Open Source Python Software Development Software for ChromeOS
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Python Software Development Software for ChromeOS
Browse free open source Python Software Development Software for ChromeOS and projects below. Use the toggles on the left to filter open source Python Software Development Software for ChromeOS by OS, license, language, programming language, and project status.
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Stay in Flow. Let Zenflow Handle the Heavy Lifting.Zenflow is your engineering control center, turning specs into shipped features. Parallel agents handle coding, testing, and refactoring with real repo context. Multi-agent workflows remove bottlenecks and automate routine work so developers stay focused and in flow.
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Auth0 for AI Agents now in GAConnect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
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Both forward-chaining and backward-chaining rules (which may include python code) are compiled into python. Can also automatically assemble python programs out of python functions which are attached to backward-chaining rules. See pyke.sourceforge.ne
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Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.
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Groq Python
The official Python Library for the Groq API
Groq Python is the official Python SDK for the Groq REST API, giving Python developers straightforward access to Groq’s LLM, chat, audio, and other AI services. Through this library, you can call Groq’s models from Python code — for example to request chat completions, code generation, transcription, or any supported endpoint — using idiomatic Python syntax. The SDK handles authentication (via environment variable or parameter), defines proper type-safe request/response data types, and supports both synchronous and asynchronous usage patterns depending on your application needs. This makes it easy to integrate Groq-powered AI capabilities into backend services, data pipelines, research notebooks, or applications written in Python. For those building AI-based tooling, automation scripts, or ML-backed backends, groq-python abstracts away HTTP request plumbing and exposes a clean API, accelerating development and reducing boilerplate. -
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. Based on one of the most widely-used Python tensor libraries: Theano. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
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LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
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learn2learn
A PyTorch Library for Meta-learning Research
Learn2Learn is a PyTorch-based library focused on meta-learning and few-shot learning research. It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. The materials are written for developers but remain friendly to newcomers, with clear setup instructions and minimal boilerplate. Because the repo is live and maintained, lessons are updated as the SDK and models evolve, and issues are used to track fixes, clarifications, and new modules. -
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Evolving Objects
This project have been merged within Paradiseo.
See the new project page: https://nojhan.github.io/paradiseo/ (Archived project page: http://eodev.sourceforge.net/) -
Leverage AI to Automate Medical CodingAs a healthcare provider, you should be paid promptly for the services you provide to patients. Slow, inefficient, and error-prone manual coding keeps you from the financial peace you deserve. XpertDox’s autonomous coding solution accelerates the revenue cycle so you can focus on providing great healthcare.
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Command Line Parser GetPot
Tool to parse the command line and configuration files.
Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more. -
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FEAR is a development kit helping software engineers create advanced artificial intelligence in synthetic characters with simplicity and efficiency. The project includes reusable AI components, portable framework and interfaces to realtime 3D games.
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NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.
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Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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Using this plugin-based framework, you can instantly start working on the *brain* of your bot (irc bot, chatterbot, robot, ...). With support for db, irc, logging and programming-language independent plugins, users can easily enhance the functionality.
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.
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Decision Analysis is an easily-extensible expert system to help users make decisions of all types. Written entirely in Python, Decision Analysis, at this time, contains a general decsion module, which uses a weighted average technique to evaluate use
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Err
err is a plugin based chatbot designed to be easily extensible
err is a plugin based chatbot designed to be easily deployable, extensible and maintainable. It allows you to start scripts interactively from your chatrooms for any reason: random humour, starting a build, monitoring commits, triggering alerts ... It is really easy to add your own feature. Features Backends support: - XMPP : Tested with hipchat, openfire and Jabber but should be compatible with any standard XMPP servers. - CampFire support - Supports MUCs (chatrooms) - Local Graphical Console (for testing/dev) - Local Text Console (for testing/dev) -
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FRODO 2
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA... -
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ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code. Now ffnet has also a GUI called ffnetui.
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FlowLens MCP
Open-source MCP server that gives your coding agent
FlowLens MCP Server is an open-source tool designed to give AI-powered coding agents (like Claude Code, Cursor, GitHub Copilot / Codex, and others) full, replayable browser context to dramatically improve debugging, bug reporting, and regression testing for web applications. It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console logs, DOM events, storage changes, and more, and exports it. The MCP server then loads this captured “flow” and exposes it to the AI agent via the Model Context Protocol (MCP), letting the agent examine, search, filter, and reason about the session just as a human developer would, without needing the agent to re-run the flow or rely on minimal reproduction data (logs, screenshots). -
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GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
An open source implementation of OpenAI's ChatGPT Code interpreter. Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services. -
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Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The
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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
