Open Source Python Software Development Software - Page 38
Sort By:
Python Software Development Software
View 6286 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.
-
Build Data Resilience - Take the Assessment TodayIs your recovery strategy as strong as you think? Take this quick self-assessment to check your recovery readiness and gain tailored insights. In only 2 minutes, you'll learn where you fall on the recovery readiness scale.
-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
1
Alfred-Workflow
Full-featured library for writing Alfred 3 & 4 workflows
Alfred-Workflow is a Python helper library for Alfred 2, 3 and 4 workflow authors, developed and hosted on GitHub. Alfred workflows typically take user input, fetch data from the Web or elsewhere, filter them and display results to the user. Alfred-Workflow takes care of a lot of the details for you, allowing you to concentrate your efforts on your workflow’s functionality. Alfred-Workflow supports macOS 10.7+ (Python 2.7). Easily launch background tasks (daemons) to keep your workflow responsive. Check for and install new workflow versions using GitHub releases. Post notifications with Notification Center (10.8+ only) Error handling and logging for easier development and support. “Magic” arguments to help development, debugging and management of the workflow. -
2
AlfredWorkflow.com
A public Collection of Alfred Workflows
AlfredWorkflow.com is a public collection of workflows created for Alfred 2 on macOS. It was designed to help users search, install, share, and discover automations without building each workflow from scratch. The repository contains hundreds of downloadable workflows alongside source code that developers can study. A Workflow Searcher lets users query the collection directly from Alfred. The project also exposes workflow metadata through a JSON API for programmatic access. GitHub-hosted backup downloads, contribution mechanisms, and update support make the repository both a workflow archive and a development resource for the Alfred community. -
3
Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset. -
4
An AI sandbox for learning game programming. It includes an integrated development environment, and functionality for rapid development of virtual characters.
-
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
5
All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts. -
6
It is a Process Monitoring Toolkit with the ability of Extend. E.g. You can use it to monitor a specified process. And set a triger like (over 50MB mem use) then do something like (report to DB).
-
7
AlluraSkel
Allura Tool Skeletons
Grab this to try your hand at developing a tool for Allura. Currently supports: - python package setup - simple per-project-installable Tool - simple Ming/MongoDB model - simple TurboGears Controllers - simple Jinja templates -
8
AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking, nonequivalence, and recombination. These contain implementations of the discovered matrix multiplication algorithms, tools to benchmark their real-world performance, proofs of nonequivalence among thousands of solutions, and methods for decomposing larger problems into smaller factorizations. Users can explore AlphaTensor’s discovered algorithms interactively using Colab notebooks or Python scripts. -
9
Alphafold
Open source code for AlphaFold
This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions. The total download size for the full databases is around 415 GB and the total size when unzipped is 2.2 TB. Please make sure you have a large enough hard drive space, bandwidth and time to download. We recommend using an SSD for better genetic search performance. -
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.
-
10
AlsaPlayer-Python is a set of python bindings. It allows you to write custom interfaces for AlsaPlayer using the full power of the Python language. Also provided is a Python module to control AlsaPlayer from external programs.
-
11
Our goal is to develop a full working solver for ATA (with 1 clock) in Python, with MTL to ATA support. The decidability for the emptiness problem was proposed by Lasota and Walukiewicz. The MTL to ATA was proposed by Ouaknine and Worrell.
-
12
Amazon Braket Ocean Plugin
A Python plugin for using Ocean with Amazon Braket
The Amazon Braket Ocean Plugin is an open-source library in Python that provides a framework that you can use to interact with Ocean tools on top of Amazon Braket. Before you begin working with the Amazon Braket Ocean Plugin, make sure that you've installed or configured the following prerequisites. Download and install Python 3.7.2 or greater from Python.org. If you are using Windows, choose Add Python to environment variables before you begin the installation. Make sure that your AWS account is onboarded to Amazon Braket. The Amazon Braket Ocean Plugin can be installed with pip. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. This package provides samplers which use Braket solvers. These samplers extend abstract base classes provided in Ocean's dimod and thus have the same interfaces as other samplers in Ocean. -
13
Amazon Braket PennyLane Plugin
A plugin for allowing Xanadu PennyLane to use Amazon Braket devices
The Amazon Braket PennyLane plugin offers two Amazon Braket quantum devices to work with PennyLane. The Amazon Braket Python SDK is an open-source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations. Once the Pennylane-Braket plugin is installed, the provided Braket devices can be accessed straight away in PennyLane, without the need to import any additional packages. While the local device helps with small-scale simulations and rapid prototyping, the remote device allows you to run larger simulations or access quantum hardware via the Amazon Braket service. -
14
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. -
15
Amazon Braket Python Schemas
A library that contains schemas for Amazon Braket
Amazon Braket Python Schemas is an open source library that contains the schemas for Braket, including intermediate representations (IR) for Amazon Braket quantum tasks and offers serialization and deserialization of those IR payloads. Think of the IR as the contract between the Amazon Braket SDK and Amazon Braket API for quantum programs. Schemas for the S3 results of each quantum task. Schemas for the device capabilities of each device. The preferred way to get Amazon Braket Python Schemas is by installing the Amazon Braket Python SDK, which will pull in the schemas. You can install from source by cloning this repository and running a pip install command in the root directory of the repository. There are currently two types of IR, including jaqcd (JsonAwsQuantumCircuitDescription) and annealing. -
16
Amazon Braket Strawberry Fields Plugin
An open source framework for using Amazon Braket devices
An open-source framework for using Amazon Braket devices with the Strawberry Fields photonic device programming library. This plugin provides a BraketEngine class for running photonic quantum circuits created in Strawberry Fields on the Amazon Braket service. The Amazon Braket Python SDK is an open source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. This plugin provides the classes BraketEngine for submitting photonic circuits to Amazon Braket and BraketJob for tracking the status of the Braket task. Strawberry Fields is an open source library for writing and running programs for photonic quantum computers. BraketEngine and BraketJob have the same interfaces as RemoteEngine in Strawberry Fields and Job in the Xanadu Cloud Client, respectively, and can be used as drop-in replacements. -
17
-
18
APLEpy stands for Algebraic Programming Language Extension for Python. It is an open source alternative to commercial products such as AMPL and GAMS. It offers the same high level of abstraction while keeping the advantages of using Python environment.
-
19
AnAviPreviewGUI is an avi preview GUI for the command line tool AviPreviewC (from www.funkster.org) AviPreviewC make avi from tempory file ".part" of eDonkey software. The GUI set size, frame, codec, resolution, ... before sand command to create preview.
-
20
These are Python bindings for Anjuta DevStudio version 2. This allows you to write Anjuta plugins in Python.
-
21
In Systems Biology models are created in various formats (Matlab, Java, C/C++, Python, ...). "Annotate Your Model" will help you to link your model to biological web resources by creating a CSV file containing MIRIAM annotations.
-
22
Anomalib
An anomaly detection library comprising state-of-the-art algorithms
Anomalib is an open-source deep learning library focused on anomaly detection and localization tasks, collecting state-of-the-art algorithms and tools under one modular framework. It provides implementations of leading anomaly detection methods drawn from current research, as well as a full set of utilities for training, evaluating, benchmarking, and deploying these models on both public and private datasets. Anomalib emphasizes flexibility and reproducibility: you can use its simple APIs to plug in custom models, track experiments, tune hyperparameters, and generate visualizations that highlight anomalous regions. Its design supports unsupervised or semi-supervised paradigms, making it especially powerful for scenarios where only “normal” data is readily available and defects must be detected without exhaustive labeling. Combined with its CLI and integration with optimization tools like OpenVINO, it’s suitable for both research and edge deployment tasks. -
23
This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.
-
24
-
25
Ansible AUR
Ansible module to manage packages from the AUR
ansible-aur is an Ansible module designed to manage packages from the Arch User Repository (AUR). It supports various AUR helpers such as yay, paru, pacaur, trizen, pikaur, and aurman, automatically selecting the first available helper or falling back to makepkg if none are found. This module facilitates the installation and upgrading of AUR packages within Ansible playbooks, streamlining package management for Arch Linux systems.
