Open Source Python Software - Page 75
Sort By:
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.
-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
Endpoint Management with Remote Control Built InSplashtop AEM handles patching, alerts, scripts, and inventory, and unattended remote control is included. When something needs hands-on work, you’re one click away instead of switching tools.
-
1
Graph Nets library
Build Graph Nets in Tensorflow
Graph Nets, developed by Google DeepMind, is a Python library designed for constructing and training graph neural networks (GNNs) using TensorFlow and Sonnet. It provides a high-level, flexible framework for building neural architectures that operate directly on graph-structured data. A graph network takes graphs as inputs, consisting of edges, nodes, and global attributes, and produces updated graphs with modified feature representations at each level. This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. Graph Nets supports both TensorFlow 1 and TensorFlow 2, working with CPU and GPU environments, and includes educational Jupyter demos for shortest path finding, sorting, and physical prediction tasks. The codebase emphasizes modularity, allowing users to easily define their own edge, node, and global update functions. -
2
Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. This project includes many examples of Jupyter notebooks. It is recommended to explore them. All of the commands and features supported by graph notebook are explained in detail with examples within the sample notebooks. You can find them here. As this project has evolved, many new features have been added. If you are already familiar with graph-notebook but want a quick summary of new features added, a good place to start is the Air-Routes notebooks in the 02-Visualization folder. -
3
HDL Checker
Repurposing existing HDL tools to help writing better code
HDL Checker is a language server that wraps VHDL/Verilg/SystemVerilog tools that aims to reduce the boilerplate code needed to set things up. It supports Language Server Protocol or a custom HTTP interface; can infer the library VHDL files likely to belong to, besides working out mixed language dependencies, compilation order, interpreting some compiler messages and providing some (limited) static checks. Notice that currently, the unused reports has caveats, namely declarations with the same name inherited from a component, function, procedure, etc. -
4
HY-Motion 1.0
HY-Motion model for 3D character animation generation
HY-Motion 1.0 is an open-source, large-scale AI model suite developed by Tencent’s Hunyuan team that generates high-quality 3D human motion from simple text prompts, enabling the automatic production of fluid, diverse, and semantically accurate animations without manual keyframing or rigging. Built on advanced deep learning architectures that combine Diffusion Transformer (DiT) and flow matching techniques, HY-Motion scales these approaches to the billion-parameter level, resulting in strong instruction-following capabilities and richer motion outputs compared to existing open-source models. The training strategy for the HY-Motion series includes extensive pre-training on thousands of hours of varied motion data, fine-tuning on curated high-quality datasets, and reinforcement learning with human feedback, which improves both the plausibility and adaptability of generated motion sequences. -
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.
-
5
HY-World 1.5
A Systematic Framework for Interactive World Modeling
HY-WorldPlay is a Hunyuan AI project focusing on immersive multimodal content generation and interaction within virtual worlds or simulated environments. It aims to empower AI agents with the capability to both understand and generate multimedia content — including text, audio, image, and potentially 3D or game-world elements — enabling lifelike dialogue, environmental interpretations, and responsive world behavior. The platform targets use cases in digital entertainment, game worlds, training simulators, and interactive storytelling, where AI agents need to adapt to real-time user inputs and changes in environment state. It blends advanced reasoning with multimodal synthesis, enabling agents to describe scenes, generate context-appropriate responses, and contribute to narrative or gameplay flows. The underlying framework typically supports large-context state tracking across extended interactions, blending temporal and spatial multimodal signals. -
6
Habit Tracker
Habit Tracker for the AI Coding Workshop
Habit Tracker is a personal habit-tracking web application designed to help users build and maintain daily habits through intuitive UI and analytics that visualize progress over time. It runs locally with a FastAPI backend (Python) and a React frontend, storing all data in a lightweight SQLite database so there’s no need for user accounts or cloud storage, which keeps habit data fully private and self-contained. The app provides streak tracking and completion rates for each habit, giving users feedback on consistency and motivation by showing how often habits are completed and where they may be lagging. A calendar view lets users see a monthly grid of their habit history with color-coded days to highlight patterns and encourage daily engagement. Habit-Tracker also supports planned absences so users can skip days without breaking their streaks, reducing frustration and keeping long-term habits on track. -
7
Harness2
Recursive Agent Harnessing for an Open World
Harness² is an open-source AI research framework developed by Google Research to improve autonomous agents through recursive optimization of their execution environments. Rather than modifying model weights, it refines the prompts, tools, memory, guardrails, and supporting components that determine agent behavior. Its task-level optimization follows a Propose–Probe–Compose process that tests alternative configurations and combines evidence from their performance. A second optimization level preserves effective configurations and lessons across successive tasks. The framework supports parallel exploration and sequential refinement without requiring ground-truth feedback. Researchers can experiment with OpenCode and Codex execution environments. It also integrates professional benchmarks for evaluating agent performance and studying improvements across different models and task categories. -
8
Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. The framework also includes monitoring mechanisms that track agent trajectories and ensure that tasks remain aligned with overall objectives. -
9
Hex-Rays IDA MCP
Official Hex-Rays IDA MCP Server
IDA MCP is the official Model Context Protocol server from Hex-Rays for connecting AI agents to IDA Pro. It lets compatible assistants inspect and operate on IDA databases through structured MCP tools. The server can work with an open IDA GUI session or run headlessly through IDA’s automation libraries. Multiple clients can share and manipulate analysis state through the underlying IDA Nexus architecture. Supported integrations include Claude Code, Codex CLI, GitHub Copilot CLI, Pi, and generic MCP clients. A dashboard and logging tools provide visibility into agent sessions and troubleshooting data. The project requires modern IDA releases and is intended for legitimate reverse engineering, debugging, and security analysis. -
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.
-
10
Howdy For Linux
Windows Hello style facial authentication for Linux
Howdy provides Windows Hello™ style authentication for Linux. Use your built-in IR emitters and camera in combination with facial recognition to prove who you are. Using the central authentication system (PAM), works everywhere you would otherwise need your password: Login, lock screen, sudo, su, etc. -
11
HunyuanWorld 1.0
Generating Immersive, Explorable, and Interactive 3D Worlds
HunyuanWorld-1.0 is an open-source, simulation-capable 3D world generation model developed by Tencent Hunyuan that creates immersive, explorable, and interactive 3D environments from text or image inputs. It combines the strengths of video-based diversity and 3D-based geometric consistency through a novel framework using panoramic world proxies and semantically layered 3D mesh representations. This approach enables 360° immersive experiences, seamless mesh export for graphics pipelines, and disentangled object representations for enhanced interactivity. The architecture integrates panoramic proxy generation, semantic layering, and hierarchical 3D reconstruction to produce high-quality scene-scale 3D worlds from both text and images. HunyuanWorld-1.0 surpasses existing open-source methods in visual quality and geometric consistency, demonstrated by superior scores in BRISQUE, NIQE, Q-Align, and CLIP metrics. -
12
IPRanges
Daily updated lists of cloud, bot, and service IP ranges
ipranges is an open source repository that provides continuously updated lists of IP address ranges associated with major cloud providers, search engine crawlers, and online services. ipranges collects IP ranges from publicly available sources and organizes them into structured files that can be easily used in security, networking, and automation workflows. It includes address ranges from providers such as Google Cloud, Amazon AWS, Microsoft, Oracle Cloud, and DigitalOcean, as well as well known service platforms like GitHub, Facebook, Twitter, and Telegram. It also tracks IP ranges used by search engine bots and automated agents including Googlebot, Bingbot, and OpenAI’s GPTBot. Lists are published in both IPv4 and IPv6 formats and are regularly updated through automated processes to keep the data current. In addition to provider specific lists, the project also offers merged and combined datasets that aggregate ranges from multiple sources into a single file. -
13
IPython
Command shell for interactive computing in multiple languages
IPython provides a rich toolkit to help you make the most of using Python interactively. Comprehensive object introspection. IPython provides input history, persistent across sessions. Caching of output results during a session with automatically generated references. Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. Extensible system of ‘magic’ commands for controlling the environment and performing many tasks related to IPython or the operating system. A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). Session logging and reloading. Extensible syntax processing for special purpose situations. Access to the system shell with user-extensible alias system. Easily embeddable in other Python programs and GUIs. Integrated access to the pdb debugger and the Python profiler. -
14
IPython Kernel for Jupyter
IPython Kernel for Jupyter
This package provides the IPython kernel for Jupyter. IPython provides a rich toolkit to help you make the most of using Python interactively. -
15
Inspect Petri
An alignment auditing agent capable of exploring alignment hypothesis
Inspect Petri is an open-source alignment auditing agent that lets researchers rapidly test concrete safety hypotheses against target models using realistic, multi-turn scenarios. Instead of building bespoke evals, Inspect Petri automatically generates audit environments from seed “special instructions,” orchestrates an auditor model to probe a target model, and simulates tool use and rollbacks to surface risky behaviors. Each interaction transcript is then scored by a judge model using a consistent rubric so results are comparable across runs and models. The system supports major model APIs and comes with starter seeds and judge dimensions, enabling minutes-to-insight workflows for questions like reward hacking, self-preservation, or eval awareness. Petri is designed for parallel exploration: it spins many audits in flight, aggregates findings, and highlights transcripts that deserve human review. -
16
InternGPT
Open source demo platform where you can easily showcase your AI models
InternGPT is an open-source multimodal AI framework designed to extend large language models beyond text interactions into visual reasoning and image manipulation tasks. The system integrates conversational AI with computer vision models so users can interact with images, videos, and visual environments through natural language instructions. Unlike traditional chat systems that rely solely on text prompts, InternGPT allows users to interact with visual content using both language and nonverbal signals such as pointing or highlighting objects within images. The framework connects multiple specialized AI models that perform tasks such as object detection, segmentation, captioning, and visual editing while coordinating them through a central conversational interface. This architecture enables the system to plan actions, execute visual operations, and return results in a coherent dialogue with the user. -
17
Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. The later chapters focus on analyzing complex models and their decisions. In an ideal future, machines will be able to explain their decisions and make a transition into an algorithmic age more human. -
18
Jev Ultrafast
A browser agent with a dynamic, indexed action space
Jev Ultrafast is an experimental browser agent built around a dynamic, indexed action space for fast web interaction. Each browser state is converted into a numbered table of currently available interactive elements. A policy chooses both an operation and a compatible target in a single network round trip. Supported actions include clicking, typing, selecting, scrolling, waiting, finishing, and reporting that a task is blocked. A small language model is invoked only when text must actually be generated for a typing action. The agent connects to Chrome through Browser Harness and provides an inspector showing elements, probabilities, decisions, and executed actions. Its design avoids site-specific scripts and instead relies on fresh observations of the live page. -
19
KJNodes for ComfyUI
Various custom nodes for ComfyUI
The ComfyUI-KJNodes project is a collection of custom nodes designed to extend the functionality of ComfyUI workflows. It provides a wide range of utility nodes that enhance control over generation processes, including scheduling, conditioning, and data manipulation. These nodes are intended to fill gaps in the default ComfyUI toolkit, offering additional flexibility for building complex pipelines. The project is often used alongside other extensions, such as video wrappers, to enable more advanced workflows. It supports tasks such as creating parameter schedules, managing conditioning inputs, and combining outputs from different nodes. By expanding the available building blocks, it allows users to design more precise and customizable workflows. The repository is frequently updated with new nodes that address emerging needs in generative AI pipelines. Overall, it serves as a foundational toolkit for advanced ComfyUI users. -
20
KVCache-Factory
Unified KV Cache Compression Methods for Auto-Regressive Models
KVCache-Factory is an open-source research framework designed to explore and implement unified key-value cache compression techniques for autoregressive transformer models. In large language models, the key-value cache stores intermediate attention states that enable efficient token generation during inference, but these caches can consume large amounts of GPU memory when handling long contexts. KVCache-Factory provides a platform for implementing and evaluating multiple compression strategies that reduce memory usage while preserving model performance. The framework integrates several state-of-the-art methods such as PyramidKV, SnapKV, H2O, and StreamingLLM, allowing researchers to compare and experiment with different approaches within the same environment. It also supports advanced inference configurations such as Flash Attention v2 and multi-GPU inference setups for very large models. -
21
Kaggle CLI
The official CLI to interact with Kaggle
Kaggle CLI is Kaggle’s official command-line interface for interacting with the Kaggle platform from a terminal. It lets users authenticate, search resources, download files, submit competition entries, manage datasets, work with models, run notebooks, and read discussion content without relying only on the web interface. The tool is useful for data scientists who want to automate Kaggle workflows inside scripts, CI jobs, notebooks, or reproducible local environments. It supports both traditional API-token authentication and an OAuth login flow, which makes it more flexible for different usage patterns. kaggle-cli is especially practical when working with large datasets or repeated competition submissions that would be slow to handle manually. Its main value is turning Kaggle’s web-based data science platform into a scriptable developer workflow. -
22
Karpathy
An agentic Machine Learning Engineer
karpathy is an experimental agentic machine learning engineer framework designed to automate many aspects of the ML development workflow. The project sets up a sandboxed environment where an AI agent can access datasets, run experiments, and generate machine learning artifacts through a web interface. Its startup script automatically prepares the environment by creating a sandbox directory, installing key ML libraries, and launching the agent interface. The system is tightly integrated with the Claude Scientific Skills ecosystem, enabling the agent to leverage specialized scientific and machine learning tools. It is intended primarily for research and experimentation with autonomous ML workflows rather than as a polished production platform. Overall, karpathy represents an early step toward fully automated machine learning engineering driven by agentic AI systems. -
23
Kimi Code CLI
Kimi Code CLI is your next CLI agent
Kimi CLI is a command-line AI agent that brings an intelligent software development assistant directly into your terminal, helping you with coding tasks, shell operations, and workflow automation without leaving your command prompt. It supports an interactive shell-like user interface where you can chat with the agent, request code edits, run shell commands, and receive contextual suggestions as you work, creating a seamless blend of AI-augmented development and traditional terminal usage. The tool includes integration with Zsh so that users can activate AI assistance via a hotkey while staying within their favorite shell environment, and it can serve as an Agent Client Protocol (ACP) server to bridge AI functionality into compatible IDEs and editors. Its support for well-established MCP tool configuration conventions lets developers connect the CLI to external tools and services during workflows, expanding its capabilities beyond simple queries into orchestrated development tasks. -
24
Kindle_download_helper
Download all your kindle books script
Kindle_download_helper is a Python script for downloading Kindle books and personal documents from a user’s Amazon content library. It supports several Amazon regions, including Amazon, Amazon.cn, Amazon.de, Amazon.co.uk, and Amazon.co.jp. The workflow requires the user to log in through the browser, retrieve a CSRF token, and then run the script with the correct regional option. It can download purchased Kindle content and, depending on the flag used, personal document files as well. The repository is archived, so it should be treated as a historical utility rather than an actively maintained tool. It is best understood as a convenience script for users who need bulk access to their own Kindle library files. -
25
Kirara AI
DIY multimodal AI chatbot
Kirara AI is a chatbot framework designed to connect large language models with mainstream chat platforms. It supports multi-turn conversation, persona presets, keyword-triggered replies, administrator commands, and conditional automation. The project can work with model providers such as OpenAI, DeepSeek, Claude, Gemini, Qwen, Mistral, Kimi, Minimax, and others. It also supports image generation models, voice replies, cross-platform message sending, and custom workflows. A WebUI helps users manage models, workflows, plugins, and platform settings without editing every detail manually. Kirara AI is especially useful for users who want a configurable AI bot that can run across multiple messaging environments.
