Open Source Python Software - Page 85
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Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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DeepSeek Engineer v2
A powerful coding assistant application
DeepSeek Engineer v2 is an AI-powered coding assistant built around DeepSeek models and an interactive terminal workflow. It lets developers discuss code, request analysis, and perform project work through natural language. Version 2.0 focuses on native function calling instead of rigid structured JSON responses. The assistant can read files, read multiple files, create files, create multiple files, and edit specific snippets when needed. It includes safeguards such as path validation, directory traversal protection, file size limits, and binary file exclusion. Overall, it is designed for developers who want a conversational coding tool that can inspect, modify, and reason about project files from the command line. -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
DeepSeek-VL2 is DeepSeek’s vision + language multimodal model—essentially the next-gen successor to their first vision-language models. It combines image and text inputs into a unified embedding / reasoning space so that you can query with text and image jointly (e.g. “What’s going on in this scene?” or “Generate a caption appropriate to context”). The model supports both image understanding (vision tasks) and multimodal reasoning, and is likely used as a component in agent systems to process visual inputs as context for downstream tasks. The repository includes evaluation results (e.g. image/text alignment scores, common VL benchmarks), configuration files, and model weights (where permitted). While the internal architecture details are not fully documented publicly, the repo suggests that VL2 introduces enhancements over prior vision-language models (e.g. better scaling, cross-modal attention, more robust alignment) to improve grounding and multimodal understanding. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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Devicon
Set of icons representing programming languages, design & development
Devicon is a set of icons representing programming languages, designing, and development tools. You can use it as a font or directly copy/paste the SVG code into your project. Devicon aims to gather all logos representing development languages and tools. Each icon comes in several versions: font/SVG, original/plain/line, colored/not colored, wordmark/no wordmark. Devicon has 150+ icons. And it's growing! Open icomoon.io and import icomoon.json. Choose yes when being asked if you would like to restore the settings stored in the configuration file. You can install devicon as a dependency to your project either with npm or yarn. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Differentiable Neural Computer
A TensorFlow implementation of the Differentiable Neural Computer
The Differentiable Neural Computer (DNC), developed by Google DeepMind, is a neural network architecture augmented with dynamic external memory, enabling it to learn algorithms and solve complex reasoning tasks. Published in Nature in 2016 under the paper “Hybrid computing using a neural network with dynamic external memory,” the DNC combines the pattern recognition power of neural networks with a memory module that can be written to and read from in a differentiable way. This allows the model to learn how to store and retrieve information across long time horizons, much like a traditional computer. The architecture consists of modular components including an access module for managing memory operations, a controller (often an LSTM or feedforward network) for issuing read/write commands, and submodules for temporal linkage and memory allocation tracking. -
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Ditto
The simplest self-building coding agent
Ditto is a simple self-building coding agent that generates multi-file Flask applications from natural language descriptions. Users describe the app they want, and the system attempts to plan and create routes, templates, static assets, and supporting files. It uses an LLM loop with basic tools to automate part of the coding process. The project is intentionally lightweight and experimental, making it easier to understand than larger agentic coding platforms. Its modular structure separates generated Flask components into cleaner directories for routes, templates, and static files. It is best suited for prototyping, learning, and exploring how natural-language app generation can work in a small local project. -
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Django OAuth Toolkit
OAuth2 goodies for the Djangonauts!
Django OAuth Toolkit can help you by providing, out of the box, all the endpoints, data, and logic needed to add OAuth2 capabilities to your Django projects. Django OAuth Toolkit makes extensive use of the excellent OAuthLib, so that everything is rfc-compliant. OAuth is an open standard for access delegation, commonly used as a way for Internet users to grant websites or applications access to their information on other websites but without giving them the passwords. Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. Your Django app exposes a web API you want to protect with OAuth2 authentication. You need to implement an OAuth2 authorization server to provide tokens management for your infrastructure. -
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Django REST framework
Powerful and flexible toolkit for building Web APIs
Django REST framework is a powerful and flexible toolkit for building Web APIs. Some reasons you might want to use REST framework: The Web browsable API is a huge usability win for your developers. Authentication policies including packages for OAuth1a and OAuth2. Serialization that supports both ORM and non-ORM data sources. Customizable all the way down - just use regular function-based views if you don't need the more powerful features. Extensive documentation, and great community support. Used and trusted by internationally recognised companies including Mozilla, Red Hat, Heroku, and Eventbrite. REST framework is a collaboratively funded project. If you use REST framework commercially we strongly encourage you to invest in its continued development by signing up for a paid plan. -
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DouZero
[ICML 2021] DouZero: Mastering DouDizhu
DouZero is a reinforcement learning-based AI for playing DouDizhu, a popular Chinese card game. It focuses on perfecting AI strategies for competitive play using value-based deep RL techniques. -
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ERAlchemy
Entity Relation Diagrams generation tool
ERAlchemy is a tool that generates Entity-Relationship (ER) diagrams from databases or SQLAlchemy models and vice versa. It’s useful for database documentation, reverse engineering, and understanding complex schemas. ERAlchemy can export diagrams in formats like Graphviz and Mermaid, making it easy to include in reports or markdown files. -
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EasyRL
Reinforcement learning (RL) tutorial series
easy-rl is a beginner-friendly reinforcement learning (RL) tutorial series and framework developed by Datawhale China. It provides educational resources and implementations of various RL algorithms to help new researchers and practitioners learn RL concepts. -
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Elementary
Open-source data observability for analytics engineers
Elementary is an open-source data observability solution for data & analytics engineers. Monitor your dbt project and data in minutes, and be the first to know of data issues. Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues. -
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EmotiVoice
Multi-Voice and Prompt-Controlled TTS Engine
EmotiVoice is a multi-voice, prompt-controlled text-to-speech engine designed to generate highly expressive speech across thousands of voices. It supports both English and Chinese and ships with over 2,000 preset voices, making it suitable for everything from characters and virtual anchors to narration and dialogue. The core idea is prompt-based emotional and style control: you can ask the engine to speak “happy,” “sad,” “excited,” or with other high-level style prompts that shape prosody, pitch, speed, and energy. EmotiVoice provides multiple ways to interact with it, including a web interface, a Docker image, an HTTP API (including an OpenAI-compatible TTS API), and Python scripts for batch synthesis. It also supports voice cloning with your own data, backed by recipes for popular datasets like DataBaker and LJSpeech, so you can train or adapt voices to custom personas. -
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source toolkit. Get a high level overview of your data distribution, explore it by customizable quality metrics, and discover any anomalies. Use powerful similarity search to find more examples of edge-cases or outliers. -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations over shared resources. The framework provides code for both supervised learning (training from human dialogue data) and reinforcement learning (via self-play and rollout-based planning). It introduces a hierarchical latent model, where high-level intents are first clustered and then translated into coherent language, improving dialogue diversity and goal consistency. The repository also includes the Negotiate dataset, comprising over 5,800 dialogues across 2,200 unique scenarios. -
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Enferno
Modern Flask framework optimized for AI-assisted development
Enferno is a framework for building developer-first cloud backends using PostgreSQL and TypeScript. It offers primitives for defining data models, APIs, and access rules directly in code, enabling quick iteration and deployment. Enferno is designed to accelerate SaaS and internal tool development by combining the benefits of traditional backends with developer ergonomics. -
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Evo 2
Genome modeling and design across all domains of life
Evo 2 is a DNA language model system designed for long-context genome modeling and biological sequence design across all domains of life. The project models DNA at single-nucleotide resolution and supports context windows of up to one million base pairs, which places it in a class of models built for very large genomic reasoning tasks. According to the repository, it uses the StripedHyena 2 architecture, was pretrained with Savanna, and was trained autoregressively on the OpenGenome2 dataset containing 8.8 trillion tokens. The codebase is focused on local inference and generation through the Vortex inference stack rather than serving as a full training framework alone, although it also points users to training and fine-tuning resources. It supports multiple ways of working with the model, including forward passes, embeddings, generation workflows, notebooks, hosted APIs, and self-hosted deployment through NVIDIA NIM. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
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Evolution Strategies Starter
Code for the paper "Evolution Strategies.."
evolution-strategies-starter is an archived OpenAI research project that provides a distributed implementation of the algorithm described in the paper “Evolution Strategies as a Scalable Alternative to Reinforcement Learning” by Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. The repository demonstrates how to scale Evolution Strategies (ES) for reinforcement learning tasks using a master-worker architecture, where the master node broadcasts parameters to multiple workers, and the workers return performance results after evaluation. This approach allows for efficient parallelization and robustness against worker termination, making it ideal for distributed execution on Amazon EC2 spot instances. The codebase supports building custom AMIs with Packer, integrates with MuJoCo for simulation-based experiments, and includes scripts for launching and managing large-scale runs. While no longer actively maintained, the repository serves as a historical and educational reference. -
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Exploitarium
A single archive of public exploit PoCs and vulnerability research
Exploitarium is a consolidated archive of public proof-of-concept vulnerability research and exploit writeups. It gathers older standalone research repositories into one place while also adding newer entries as self-contained folders. The project includes tracked examples involving software such as 7-Zip, AnyDesk, c-ares, curl, Discourse, Docker, and other targets. Its purpose is research visibility, vulnerability study, and educational review rather than normal application deployment. Because the repository contains exploit proof-of-concepts, it should only be used in authorized labs, defensive analysis, or coordinated research contexts. It is best categorized as a high-risk security research archive that requires careful legal and ethical handling. -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. While here the work is presented as a black box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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FastAPI Python
FastAPI framework, high performance, easy to learn, fast to code
FastAPI framework, high performance, easy to learn, fast to code, ready for production. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints. -
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FastChat
Open platform for training, serving, and evaluating language models
FastChat is an open platform for training, serving, and evaluating large language model-based chatbots. If you do not have enough memory, you can enable 8-bit compression by adding --load-8bit to the commands above. This can reduce memory usage by around half with slightly degraded model quality. It is compatible with the CPU, GPU, and Metal backend. Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/T4/V100(16GB) GPU. In addition to that, you can add --cpu-offloading to commands above to offload weights that don't fit on your GPU onto the CPU memory. This requires 8-bit compression to be enabled and the bitsandbytes package to be installed, which is only available on linux operating systems. -
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FastMCP
The fast, Pythonic way to build Model Context Protocol servers
FastMCP is a fast, Pythonic framework for building servers and clients using the Model Context Protocol (MCP). It abstracts away protocol complexity like serialization, validation, and error handling, letting developers focus entirely on their business logic. With simple decorators, you can expose Python functions as tools, resources, or prompts that AI agents can safely and efficiently use. FastMCP introduces clear abstractions—components, providers, and transforms—that make it easy to control what agents see and how they interact with your system. The framework is opinionated by design, ensuring best practices and protocol compliance are the default rather than an extra burden. Actively maintained and widely adopted, FastMCP powers a majority of MCP servers and has become the de facto standard for production-ready MCP applications.
