Open Source Python Software Development Software - Page 12
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Python Software Development Software
View 6284 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.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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One Monitoring Tool for IT, OT and Cloud | Free TrialModern infrastructure spans data centers, cloud platforms and factory floors, and every blind spot between them is a risk. PRTG supports SNMP, WMI, SSH and other standard protocols to monitor IT, OT and hybrid environments through one customizable dashboard. Build the views your team needs, from network health to application performance, without switching tools. Try PRTG free for 30 days now.
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Websoft9
Applications self-hosting and DevOps platform
Websoft9 is a web-based PaaS and Linux panel for self-hosting open-source applications on a user’s own server. It is designed for running many web apps on a single machine instead of requiring a complex Kubernetes-first architecture. The platform provides a graphical interface for installing, managing, and operating more than 200 application templates. It combines application deployment with server administration tools, so users can manage files, containers, users, proxies, SSL, logs, storage, firewalls, and system services from one place. Websoft9 uses familiar technologies such as Docker, Docker Compose, Nginx, Cockpit, Kubernetes, and Swarm rather than introducing a completely new stack. It is useful for individuals, teams, and organizations that want lightweight self-hosting with DevOps-style controls and less manual configuration. -
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any-llm
Communicate with an LLM provider using a single interface
any-llm is a unified SDK and platform developed by Mozilla AI that allows developers to interact with multiple large language model providers through a single, consistent interface. Instead of rewriting code for each provider, developers can switch between services like OpenAI, Anthropic, Mistral, and Ollama simply by changing configuration parameters. The project is designed to be framework-agnostic, making it easy to integrate into scripts, applications, or production systems without being tied to a specific stack. It includes a core SDK for direct usage as well as an optional gateway layer that adds enterprise features such as budget management, API key control, usage analytics, and multi-tenant support. The gateway exposes an OpenAI-compatible API, allowing existing tools and workflows to connect without modification. The system emphasizes developer experience with clear error handling, strong typing, and minimal configuration overhead. -
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bitnet.cpp
Official inference framework for 1-bit LLMs
bitnet.cpp is the official open-source inference framework and ecosystem designed to enable ultra-efficient execution of 1-bit large language models (LLMs), which quantize most model parameters to ternary values (-1, 0, +1) while maintaining competitive performance with full-precision counterparts. At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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gpt-engineer
Full stack AI software engineer
gpt-engineer is an open-source platform designed to help developers automate the software development process using natural language. The platform allows users to specify software requirements in plain language, and the AI generates and executes the corresponding code. It can also handle improvements and iterative development, giving users more control over the software they’re building. Built with a terminal-based interface, gpt-engineer is customizable, enabling developers to experiment with AI-assisted programming and refine their development process. It is especially useful for automating the coding and iterative feedback loop in software development. -
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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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. Typical use cases include turning analytical utilities—like probability mass functions, activation formulas, or recurrence relations—into equations suitable for papers, notebooks, and slide decks. The tool aims to preserve semantics such as exponentiation, summations, products, piecewise definitions, and function application while hiding Pythonic scaffolding. Users can control rendering details for names and operators so the output conforms to a project’s notation style. Because it works directly from Python source, it enables a tight authoring loop. -
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n8n Workflows
All of the workflows of n8n i could find (also from the site itself)
This repository aggregates a well-organized collection of community-submitted automation workflows built for n8n, a fair-code, self-hostable automation tool. It serves as an index for discovering ready-made flows to automate tasks across multiple services and platforms. 2,057 workflows with meaningful, searchable names. 365 unique integrations across popular platforms. 29,445 total nodes with professional categorization. Quality assurance - All workflows analyzed and categorized. -
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ruff-pre-commit
A pre-commit hook for Ruff
ruff-pre-commit is a lightweight integration that enables the Ruff linter and formatter to run automatically as part of Git pre-commit hooks, ensuring code quality checks are enforced before changes are committed to a repository. It is distributed as a standalone repository to simplify installation and allow developers to use prebuilt wheels directly from package managers. By integrating Ruff into the pre-commit workflow, it helps maintain consistent coding standards across teams by automatically detecting and optionally fixing issues before code reaches the repository. The tool supports both linting and formatting hooks, allowing developers to configure workflows that enforce style and correctness in a single step. It also provides flexibility in configuration, including selective file type targeting and optional automatic fixes through command-line arguments. -
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The CvsGui project is providing several high-end interface clients (WinCvs, MacCvs, gCvs) written in C++ using popular frameworks (MFC, PowerPlant, GTK+) with a scripting support via Python or TCL.
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pythondialog
Python module to build dialogs for terminal-based applications
This is a Python module for doing terminal-based user interaction. It wraps the dialog/Xdialog program, and provides a nice, object-oriented programming model. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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GtkGLExt is an OpenGL extension to GTK+. It provides additional GDK objects which support OpenGL rendering in GTK+, and GtkWidget API add-ons to make GTK+ widgets OpenGL-capable.
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PC-BASIC - a GW-BASIC emulator
A free, cross-platform emulator for GW-BASIC, PCjr & Tandy BASIC
PC-BASIC is a free, cross-platform interpreter for GW-BASIC, BASICA, PCjr Cartridge Basic and Tandy 1000 GWBASIC. -- This page is an archive and hosts the legacy version 1.2 of PC-BASIC only. -- For documentation and the latest releases, please see the official homepage: www.pc-basic.org Four source code, discussions and to report bugs please see the GitHub project page https://github.com/robhagemans/pcbasic -
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ZK is an open-source Java framework for building modern web and mobile applications. It enables developers to create rich, interactive UIs using only Java — no JavaScript required. With 200+ Ajax-powered components, event-driven architecture, and support for popular technologies like Spring, Java EE, and JSP/JSF, ZK makes it simple to deliver powerful and user-friendly web applications.
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AI Chatbot Framework
Python chatbot framework with Natural Language Understanding
Building a chatbot can sound daunting, but it’s totally doable. AI Chatbot Framework is an AI powered conversational dialog interface built in Python. With this tool, it’s easy to create Natural Language conversational scenarios with no coding efforts whatsoever. The smooth UI makes it effortless to create and train conversations to the bot and it continuously gets smarter as it learns from conversations it has with people. AI Chatbot Framework can live on any channel of your choice (such as Messenger, Slack etc.) by integrating it’s API with that platform. You don’t need to be an expert at artificial intelligence to create an awesome chatbot that has AI capabilities. With this boilerplate project you can create an AI-powered chatting machine in no time. -
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Ansible Examples
A few starter examples of ansible playbooks, to show features
This repository collects practical, real-world examples of using Ansible to automate infrastructure, deployments, and configurations. Each directory demonstrates a specific use case—ranging from setting up web servers, load balancers, and databases to orchestrating multi-tier applications in cloud environments. The examples highlight common Ansible practices such as organizing inventories, writing reusable playbooks, using roles, and handling variables and templates. They’re designed to be adapted directly into your own infrastructure or to serve as reference blueprints when learning how to structure automation projects. Whether you’re managing a handful of servers or deploying at scale, this repo provides starting points that illustrate how Ansible can streamline repetitive operational tasks. -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production may be older. As any other linter, it is opinionated. Still, its rules are the result of community contributions and they can always be disabled based individually or by category by each user. ansible-lint checks playbooks for practices and behavior that could potentially be improved. As a community-backed project ansible-lint supports only the last two major versions of Ansible. -
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Azure SDK for Python
Active development of the Azure SDK for Python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs or our versioned developer docs. For your convenience, each service has a separate set of libraries that you can choose to use instead of one, large Azure package. To get started with a specific library, see the README.md (or README.rst) file located in the library's project folder. Last stable versions of packages that have been provided for usage with Azure and are production-ready. These libraries provide you with similar functionalities to the Preview ones as they allow you to use and consume existing resources and interact with them, for example: upload a blob. They might not implement the guidelines or have the same feature set as the November releases. They do however offer wider coverage of services. A new set of management libraries that follow the Azure SDK Design Guidelines for Python are now available. -
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Bandit
Bandit is a tool designed to find common security issues in Python
Bandit is a tool designed to find common security issues in Python code. To do this, Bandit processes each file, builds an AST from it, and runs appropriate plugins against the AST nodes. Once Bandit has finished scanning all the files, it generates a report. Bandit was originally developed within the OpenStack Security Project and later rehomed to PyCQA. -
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Black
The uncompromising Python code formatter
Black is the uncompromising Python code formatter that takes total control over your Python code formatting in the best way. Black delivers speed and certainty, while freeing you from constant pycodestyle nagging. Your code review becomes a lot faster and easier, so you can spend more time and mental energy on more important things. Blackened code makes the smallest diffs possible and looks the same no matter the project. Its formatting eventually becomes transparent, so you can simply forget about it and focus on your task at hand. Black has been successfully used in many projects, and has gained stellar user reviews as an exceptional, uncompromising PEP 8 compliant opinionated formatter. -
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Django RQ
A simple app that provides django integration for RQ
A simple app that provides django integration for RQ (Redis Queue). Django integration with RQ, a Redis-based Python queuing library. Django-RQ is a simple app that allows you to configure your queues in django's settings.py and easily use them in your project. Django-RQ allows you to easily put jobs into any of the queues defined in settings.py. You can provide your own singleton Redis connection object to this function so that it will not create a new connection object for each queue definition. If you have django-redis or django-redis-cache installed, you can instruct django_rq to use the same connection information from your Redis cache. This has two advantages, it's DRY and it takes advantage of any optimization that may be going on in your cache setup (like using connection pooling or Hiredis.) -
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Django jazzmin
Jazzy theme for Django
Welcome to Jazzmin, intended as a drop-in app to jazz up your django admin site, with plenty of things you can easily customize, including a built-in UI customizer. 4 different Change form templates (horizontal tabs, vertical tabs, carousel, collapsible). Bootstrap 4 modal (instead of the old popup window, optional). Search bar for any given model admin. Customizable UI (via Live UI changes, or custom CSS/JS). Select2 drop-downs. Bootstrap 4 & AdminLTE UI components. You can add links to the user menu on the top right of the screen using the "usermenu_links" settings key, the format of these links is the same as with top menu, though submenus via "app" are not currently supported and will not be rendered. The side menu gets a list of all installed apps and their models that have admin classes, and creates a tree of apps and links to model admin pages. -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints. -
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Eel
A Python library for making simple Electron-like HTML/JS GUI apps
Eel is a little Python library for making simple Electron-like offline HTML/JS GUI apps, with full access to Python capabilities and libraries. Eel hosts a local webserver, then lets you annotate functions in Python so that they can be called from Javascript, and vice versa. Eel is designed to take the hassle out of writing short and simple GUI applications. If you are familiar with Python and web development, probably just jump to this example which picks random file names out of the given folder (something that is impossible from a browser). There are several options for making GUI apps in Python, but if you want to use HTML/JS (in order to use jQueryUI or Bootstrap, for example) then you generally have to write a lot of boilerplate code to communicate from the Client (Javascript) side to the Server (Python) side. -
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FISSURE
The RF and reverse engineering framework for everyone
FISSURE is an open-source radio frequency analysis and signal intelligence framework built to support software-defined radio research, wireless security experimentation, and protocol reverse engineering. The project brings together tools for capturing, inspecting, decoding, replaying, and analyzing RF signals across a wide range of wireless technologies. It is designed as a practical environment for researchers and operators who need to move from raw spectrum observation to structured investigation without stitching together too many separate utilities by hand. The platform supports workflows related to signal discovery, demodulation, packet inspection, fuzzing, and attack simulation, making it useful for both defensive research and controlled lab testing. Its architecture is oriented toward extensibility, so users can integrate additional hardware, signal-processing components, and protocol-specific modules depending on their needs. -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share. One of the best ways to share your machine learning model, API, or data science workflow with others is to create an interactive demo that allows your users or colleagues to try out the demo in their browsers.
