Open Source Python Software - Page 80
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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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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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Agent Starter Pack
Ship AI Agents to Google Cloud in minutes, not months
Agent Starter Pack is a production-focused framework that provides pre-built templates and infrastructure for rapidly developing and deploying generative AI agents on Google Cloud. It is designed to eliminate the complexity of moving from prototype to production by bundling essential components such as deployment pipelines, monitoring, security, and evaluation tools into a single package. Developers can create fully functional agent projects with a single command, generating both backend and frontend structures along with deployment-ready configurations. The framework supports multiple agent architectures, including ReAct, retrieval-augmented generation, and multi-agent systems, allowing flexibility across use cases. It integrates tightly with Google Cloud services like Vertex AI, Cloud Run, and Terraform-based infrastructure provisioning, enabling scalable and reliable deployments. -
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AgentHandover
AgentHandover observes, learns and teaches agents with skills
AgentHandover is a Mac-focused system that observes how a user works and converts repeated workflows into reusable, self-improving skills for AI agents. It is designed for tools such as Claude Code, OpenClaw, Codex, Hermes, Cursor, Windsurf, and other MCP-compatible environments. Instead of asking users to manually write long prompts or static automation instructions, it records real actions, infers decision logic, and produces skills that include steps, strategy, guardrails, selection criteria, and writing style. The project supports both focused recording for specific tasks and passive discovery for workflows that appear repeatedly over time. It stores learned knowledge locally and uses feedback from later executions to improve confidence, add decision branches, and demote stale or failing skills. Its main value is helping agents learn how a person actually works, so recurring tasks can be handed off with more context, consistency, and trust. -
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Agentic Data Scientist
An end-to-end Data Scientist
Agentic Data Scientist is an experimental AI-driven research framework that orchestrates data science workflows through autonomous agents that can reason, plan, and execute complex analytics tasks. Unlike traditional scripted pipelines, this project lets AI agents break down high-level research goals into sub-tasks such as data acquisition, cleaning, modeling, evaluation, and reporting, with minimal human direction. Each agent is designed to independently call functions, interact with data sources, and adapt to uncertainties during processing, enabling iterative refinement of models without manual coordination. The framework supports interoperability with existing data tools and libraries, letting the agents leverage libraries like pandas, scikit-learn, and visualization frameworks to perform real computations rather than mock demonstrations. -
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Agently
AI Agent Application Development Framework
Build AI agent native application in very little code. Easy to interact with AI agents in code using structure data and chained-calls syntax. Enhance AI Agent using plugins instead of rebuilding a whole new agent. Agently is a development framework that helps developers build AI agent native applications really fast. You can use and build AI agents in your code in an extremely simple way. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Agently 4
Build GenAI application quick and easy
Agently is a Python framework for building generative-AI (“GenAI”) applications; it focuses on enabling developers to orchestrate AI agents, workflows, and event-driven logic in a robust, reusable way. With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at production-grade GenAI application development rather than just one-off scripts — you’ll find examples of news gathering, agentic workflows, control systems, etc. It is licensed under Apache-2.0, allowing commercial use and modification. Because it's built in Python, it integrates easily with existing data pipelines, databases, and agent architectures. -
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Agno
Lightweight framework for building Agents with memory, knowledge, etc.
Agno is a modular, open-source artificial general intelligence (AGI) research platform that allows developers to build, evaluate, and experiment with cognitive architectures in a composable way. It provides a flexible framework for modeling reasoning, memory, decision-making, and planning, aimed at long-term AI research beyond narrow learning. Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge, goal-oriented behaviors, and meta-learning. It’s designed for researchers seeking an extensible platform to explore AGI components without being tied to black-box models. -
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Airweave
Airweave lets agents search any app
Airweave is an open-source platform that enables agents to semantically search across various applications, databases, and APIs. By transforming disparate data sources into a unified, searchable knowledge base, Airweave facilitates intelligent information retrieval through REST APIs or the MCP protocol. It's particularly useful for building AI agents that require access to structured and unstructured data across multiple platforms. -
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All-in-RAG
Big Model Application Development Practice 1
All-in-RAG is an open-source educational project designed to teach developers how to build applications using retrieval-augmented generation techniques. The repository provides a structured learning path that covers both theoretical foundations and practical implementation steps for RAG systems. It explains the full development pipeline required to create knowledge-aware AI assistants, including data preparation, document indexing, vector embedding generation, and retrieval strategies. The project also explores advanced topics such as hybrid retrieval methods, query optimization, and evaluation techniques for improving system accuracy. Alongside theoretical explanations, the repository includes hands-on exercises and example projects that demonstrate how to build production-ready RAG systems. These projects guide developers through the process of integrating vector databases, embedding models, and large language models into a unified application. -
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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
To eventually become an unofficial working Pytorch implementation of Alphafold2, the breathtaking attention network that solved CASP14. Will be gradually implemented as more details of the architecture is released. Once this is replicated, I intend to fold all available amino acid sequences out there in-silico and release it as an academic torrent, to further science. Deepmind has open sourced the official code in Jax, along with the weights! This repository will now be geared towards a straight pytorch translation with some improvements on positional encoding. lhatsk has reported training a modified trunk of this repository, using the same setup as trRosetta, with competitive results. The underlying assumption is that the trunk works on the residue level, and then constitutes to atomic level for the structure module, whether it be SE3 Transformers, E(n)-Transformer, or EGNN doing the refinement. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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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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Anything to NotebookLM
Multi-source content processor for NotebookLM
Qiaomu Anything to NotebookLM is a Claude Code skill that turns many types of source material into structured NotebookLM-ready outputs. It is built for users who want to convert articles, web pages, videos, PDFs, office files, podcasts, images, and search results into more usable study or presentation formats. The project uses natural-language commands, so the user can ask for a podcast, slide deck, mind map, report, quiz, flashcards, or infographic without manually building the workflow. It supports multilingual material, with especially strong use cases for Chinese and English content. The tool can process files locally, extract or transcribe content when needed, and hand the cleaned material to NotebookLM for generation. It is best suited for researchers, students, content curators, and knowledge workers who regularly turn scattered information into organized learning assets. -
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AppWorld
World of apps for benchmarking interactive coding agent
AppWorld is a framework developed by Stony Brook University's NLP group to simulate environments for training and evaluating dialogue agents in task-oriented applications. -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems. Extending the toolkit with new features is simple. Add a new filterbank, separator architecture, dataset or even recipe very easily. Recipes provide an easy way to reproduce results with data preparation, system design, training and evaluation in a single script. This is an essential tool for the community! The default logger is TensorBoard in all the recipes. From the recipe folder, you can run the following to visualize the logs of all your runs. -
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Astron Agent
Enterprise platform for building and orchestrating AI agent workflows
Astron Agent is an enterprise-grade platform designed for building and managing intelligent AI agent workflows in production environments. It provides a development environment that combines workflow orchestration, model management, and integration with various AI tools and services. Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. Astron Agent supports scalable and high-availability deployments, allowing teams to run reliable AI agent infrastructure in distributed environments. It includes collaboration features that allow teams to develop, manage, and operate AI applications together. With its extensible architecture and enterprise-focused design, it aims to help organizations build production-ready intelligent agent solutions. -
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AstronRPA
Agent-ready RPA suite with visual workflow automation tools engine
Astron RPA is an enterprise-grade robotic process automation platform designed to help organizations and developers build automated workflows for desktop and web applications. It provides a visual workflow designer that supports low-code and no-code development, allowing users to create automation processes through a drag-and-drop interface instead of writing extensive code. It enables automation of common desktop software and browser-based tasks, making it suitable for repetitive business operations and system integrations. Astron RPA includes a large library of reusable components that handle tasks such as user interface operations, data processing, and system interactions, allowing workflows to be assembled from modular building blocks. Astron RPA also integrates with intelligent agent systems so that automated processes and AI-driven workflows can work together in broader automation scenarios. -
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Atomic Agents
Building AI agents, atomically
The Atomic Agents framework is designed around the concept of atomicity to be an extremely lightweight and modular framework for building Agentic AI pipelines and applications without sacrificing developer experience and maintainability. The framework provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization. All logic and control flows are written in Python, enabling developers to apply familiar best practices and workflows from traditional software development without compromising flexibility or clarity. -
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Audio Webui
A webui for different audio related Neural Networks
Audio Webui is a Gradio-based web user interface that unifies a wide range of audio-related neural networks under a single, accessible front end. It is designed as an “all-in-one” environment where users can experiment with text-to-speech, voice cloning, generative music, and other neural audio models without writing boilerplate code. The project supports multiple back-end models and toolchains (such as Bark, RVC, AudioLDM, Audiocraft, and other text-to-audio or voice-cloning tools), exposing them through a consistent UI for inference and experimentation. Installation is streamlined through automatic installers and platform-specific scripts that create a virtual environment, install dependencies, and launch the web app with minimal manual setup. For more advanced users, it exposes a rich set of command-line flags to control behavior such as skipping installation, disabling venv, changing model cache directories, sharing Gradio links, setting passwords, and specifying themes or ports. -
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AudioLM - Pytorch
Implementation of AudioLM audio generation model in Pytorch
Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same. This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it. -
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AutoAgent AI
Autonomous harness engineering
AutoAgent is an experimental AI framework focused on autonomous agent engineering, where a meta-agent iteratively improves another agent’s architecture without direct human intervention. Instead of manually tuning prompts or workflows, developers define high-level goals in a configuration file, and the system continuously modifies its own tools, orchestration, and logic based on benchmark performance. It operates through a loop of testing, analyzing failures, and refining the agent’s configuration to maximize a scoring metric. The framework uses a single-file agent harness combined with structured tasks and evaluation suites to guide optimization. It runs inside Docker for safe execution and reproducibility. This approach shifts agent development from manual design to automated optimization. The system is particularly useful for building domain-specific agents that need continuous performance improvement. -
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AutoCoder
A long-running autonomous coding agent powered by the Claude Agent
Autocoder is an experimental auto-generation engine that transforms high-level prompts or structured descriptions into functioning source code, models, or systems with minimal manual intervention. Rather than hand-writing boilerplate or repetitive patterns, users supply a specification—such as a description of a feature, a function prototype, or a module outline—and Autocoder fills in complete implementations that compile and run. It is built to support iterative refinement: after generating an initial draft, you can provide feedback or corrections, and the system will adjust the output to match evolving intentions. The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
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AutoPkg
Automating packaging and software distribution on macOS
AutoPkg is a system that automatically prepares software for distribution to managed clients. Recipes allow you to specify a series of simple actions which combined together can perform complex tasks, similar to Automator workflows or Unix pipes. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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AutoTyper-with-python
A program to auto type a text and enter
A program to auto type a text and enter made with python programming language. -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision). Provides all the necessary utilities concerning model training. This includes simple and efficient ways of implementing new continual learning strategies as well as a set of pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison! Avalanche the first experiment of an End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, etc.
