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Showing 16864 open source projects for "python windows"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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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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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant... -
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
AReaL is an open source, fully asynchronous reinforcement learning training system. AReal is designed for large reasoning and agentic models. It works with models that perform reasoning over multiple steps, agents interacting with environments. It is developed by the AReaL Team at Ant Group (inclusionAI) and builds upon the ReaLHF project. Release of training details, datasets, and models for reproducibility. It is intended to facilitate reproducible RL training on reasoning / agentic tasks,... -
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Pfl Research
Simulation framework for accelerating research
A fast, modular Python framework released by Apple for privacy-preserving federated learning (PFL) simulation. Integrates with TensorFlow, PyTorch, and classical ML, and offers high-speed distributed simulation (7–72× faster than alternatives). -
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Perspective
A data visualization and analytics component
Perspective is a high-performance data visualization library for building real-time, interactive analytics dashboards. Developed by FINOS, it supports WebAssembly-powered pivot tables and can handle large streaming datasets with speed and flexibility. Perspective is ideal for fintech, trading, and IoT applications where insights from live data need to be visualized, sliced, and explored quickly in a browser. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Hyperledger Cello
Operating System for Enterprise Blockchain
Hyperledger Cello is a blockchain operation and provisioning system designed to automate the deployment, management, and scaling of Hyperledger Fabric networks. As part of the Hyperledger project under the Linux Foundation, Cello aims to offer Blockchain-as-a-Service (BaaS) by abstracting the complexity of infrastructure setup for consortiums and enterprises. It provides a dashboard, APIs, and orchestration tools to help users create, monitor, and manage blockchain nodes, ledgers, and... -
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FreeOrion
Source code repository of the FreeOrion project
FreeOrion is a turn-based space empire-building game inspired by the classic Master of Orion series, with an emphasis on modularity, modding, and strategic depth. Built using C++ and Python, it features a flexible game engine and a custom UI tailored to 4X (eXplore, eXpand, eXploit, eXterminate) gameplay. Players manage planets, design ships, conduct research, engage in diplomacy, and wage war across a procedurally generated galaxy. FreeOrion offers a unique tech tree, customizable empires,... -
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PaperQA2
High accuracy RAG for answering questions from scientific documents
PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature. See our recent 2024 paper to see examples of PaperQA2's superhuman performance in scientific tasks like question answering, summarization, and contradiction detection. In this example we take a folder of research paper PDFs, magically get their metadata - including citation counts and a retraction check, then parse and cache PDFs into a... -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry... -
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Kapitan
Generic templated configuration management for Kubernetes
Generic templated configuration management for Kubernetes, Terraform, and other things. Kapitan aims to be your one-stop configuration management solution to help you manage the ever-growing complexity of your configurations by enabling Platform Engineering and GitOps workflows. It streamlines complex deployments across heterogeneous environments while providing a secure and adaptable framework for managing infrastructure configurations. Kapitan's inventory-driven model, powerful templating... -
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Tequila
A High-Level Abstraction Framework for Quantum Algorithms
Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state-of-the-art simulators as well as on real quantum devices. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed),... -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. At the same time, it is... -
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Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems... -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
Export Django monitoring metrics for Prometheus.io. This library provides Prometheus metrics for Django-related operations. Prometheus uses Histogram based grouping for monitoring latencies. You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy. SQLite, MySQL, and PostgreSQL databases can be monitored. Just replace the ENGINE property of your database, replacing django.db.backends with django_prometheus.db.backends. You may want to... -
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Graphene-Django
Integrate GraphQL into your Django project
Graphene-Django is built on top of Graphene. Graphene-Django provides some additional abstractions that make it easy to add GraphQL functionality to your Django project. First time? We recommend you start with the installation guide to get set up and the basic tutorial. It is worth reading the core graphene docs to familiarize yourself with the basic utilities. Graphene Django has a number of additional features that are designed to make working with Django easy. Our primary focus in this... -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render... -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference... -
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Cryptocurrency Exchange Feed Handler
Cryptocurrency Exchange Websocket data feed handler
Handles multiple cryptocurrency exchange data feeds and returns normalized and standardized results to client registered callbacks for events like trades, book updates, ticker updates, etc. Utilizes WebSockets when possible, but can also poll data via REST endpoints if a WebSocket is not provided. Create a FeedHandler object and add subscriptions. For the various data channels that an exchange supports, you can supply callbacks for data events, or use provided backends to handle the data for... -
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Trellis
WordPress LEMP stack with PHP 8.1, Composer, WP-CLI
Trellis uses Vagrant to automatically create a self-contained virtual machine. Stop cluttering up your host machine with software like MAMP and use the same software you would in production. You’ll get a complete WordPress server running all the software you need to be configured according to the best practices. All of this is powered by Ansible for configuration management. You don’t have to use brittle and confusing Bash scripts or worry about commands you found to copy and paste. Trellis... -
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ChatterBot
Machine learning, conversational dialog engine for creating chat bots
ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally,... -
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Harbor LLM
Run a full local LLM stack with one command using Docker
Harbor is an open source, containerized toolkit designed to simplify running local large language model (LLM) environments. It combines a CLI and companion app to launch backends, frontends, and supporting services with minimal setup. With a single command, users can start preconfigured tools like Ollama and Open WebUI, enabling chat, workflows, and integrations immediately. Harbor supports multiple inference engines, including llama.cpp and vLLM, and connects them seamlessly to user... -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development... -
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AIQuant
AI-powered platform for quantitative trading
ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models,... -
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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,... -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++)...
