10 Modern Python Tools Every Developer Should Master in 2025

Master 10 modern Python tools that will boost your productivity and efficiency in 2025.

Aashish Kumar

08 Mar 2025 — 9 min read

Stay ahead with these game-changing Python tools!

10 Modern Python Tools Every Developer Should Master in 2025

The Python ecosystem keeps evolving, and new tools introduced to make development faster, cleaner, and more efficient. If you’re still relying on outdated workflows, it’s time to upgrade your toolkit!

Here are 10 more cutting-edge Python tools that will boost your productivity in 2025!

1. Django — Full-Stack Web Framework

A Django high-level Python web framework for building secure, scalable applications quickly. A must have tool every developer should master in 2025.

Installation:

pip install django

Example:

Create a new Django project:

django-admin startproject myproject 
cd myproject 
python manage.py runserver

You should see you app is running on http:127.0.0.1:8000/

Key Features of Django:

Built-in ORM (Object-Relational Mapping)
Authentication system included
Scalable & Secure and many more

2. FastAPI — High-Performance APIs

FastAPI is a lightweight and fast Python web framework for building RESTful APIs with async support. A must have tool every developer should master in 2025.

Installation:

pip install fastapi uvicorn

Example:

A simple example to create api using FastAPI:

# main.py 
from fastapi import FastAPI 
 
app = FastAPI() 
 
@app.get("/") 
def read_root(): 
    return {"message": "Hello, FastAPI!"}

Run the server:

uvicorn main:app --reload

You should see you app is running on http:127.0.0.1:8000/

Key Features:

Asynchronous support (built-in)
Automatic OpenAPI & Swagger UI
Fast (built on Starlette & Pydantic)

3. LangChain — AI-Powered Applications

LangChain is python framework that simplifies working with LLMs (Large Language Models) like OpenAI’s GPT. A must have tool every developer should master in 2025.

Installation:

pip install langchain

Example:

A simple example to create chatbot, using openAI model:

pip install -qU "langchain[openai]"

import getpass 
import os 
from langchain.chat_models import init_chat_model 
 
if not os.environ.get("OPENAI_API_KEY"): 
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ") 
 
model = init_chat_model("gpt-4o-mini", model_provider="openai")

model.invoke("Hello, world!")

You will see a response from chatbot

Key Features:

Integrates with OpenAI, Hugging Face, and more
Chain multiple LLM calls together
Supports memory & retrieval-based queries

4. Pydantic — Data Validation & Parsing

Pydantic provides data validation using Python type hints, used in FastAPI. A must have tool every developer should master in 2025.

Installation:

pip install pydantic

Example:

from pydantic import BaseModel 
 
class User(BaseModel): 
    name: str 
    age: int 
 
user = User(name="Aashish Kumar", age=25) 
 
print(user)      # User(name='Aashish Kumar', age=25) 
print(user.name) # 'Aashish Kumar' 
print(user.age)  # 25

Key Features:

Automatic data validation
Type hint-based parsing
Works well with FastAPI

5. Ruff — Fast Python Linter & Formatter

Ruff is a blazing-fast linter written in Rust, designed to replace Flake8, Black, and isort in one tool. A must have tool every developer should master in 2025.

Installation:

pip install ruff

Example

Let’s use uv to initialize a project:

uv init --lib demo

This command creates a Python project with the following structure:

demo 
  ├── README.md 
  ├── pyproject.toml 
  └── src 
      └── demo 
          ├── __init__.py 
          └── py.typed

We’ll then replace the contents of src/demo/__init__.py with the following code:

from typing import Iterable 
 
import os 
 
 
def sum_even_numbers(numbers: Iterable[int]) -> int: 
    """Given an iterable of integers, return the sum of all even numbers in the iterable.""" 
    return sum( 
        num for num in numbers 
        if num % 2 == 0 
    )

Next, we’ll add Ruff to our project:

uv add --dev ruff

We can then run the Ruff linter over our project via uv run ruff check:

$ uv run ruff check 
src/numbers/__init__.py:3:8: F401 [*] `os` imported but unused 
Found 1 error. 
[*] 1 fixable with the `--fix` option.

we can resolve the issue automatically by running ruff check --fix:

$ uv run ruff check --fix 
Found 1 error (1 fixed, 0 remaining).

Key Features:

Faster than Flake8 & Black
Supports auto-fixing
Works with pre-commit hooks

6. Flet — Python for Web, Mobile & Desktop UI

Flet build modern web, desktop, and mobile apps using Python only (no HTML/CSS/JS). A must have tool every developer should master in 2025.

Installation:

pip install flet

Example:

Let’s build a simple Counter App:

import flet 
from flet import IconButton, Page, Row, TextField, icons 
 
def main(page: Page): 
    page.title = "Flet counter example" 
    page.vertical_alignment = "center" 
 
    txt_number = TextField(value="0", text_align="right", width=100) 
 
    def minus_click(e): 
        txt_number.value = str(int(txt_number.value) - 1) 
        page.update() 
 
    def plus_click(e): 
        txt_number.value = str(int(txt_number.value) + 1) 
        page.update() 
 
    page.add( 
        Row( 
            [ 
                IconButton(icons.REMOVE, on_click=minus_click), 
                txt_number, 
                IconButton(icons.ADD, on_click=plus_click), 
            ], 
            alignment="center", 
        ) 
    ) 
 
flet.app(target=main)

Run the program:

python counter.py

The app will be started in a native OS window — what a nice alternative to Electron!

If you want to run the app as a web app, just replace the last line with:

flet.app(target=main, view=flet.AppView.WEB_BROWSER)

Run again and now you instantly get a web app:

Key Features:

No need for JavaScript or frontend knowledge
Works on Web, Windows, macOS, and Linux
Reactive UI framework

7. Weaviate — Vector Database for AI & Search

Weaviate is a fast, open-source vector database for semantic search and AI applications. A must have tool every developer should master in 2025.

Installation:

pip install -U weaviate-client

Example:

To run Weaviate with Docker using default settings, run this command from from your shell:

docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:1.29.0

Docker instances default to http://localhost:8080

To connect to a local instance without authentication :

import weaviate 
 
client = weaviate.connect_to_local() 
 
print(client.is_ready())

Key Features:

Ideal for AI-powered search
Stores text, images, and embeddings
Scales for large datasets

8. Reflex — Web Apps in Python (Frontend + Backend)

Reflex is a full-stack web framework to build modern web apps in Python, similar to Streamlit but more customizable. A must have tool every developer should master in 2025.

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

Create a simple app :

# app.py 
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")

Run development server :

reflex run

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

Key Features:

Build React-like UI in Python
State management included
Backend + frontend in one place

9. Beanie — Async ODM for MongoDB

Beanie is an asynchronous Object-Document Mapper (ODM) for MongoDB, built on top of Motor and Pydantic. It allows you to define MongoDB models using Pydantic and provides easy CRUD operations. A must have tool every developer should master in 2025.

Installation

pip install beanie

Example

Simple example to work with Beanie:

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())

Key Features

Asynchronous support with asyncio
Pydantic-based schema validation
Built-in CRUD operations
MongoDB aggregation & relations support
Indexes and transactions

10. uv — A Fast Python Package and Project Manager

uv is an ultra-fast Python package and project manager written in Rust. It aims to be a drop-in replacement for pip, pip-tools, venv, and virtualenv, providing significant performance improvements while maintaining compatibility with existing Python package management workflows. A must have tool every developer should master in 2025.

Installation

pip install uv

Example

Let’s create a project with uv:

$ uv init --lib demo

This will create this project structure:

demo 
  ├── README.md 
  ├── pyproject.toml 
  └── src 
      └── demo 
          ├── __init__.py 
          └── py.typed

Add dependency:

$ uv add django

Key Features

Blazing fast — Written in Rust for optimal speed
Drop-in replacement for pip, pip-tools, venv, and virtualenv
Supports environment management – Works like venv
Efficient dependency resolution
No need for Python pre-installation – Works standalone

Final Thoughts

These 10 modern Python tools 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!

Stay ahead with these game-changing Python tools!

10 Modern Python Tools Every Developer Should Master in 2025

1. Django — Full-Stack Web Framework

Installation:

Example:

Key Features of Django:

2. FastAPI — High-Performance APIs

Installation:

Example:

Key Features:

3. LangChain — AI-Powered Applications

Installation:

Example:

Key Features:

4. Pydantic — Data Validation & Parsing

Installation:

Example:

Key Features:

5. Ruff — Fast Python Linter & Formatter

Installation:

Example

Key Features:

6. Flet — Python for Web, Mobile & Desktop UI

Installation:

Example:

Key Features:

7. Weaviate — Vector Database for AI & Search

Installation:

Example:

Key Features:

8. Reflex — Web Apps in Python (Frontend + Backend)

Installation

Example

Key Features:

9. Beanie — Async ODM for MongoDB

Installation

Example

Key Features

10. uv — A Fast Python Package and Project Manager

Installation

Example

Key Features

Final Thoughts

Read more

I Wrote Python Without Using Lists for a Week — Here’s What Happened

How to Make Any Python Script 10x Faster with One Change

This Python Feature Has Been There for Years — But Nobody Talks About It

I Wasted 5 Years in College — Here’s What I’d Do Differently