Open Source Python Software - Page 61
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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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Flask-MongoEngine
MongoEngine flask extension with WTF model forms support
Flask-MongoEngine is a Flask extension that provides integration with MongoEngine, WtfForms and FlaskDebugToolbar. By default, Flask-MongoEngine will install integration only between Flask and MongoEngine. Integration with WTFForms and FlaskDebugToolbar are optional and should be selected as extra option, if required. This is done by users request, to limit amount of external dependencies in different production setup environments. All methods end extras described below are compatible between each other and can be used together. We still maintain special case for Flask = 1.1.4 support (the latest version in 1.x.x branch). To install flask-mongoengine with required dependencies use legacy extra option. Flask-mongoengine can be installed with Flask-WTF and WTFForms support. This will extend project dependencies with Flask-WTF, WTFForms and related packages. -
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FreeTAKServer
Situational Awareness Server compatible with TAK clients
FTS is a Python3 implementation of a TAK Server for devices like ATAK, WinTAK, and ITAK, it is cross-platform and runs from a multi-node installation on AWS down to the Android edition. It's free and open source (released under the Eclipse Public License. FTS allows you to connect ATAK clients to share geo-information, to chat with all the connected clients, exchange files and more. It intends to support all the major use cases of the original TAK server. -
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Full Stack FastAPI Couchbase
Full stack, modern web application generator
Full stack, modern web application generator. Using FastAPI, Couchbase as a database, Docker, automatic HTTPS, and more. Couchbase has a great set of features that is not easily or commonly found in alternatives. REST backend tests based on Pytest, integrated with Docker, so you can test the full API interaction, independent on the database. As it runs in Docker, it can build a new data store from scratch each time (so you can use ElasticSearch, MongoDB, or whatever you want, and just test that the API works). Load balancing between frontend and backend with Traefik, so you can have both under the same domain, separated by path, but served by different containers. -
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Full Stack FastAPI and PostgreSQL
Full stack, modern web application generator
Generate a backend and frontend stack using Python, including interactive API documentation. Production ready Python web server using Uvicorn and Gunicorn. Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. Minimize code duplication. Multiple features from each parameter declaration. Get production-ready code. With automatic interactive documentation. Many other features including automatic validation, serialization, interactive documentation, authentication with OAuth2 JWT tokens, etc. Celery worker that can import and use models and code from the rest of the backend selectively. REST backend tests based on Pytest, integrated with Docker, so you can test the full API interaction, independent on the database. As it runs in Docker, it can build a new data store from scratch each time. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
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Google Spreadsheets Python
Google Sheets Python API
gspread is a Python API for Google Sheets. A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs [sic]. Since it’s a separate account, by default it does not have access to any spreadsheet until you share it with this account. Just like any other Google account. To access spreadsheets via Google Sheets API you need to authenticate and authorize your application. Older versions of gspread have used oauth2client. Google has deprecated it in favor of google-auth. If you’re still using oauth2client credentials, the library will convert these to google-auth for you, but you can change your code to use the new credentials to make sure nothing breaks in the future. If you familiar with the Jupyter Notebook, Google Colaboratory is probably the easiest way to get started using gspread. -
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Google Toolbox for Mac
Google Toolbox for Mac
Google Toolbox for Mac (GTMSession) is a comprehensive collection of open source Objective-C utilities and frameworks developed by Google to support macOS and iOS application development. It consolidates reusable code components drawn from various internal Google projects, offering developers a wide range of tools for building efficient, maintainable Apple platform software. The library includes modules for networking, logging, testing, data handling, and user interface extensions, helping developers avoid reinventing common functionality. Its modular design allows developers to integrate only the components they need, improving project flexibility and performance. With well-documented interfaces and consistent coding standards, Google Toolbox for Mac serves as a reliable foundation for both small and large-scale applications. It continues to be widely used across open source and internal projects that target Apple ecosystems. -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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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 tutorial is to give a good understanding of how to connect models from Django ORM to Graphene object types. GraphQL presents your objects to the world as a graph structure rather than a more hierarchical structure to which you may be accustomed. In order to create this representation, Graphene needs to know about each type of object which will appear in the graph. -
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Graylog Ansible Role
Ansible role which installs and configures Graylog
Ansible role which installs and configures Graylog. -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case. Griptape not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Griptape maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities. -
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Guidance
A guidance language for controlling large language models
Guidance is an efficient programming paradigm for steering language models. With Guidance, you can control how output is structured and get high-quality output for your use case—while reducing latency and cost vs. conventional prompting or fine-tuning. It allows users to constrain generation (e.g. with regex and CFGs) as well as to interleave control (conditionals, loops, tool use) and generation seamlessly. -
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Gymnasium
An API standard for single-agent reinforcement learning environments
Gymnasium is a fork of OpenAI Gym, maintained by the Farama Foundation, that provides a standardized API for reinforcement learning environments. It improves upon Gym with better support, maintenance, and additional features while maintaining backward compatibility. -
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start training your model. Start by creating an experiment. You can then monitor and manage your experiment, compare experiments, or push the model to Hugging Face to share it with the community. -
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Habit Tracker
Habit Tracker for the AI Coding Workshop
Habit Tracker is a personal habit-tracking web application designed to help users build and maintain daily habits through intuitive UI and analytics that visualize progress over time. It runs locally with a FastAPI backend (Python) and a React frontend, storing all data in a lightweight SQLite database so there’s no need for user accounts or cloud storage, which keeps habit data fully private and self-contained. The app provides streak tracking and completion rates for each habit, giving users feedback on consistency and motivation by showing how often habits are completed and where they may be lagging. A calendar view lets users see a monthly grid of their habit history with color-coded days to highlight patterns and encourage daily engagement. Habit-Tracker also supports planned absences so users can skip days without breaking their streaks, reducing frustration and keeping long-term habits on track. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks. Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
Contains significant improvements, bug fixes, and additional support. Get it from the releases, or pull the master branch. This package provides a few things. A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. You also have access to the standard evaluation and prediction functions. Torchsample provides a wide range of callbacks, generally mimicking the interface found in Keras. -
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HuixiangDou
Overcoming Group Chat Scenarios with LLM-based Technical Assistance
HuixiangDou is an open-source large language model assistant designed specifically for technical question answering in group chat environments. The project addresses a common problem in developer communities where discussion channels become overwhelmed by repeated or irrelevant questions. To solve this issue, HuixiangDou implements a multi-stage pipeline that analyzes incoming messages, filters irrelevant conversations, and selectively generates responses when the assistant determines it can provide useful information. This design allows the system to participate in group discussions without flooding the chat with unnecessary messages. The assistant uses retrieval and ranking methods along with language model reasoning to produce accurate answers for technical topics such as computer vision and machine learning projects. It can be integrated into messaging platforms such as WeChat or other team collaboration tools to assist developer communities. -
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Image-Editor
AI based photo editing website for changing image background
Welcome to Image-Editor, the AI-based photo editing website that lets you change backgrounds, colors, crop, sharpen images, and much more with just a single click. With exceptional image quality and fast processing times, Image-Editor is the ultimate tool for all your photo editing needs. To get started, simply run pip install -r requirements.txt to download all the necessary libraries. Then to, create a new Django project using django-admin startproject Website1, replacing 'Website1' with the name of your choice. Image-Editor uses Python's cv2 library, which provides an easy and efficient way to work with images and videos, including a wide range of image processing and computer vision algorithms. With cv2, you can easily read, write, filter, and display images, and much more. Image-Editor uses Mediapipe's selfie_segmentation model for background removal in real-time video streams. This advanced model uses deep neural networks to detect and remove the background. -
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Indico
A feature-rich event management system
The effortless open-source tool for event organization, archival, and collaboration. Event-organization workflow that fits lectures, meetings, workshops, and conferences. A feature-rich event management system, made @ CERN, the place where the Web was born. A powerful and flexible hierarchical content management system for events, a full-blown conference organization workflow with call for Abstracts and abstract reviewing modules; flexible registration form creation and configuration; integration with existing payment systems; a paper reviewing workflow; a drag and drop timetable management interface; a simple badge editor with the possibility to print badges and tickets for participants; tools for meeting management and archival of presentation materials; a powerful room booking interface; integration with existing video conferencing solutions. -
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IntelOwl
Centralized platform for automated threat intelligence analysis
IntelOwl is an open source platform designed to manage and enrich threat intelligence data at scale. It provides a centralized environment where security analysts can gather information about suspicious files and observables such as IP addresses, domains, URLs, or hashes using a single API request. The platform integrates numerous online intelligence sources and advanced malware analysis tools, enabling users to obtain comprehensive threat intelligence without manually querying multiple services. IntelOwl was created to automate repetitive investigation tasks typically performed by security operations center (SOC) analysts, helping teams focus on deeper analysis and incident response. The system features a modular architecture built around plugins that allow new analyzers, connectors, and integrations to be added easily. These plugins can collect data from external intelligence platforms or generate insights using internal analysis tools such as YARA or static malware analyzers. -
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K8s MCP Server
K8s-mcp-server is a Model Context Protocol (MCP) server
An MCP server that enables AI assistants like Claude to securely execute Kubernetes commands, providing a bridge between language models and essential Kubernetes CLI tools for cluster management and deployments. -
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Karellen Gevent Websocket Library
Karellen Gevent Websocket Library
This is a Karellen fork of gevent-websocket. The goal of this fork is to maintain the project to support Python 3.3, 3.4 and 3.5+ as well as latest WS standards errata. -
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Kitty-Tools
Kitty-Tools is a Kahoot Cheating Client for all devices and interfaces
Kitty-Tools is a multi-platform client designed to interact with Kahoot games by providing automation and enhanced control features that go beyond the standard user experience. It is built to run across a wide range of environments including desktop systems, web interfaces, and even embedded or microcontroller-based setups, emphasizing accessibility and ease of use. The project focuses on simplifying interaction with Kahoot sessions by offering tools that can automate participation, manipulate answer selection, and streamline gameplay workflows. Its architecture allows users to connect to live game sessions and perform actions programmatically, which can be useful for testing, demonstration, or experimental purposes. The software is designed with a lightweight approach so that it can operate efficiently across devices with varying capabilities. Additionally, it includes a user-friendly interface layer that abstracts the complexity of network interactions with Kahoot servers. -
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Kopf
A Python framework to write Kubernetes operators
Kopf —Kubernetes Operator Pythonic Framework, is a framework and a library to make Kubernetes operator's development easier, just in a few lines of Python code. The main goal is to bring the Domain-Driven Design to the infrastructure level, with Kubernetes being an orchestrator/database of the domain objects (custom resources), and the operators containing the domain logic (with no or minimal infrastructure logic).
