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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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the original series, enhanced with detailed explanations, formula derivations, and optimized solutions. In July 2023, work on the second edition was announced, which expands the project with updated content, new problems inspired by recent big-tech interviews, and introductions to modern machine learning techniques such as XGBoost, CNNs, RNNs, and LSTMs. This collection serves both as a historical record of algorithm problem-solving and as a living resource for programmers preparing for interviews. -
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Three.js Object Sculptor
Codex plugin that turns attached object images into code-only
Three.js Object Sculptor is a Codex plugin that converts a reference image into a procedural Three.js object written entirely in code. It first evaluates whether the image is suitable and produces an ObjectSculptSpec describing geometry, materials, lighting, hierarchy, pivots, and quality targets. Codex then follows staged passes from blockout and structural work through surface detail, interaction design, and optimization. Generated models include meaningful sockets and anchors for animation, transformation, physics, and destruction. Reference and rendered screenshots can be combined for AI vision review and scored self-correction. The workflow targets browser-friendly props and stylized reconstructions rather than photogrammetry or exact mesh extraction. -
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TikZ
TikZ figures for concepts in physics/chemistry/ML
Collection of 111 standalone TikZ figures for illustrating concepts in physics, chemistry, and machine learning. Check out janosh.github.io to search, sort, open in Overleaf, and download figures (PDF/SVG/PNG) from this collection. -
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Tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models
tiktoken is a high-performance, tokenizer library (based on byte-pair encoding, BPE) designed for use with OpenAI’s models. It handles encoding and decoding text to token IDs efficiently, with minimal overhead. Because tokenization is a fundamental step in preparing text for models, tiktoken is optimized for speed, memory, and correctness in model contexts (e.g. matching OpenAI’s internal tokenization). The repo supports multiple encodings (e.g. “cl100k_base”) and lets users switch encoding names to match different model contexts. It also offers extension mechanisms so that custom encodings can be registered. Internally, it includes the core tokenizer logic (often implemented in Rust or efficient lower-level code), APIs for encoding, decoding, and counting tokens, and binding layers to Python (and sometimes other languages) for easy use. -
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Tilf
Tilf (Tiny Elf) is a free, simple yet powerful pixel art editor
Tilf (Tiny Elf) is a lightweight, cross-platform pixel art editor developed in Python with PySide6, designed for simplicity, speed, and freedom from account systems or installation overhead. It focuses on enabling artists to create sprites, icons, and small 2D assets quickly, without requiring setup, dependencies, or internet connectivity. Tilf provides a familiar drawing environment with essential tools—such as pencil, eraser, fill, eyedropper, rectangle, and ellipse—along with zoom, grid display, real-time preview, and undo/redo capabilities. It supports importing and exporting images in PNG, JPG, and BMP formats, including transparency options. With its single-executable builds for Windows, macOS, and Linux, Tilf can be run instantly and is ideal for both hobbyist pixel artists and developers needing a quick sketching tool for sprite work. The project emphasizes accessibility and minimalism over complexity, making it approachable even for users with no technical background. -
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TinyDB
Document oriented database optimized for you
TinyDB is a lightweight document oriented database optimized for your happiness :) It's written in pure Python and has no external dependencies. The target are small apps that would be blown away by a SQL-DB or an external database server. The current source code has 1800 lines of code (with about 40% documentation) and 1600 lines tests. Like MongoDB, you can store any document (represented as dict) in TinyDB. TinyDB is designed to be simple and fun to use by providing a simple and clean API. TinyDB neither needs an external server (as e.g. PyMongo) nor any dependencies from PyPI. TinyDB works on all modern versions of Python and PyPy. You can easily extend TinyDB by writing new storages or modify the behaviour of storages with Middlewares. TinyDB has been tested with Python 3.6 - 3.10 and PyPy3. -
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Toapi
Convert websites into structured APIs automatically with Python tool
Toapi is a Python library designed to transform ordinary websites into usable API services. Instead of building a traditional web crawler that collects and stores data before exposing it through an API, Toapi simplifies the process by allowing developers to define data structures that automatically generate an API layer from existing web pages. It works by parsing HTML content from a source site and mapping selected elements into structured data that can be returned as JSON through API endpoints. Developers define items and routes that determine how web pages are parsed and how the resulting data is exposed through the API interface. It also includes mechanisms for caching both page content and API requests, helping reduce repeated network calls and improving performance. Because the generated service is built on top of a Flask application, it can be deployed like any other Flask-based project and integrated into existing Python workflows. -
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TokenCost
Easy token price estimates for 400+ LLMs. TokenOps
TokenCost is an open-source developer utility designed to estimate the cost of using large language model APIs by calculating token usage and translating it into real monetary values. The tool focuses on helping developers understand how much their prompts and generated completions cost when interacting with commercial AI models. It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. This allows engineers building AI applications, chatbots, or autonomous agents to monitor and predict API expenses during development and production. The library includes pricing information for hundreds of language models and is frequently updated to reflect pricing changes from major AI providers. -
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Toonily Downloader
A python tool for downloading manga from Toonily
Toonily Downloader is a Python-based scraping and downloading tool designed specifically for manga and manhwa hosted on Toonily, enabling users to fetch entire series efficiently while preserving original image quality and structure. It provides both a command-line interface and a graphical user interface, making it accessible for both technical and non-technical users. The software supports downloading full series or selected chapters by parsing Toonily URLs and organizing content into clean, chapter-based directories. It uses concurrent downloading techniques to significantly speed up the process and includes robust error handling to recover from interruptions or failed downloads. Additionally, the tool allows users to convert downloaded chapters into high-quality PDF files without re-encoding images, ensuring fidelity to the original content. -
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TradingAgents
Chinese Financial Trading Framework Based on Multi-Agent LLM
TradingAgents-CN is a Chinese-enhanced, multi-agent LLM framework aimed at building financial analysis and trading-oriented workflows, with an emphasis on collaboration between specialized agents rather than a single monolithic prompt. It organizes market-related tasks into roles and stages so different agents can contribute research, reasoning, aggregation, and decision support in a structured pipeline. The project is oriented toward practical usage, including a stack that can be run in a modern development environment and commonly paired with containerized backends, configuration files, and service components. It also pays attention to distribution and misuse risks, clearly warning users about unauthorized commercial repackaging and stating that commercial use requires explicit authorization while personal use is open. -
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Trae Agent
LLM-based agent for general purpose software engineering tasks
Trae Agent is an open-source, LLM-based agent system also developed by ByteDance, focused primarily on automating software engineering workflows. It provides a command-line interface (CLI) that accepts natural-language instructions (e.g. “refactor this module,” “write a unit test,” “generate a REST API skeleton”), and then orchestrates tool-based workflows — such as file editing, shell/batch commands, code generation, code formatting or refactoring — to carry out complex engineering tasks. Under the hood, Trae Agent supports multiple LLM backends (so you can choose your preferred model provider), and comes with a modular architecture that makes it easy to study, extend, or modify. Because of its transparent, research-friendly design and detailed logging (trajectory recording), it is positioned not just as a productivity tool but also as a platform for researchers to explore, analyze, or extend AI-based code automation strategies. -
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Trail of Bits Skills Marketplace
Trail of Bits Claude Code skills for security research, vulnerability
Trail of Bits Skills Marketplace is a specialized Claude Code skills marketplace built by the security research firm Trail of Bits that focuses on enhancing AI-assisted workflows for vulnerability discovery, testing, and secure development. The repository groups a set of plug-in skills tailored toward static analysis, code auditing, secure defaults detection, and other practices that matter in software security. Users can easily add the marketplace to a Claude Code environment, browse available plugins, and install specific skills for tasks like automatic Semgrep rule creation, entry-point analysis in smart contracts, or insecure defaults detection. This project leverages the agent skills architecture to let AI assistants take on detailed, repeatable security procedures that are typically manual, such as parsing Burp Suite projects or conducting variant analysis across codebases. -
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Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
Transfer Learning Repo is an open-source repository that compiles resources, code implementations, and academic references related to transfer learning and its related research areas. The project functions as a large knowledge hub that organizes papers, tutorials, datasets, and software implementations across topics such as domain adaptation, domain generalization, multi-task learning, and few-shot learning. The repository includes surveys and theoretical explanations that help readers understand how transfer learning methods allow models trained in one domain to adapt to new tasks or datasets. In addition to academic references, the project provides practical code implementations of many transfer learning algorithms so that researchers can reproduce experiments or build their own applications. The repository also catalogs well-known scholars, research laboratories, and datasets relevant to transfer learning studies. -
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Trellis AI
All-in-one AI framework & toolkit for Claude Code & Cursor
Trellis is an advanced workflow and agent orchestration framework designed for building, managing, and scaling intelligent applications that coordinate numerous autonomous components. At its core, Trellis lets developers define units of work — called tasks or agents — and compose them into rich workflows that can operate with concurrency, conditional logic, and dynamic branching, all without sacrificing readability or control. It emphasizes modular design, encouraging users to encapsulate logic into reusable pieces that can be tested, versioned, and reused across projects. Trellis also includes tooling for monitoring, scheduling, and tracing the execution of complex multi-step jobs, helping teams maintain visibility into how work progresses and where bottlenecks emerge. The platform can integrate with external services, databases, and model endpoints, making it suitable for automation, ETL pipelines, AI-driven processes, and business logic orchestration. -
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TrendRadar
AI-driven public opinion trend monitor with multi-platform aggregation
TrendRadar is an AI-powered trend and hotspot tracking system that aggregates information from dozens of news, social, and content platforms to help users cut through information overload and focus on what matters. It automatically crawls and monitors trends across 30+ sources with smart filtering, keyword triggers, sentiment analysis, and natural language summarization to give actionable insights. The tool supports multiple alert modes—such as daily summaries, incremental change monitoring, and current rankings—and can push notifications through messaging platforms like Telegram, Slack, WeChat, DingTalk, and email. Users can deploy it quickly via Python and GitHub Actions, and it also supports RSS feeds and Docker deployment for flexible integration. By combining AI judgment with broad data ingestion, TrendRadar empowers individuals and teams to track public opinion, news cycles, and emerging topics without manual scanning of dozens of websites. -
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TurboQuant PyTorch
From-scratch PyTorch implementation of Google's TurboQuant
TurboQuant PyTorch is a specialized deep learning optimization framework designed to accelerate neural network inference and training through advanced quantization techniques within the PyTorch ecosystem. The project focuses on reducing the computational and memory footprint of models by converting floating-point representations into lower-precision formats while preserving performance. It provides tools for experimenting with different quantization strategies, enabling developers to balance accuracy and efficiency depending on their application. The framework integrates directly with PyTorch workflows, making it accessible for researchers and engineers already familiar with the ecosystem. It is particularly useful for deploying models in resource-constrained environments such as edge devices or real-time systems. -
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USO
Open-sourced unified customization model
USO is ByteDance’s “Unified Style and Subject-Driven Generation” framework, open-sourced to allow customization in generative modeling by disentangling style and subject representation and using reward learning to guide generation. The system is designed such that users can control both “what” is generated (the subject: e.g. a person, object, scene) and “how” it is generated (the style: artistic style, color palette, aesthetic) separately, giving much more flexibility than conventional monolithic generative models. By decoupling style and subject, USO enables reuse of learned style/style-embeddings across different subjects, or vice versa, which makes generation more modular and controllable. The project provides tooling (in Python) including inference and workflow scripts, example configurations, and support for generation pipelines; and as of 2025, USO is also natively supported in some mainstream generative-art UI pipelines (e.g. ComfyUI) to ease adoption. -
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Ultralytics
Ultralytics YOLO
Ultralytics is a comprehensive computer vision framework that provides state-of-the-art implementations of the YOLO (You Only Look Once) family of models, enabling developers to perform tasks such as object detection, segmentation, classification, tracking, and pose estimation within a unified system. It is designed to be fast, accurate, and easy to use, offering both command-line and Python-based interfaces for training, validation, and deployment of machine learning models. The framework supports a full end-to-end workflow, including dataset preparation, model training, evaluation, and export to various deployment formats. Its architecture emphasizes performance optimization, balancing speed and accuracy to support real-time applications across industries. Ultralytics also provides pretrained models and flexible configuration options, allowing users to adapt the system to different datasets and use cases with minimal effort. -
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Ultravox
Fast multimodal LLM for real-time voice interaction and AI apps
Ultravox is an open source multimodal large language model designed specifically for real-time voice-based interactions. It is built to process both text and spoken audio directly, eliminating the need for a separate speech recognition stage and enabling more seamless conversational experiences. Ultravox works by combining text prompts with encoded audio inputs, allowing it to understand spoken language alongside written instructions in a unified pipeline. Internally, it leverages pretrained language models and speech encoders, with a multimodal adapter that integrates both modalities for inference and training. Ultravox is optimized for low latency, achieving fast response times suitable for interactive voice agents and real-time applications. It supports use cases such as conversational AI agents, speech-to-speech translation, and analysis of spoken audio content. Ultravox also includes tooling and configuration systems for training, evaluation, and dataset integration. -
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VADER
Lexicon and rule-based sentiment analysis tool
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments. -
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VCPlayerBot
Telegram bot to stream videos in telegram voicechat for both groups
Telegram bot to stream videos in telegram voice chat for both groups and channels. Supports live streams, YouTube videos and telegram media. With record stream support, Schedule streams, and many more. -
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VGGSfM
VGGSfM: Visual Geometry Grounded Deep Structure From Motion
VGGSfM is an advanced structure-from-motion (SfM) framework jointly developed by Meta AI Research (GenAI) and the University of Oxford’s Visual Geometry Group (VGG). It reconstructs 3D geometry, dense depth, and camera poses directly from unordered or sequential images and videos. The system combines learned feature matching and geometric optimization to generate high-quality camera calibrations, sparse/dense point clouds, and depth maps in standard COLMAP format. Version 2.0 adds support for dynamic scene handling, dense point cloud export, video-based reconstruction (1000+ frames), and integration with Gaussian Splatting pipelines. It leverages tools like PyCOLMAP, poselib, LightGlue, and PyTorch3D for feature matching, pose estimation, and visualization. With minimal configuration, users can process single scenes or full video sequences, apply motion masks to exclude moving objects, and train neural radiance or splatting models directly from reconstructed outputs. -
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VGGT
[CVPR 2025 Best Paper Award] VGGT
VGGT is a transformer-based framework aimed at unifying classic visual geometry tasks—such as depth estimation, camera pose recovery, point tracking, and correspondence—under a single model. Rather than training separate networks per task, it shares an encoder and leverages geometric heads/decoders to infer structure and motion from images or short clips. The design emphasizes consistent geometric reasoning: outputs from one head (e.g., correspondences or tracks) reinforce others (e.g., pose or depth), making the system more robust to challenging viewpoints and textures. The repo provides inference pipelines to estimate geometry from monocular inputs, stereo pairs, or brief sequences, together with evaluation harnesses for common geometry benchmarks. Training utilities highlight data curation and augmentations that preserve geometric cues while improving generalization across scenes and cameras. -
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VMZ (Video Model Zoo)
VMZ: Model Zoo for Video Modeling
The codebase was designed to help researchers and practitioners quickly reproduce FAIR’s results and leverage robust pre-trained backbones for downstream tasks. It also integrates Gradient Blending, an audio-visual modeling method that fuses modalities effectively (available in the Caffe2 implementation). Although VMZ is now archived and no longer actively maintained, it remains a valuable reference for understanding early large-scale video model training, transfer learning, and multimodal integration strategies that influenced modern architectures like SlowFast and X3D. -
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Watcher
Open Source Cybersecurity Threat Hunting Platform
Watcher is a file integrity monitoring tool that detects unauthorized changes to files, helping organizations maintain compliance and security.
