Open Source Python Software - Page 44
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Python Software
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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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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ClusterFuzz
Scalable fuzzing infrastructure
ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a software project's development process. Can run on any size cluster (e.g. OSS-Fuzz instance runs on 100,000 VMs). Fully automatic bug filing, triage and closing for various issue trackers (e.g. Monorail, Jira). Supports multiple coverage guided fuzzing engines (libFuzzer, AFL, AFL++ and Honggfuzz) for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports over 100 programming languages. With improved inference efficiency, quantization options, and multi-query/flash attention, CodeGeeX2 achieves faster generation speeds and lightweight deployment, requiring as little as 6GB GPU memory at INT4 precision. Its backend powers the CodeGeeX IDE plugins for VS Code, JetBrains, and other editors, offering developers interactive AI assistance with features like infilling and cross-file completion. -
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. It is built with compatibility in mind, supporting popular model families such as LLaMA, Qwen, and diffusion-based world models, as well as integration with Hugging Face ecosystems. cosmos-rl also includes support for advanced RL algorithms, low-precision training, and fault-tolerant execution, making it suitable for large-scale production workloads. -
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CosyVoice
Multi-lingual large voice generation model, providing inference
CosyVoice is a multilingual large voice generation model that offers a full-stack solution for training, inference, and deployment of high-quality TTS systems. The model supports multiple languages, including Chinese, English, Japanese, Korean, and a range of Chinese dialects such as Cantonese, Sichuanese, Shanghainese, Tianjinese, and Wuhanese. It is designed for zero-shot voice cloning and cross-lingual or mix-lingual scenarios, so a single reference voice can be used to synthesize speech across languages and in code-switching contexts. CosyVoice 2.0 significantly improves on version 1.0 by boosting accuracy, stability, speed, and overall speech quality, making it more suitable for production environments. The repository contains training recipes, inference pipelines, deployment scripts, and integration examples, positioning it as a comprehensive toolkit rather than just a set of model weights. -
Application Monitoring That Won't Slow Your App DownFull APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
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CowAgent
AI assistant based on large models that can actively think and plan
CowAgent, based on the chatgpt-on-wechat project, is an open-source AI agent framework that integrates large language models into the WeChat ecosystem to create intelligent conversational assistants. It enables automated message handling by connecting WeChat accounts with AI models that can generate contextual replies, process voice messages, and produce images directly inside chats. The platform has evolved beyond a simple chatbot into a more autonomous agent capable of planning complex tasks, maintaining long-term memory, and invoking external tools to complete workflows. It supports multi-turn conversations with per-user context tracking, allowing more natural and persistent interactions across private and group chats. Developers can extend functionality through a plugin architecture and customizable rules, making it suitable for both personal assistants and enterprise automation scenarios. -
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Crawlab
Distributed web crawler admin platform for spiders management
Golang-based distributed web crawler management platform, supporting various languages including Python, NodeJS, Go, Java, PHP and various web crawler frameworks including Scrapy, Puppeteer, Selenium. Please use docker-compose to one-click to start up. By doing so, you don't even have to configure MongoDB database. The frontend app interacts with the master node, which communicates with other components such as MongoDB, SeaweedFS and worker nodes. Master node and worker nodes communicate with each other via gRPC (a RPC framework). Tasks are scheduled by the task scheduler module in the master node, and received by the task handler module in worker nodes, which executes these tasks in task runners. Task runners are actually processes running spider or crawler programs, and can also send data through gRPC (integrated in SDK) to other data sources, e.g. MongoDB. -
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Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes
Cua is an open-source command-line utility and workflow orchestrator designed to help developers define, compose, and run common tasks with a unified interface, promoting consistency and reuse across projects. It introduces a declarative syntax for specifying build scripts, automation pipelines, environment setups, and project-specific commands so contributors don’t need to memorize disparate scripts or tooling across languages and ecosystems. Cua can also manage task dependencies, handle cross-platform invocations, and simplify complex workflows into simple aliases or compound commands that are easy to share in teams. By centralizing shared commands in a structured, documented config, it helps reduce errors, accelerates onboarding of new contributors, and keeps task definitions versioned with the codebase. The CLI is typically lightweight, easy to install, and designed to integrate with existing toolchains and shells without friction. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
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DNF
Package manager based on libdnf and libsolv. Replaces YUM
DNF (Dandified YUM) is the next-generation package manager for RPM-based distributions, replacing the traditional YUM tool. It utilizes modern libraries like libsolv and librepo to provide efficient dependency resolution and package management. DNF offers a more robust and user-friendly experience, with enhanced performance and a cleaner codebase. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Dear ImGui Bundle
Dear ImGui Bundle: easily create ImGui applications in Python and C++
Dear ImGui Bundle is a bundle for Dear ImGui, including various powerful libraries from its ecosystem. It enables to easily create ImGui applications in C++ and Python, under Windows, macOS, and Linux. It is aimed at application developers, researchers, and beginner developers who want to quickly get started. -
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DeepSeek-OCR 2
Visual Causal Flow
DeepSeek-OCR-2 is the second-generation optical character recognition system developed to improve document understanding by introducing a “visual causal flow” mechanism, enabling the encoder to reorder visual tokens in a way that better reflects semantic structure rather than strict raster scan order. It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents with rich spatial structure. The repository provides model code and inference scripts that let researchers and developers run and benchmark the system on both images and PDFs, with support for batch evaluation and optimized pipelines leveraging vLLM and transformers. -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page. -
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Diagrams
Diagram as Code for prototyping cloud system architectures
Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture without any design tools. You can also describe or visualize the existing system architecture as well. Diagram as Code allows you to track the architecture diagram changes in any version control system. Diagrams currently support main major providers including AWS, Azure, GCP, Kubernetes, Alibaba Cloud, Oracle Cloud, etc. It also supports On-Premise nodes, SaaS and major Programming frameworks and languages. It does not control any actual cloud resources nor does it generate cloud formation or terraform code. It is just for drawing the cloud system architecture diagrams. -
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Django Hijack
With Django Hijack, admins can log in and work on behalf of others
With Django Hijack, admins can log in and work on behalf of other users without having to know their credentials. 3.x docs are available in the docs folder. This version provides a security-first design, easy integration, customization, out-of-the-box Django admin support and dark mode. It is a complete rewrite and all former APIs are broken. A form is used to perform a POST including a CSRF-token for security reasons. The field user_pk is mandatory and the value must be set to the target users' primary key. The optional field next determines where a user is forwarded after a successful hijack. If not provided, users are forwarded to the LOGIN_REDIRECT_URL. Do not forget to load the hijack template tags to use the can_hijack filter. The can_hijack returns a boolean value, the first argument should be user hijacker, the second value should be the hijacked. -
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Django LMS
A learning management system using django web framework
django-lms is an open-source Learning Management System (LMS) built with Django and designed for ease of use and extensibility. It allows administrators to manage courses, lessons, quizzes, and users in an educational environment. The project includes a clean UI and backend tools to help educators create and track learning content. -
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Django Lifecycle Hooks
Declarative model lifecycle hooks, an alternative to Signals
This project provides a @hook decorator as well as a base model and mixin to add lifecycle hooks to your Django models. Django's built-in approach to offering lifecycle hooks is Signals. However, my team often finds that Signals introduce unnecessary indirection and are at odds with Django's "fat models" approach. Django Lifecycle Hooks supports Python 3.7, 3.8 and 3.9, Django 2.0.x, 2.1.x, 2.2.x, 3.0.x, 3.1.x, and 3.2.x. For simple cases, you might always want something to happen at a certain point, such as after saving or before deleting a model instance. When a user is first created, you could process a thumbnail image in the background and send the user an email. -
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Dynamiq
An orchestration framework for agentic AI and LLM applications
Dynamiq is an open-source orchestration framework designed to streamline the development of generative AI applications that rely on large language models and autonomous agents. The framework focuses on simplifying the creation of complex AI workflows that involve multiple agents, retrieval systems, and reasoning steps. Instead of building each component manually, developers can use Dynamiq’s structured APIs and modular architecture to connect language models, vector databases, and external tools into cohesive pipelines. The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
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EvaDB
Database system for building simpler and faster AI-powered application
Over the last decade, AI models have radically changed the world of natural language processing and computer vision. They are accurate on various tasks ranging from question answering to object tracking in videos. To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents people who are experts in other domains from benefiting from these models. Running these deep learning models on large document or video datasets is costly and time-consuming. For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. Besides the money spent on hardware, these models also increase the time that you spend waiting for the model inference to finish. -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
EverMemOS is an open-source memory operating system built to give AI agents long-term, structured memory. It captures conversations, transforms them into organized memory units, and enables agents to recall past interactions with context and meaning. Instead of treating each prompt independently, it builds evolving user profiles, tracks preferences, and connects related events into coherent narratives. Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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ExtractThinker
ExtractThinker is a Document Intelligence library for LLMs
ExtractThinker is a tool designed to facilitate the extraction and analysis of information from various data sources, aiding in data processing and knowledge discovery. -
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FastUI
Build better UIs faster
FastUI is a library that lets developers build interactive user interfaces for FastAPI applications using Pydantic models. It automatically generates frontend components based on data schemas and endpoint logic, reducing the need for manual UI development. Designed to be type-safe, reactive, and fast, FastUI streamlines the creation of web dashboards, admin panels, and internal tools within a FastAPI backend. -
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to boost the performance of your model. FiftyOne provides the building blocks for optimizing your dataset analysis pipeline. Use it to get hands-on with your data, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more! Surveys show that machine learning engineers spend over half of their time wrangling data, but it doesn't have to be that way. -
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FinalRecon
All-in-one Python web reconnaissance tool for fast target analysis
FinalRecon is an all-in-one web reconnaissance tool written in Python that helps security professionals gather information about a target website quickly and efficiently. It combines multiple reconnaissance techniques into a single command-line utility so users do not need to run several separate tools to collect similar data. FinalRecon focuses on providing a fast overview of a web target while maintaining accuracy in the collected results. It includes modules for gathering server information, analyzing SSL certificates, performing WHOIS lookups, and crawling website resources. FinalRecon can also enumerate DNS records, discover subdomains, search for directories and files, and scan common network ports. Historical URLs and resources can be retrieved from archived sources to help analyze changes in a website over time. Designed primarily for penetration testers and security researchers, FinalRecon simplifies the reconnaissance phase of security assessments. -
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Flagsmith
Open source feature flagging and remote config service
Release features with confidence; manage feature flags across web, mobile, and server-side applications. Use our hosted API, deploy to your own private cloud, or run on-premises. Flagsmith provides an all-in-one platform for developing, implementing, and managing your feature flags. Whether you are moving off an in-house solution or using toggles for the first time, you will be amazed by the power and efficiency gained by using Flagsmith. Flagsmith makes it easy to create and manage feature toggles across web, mobile, and server-side applications. Just wrap a section of code with a flag, and then use Flagsmith to manage that feature. Manage feature flags by the development environment, and for individual users, a segment of users, or a percentage. This means quickly implementing practices like canary deployments. Multivariate flags allow you to use a percentage split across two or more variations for precise A/B/n testing and experimentation. -
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Flask RESTX
Fully featured framework for fast, easy and documented API development
Fork of Flask-RESTPlus fully featured framework for fast, easy and documented API development with Flask. Flask-RESTX is an extension for Flask that adds support for quickly building REST APIs. Flask-RESTX encourages best practices with minimal setup. If you are familiar with Flask, Flask-RESTX should be easy to pick up. It provides a coherent collection of decorators and tools to describe your API and expose its documentation properly using Swagger. With Flask-RESTX, you only import the api instance to route and document your endpoints.
