Open Source Python Frameworks - Page 14

JyConsole is an advanced Java graphic console for Jython. JyConsole provides an object-oriented completion on Java and Python objects and allows the direct manipulation of objects. JyConsole includes command history, script loading and GUI preference.

Kalliope is a modular always-on voice-controlled personal assistant designed for home automation. Kalliope is a framework that will help you to create your own personal assistant. The concept is to create the brain of your assistant by attaching an input signal (vocal order, scheduled event, MQTT message, GPIO event, etc..) to one or multiple actions called neurons. You can create your own Kalliope bot, by simply choosing and composing the existing neurons without writing any code. But, if you need a particular module, you can write it by yourself, add it to your project, and propose it to the community. Kalliope can run on all Linux Debian-based distributions including a Raspberry Pi and its multi-lang. The only thing you need is a microphone.

A simple Python-based web framework for viewing and editing genealogy data. Allows users to browse your family tree and make changes, such as adding new people, adding or deleting images, or notes and stories. Works with GEDCOM v5.5 and Spyce.

Python web development framework with a focus on efficiency/speed and extensibility, with a large library of functions to aid in the development of modern web applications.

Python port of Platform LSF API using swig. Access is provided directly to the API call in addition to server Object classes.

Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises.

Leilany is a framework to build GUI applications with Python and GTK, using de MVC pattern design. Interested in build the gadgets for invoicing and the necessary requirements. The main goal is make applications with low memory.

Lino is a suite of Python packages for IT professionals who offer business applications for small and medium-sized enterprises.

Framework for making Windows applications that are one .exe file in AutoHotKey_L,C++,C#, VB.NET,Java,Groovy,Common Lisp,Nemerle,Ruby,Python,PHP,Lua,Tcl,Perl,Jint,S#,WSH VBScript,HTML/JavaScript/CSS,COM, PowerShell without compiling . For .NET 4.

The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).

Lusca is a test-framework for testing network applications under unix-like operating systems. Provides remote equipment control, automated script execution, event logging and more.

MITMf aims to provide a one-stop-shop for Man-In-The-Middle and network attacks while updating and improving existing attacks and techniques. Originally built to address the significant shortcomings of other tools (e.g Ettercap, Mallory), it's been almost completely rewritten from scratch to provide a modular and easily extendible framework that anyone can use to implement their own MITM attack. The framework contains a built-in SMB, HTTP and DNS server that can be controlled and used by the various plugins, it also contains a modified version of the SSLStrip proxy that allows for HTTP modification and a partial HSTS bypass. As of version 0.9.8, MITMf supports active packet filtering and manipulation (basically what better filters did, only better), allowing users to modify any type of traffic or protocol. The configuration file can be edited on-the-fly while MITMf is running, the changes will be passed down through the framework.

MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy.

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. MMF is built on top of PyTorch that brings all of its power in your hands. MMF is not strongly opinionated. So you can use all of your PyTorch knowledge here. MMF is created to be easily extensible and composable. Through our modular design, you can use specific components from MMF that you care about. Our configuration system allows MMF to easily adapt to your needs.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform.

MRV is a python framework based on wrapping the Maya API to speed up development and ease of use within pipelines using Autodesk Maya.

MVCPython is a framework which automatically generates a MVC architecture (MVC= Model View Controller) just from Python Classes and Object defintions. MVCPython achieves this through a strong use of Python's introspection capability.

You assemble *blocks* in your django template, with each block wired to a tastypie resource, which in turn is mapped to a django model. Each *block* will reside in a dijit ContentPane which you must define in your template. You have a choice of 2 layouts for each *block*, Form or Grid based. Form displays a single record in either a 1, 2 or 3 column layout while Grid displays many rows in a spreadsheet like grid. You can perform edits in place on fields in both layouts. You can define the master-detail relationship between the *blocks* and enblok will automatically display the related detail *block* rows as the user navigates the master *block*'s rows. You get for free the default *CRUD* functionality for each block, together with Search. You are not limited to just a 2 level master-detail relationship, in fact enblok can support n-th level relationships but we do not advise you to go beyond 3. Read the distribution's enblok/readme.txt for further instructions.

Mangum is an adapter for running ASGI applications in AWS Lambda to handle Function URL, API Gateway, ALB, and Lambda@Edge events. Event handlers for API Gateway HTTP and REST APIs, Application Load Balancer, Function URLs, and CloudFront Lambda@Edge. Compatibility with ASGI application frameworks, such as Starlette, FastAPI, Quart and Django. Support for binary media types and payload compression in API Gateway using GZip or Brotli. Works with existing deployment and configuration tools, including Serverless Framework and AWS SAM. The heart of Mangum is the adapter class. It is a configurable wrapper that allows any ASGI application (or framework) to run in an AWS Lambda deployment. The adapter accepts a number of keyword arguments to configure settings related to logging, HTTP responses, ASGI lifespan, and API Gateway configuration.

Manticore helps us quickly take advantage of symbolic execution, taint analysis, and instrumentation to analyze binaries. Parts of Manticore underpinned our symbolic execution capabilities in the Cyber Grand Challenge. As an open-source tool, we hope that others can take advantage of these capabilities in their own projects. We prioritized simplicity and usability while building Manticore. We used minimal external dependencies and our API should look familiar to anyone with exploitation or reversing background. If you have never used such a tool before, give Manticore a try. Manticore comes with an easy-to-use command line tool that quickly generates new program “test cases” (or sample inputs) with symbolic execution. Each test case results in a unique outcome when running the program, like a normal process exit or crash (e.g., invalid program counter, invalid memory read/write).

Mantikhor is an *information representation* language. Like RDF, it models information as directed-edge arc-node graphs, although Mantikhor's use of such constructs is more structured and constrained.

Math tools in Python to tackle down problems in Operational Research fields. Comes with a Django based web interface to allow remote access to complex simulation means.

Metatest - a simple and elegant Python framework for writing tests

Mobly is a Python-based test framework that specializes in supporting test cases that require multiple devices, complex environments, or custom hardware setups. P2P data transfer between two devices. Conference calls across three phones. Wearable device interacting with a phone. Internet-Of-Things devices interacting with each other. Testing RF characteristics of devices with special equipment. Testing LTE network by controlling phones, base stations, and eNBs. Mobly can support many different types of devices and equipment, and it's easy to plug your own device or custom equipment/service into Mobly. Mobly comes with a set of libs to control common devices like Android devices. While developed by Googlers, Mobly is not an official Google product.

Model Search is an AutoML research system for discovering neural network architectures with minimal human intervention. Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of candidates are orchestrated automatically, with strong emphasis on reproducibility and fair comparisons. The framework logs trials, metrics, and artifacts so you can analyze what the search learned and why certain designs dominate. It’s intended as a platform for method development as much as for model discovery.