5 Modern Python Libraries You Must Know in 2025! 🚀

LEVEL UP YOUR PYTHON SKILLS WITH THESE

5 Modern Python Libraries You Must Know in 2025! 🚀

Hey Everyone, as we know python has a vast set of amazing libraries and many of them are really a game changer.

So, i have discovered these 5 few game changer modern set of libraries that you should must know if you want to stay ahead in 2025.

9 Modern Python Libraries You Must Know in 2025! 🚀

I have discovered these few game changer modern set of libraries that you should must know in 2025.

1. Reflex — Build Full-Stack Web Apps in Pure Python

Reflex is amazing library allows you to build full-stack web applications using only Python — no JavaScript required!

Docs : https://github.com/reflex-dev/reflex

Installation

pip install reflex

Example

You can use these command to create a reflex project:

mkdir my_app_name 
cd my_app_name 
reflex init

Let’s create a simple app :

import reflex as rx 
import openai 
 
openai_client = openai.OpenAI() 
 
 
# Backend code 
class State(rx.State): 
    """The app state.""" 
 
    prompt = "" 
    image_url = "" 
    processing = False 
    complete = False 
 
    def get_image(self): 
        """Get the image from the prompt.""" 
        if self.prompt == "": 
            return rx.window_alert("Prompt Empty") 
 
        self.processing, self.complete = True, False 
        yield 
        response = openai_client.images.generate( 
            prompt=self.prompt, n=1, size="1024x1024" 
        ) 
        self.image_url = response.data[0].url 
        self.processing, self.complete = False, True 
 
 
# Frontend code 
def index(): 
    return rx.center( 
        rx.vstack( 
            rx.heading("DALL-E", font_size="1.5em"), 
            rx.input( 
                placeholder="Enter a prompt..", 
                on_blur=State.set_prompt, 
                width="25em", 
            ), 
            rx.button( 
                "Generate Image",  
                on_click=State.get_image, 
                width="25em", 
                loading=State.processing 
            ), 
            rx.cond( 
                State.complete, 
                rx.image(src=State.image_url, width="20em"), 
            ), 
            align="center", 
        ), 
        width="100%", 
        height="100vh", 
    ) 
 
# Add state and page to the app. 
app = rx.App() 
app.add_page(index, title="Reflex:DALL-E")

Hit this command to run development server :

reflex run

You should see your app running at http://localhost:3000.

2. FastStream — The Future of Message Queue Processing

FastStream is a modern async messaging framework built for RabbitMQ, Kafka, and NATS, making distributed event-driven systems easier.

Docs : https://github.com/airtai/faststream

Installation

pip install faststream[kafka] 
# or 
pip install faststream[rabbit] 
# or 
pip install faststream[nats] 
# or 
pip install faststream[redis]

Example

Here is an example Python app using FastStream that consumes data from an incoming data stream and outputs the data to another one:

from faststream import FastStream 
from faststream.kafka import KafkaBroker 
# from faststream.rabbit import RabbitBroker 
# from faststream.nats import NatsBroker 
# from faststream.redis import RedisBroker 
 
broker = KafkaBroker("localhost:9092") 
# broker = RabbitBroker("amqp://guest:guest@localhost:5672/") 
# broker = NatsBroker("nats://localhost:4222/") 
# broker = RedisBroker("redis://localhost:6379/") 
 
app = FastStream(broker) 
 
@broker.subscriber("in") 
@broker.publisher("out") 
async def handle_msg(user: str, user_id: int) -> str: 
    return f"User: {user_id} - {user} registered"

Running the application :

faststream run basic:app --reload

3. Beanie — Async ODM for MongoDB

Beanie provides a modern, async-first Object Document Mapper (ODM) for MongoDB, making it easy to work with NoSQL databases.

Docs : https://github.com/BeanieODM/beanie

Installation

pip install beanie

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())

4. Haystack — Build Smart AI Search & Q&A Systems

Haystack enables real-time AI-powered search and question-answering systems, making it a must-have for LLM-based apps.

Docs : https://docs.haystack.deepset.ai/docs/get_started

Installation

pip install haystack-ai

Example

from haystack import Pipeline, Document 
from haystack.utils import Secret 
from haystack.document_stores.in_memory import InMemoryDocumentStore 
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever 
from haystack.components.generators import OpenAIGenerator 
from haystack.components.builders.prompt_builder import PromptBuilder 
 
# Write documents to InMemoryDocumentStore 
document_store = InMemoryDocumentStore() 
document_store.write_documents([ 
    Document(content="My name is Jean and I live in Paris."),  
    Document(content="My name is Mark and I live in Berlin."),  
    Document(content="My name is Giorgio and I live in Rome.") 
]) 
 
# Build a RAG pipeline 
prompt_template = """ 
Given these documents, answer the question. 
Documents: 
{% for doc in documents %} 
    {{ doc.content }} 
{% endfor %} 
Question: {{question}} 
Answer: 
""" 
 
retriever = InMemoryBM25Retriever(document_store=document_store) 
prompt_builder = PromptBuilder(template=prompt_template) 
llm = OpenAIGenerator(api_key=Secret.from_token(api_key)) 
 
rag_pipeline = Pipeline() 
rag_pipeline.add_component("retriever", retriever) 
rag_pipeline.add_component("prompt_builder", prompt_builder) 
rag_pipeline.add_component("llm", llm) 
rag_pipeline.connect("retriever", "prompt_builder.documents") 
rag_pipeline.connect("prompt_builder", "llm") 
 
# Ask a question 
question = "Who lives in Paris?" 
results = rag_pipeline.run( 
    { 
        "retriever": {"query": question}, 
        "prompt_builder": {"question": question}, 
    } 
) 
 
print(results["llm"]["replies"])

5. Litestar — A Next-Gen API Framework

Litestar is a modern, async-first alternative to FastAPI, offering speed, flexibility, and powerful dependency injection.

Docs : https://docs.litestar.dev/latest/

Installation

pip install litestar

Example

First, create a file named app.py with the following code:

# app.py 
from litestar import Litestar, get 
 
 
@get("/") 
async def index() -> str: 
    return "Hello, world!" 
 
 
@get("/user/{book_id:int}") 
async def get_user(user_id: int) -> dict[str, int]: 
    return {"user_id": user_id} 
 
 
app = Litestar([index, get_user])

Then, run the following command:

litestar run 
# Or you can run Uvicorn directly: 
uvicorn app:app --reload

You can now visit http://localhost:8000/ and http://localhost:8000/user/1 in your browser and you should see the responses of your two endpoints:

"Hello, world!"

and

{"user_id": 1}

Final Thoughts

These 5 modern Python libraries will help you build faster, smarter, and more scalable applications in 2025.

Which library are you most excited to try? Let me know in the comments!