Open Source Python Frameworks - Page 8
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Python Frameworks
View 166 business solutionsBrowse free open source Python Frameworks and projects below. Use the toggles on the left to filter open source Python Frameworks by OS, license, language, programming language, and project status.
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Bot Framework SDK for Python
Build and connect intelligent bots that interact naturally
This repository contains code for the Python version of the Microsoft Bot Framework SDK, which is part of the Microsoft Bot Framework - a comprehensive framework for building enterprise-grade conversational AI experiences. This SDK enables developers to model conversation and build sophisticated bot applications using Python. SDKs for JavaScript and .NET are also available. The Microsoft Bot Framework provides what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services. -
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Bottle
bottle.py is a fast and simple micro-framework for python applications
Bottle is a minimalist web framework for building small web applications and APIs in Python. It is distributed as a single file with no external dependencies, making it perfect for rapid development, prototyping, or embedded use. Despite its small size, Bottle supports routing, templates, request handling, and plugin support, offering a full-featured toolkit in an extremely compact package. -
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Bowtie
Create a dashboard with python!
Bowtie is a library for writing dashboards in Python. No need to know web frameworks or JavaScript, focus on building functionality in Python. Interactively explore your data in new ways! Deploy and share with others! Bowtie uses Yarn to manage node packages. If you installed Bowtie through conda, Yarn was also installed as a dependency. Yarn can be installed through conda. An early integration with Jupyter has been prototyped. I encourage you to try it out and share feedback. I hope this will make it easier to make Bowtie apps. Bowtie helps you visualize your data interactively. No Javascript required, you build your dashboard in pure Python. Easy to deploy so you can share results with others. Ships with many useful widgets including charts, tables, dropdown menus, sliders, and markdown. All widgets come equipped with events and commands for interaction. Compiles a single Javascript bundle speeding up load times and removes CDN dependencies. -
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Brighton Server
Python Framework for Multipurpose Server
Brighton Server is a framework that help the server implementation based on python and console interface with feature like a screen control, logging control for the moment. -
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Brownie
A Python-based development and testing framework for smart contracts
Brownie is a Python-based development and testing framework for smart contracts targeting the Ethereum Virtual Machine. Powerful debugging tools, including python-style tracebacks and custom error strings. The recommended way to install Brownie is via pipx. pipx installs Brownie into a virtual environment and makes it available directly from the command-line. Once installed, you will never have to activate a virtual environment prior to using Brownie. Brownie documentation is hosted at Read the Docs. Use tox to run the complete suite against the full set of build targets, or pytest to run tests against a specific version of Python. If you are using pytest you must include the -p no:pytest-brownie flag to prevent it from loading the Brownie plugin. -
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Buildbot
Python-based continuous integration testing framework
Buildbot is an open-source framework for automating software build, test, and release processes. At its core, Buildbot is a job scheduling system: it queues jobs, executes the jobs when the required resources are available, and reports the results. Your Buildbot installation has one or more masters and a collection of workers. The masters monitor source-code repositories for changes, coordinate the activities of the workers, and report results to users and developers. Workers run on a variety of operating systems. You configure Buildbot by providing a Python configuration script to the master. This script can be very simple, configuring built-in components, but the full expressive power of Python is available. This allows dynamic generation of configuration, customized components, and anything else you can devise. The framework itself is implemented in Twisted Python, and compatible with all major operating systems. -
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An intuitive and easily expandable platform for recognizing multiple users based on their individual Bluetooth devices; allowing a systems to act upon the presence or absence of individuals and groups.
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COFILOS
A Development Framework for Coldfire
Contains a framework for Coldfire MCUs like 52254. The framework supports a Command Line Interface (CLI) that may work from Serial port, USB or ENET. The framework uses Processor Expert and IDE requirement is MCU Eclipse 10.4 from Freescale. Includes the FunkOS Realtime Operating System by Funkenstein Software Consulting, available at http://funkos.sourceforge.net Mainly it is a support package for the development board Perseus, but I have ported also the RTOS to MCF52233DEMO board. More to come.... -
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Provides a voice interface for applications via a plug in system. Allows the inclusion of voice recognition in an application with a minimum of effort.
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Chapman is a modern, model-view-controller based gui component framework developed in python, inspired by Smalltalk's VisualWorks framework.
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This is intended to ease the development of web applications using CherryPy (web server) and Elixir (DB access) with python. It will contain structures to easily setup the database structure and populate with data, and to configure the server
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Cimarrón is an application development framework which uses the MVC pattern as its basis. Note that while cimarrón provides both View's and some Controller's, it does not Provide any Model, leaving the developer the posibility to choose it freely.
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Clairv is a Java framework based on Apache Lucene that adds search capability to your applications. Distributed indices over different machines are supported. A web front end will also be provided.
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ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring distributed training, reproducible experiments, and flexible configuration tools. It offers high performance and scalability—capable of training models like ResNet-50 on ImageNet in just minutes—while remaining accessible to both researchers and production engineers. The library integrates seamlessly with PyTorch Hub for easy access to pretrained models and supports elastic training using PyTorch Elastic, making distributed training robust to changes in cluster resources or hardware failures. -
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This project allow to test network availability on multiple nodes. It is built as a plugin architecture in order to use it on multiples networks. Test is based on three steps : module status, local visibility of network, and interconnection between nodes
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CoSimA+
The Co-Simulation Adaptation Platform
The co-simulation adapation platform serves as programming framework and middleware to enable coupling of distributed, heterogeneous numerical models. The framework facilitates the adaptation and integration of new sub-models into a common simulation platform. -
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Connexion
Swagger/OpenAPI First framework for Python on top of Flask
Connexion is a framework on top of Flask that automagically handles HTTP requests defined using OpenAPI (formerly known as Swagger), supporting both v2.0 and v3.0 of the specification. Connexion allows you to write these specifications, then maps the endpoints to your Python functions. This is what makes it unique from other tools that generate the specification based on your Python code. You are free to describe your REST API with as much detail as you want and then Connexion guarantees that it will work as you specified. We built Connexion this way in order to simplify the development process. Reduce misinterpretation about what an API is going to look like. With Connexion, you write the spec first. Connexion then calls your Python code, handling the mapping from the specification to the code. This incentivizes you to write the specification so that all of your developers can understand what your API does, even before you write a single line of code. -
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Cormoran is a Python persistence framework. Currently the development is carried out on https://github.com/jaimegildesagredo/cormoran
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CrypTen
A framework for Privacy Preserving Machine Learning
CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data. -
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DBOS Transact PY
Lightweight Durable Python Workflows
dbos-transact-py is the Python counterpart to dbos-transact-ts, offering durable transactional programming with automatic state persistence in PostgreSQL. It simplifies building resilient and idempotent applications by enabling Python functions to retain their state, restart after failure, and guarantee consistency. It's designed for data-heavy and fault-intolerant use cases. -
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Database-backed Periodic Tasks
Celery Periodic Tasks backed by the Django ORM
This extension enables you to store the periodic task schedule in the database. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run. Usage and installation instructions for this extension are available from the Celery documentation. If you change the Django TIME_ZONE setting your periodic task schedule will still be based on the old timezone. To create a periodic task executing at an interval you must first create the interval object. If you have multiple periodic tasks executing every 10 seconds, then they should all point to the same schedule object. -
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Digital Preservation Software Platform (DPSP) consists of a number of open source products such as Xena and Digital Preservation Recorder. This installs and configures the complete platform for preserving digital records - just install and use! NO LONGER MAINTAINED, NO LONGER SUPPORTED
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Dj-Rest-Auth
Authentication for Django Rest Framework
Since the introduction of django-rest-framework, Django apps have been able to serve up app-level REST API endpoints. As a result, we saw a lot of instances where developers implemented their own REST registration API endpoints here and there, snippets, and so on. We aim to solve this demand by providing dj-rest-auth, a set of REST API endpoints to handle User Registration and Authentication tasks. By having these API endpoints, your client apps such as AngularJS, iOS, Android, and others can communicate to your Django backend site independently via REST APIs for User Management. Of course, we’ll add more API endpoints as we see the demand. -
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Django Autocomplete Light
A fresh approach to autocomplete implementations
A fresh approach to autocomplete implementations, specially for Django. Python 2.7, 3.4, Django 2.0+ support (Django 1.11 (LTS), is supported until django-autocomplete-light-3.2.10), Django (multiple) choice support, Django (multiple) model choice support, Django generic foreign key support (through django-querysetsequence), Django generic many to many relation support (through django-generic-m2m and django-gm2m). Multiple widget support: select2.js, easy to add more. Creating choices that don't exist in the autocomplete, offering choices that depend on other fields in the form, in an elegant and innovative way. Dynamic widget creation (ie. inlines), supports YOUR custom scripts too. Provides a test API for your awesome autocompletes, to support YOUR custom use cases too. A documented automatically tested example for each use case in test_project.
