely resembles the objects we manipulate with our code — these techniques and libraries are often called object-document mapping (ODM).

The MongoDB document model

The MongoDB data structure closely resembles the objects we usually manipulate with our code. It uses JSON (in practice, BSON under the hood), similar to JavaScript objects, as the preferred human-readable data interchange format.

Because we can embed objects and arrays as values in documents, it becomes easier to integrate the data layer with the application layer. MongoDB’s flexible schema also allows rapid prototyping and fast iteration without the need to manage migrations and database schema versions as strictly as in traditional relational databases.

From this perspective, you might think that ODMs are less necessary than ORMs. Although the official MongoDB drivers already “speak” the language of objects, ODMs can be very useful to:

• Speed up development by generating boilerplate code for CRUD operations and object mapping.
• Make data access code more abstract and portable.
• Integrate data validation in a consistent way.

When coupled with a data validation library like Pydantic, ODMs can help guarantee data consistency by enforcing validation rules at the application level.

In this article, we’ll explore the main features offered by some of the most used and loved object-document mapping libraries for Python and see how they complement the native capabilities of the official MongoDB drivers.

Python ODMs

Beanie

Beanie is an asynchronous ODM whose data models are based on Pydantic, the most widely used data validation library for Python. It is lightweight, very intuitive, and has been around for several years, making it one of the most used and loved ODMs available.

You can use modern Python language features such as type hints, decorators, and of course asynchronous calls (using non-blocking access to MongoDB provided by Async PyMongo, which is built on top of Python’s asyncio library).

Installation is easily accomplished using PIP:

py
pip install beanie

or Poetry:

py
poetry add beanie

Beanie uses Async PyMongo as an async database engine. You provide an AsyncMongoClient instance and a list of your document models to the init_beanie(...) function.

In Beanie, each database collection is represented by a class that extends the Document base class. By extending that class, you define the schema and perform all the read and write operations on the corresponding collection.

You can also use pydantic.BaseModel directly by extending its class for embedded documents. For example:

py
import asyncio
from typing import Optional
from pydantic import BaseModel
from beanie import init_beanie, Document, Indexed
from pymongo import AsyncMongoClient

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

class Product(Document):
name: str
description: Optional[str] = None
price: Indexed(float)
category: Category

async def main():
client = AsyncMongoClient("mongodb://root:root@localhost:27017/")
await init_beanie(database=client.shop, document_models=[Product])

coffee = Category(
    name="Coffee",
    description="A preparation of roasted and ground coffee seeds.",
)
espresso = Product(
    name="Italian Espresso",
    price=2.95,
    category=coffee,
)

await espresso.insert()
if name == "main":
asyncio.run(main())

This defines two models:

• Category, based on Pydantic, used as an embedded document inside Product.
• Product, the actual document that will be inserted into the corresponding MongoDB collection.

Notice that price is defined as an indexed field via Indexed(float): this will create an index on price in the underlying collection. The insert() method stores the Product instance in MongoDB.

The Beanie website provides a clear, easy-to-follow tutorial on defining documents along with comprehensive documentation for more advanced use cases.

MongoEngine

MongoEngine is a mature and widely adopted ODM. It has been around for many years and is considered one of the most stable options in the Python ecosystem.

MongoEngine follows a synchronous programming model and offers a very Django-like developer experience, with:

• Explicit field definitions.
• Built-in validation.
• Rich query capabilities.

Similarly to Beanie, each collection is represented by a class that extends the Document base class. Fields are declared explicitly using typed field classes, and embedded documents are modeled by extending EmbeddedDocument.

Installation is done with the usual pip command: pip install mongoengine

A simple example looks like this:

py
from mongoengine import (
connect,
Document,
EmbeddedDocument,
StringField,
FloatField,
EmbeddedDocumentField,
)

class Category(EmbeddedDocument):
name = StringField(required=True)
description = StringField()

class Product(Document):
name = StringField(required=True)
description = StringField()
price = FloatField(required=True)
category = EmbeddedDocumentField(Category)

connect(db="shop", host="mongodb://localhost:27017/shop")

coffee = Category(
name="Coffee",
description="A preparation of roasted and ground coffee seeds.",
)
espresso = Product(
name="Italian Espresso",
price=2.95,
category=coffee,
)
espresso.save()

Calling save() persists the document to MongoDB, automatically handling inserts or updates as needed.

The synchronous nature of MongoEngine offers a concise syntax and fine-grained control over the sequence of operations. For workloads where you don’t need concurrency at scale, this can make application logic easier to reason about, even if asynchronous ODMs may use resources more efficiently under heavy I/O load.

MongoEngine’s online documentation provides both a comprehensive API reference and a getting started tutorial.

ODMantic

ODMantic is a modern asynchronous ODM built on top of Pydantic and Motor, designed to provide strong typing, data validation, and native asyncio support.

Its design philosophy is quite close to Beanie: both libraries offer “Pythonic” ways to interact with MongoDB. ODMantic, however, is somewhat less focused on data migration and more on ergonomic models and async workflows.

Here is a basic example:

py
import asyncio
from typing import Optional
from odmantic import Model, Field, EmbeddedModel, AIOEngine
from motor.motor_asyncio import AsyncIOMotorClient

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

class Product(Model):
name: str
description: Optional[str] = None
price: float = Field(index=True)
category: Category

async def main():
client = AsyncIOMotorClient("mongodb://localhost:27017")
engine = AIOEngine(client=client, database="shop")

coffee = Category(
    name="Coffee",
    description="A preparation of roasted and ground coffee seeds.",
)
espresso = Product(
    name="Italian Espresso",
    price=2.95,
    category=coffee,
)

await engine.save(espresso)
if name == "main":
asyncio.run(main())

The model definitions and asynchronous syntax strongly resemble Beanie, but the way you persist data (engine.save(...)) feels similar to MongoEngine and many other ODMs/ORMs.

ODMantic can also interact with MongoDB synchronously in specific scenarios, but its primary strength lies in asynchronous code paths.

Documentation is available on the ODMantic website, including a concise quick-start example.

Django

Django is not an ODM by design, but through third-party integrations it can be used to model and interact with MongoDB using a familiar ORM-like interface.

Libraries such as Djongo and the MongoEngine–Django integration allow developers to reuse Django’s model layer while targeting MongoDB as the backing datastore. This approach is often chosen when MongoDB is introduced into an existing Django project and you want to minimize changes to the application layer.

Inside an existing Django backend, you can use a MongoDB-backed Django model like this (using Djongo):

py
from djongo import models
class Category(models.Model):
name = models.CharField(max_length=200)
description = models.TextField()

class Meta:
    abstract = True
class Product(models.Model):
name = models.CharField(max_length=200)
description = models.TextField(null=True, blank=True)
price = models.FloatField(db_index=True)
category = models.EmbeddedField(
model_container=Category,
)

coffee = Category(
name="Coffee",
description="A preparation of roasted and ground coffee seeds.",
)
espresso = Product.objects.create(
name="Italian Espresso",
price=2.95,
category=coffee,
)

The Meta class definition with abstract = True tells Django not to create a separate collection for Category. It is just an abstract class that will be used for embedded objects. The objects.create(...) call then inserts the document into MongoDB using Django’s familiar query API.

Documentation for Djongo is available on its website, while the Django–MongoEngine integration can be found on GitHub.

Asynchronous vs. synchronous

In the overview above, we’ve seen both synchronous and asynchronous ODM libraries. So what is the difference, and which one is best for your use case?

At a high level:

• Synchronous code executes operations one after another. Each I/O operation (like a database call) blocks the current thread until it finishes.
• Asynchronous code is non-blocking. When an I/O operation is waiting on the network or disk, the event loop can switch to other tasks instead of blocking.

In Python, a mechanism called the Global Interpreter Lock (GIL) prevents true parallel execution of Python bytecode in multiple threads at the same time, ensuring memory consistency and state predictability. This is a separate concern from async I/O, but it’s worth keeping in mind when considering concurrency strategies.

Python’s asyncio module is designed for managing asynchronous operations using coroutines and an event loop. Python also supports concurrency via the threading module and can leverage multiple CPUs via multiprocessing, which bypasses the GIL by using separate processes.

As mentioned earlier, ODM libraries like Beanie and ODMantic provide asynchronous capabilities using asyncio, while Django typically relies on the ASGI gateway and its ecosystem for async support.

Another important piece in the async story is Motor, the official asynchronous driver for MongoDB in Python. Motor has historically been the go-to choice for async access in Python, but it was deprecated in May 2025 in favor of the PyMongo Async API in the pymongo library (in GA at the time of writing). MongoDB will not add new features to Motor and will provide only bug fixes until its end of life on May 14, 2026.

From a performance standpoint:

• Asynchronous libraries tend to shine when tasks are frequently blocked by I/O and do not use much CPU — for example, web applications that handle a high volume of concurrent client requests. When a task is waiting for input/output, it can yield control so other tasks can proceed.
• Synchronous programming can still be a great fit when there are relatively few concurrent requests and tasks are CPU-bound. In these cases, the overhead of managing an event loop might not pay off, and a straightforward synchronous approach can be simpler and efficient enough.

Conclusion

We have explored different approaches to ODMs in Python:

• Beanie and ODMantic are modern, lightweight, and support asynchronous programming, making them attractive for new projects, microservices architectures, and applications that need to handle many concurrent requests efficiently.
• MongoEngine offers a solid foundation for synchronous software and a mature ecosystem, with a familiar, Django-like style.
• Given the popularity and historical importance of Django in the Python web ecosystem, we also looked at how to integrate MongoDB via Django-friendly tools like Djongo and the Django–MongoEngine integration, which can be particularly useful when you are adding MongoDB to an existing Django project.

Choosing the “best” Python ODM for MongoDB depends on your context:

• If you are building a new async-first API or microservice, Beanie or ODMantic are strong candidates.
• If you prefer a synchronous style and value maturity and stability, MongoEngine is a solid choice.
• If your project already relies heavily on Django, leveraging Djongo or the Django–MongoEngine integration lets you take advantage of MongoDB while preserving familiar patterns.

Ultimately, all of these tools help you align Python’s object-oriented programming model with MongoDB’s flexible, document-oriented data model — so you can focus more on your application’s logic and less on boilerplate data-access code.