Open Source Python Frameworks - Page 13
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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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MITMf
Framework for Man-In-The-Middle attacks
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. -
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MMF
A modular framework for vision & language multimodal research
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. -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
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. -
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MRV is a python framework based on wrapping the Maya API to speed up development and ease of use within pipelines using Autodesk Maya.
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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.
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MaeBlok
Rapid web development tool for business applications
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. -
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Maestro Framework
A framework for Claude Opus to intelligently orchestrate subagents
Maestro Framework is a Python framework for orchestrating AI subagents across complex tasks. It breaks a user objective into smaller subtasks, assigns those subtasks to worker models, and refines the results into a final output. The original workflow used Claude Opus and Haiku, while newer variants support Claude 3.5 Sonnet, GPT models, Gemini, Cohere, Groq, LM Studio, and Ollama through different scripts and LiteLLM support. It can maintain context between subtasks so later steps can build on earlier work. The project can also generate exchange logs and save them as Markdown for review. Overall, it is useful for experimenting with multi-agent task decomposition, AI-assisted planning, and model orchestration workflows. -
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Mangum
AWS Lambda support for ASGI applications
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. -
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Manticore
Symbolic execution tool
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). -
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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.
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Masonite
The Modern And Developer Centric Python Web Framework
Stop using old frameworks with just a few confusing features. Masonite is the developer-focused dev tool with all the features you need for the rapid development you deserve. Masonite is perfect for beginners getting their first web app deployed or advanced developers and businesses that need to reach for the full fleet of features available. Mail support for sending emails quickly. Queue support to speed your application up by sending jobs to run on a queue or asynchronously. Notifications for sending notifications to your users simply and effectively. Task scheduling to run your jobs on a schedule (like everyday at midnight) so you can set and forget your tasks. Events you can listen for to execute listeners that perform your tasks when certain events happen in your app. A BEAUTIFUL Active Record style ORM called Masonite ORM. Amazingness at your fingertips. Many more features you need which you can find in the docs! -
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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.
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Metarget
Framework for automatic construction of vulnerable infrastructures
Metarget = meta- + target, a framework providing automatic constructions of vulnerable infrastructures, used to deploy simple or complicated vulnerable cloud native targets swiftly and automatically. During security research, we might find that the deployment of a vulnerable environment often takes much time, while the time spent on testing PoC or ExP is comparatively short. In the field of cloud-native security, thanks to the complexity of cloud-native systems, this issue is more terrible. There are already some excellent security projects like Vulhub, and VulApps in the open-source community, which pack vulnerable scenes into container images so that researchers could utilize them and deploy scenes quickly. Hence, we develop Metarget and hope to solve the deployment issue above to some extent. Furthermore, we also expect that Metarget could help to construct multilayer vulnerable cloud native scenes automatically. -
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Model Search
Framework that implements AutoML algorithms
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. -
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Modeltranslation
Translates Django models using a registration approach
The modeltranslation application is used to translate dynamic content of existing Django models to an arbitrary number of languages without having to change the original model classes. It uses a registration approach (comparable to Django's admin app) to be able to add translations to existing or new projects and is fully integrated into the Django admin backend. The advantage of a registration approach is the ability to add translations to models on a per-app basis. You can use the same app in different projects, may they use translations or not, and you never have to touch the original model class. -
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Networking and sockets in scripting languages is difficult and often overcomplicated! Monasic is a server daemon (service) which does all the low level work to create a simple message-based environment, allowing easy access to networking via scripts.
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The main purpose of the Moonlight Engine is to be a general 2D game engine geared towards tile-based games, and RPGs specifically. Support for online games is also planned.
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Motinha
Information Gathering and Network Exploitation Framework
Motinha is a Simple Information Gathering and Network Exploitation Framework coded in Python. Here we have a bridge between the final user and the most futurists’ tools on the Internet to find juice info around any network, website, domain, company or persons and in some cases exploit some features to have fun , now let’s Shut Up And Hack! -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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A platform independent framework to remote control Lego Mindstorms NXT from Python. Using PyBluez http://org.csail.mit.edu/pybluez/ for Bluetooth connectivity and wxPython http://www.wxpython.org/ for UI.
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Nameko
Python framework for building microservices
Write regular Python methods and classes to implement your service. Nameko will manage connections, transports, and concurrency for you. Spin up multiple service instances to easily scale out. Nameko gives you effortless concurrency by yielding workers when they wait for I/O, leaving you free to handle many requests without the worry of threading. Nameko is compatible with almost any protocol, transport or database. Simply use the built-in extensions, build your own or leverage the community. Nameko includes an implementation of RPC over AMQP. It comprises the @rpc entry point, a proxy for services to talk to other services, and a standalone proxy that non-Nameko clients can use to make RPC calls to a cluster. The HTTP entry point is built on top of werkzeug, and supports all the standard HTTP methods (GET/POST/DELETE/PUT etc). -
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Needle
The iOS Security Testing Framework
Needle is an open-source, modular framework to streamline the process of conducting security assessments of iOS apps. Needle was originally made to work with iOS 9 and iOS 10. Since then, Frida was released and become the defacto tool to use with mobile security assessments. Some common Frida mobile security scripts were later implemented within Needle, as some of these scripts worked better or addressed some issues that were present in Needle's custom tooling. Assessing the security of an iOS application typically requires a plethora of tools, each developed for a specific need and all with different modes of operation and syntax. The Android ecosystem has tools like "drozer" that have solved this problem and aim to be a ‘one-stop shop’ for the majority of use cases, however, iOS does not have an equivalent. Needle is the MWR's iOS Security Testing Framework, released at Black Hat USA in August 2016. It is an open-source modular framework. -
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Neomake
Asynchronous linting and make framework for Neovim/Vim
Neomake is an asynchronous linting and build framework for Vim and Neovim that predates and inspires newer tooling in this space. It runs “makers” (linters, compilers, format checkers, test commands) in the background and surfaces results as signs, virtual text, or via quickfix/location lists. The system is highly configurable: you can define per-filetype makers, chain multiple tools, and control when they run (on save, on write, on cursor hold, or manually). Run several makers concurrently and aggregate results. Because it uses job control and timers, it scales well on large codebases and keeps the editor responsive. It’s useful both as a general “make” front end and as a diagnostic engine for code quality. Async execution of linters, compilers, and test commands via “makers”. Teams often adopt it to standardize checks across languages while keeping developers inside Vim.
