Open Source Python Software Development Software - Page 8
Sort By:
Python Software Development Software
View 5979 business solutionsBrowse free open source Python Software Development Software and projects below. Use the toggles on the left to filter open source Python Software Development Software by OS, license, language, programming language, and project status.
-
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.
-
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
1
Ralph
Ralph is the CMDB / Asset Management system for data center
Ralph is built on top of Django and Python 3 and is easy to extend and customize without writing boilerplate code. REST API, Workflows code extensions allow for easy customization. We've chosen the best features of DCIM, Asset Mgmt and CMDB systems to create one, easy and well-integrated system. One interface is easier than 3. Keep track of assets purchases and their life cycle. Flexible flow system for assets life cycle. Data center and back office support. DC visualization built-in. Ralph is a simple yet powerful Asset Management, DCIM and CMDB system for data center and back office. -
2
SQL Explorer
Easily share data across your company via SQL queries
SQL Explorer aims to make the flow of data between people fast, simple, and confusion-free. It is a Django-based application that you can add to an existing Django site, or use as a standalone business intelligence tool. Quickly write and share SQL queries in a simple, usable SQL editor, preview the results in the browser, share links, download CSV, JSON, or Excel files (and even expose queries as API endpoints, if desired), and keep the information flowing! Comes with support for multiple connections, to many different SQL database types, a schema explorer, query history (e.g. lightweight version control), a basic security model, in-browser pivot tables, and more. SQL Explorer values simplicity, intuitive use, unobtrusiveness, stability, and the principle of least surprise. SQL Explorer is inspired by any number of great query and reporting tools out there. -
3
Slither
Static Analyzer for Solidity
Slither is a Solidity static analysis framework written in Python 3. It runs a suite of vulnerability detectors, prints visual information about contract details, and provides an API to easily write custom analyses. Slither enables developers to find vulnerabilities, enhance their code comprehension, and quickly prototype custom analyses. Slither is the first open-source static analysis framework for Solidity. Slither is fast and precise; it can find real vulnerabilities in a few seconds without user intervention. It is highly customizable and provides a set of APIs to inspect and analyze Solidity code easily. We use it in all of our security reviews. Now you can integrate it into your code-review process. We are open sourcing the core analysis engine of Slither. This core provides advanced static-analysis features, including an intermediate representation (SlithIR) with taint tracking capabilities on top of which complex analyses (“detectors”) can be built. -
4
colleague-skill
Transform a cold separation into a warm Skill
colleague-skill is a specialized agent skill designed to simulate a collaborative teammate within AI-driven workflows, enabling agents to behave more like human colleagues in problem-solving scenarios. The project focuses on enhancing interaction quality by introducing role-based behavior, contextual awareness, and cooperative task execution. It allows agents to provide suggestions, feedback, and alternative approaches, mimicking real-world collaboration dynamics. The system likely integrates with broader agent frameworks, enabling seamless inclusion in multi-agent environments. It emphasizes communication and coordination, ensuring that agents can contribute meaningfully to shared objectives. This makes it particularly useful for complex projects where multiple perspectives or iterative refinement are needed. -
Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
5
ggshield
Detect and validate 500+ types of hardcoded secrets
GitGuardian’s ggshield is an open-source command-line interface (CLI) tool designed to help developers and security teams detect hardcoded secrets and sensitive credentials early in the development process, either locally or in CI/CD pipelines. It scans source code, configuration files, commit history, and other artifacts to automatically detect hundreds of different secret types — such as API keys, tokens, and passwords — helping prevent accidental leaks before they reach version control or production environments. ggshield can be used interactively on a developer’s machine, integrated as a pre-commit or pre-push git hook, and run as part of automated build or merge workflows to enforce security policies consistently across teams. It works across major operating systems using Python, and offers standalone packaged binaries for environments where Python isn’t available, making it adaptable to a wide range of developer setups. -
6
git-cola
git-cola: The highly caffeinated Git GUI
Git Cola is a sleek and powerful graphical user interface for Git. Git Cola is free software and written in Python (v2 + v3). Git Cola uses QtPy, so you can choose between PyQt6, PyQt5 and PySide2 by setting the QT_API environment variable to pyqt6, pyqt5 or pyside2 as desired. qtpy defaults to pyqt6 and falls back to pyqt6 and pyside2 if pyqt5 is not installed. Git Cola enables additional features when the following Python modules are installed. send2trash enables cross-platform "Send to Trash" functionality. Never run pip install or make install as root or outside of a Python virtualenv! If you don't have PyQt installed then the easiest way to get it is to use a Python virtualenv and install Git Cola into it in "editable" mode. This install method lets you upgrade Git Cola by running git pull. -
7
nbstripout
strip output from Jupyter and IPython notebooks
Opens a notebook, strips its output, and writes the outputless version to the original file. Useful mainly as a git filter or pre-commit hook for users who don't want to track output in VCS. This does mostly the same thing as the Clear All Output command in the notebook UI. You can download and install the latest version of nbstripout from PyPI, the Python package index. Strip output from IPython / Jupyter / Zeppelin notebook (modifies the file in-place). Usually, nbstripout is installed per repository so you can choose where to use it or not. You can choose to set the attributes in .gitattributes and commit this file to your repository, however there is no way to have git set up the filters automatically when someone clones a repository. This is by design, to prevent you from executing arbitrary and potentially malicious code when cloning a repository. -
8
AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. This project is licensed under the Apache-2.0 License. Ensure you have access to an AWS account i.e. setup your environment such that awscli can access your account via either an IAM user or an IAM role. -
9
Amazon Braket Python SDK
A python SDK for interacting with quantum devices on Amazon Braket
The Amazon Braket Python SDK is an open-source library to design and build quantum circuits, submit them to Amazon Braket devices as quantum tasks, and monitor their execution. Before you begin working with the Amazon Braket SDK, make sure that you've installed or configured the following prerequisites. Download and install Python 3.7.2 or greater from Python.org. As a managed service, Amazon Braket performs operations on your behalf on the AWS hardware that is managed by Amazon Braket. Amazon Braket can perform only operations that the user permits. You can read more about which permissions are necessary in the AWS Documentation. The Braket Python SDK should not require any additional permissions aside from what is required for using Braket. However, if you are using an IAM role with a path in it, you should grant permission for iam:GetRole. -
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.
-
10
Buildbot
Python-based continuous integration testing framework
Buildbot is an open-source framework for automating software build, test, and release processes. At its core, Buildbot is a job scheduling system: it queues jobs, executes the jobs when the required resources are available, and reports the results. Your Buildbot installation has one or more masters and a collection of workers. The masters monitor source-code repositories for changes, coordinate the activities of the workers, and report results to users and developers. Workers run on a variety of operating systems. You configure Buildbot by providing a Python configuration script to the master. This script can be very simple, configuring built-in components, but the full expressive power of Python is available. This allows dynamic generation of configuration, customized components, and anything else you can devise. The framework itself is implemented in Twisted Python, and compatible with all major operating systems. -
11
Cassowary
Run Windows Applications on Linux as if they are native
Run Windows Applications on Linux as if they are native, Use Linux applications to launch files located in the windows vm without needing to install applications on vm. With easy-to-use configuration GUI. -
12
DeepSea
The new All-in-One CFW package for the Nintendo Switch
The new All-in-One CFW package for the Nintendo Switch. Background FTP server for filetransfers. Install NSP, NSZ, XCI & XCZ files from Harddrive, Google Drive, WLAN or wired through PC, Smartphone, etc. Over & underclocking. Update OFW & CFW through homebrew. Find new homebrew through the Appstore. Savegame management. Cheating in games (please don't cheat online) Emulate Amiibo. Use all kinds of 3rd party controllers. Lan play (like Hamachi for your Switch) Tesla overlay to control all those features (press L1+DpadDown+RightStick). -
13
DocTR
Library for OCR-related tasks powered by Deep Learning
DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document datasets, comparable with GoogleVision/AWS Textract. Easy integration (available templates for browser demo & API deployment). End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). As such, you can select the architecture used for text detection, and the one for text recognition from the list of available implementations. -
14
My Python Eggs
Python Examples
My Python Eggs, commonly associated with the geekcomputers Python repository, is a large collection of practical Python scripts and small programs created primarily for experimentation, automation, and educational purposes. Rather than being a single cohesive application, it functions as a repository of utilities that demonstrate how Python can be used to solve everyday problems and automate repetitive tasks. The scripts cover a wide range of topics, including file management, networking, system monitoring, web scraping, and even simple games, making it a versatile learning resource. Many of the programs are designed to reduce manual workload by automating tasks such as renaming files, scanning directories, or checking system information. The repository also includes examples of more advanced concepts like multithreading, API interaction, and GUI development, providing a gradual learning curve for beginners. -
15
NiceGUI
Create web-based user interfaces with Python
NiceGUI is a Python-based UI framework that enables developers to create interactive web applications using only Python code. It abstracts away the complexities of HTML, CSS, and JavaScript, allowing for rapid development of web interfaces directly from Python scripts. NiceGUI is suitable for building dashboards, control panels, and other web-based tools, especially in contexts like robotics and data visualization. -
16
OPI
OBS Package Installer (CLI)
OPI is a command-line tool designed to simplify the installation of software packages from the openSUSE Build Service (OBS) and other repositories. It streamlines the process of searching for and installing packages, making software management more accessible for openSUSE users. -
17
PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research
Scale your models, not your boilerplate with PyTorch Lightning! PyTorch Lightning is the ultimate PyTorch research framework that allows you to focus on the research while it takes care of everything else. It's designed to decouple the science from the engineering in your PyTorch code, simplifying complex network coding and giving you maximum flexibility. PyTorch Lightning can be used for just about any type of research, and was built for the fast inference needed in AI research and production. When you need to scale up things like BERT and self-supervised learning, Lightning responds accordingly by automatically exporting to ONNX or TorchScript. PyTorch Lightning can easily be applied for any use case. With just a quick refactor you can run your code on any hardware, run distributed training, perform logging, metrics, visualization and so much more! -
18
Python LSP Server
Fork of the python-language-server project
Fork of the python-language-server project, maintained by the Spyder IDE team and the community. A Python 3.7+ implementation of the Language Server Protocol. The base language server requires Jedi to provide Completions, Definitions, Hover, References, Signature Help, and Symbols. Like all language servers, the configuration can be passed from the client that talks to this server (i.e. your editor/IDE or another tool that has the same purpose). The details of how this is done depend on the editor or plugin that you are using to communicate with python-lsp-server. The configuration options are available at that level are documented in CONFIGURATION.md. Overall configuration is computed first from user configuration (in home directory), overridden by configuration passed in by the language client, and then overridden by configuration discovered in the workspace. -
19
SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
20
Sentry
Cross-platform application monitoring and error tracking software
Sentry is a cross-platform, self-hosted error monitoring solution that helps software teams discover, monitor and fix errors in real-time. The most users and logs will have to provide are the clues, and Sentry provides the answers. Sentry offers enhanced application performance monitoring through information-laden stack traces. It lets you build better software faster and more efficiently by showing you all issues in one place and providing the trail of events that lead to errors. It also provides real-time monitoring and data visualization through dashboards. Sentry’s server is in Python, but its API enables for sending events from any language, in any application. More than fifty-thousand companies already ship better software faster thanks to Sentry; let yours be one of them! -
21
Spack
A flexible package manager that supports multiple versions
A flexible package manager supporting multiple versions, configurations, platforms, and compilers. Spack is a package manager for supercomputers, Linux, and macOS. It makes installing scientific software easy. Spack isn’t tied to a particular language; you can build a software stack in Python or R, link to libraries written in C, C++, or Fortran, and easily swap compilers or target specific microarchitectures. Spack offers a simple "spec" syntax that allows users to specify versions and configuration options. Package files are written in pure Python, and specs allow package authors to write a single script for many different builds of the same package. With Spack, you can build your software all the ways you want to. -
22
aeneas
Automagically synchronize audio and text (aka forced alignment)
aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment). aeneas automatically generates a synchronization map between a list of text fragments and an audio file containing the narration of the text. In computer science this task is known as (automatically computing a) forced alignment. -
23
attrs
Python Classes Without Boilerplate
attrs is a Python package that lets you write classes without all the usual drudgery. Its ultimate goal is to help you write concise and correct software without slowing down your code. attrs provides a class decorator and a means to declaratively define the attributes on that class. This results in a concise and explicit overview of the class's attributes, a human-readable __repr__, a complete set of comparison methods and more, all without having to repetitively write dull boilerplate code and without negatively affecting your runtime performance. With attrs you can write correct and self-documenting code, and you'll find joy in writing classes again! -
24
ModbusPal - a Java MODBUS simulator
Modbus slave simulator
ModbusPal is a project to develop a PC-based Modbus simulator. Its goal is to reproduce a realistic environment, with many slaves and animated register values. Almost everything in ModbusPal can be customized and controlled by scripts. -
25
PyInstaller Extractor
Extract contents of a Windows executable file created by pyinstaller
MIGRATED TO GITHUB https://github.com/extremecoders-re/pyinstxtractor This is a python script to extract the contents of a PyInstaller generated Windows executable file. The contents of the pyz file (usually pyc files) present inside the executable are also extracted. The pyc files are made valid so that a python bytecode decompiler will recognise it. Script can run on both python 2.x and 3.x Pyinstaller versions 2.0, 2.1, 3.0, 3.1 and 3.2 are supported
