Open Source Python Frameworks - Page 14
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Python Frameworks
View 168 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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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
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. -
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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.
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Linderdaum Engine is an open source purely object-oriented 3D gaming engine written in C++. It is designed to be an integrated solution for the development of interactive 3D applications, for game, industrial and scientific visualization.
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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.
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).
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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.
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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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Make It heavy
A Python framework that emulates Grok Heavy functionality
Make It heavy is a Python framework for producing deeper AI analysis through multi-agent orchestration. It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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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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Marin
Framework for research and development of foundation models
Marin is an open-source research platform and community for developing foundation models through transparent, reproducible experimentation. It covers the complete model-building pipeline from data curation and filtering through tokenization, pretraining, post-training, and evaluation. Experiments and decisions are documented as they occur, including unsuccessful approaches. The framework is primarily used for large language models but has also supported audio-text, DNA, and protein modeling research. Experiments are expressed as dependent steps that execute in topological order, enabling reproducible training workflows. Marin also provides model checkpoints, training recipes, scaling research, documentation, and reusable infrastructure for large-scale experiments. -
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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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Miasm
Reverse engineering framework in Python
The Miasm intermediate representation is used for multiple task: emulation through its jitter engine, symbolic execution, DSE, program analysis, but the intermediate representation can be a bit hard to read. We will present in this article new tricks Miasm has learned in 2018. Among them, the SSA/Out-of-SSA transformation, expression propagation and high-level operators can be joined to “lift” Miasm IR to a more human-readable language. We use graphviz to illustrate some graphs. Its layout does not always totally conform with a reverse engineering “ideal view”, so please be tolerant of those odd graphs. Miasm is not the first tool to implement this feature. But, well, as the tool already had everything needed to implement DSE, it was just a matter of time before these features landed in the main branch. -
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Mobly
E2E test framework for tests with complex environment requirements
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. -
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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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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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