Open Source Python Software - Page 93
Sort By:
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.
-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
OpenSage
An agent framework that enables AI to create their own agent
OpenSage is an emerging open-source AI agent development framework designed to automate the creation, orchestration, and evolution of intelligent agents through a self-programming paradigm. Unlike traditional agent frameworks that require developers to manually define workflows, tools, and structures, OpenSage introduces a system where large language models can dynamically generate their own agent architectures, including sub-agents, toolchains, and execution strategies. The framework is built around the concept of an Agent Development Kit (ADK), providing structured components for memory, reasoning, and task decomposition while allowing agents to iteratively improve their own design. A key innovation is its hierarchical and graph-based memory system, which enables agents to store, retrieve, and organize information across complex workflows with improved efficiency and contextual awareness. -
2
OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. It also focuses on cost efficiency by reducing redundant computations and reusing successful workflows, significantly lowering token usage in repeated tasks. The framework includes monitoring and evaluation mechanisms to track skill performance and ensure reliability as systems evolve. It supports integration with various agent platforms, making it flexible and extensible across different environments. -
3
OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
openvino_notebooks is a collection of interactive Jupyter notebooks designed to demonstrate how to build, optimize, and deploy artificial intelligence applications using the OpenVINO toolkit. The repository provides practical tutorials that guide developers through various AI workflows including computer vision, natural language processing, and generative AI tasks. Each notebook demonstrates how to run pre-trained models, optimize inference performance, and deploy models across hardware such as CPUs, GPUs, and specialized accelerators. The tutorials also illustrate how OpenVINO integrates with models from frameworks like PyTorch, TensorFlow, and ONNX to accelerate inference workloads. Many notebooks include end-to-end examples that show how to prepare input data, load optimized models, run inference, and visualize results. The project is particularly useful for developers who want to learn how to optimize machine learning inference pipelines for production environments. -
4
Oppia
A free, online learning platform to make quality education accessible
Oppia is an online learning tool that enables anyone to easily create and share interactive activities (called 'explorations'). These activities simulate a one-on-one conversation with a tutor, making it possible for students to learn by doing while getting feedback. Oppia identifies common wrong answers and provides tailored feedback, so that students get a personalized experience. Our lessons keep students engaged through playful characters and use different strategies to solidify their knowledge. Check out our math lessons with proven results! In addition to developing the Oppia platform, the team is also developing and piloting a set of free and effective lessons on basic mathematics. These lessons are targeted at learners who lack access to educational resources. Oppia is written using Python and AngularJS, and is built on top of Google App Engine. -
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
-
5
Orpheus TTS
Towards Human-Sounding Speech
Orpheus TTS is a state-of-the-art open-source text-to-speech system built on a Llama-3B backbone, treating speech synthesis as a large language model problem instead of a traditional TTS pipeline. It is designed to produce human-like speech with natural intonation, emotion, and rhythm, targeting quality comparable to or better than many closed-source systems. The project ships both pretrained and finetuned English models, as well as a family of multilingual models released as a research preview, and includes data-processing scripts so users can train or finetune their own variants. Inference is provided through a Python package that uses vLLM under the hood for high-throughput, low-latency generation, including streaming examples that show how to generate audio chunks in real time. The maintainers provide Colab notebooks, a standardized prompting format, and one-click deployment via Baseten for production-grade, FP8/FP16 optimized inference with ~200 ms streaming latency. -
6
OuteTTS
Interface for OuteTTS models
OuteTTS is an interface library for running OuteTTS text-to-speech models across a range of backends, making it easier to deploy the same model on different hardware and runtimes. It provides a high-level Interface API that wraps model configuration, speaker handling, and audio generation so you can focus on integrating speech into your application rather than wiring up low-level engines. The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face Transformers, ExLlamaV2, VLLM and a JavaScript interface via Transformers.js, allowing it to run on CPUs, NVIDIA CUDA GPUs, AMD ROCm, Vulkan-capable GPUs, and Apple Metal. It also includes a notion of speaker profiles: you can create a speaker from a short audio sample, save it as JSON, and reuse it for consistent voice identity across generations and sessions. For best quality, the model is designed to work with a reference speaker clip and will inherit emotion, style, and accent from that reference. -
7
PDF Merge and Edit
Python script to merge and edit sensitive PDF files
Python script to merge and edit sensitive PDF files you don't want to upload to random sites you find on Google. Merge PDFs by adding one to another. Update a single page in a PDF (good for adding a signed page to a form) Insert a page into an existing PDF. Delete a page. Click on one of the buttons and a new window will pop up depending on the function. Pick your files and enter in the data. If there are no problems, a confirmation will pop up. -
8
PDM
A modern Python package and dependency manager
PDM (Python Development Master) is a modern Python package and dependency manager that adheres to the latest PEP standards. It emphasizes a declarative approach to project configuration using pyproject.toml, facilitating reproducible builds and streamlined workflows. PDM's focus on simplicity and compliance with Python's evolving ecosystem makes it a valuable tool for developers seeking modern project management solutions. -
9
POT
Python Optimal Transport
This open source Python library provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
-
10
PRIME
Scalable RL solution for advanced reasoning of language models
PRIME is an open-source reinforcement learning framework designed to improve the reasoning capabilities of large language models through process-level rewards rather than relying only on final outputs. The system introduces the concept of process reinforcement through implicit rewards, allowing models to receive feedback on intermediate reasoning steps instead of evaluating only the final answer. This approach helps models learn better reasoning strategies and encourages them to generate more reliable multi-step solutions to complex tasks. PRIME provides training pipelines, datasets, and experimental infrastructure that allow researchers to train models with reinforcement learning tailored for reasoning improvement. The framework also includes data preprocessing utilities and example datasets such as mathematical reasoning tasks that are well suited for process-based reward signals. -
11
PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
PRM800K is a process supervision dataset accompanying the paper Let’s Verify Step by Step, providing 800,000 step-level correctness labels on model-generated solutions to problems from the MATH dataset. The repository releases the raw labels and the labeler instructions used in two project phases, enabling researchers to study how human raters graded intermediate reasoning. Data are stored as newline-delimited JSONL files tracked with Git LFS, where each line is a full solution sample that can contain many step-level labels and rich metadata such as labeler UUIDs, timestamps, generation identifiers, and quality-control flags. Each labeled step can include multiple candidate completions with ratings of -1, 0, or +1, optional human-written corrections (phase 1), and a chosen completion index, along with a final finish reason such as found_error, solution, bad_problem, or give_up. -
12
PRML
PRML algorithms implemented in Python
PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM algorithms, approximate inference, and sequential data methods — all following the book’s structure and notation. Many of these algorithms are paired with Jupyter notebooks that let users interact with the code, visualize results, and experiment with parameters in a way that deeply strengthens theoretical understanding. -
13
PaLM + RLHF - Pytorch
Implementation of RLHF (Reinforcement Learning with Human Feedback)
PaLM-rlhf-pytorch is a PyTorch implementation of Pathways Language Model (PaLM) with Reinforcement Learning from Human Feedback (RLHF). It is designed for fine-tuning large-scale language models with human preference alignment, similar to OpenAI’s approach for training models like ChatGPT. -
14
PaaSTA
An open, distributed platform as a service
PaaSTA is a highly-available, distributed system for building, deploying, and running services using containers and Kubernetes. PaaSTA has been running production services at Yelp since 2016. It was originally designed to run on top of Apache Mesos but has subsequently been updated to use Kubernetes. Over time the features and functionality that PaaSTA provides have increased but the principal design remains the same. PaaSTA aims to take a declarative description of the services that teams need to run and then ensures that those services are deployed safely, efficiently, and in a manner that is easy for the teams to maintain. Rather than managing Kubernetes YAML files, PaaSTA provides a simplified schema to describe your service and in addition to configuring Kubernetes it can also configure other infrastructure tools to provide monitoring, logging, cost management etc. -
15
Pacu
The AWS exploitation framework, designed for testing security
Pacu (named after a type of Piranha in the Amazon) is a comprehensive AWS security-testing toolkit designed for offensive security practitioners. While several AWS security scanners currently serve as the proverbial “Nessus” of the cloud, Pacu is designed to be the Metasploit equivalent. Written in Python 3 with a modular architecture, Pacu has tools for every step of the pen testing process, covering the full cyber kill chain. Pacu is the aggregation of all of the exploitation experience and research from our countless prior AWS red team engagements. Automating components of the assessment not only improves efficiency but also allows our assessment team to be much more thorough in large environments. What used to take days to manually enumerate can be now be achieved in minutes. There are currently over 35 modules that range from reconnaissance, persistence, privilege escalation, enumeration, data exfiltration, log manipulation, and miscellaneous general exploitation. -
16
Pal
A personal context-agent that learns how you work
Pal is an open-source AI personal agent built within the Agno ecosystem that functions as an intelligent digital assistant designed to learn from user activity over time. The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can answer questions, recall information, and assist with future tasks more effectively. The agent can perform web research, summarize information, and store insights so that useful discoveries are not lost across conversations or sessions. Over time, the agent learns from interactions, remembers patterns that worked well, and applies those learnings to similar tasks in the future, allowing it to improve without requiring additional model training. -
17
Paper Icon Theme
Paper Icon Theme
Paper is a modern freedesktop icon theme whose design is based around the use of bold colours and simple geometric shapes to compose icons. Each icon has been meticulously designed for pixel-perfect viewing. While it does take some inspiration from the icons in Google's Material Design, some aspects have been adjusted to better suit a desktop environment. This project has mixed licencing. You are free to copy, redistribute and/or modify aspects of this work under the terms of each licence accordingly (unless otherwise specified). You can build and install Paper from source using Meson. -
18
Paper2GUI
Convert AI papers to GUI
Convert AI papers to GUI，Make it easy and convenient for everyone to use artificial intelligence technology。让每个人都简单方便的使用前沿人工智能技术 Paper2GUI: An AI desktop APP toolbox for ordinary people. It can be used immediately without installation. It already supports 40+ AI models, covering AI painting, speech synthesis, video frame complementing, video super-resolution, object detection, and image stylization. , OCR recognition and other fields. Support Windows, Mac, Linux systems. Paper2GUI: 一款面向普通人的 AI 桌面 APP 工具箱，免安装即开即用，已支持 40+AI 模型，内容涵盖 AI 绘画、语音合成、视频补帧、视频超分、目标检测、图片风格化、OCR 识别等领域。支持 Windows、Mac、Linux 系统。 -
19
Parallax
Parallax is a distributed model serving framework
Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to available hardware and how requests are routed across nodes during execution. This scheduling system optimizes latency, throughput, and hardware utilization even when nodes have different computational capabilities. The platform also supports model sharding and pipeline parallelism, allowing very large models to run across distributed resources. -
20
Patroni
A template for PostgreSQL high availability with Etcd, Consul, etc.
Patroni is a template for you to create your own customized, high-availability solution using Python and - for maximum accessibility - a distributed configuration store like ZooKeeper, etcd, Consul or Kubernetes. Database engineers, DBAs, DevOps engineers, and SREs who are looking to quickly deploy HA PostgreSQL in the datacenter-or anywhere else-will hopefully find it useful. We call Patroni a "template" because it is far from being a one-size-fits-all or plug-and-play replication system. It will have its own caveats. Use wisely. Currently supported PostgreSQL versions 9.3 to 14. Patroni originated as a fork of Governor, the project from Compose. It includes plenty of new features. For an example of a Docker-based deployment with Patroni, see Spilo, currently in use at Zalando. -
21
PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK. -
22
Pesterchum-Discord
A Discord client mimicking the Pesterchum chat client from Homestuck
A Discord client mimicking the Pesterchum chat client from Homestuck, Uses a lot of code from my Pesterchum Client. A Discord client mimicking the Pesterchum chat client from Homestuck, for the few people who are still interested in that. If you have any questions either contact me the GitHub or add me at Henry#8808 on Discord. This app is not an all-in-one client! It is meant to enhance RP and anything else you wish to do with it, however, it is recommended you still use regular Discord for technical functions. The release download is distributed as a .zip file. Unzip the file and inside will be a file called pesterchum.exe If there is an update, updater.exe will launch in a console. It doesn't install anywhere, its fairly portable. It is not yet for OSX/Linux, contact me if anyone is interested in supporting it. -
23
Petastorm
Petastorm library enables single machine or distributed training
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. It can also be used from pure Python code. A dataset created using Petastorm is stored in Apache Parquet format. On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own. -
24
PilottAI
Python framework for building scalable multi-agent systems
pilottai is an AI-based autonomous drone navigation system utilizing reinforcement learning for real-time decision-making. It is designed for simulating and training drones to fly safely through dynamic environments using AI-based controllers. -
25
PixelCNN
Code for the paper "PixelCNN++: A PixelCNN Implementation..."
PixelCNN is the official implementation from OpenAI of the autoregressive generative model described in the paper Conditional Image Generation with PixelCNN Decoders. It provides code for training and evaluating PixelCNN models on image datasets, focusing on conditional image modeling where pixels are generated sequentially based on the values of previously generated pixels. The repository demonstrates how to apply masked convolutions to enforce autoregressive dependencies and achieve tractable likelihood-based training. It also includes scripts for reproducing key experimental results from the paper, such as conditional sampling on datasets like CIFAR-10. The project serves as both a research reference and a practical framework for experimenting with autoregressive generative models. Although archived, PixelCNN has influenced a wide range of later work in generative modeling, including advancements in image transformers and diffusion models.
