Open Source Python Software - Page 42
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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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8mb.local
Ffree local self hosted video compressor webui
8mb.local is a lightweight, self-hosted local network status dashboard and utilities hub designed to help users monitor devices and services connected to their home or small business network. The platform presents a simple yet informative web dashboard that dynamically updates with device statuses, IP addresses, and latency information, giving users a centralized view of network health without complex dashboards. It also includes basic tools such as ping monitoring, service reachability checks, and port scanning, enabling users to diagnose connection issues quickly and without external services. The emphasis is on minimalism and performance, so it runs efficiently on low-power devices like Raspberry Pis or small containers, making it ideal for home labs or edge deployments. 8mb.local can also notify users of offline devices or connection drops through configurable alerts, helping preempt outages before they become serious. -
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APKiD
Android Application Identifier for Packers, Protectors and Obfuscators
APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android. -
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ATX
Smart phone automation tool. Support iOS, Android, WebApp and game
ATX (AutomatorX) is an open source automated testing tool that supports testing native applications, games, and Web applications on iOS and Android platforms. Use Python to write test cases, and use image recognition and control positioning technology to complete the automation of the game. Attach a dedicated IDE to complete scripting quickly. The libraries for Android and iOS native application testing have been separated and can be used alone (it is strongly recommended to use it alone, firstly, it has less dependencies and high stability, and secondly, it can be automatically completed when writing code) -
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AV1 AVIF
AV1 Image File Format Specification - ISO-BMFF/HEIF derivative
AV1 AVIF is the official specification and reference design for the AV1 Image File Format (AVIF), defining how AV1-encoded bitstreams are packaged into the HEIF container format (based on ISOBMFF) to produce AVIF files. The project outlines the syntax and semantics required for AVIF compliance, including support for multiple image profiles, color depths, chroma subsampling modes, HDR/WCG, alpha channels, animation/image sequences, and various color-space/bit-depth combinations — making AVIF a versatile, modern image format suitable for both simple photos and advanced imagery needing high fidelity. The specification ensures interoperability across encoders and decoders, providing guidelines so that images created by any compliant AVIF encoder can be reliably decoded by compliant decoders. As adoption grows, AV1 AVIF plays a crucial role in promoting a royalty-free, open, high-efficiency image standard that competes with older formats such as JPEG and newer proprietary ones. -
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AWS Data Wrangler
Pandas on AWS, easy integration with Athena, Glue, Redshift, etc.
An AWS Professional Service open-source python initiative that extends the power of Pandas library to AWS connecting DataFrames and AWS data-related services. Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON, and EXCEL). Built on top of other open-source projects like Pandas, Apache Arrow and Boto3, it offers abstracted functions to execute usual ETL tasks like load/unload data from Data Lakes, Data Warehouses, and Databases. Convert the column name to be compatible with Amazon Athena and the AWS Glue Catalog. Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame. Get QuickSight dashboard ID given a name and fails if there is more than 1 ID associated with this name. List IAM policy assignments in the current Amazon QuickSight account. -
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AWS SAM CLI
CLI tool to build, test, debug, and deploy Serverless applications
The AWS Serverless Application Model (SAM) CLI is an open-source CLI tool that helps you develop serverless applications containing Lambda functions, Step Functions, API Gateway, EventBridge, SQS, SNS and more. The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. To get started with building SAM-based applications, use the SAM CLI. SAM CLI provides a Lambda-like execution environment that lets you locally build, test, debug, and deploy AWS serverless applications. -
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Academic Research Skills for Claude Code
Academic Research Skills for Claude Code
Academic Research Skills is a structured learning repository aimed at improving users’ ability to conduct rigorous academic research, particularly in technical and scientific domains. It compiles methodologies, frameworks, and best practices for literature review, critical analysis, and research writing. The project is designed as a self-guided resource, helping learners understand how to evaluate sources, synthesize information, and develop strong arguments. It likely integrates examples, templates, and conceptual explanations to bridge the gap between theory and practical research execution. The repository emphasizes skill-building rather than automation, making it especially useful for students and early-career researchers. Its overall goal is to enhance research literacy and reproducibility in academic workflows. -
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Agent Skills
Specification and documentation for Agent Skills
agentskills is the specification and documentation repository for the Agent Skills open format, which defines a standardized way to package capabilities that AI agents can discover and use. A “skill” is treated as a foldered bundle containing instructions, optional scripts, and supporting resources, so agents can reliably apply a workflow or expertise area when it becomes relevant. The central goal is portability: you can write a skill once and reuse it across different agent runtimes and developer tools that implement the format. This repo serves as the canonical reference for how skills should be structured, what metadata they should include, and how an SDK can load and apply them consistently. It also includes supporting materials like guides and examples so builders can create skills that are predictable, testable, and shareable with teams. -
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Agentic Security
Agentic LLM Vulnerability Scanner / AI red teaming kit
The open-source Agentic LLM Vulnerability Scanner. -
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AnimeGAN
A simple PyTorch Implementation of Generative Adversarial Networks
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. The images are generated from a DCGAN model trained on 143,000 anime character faces for 100 epochs. Manipulating latent codes enables the transition from images in the first row to the last row. The images are not clean, some outliers can be observed, which degrades the quality of the generated images. Anime-style images of 126 tags are collected from danbooru.donmai.us using the crawler tool gallery-dl. The images are then processed by an anime face detector python-anime face. The resulting dataset contains ~143,000 anime faces. Note that some of the tags may no longer be meaningful after cropping, i.e. the cropped face images under the 'uniform' tag may not contain visible parts of uniforms. -
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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. -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds. -
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Buster
OSINT tool for discovering information linked to email addresses
Buster is an open source OSINT tool designed for email reconnaissance and information gathering. It helps investigators, security researchers, and penetration testers discover publicly available information related to email addresses and usernames. It can analyze an email address to identify associated social media accounts, references across the web, and potential data breaches linked to that email. It also performs reverse WHOIS lookups to discover domains that may have been registered using a specific email address. In addition to investigating existing addresses, Buster can generate possible email combinations and usernames based on personal details such as a person’s name, birthdate, or additional hints. Buster supports validating generated email addresses and retrieving contextual information about them. By combining multiple online sources and services, Buster helps automate the process of gathering intelligence related to digital identities. -
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ChatDev
Create Customized Software using Natural Language Idea
ChatDev is an AI-powered development tool designed to simulate the software development lifecycle using multi-agent collaboration. It allows multiple AI agents to take on roles such as product managers, developers, and testers to collaboratively generate, refine, and evaluate software code. This project explores how AI can be leveraged to automate and optimize development workflows. -
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ChatGPT Academic
ChatGPT extension for scientific research work
ChatGPT extension for scientific research work, specially optimized academic paper polishing experience, supports custom shortcut buttons, supports custom function plug-ins, supports markdown table display, double display of Tex formulas, complete code display function, new local Python/C++/Go project tree Analysis function/Project source code self-translation ability, newly added PDF and Word document batch summary function/PDF paper full-text translation function. All buttons are dynamically generated by reading functional.py, you can add custom functions at will, and liberate the pasteboard. Support for markdown tables output by GPT. If the output contains a formula, it will be displayed in tex form and rendered form at the same time, which is convenient for copying and reading. -
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Checkov
Prevent cloud misconfigurations during build-time for Terraform
Checkov scans cloud infrastructure configurations to find misconfigurations before they're deployed. Checkov uses a common command-line interface to manage and analyze infrastructure as code (IaC) scan results across platforms such as Terraform, CloudFormation, Kubernetes, Helm, ARM Templates and Serverless framework. Verify changes to hundreds of supported resource types in all major cloud providers. Checkov supports developers using Terraform, Terraform plan, CloudFormation, Kubernetes, ARM Templates, Serverless, Helm, and AWS CDK. Scan cloud resources in build-time for misconfigured attributes with a simple Python policy-as-code framework. Analyze relationships between cloud resources using Checkov’s graph-based YAML policies. Execute, test, and modify runner parameters in the context of a subject repository CI/CD and version control integrations. -
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Claude Blog
Claude Code blog skill suite
Claude Blog is a Claude Code blog skill suite for planning, writing, optimizing, and auditing long-form content. It is built as a full-lifecycle blog engine with 30 sub-skills, 5 agents, 12 content templates, on-demand references, Python scripts, and automated tests. The workflow is designed to produce production-ready content rather than one-shot AI drafts. It uses a five-gate delivery contract covering capability, format, visual quality, content review, and asset integrity. Commands support new blog posts, rewrites, content audits, briefs, editorial calendars, strategy, outlines, SEO checks, personas, taxonomy, multilingual workflows, research, audio narration, and Google data. Claude Blog is best suited for solo publishers, marketing teams, agencies, and Claude Code skill builders who want structured editorial automation. -
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Claude How-To
A visual, example-driven guide to Claude Code
Claude How-To is a visual, example-driven guide for learning Claude Code. It covers basic concepts, memory, slash commands, hooks, skills, subagents, MCP configuration, plugins, and advanced agent workflows. The project is designed as a practical learning path rather than a simple list of notes. It includes copy-paste templates that users can apply directly to their own projects. The guide also uses diagrams and structured examples to explain not only how features work, but why they matter in a real development workflow. Its main value is helping developers move from casual Claude Code usage to more organized, automated, and agent-driven software development. -
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Code-Graph-RAG
The ultimate RAG for your monorepo
Code-Graph-RAG is an advanced retrieval-augmented generation system designed specifically for understanding and interacting with large, multi-language codebases by transforming them into structured knowledge graphs. It uses Tree-sitter to parse source code into abstract syntax trees, extracting relationships between functions, classes, and modules to build a graph-based representation of the entire codebase. This structured approach enables more accurate and context-aware querying compared to traditional text-based search methods, allowing users to ask natural language questions about code structure and functionality. The system integrates with graph databases such as Memgraph to store and manage relationships, enabling efficient querying and visualization of complex dependencies. It also supports AI-driven query translation, converting natural language into graph queries for deeper analysis and interaction. -
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Conan
The open-source C/C++ package manager
The open-source, decentralized and multi-platform package manager to create and share all your native binaries. All platforms. Windows, Linux, Apple, FreeBSD, Android, iOS, embedded, cross-building, bare metal, etc. All build systems. Visual Studio MSBuild, CMake, Makefiles, SCons, etc. Extensible to any build system. Full management of binaries. Create, manage and reuse any number of binaries, for any configuration: platform, compiler, version, architectures, or build from sources at will. Fully automated dependency management. Transitive dependencies, conflicts detection, dependency overriding, conditional dependencies. Decentralized client-server architecture. Run your own server for free with JFrog Artifactory on-prem to fully own your packages and binaries. Conan is Free, open-source software with a permissive MIT license. Use, modify, redistribute, and extend it - even for commercial purposes. -
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ConsistencyDecoder
Consistency Distilled Diff VAE
ConsistencyDecoder is a Python package from OpenAI that introduces an improved decoding method for variational autoencoders (VAEs) used in Stable Diffusion pipelines. Instead of relying solely on the standard GAN or VAE decoder, this approach leverages a Consistency Distilled Diff VAE, designed to produce higher-quality and more stable outputs from encoded latents. The project provides a simple API for encoding with a Stable Diffusion VAE and decoding using the new consistency model, allowing for side-by-side comparisons with traditional decoders. It demonstrates how consistency models can enhance visual fidelity while maintaining efficiency, reducing artifacts common in GAN-decoded outputs. The repository includes installation instructions, usage examples, and visual comparisons to highlight improvements. Though compact, it provides an accessible entry point for experimenting with advanced decoding strategies in diffusion-based generative models. -
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Crawl4AI
Open-source LLM Friendly Web Crawler & Scraper
Crawl4AI is a high-performance, AI‑ready web crawler tailored for LLM data ingestion and RAG pipelines. It supports adaptive crawling heuristics (stopping when enough info is gathered), structured markdown output, and high-speed parallel execution. Designed to operate at scale with optional Docker deployment and framework integrations. -
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Cryptocurrency Exchange Feed Handler
Cryptocurrency Exchange Websocket data feed handler
Handles multiple cryptocurrency exchange data feeds and returns normalized and standardized results to client registered callbacks for events like trades, book updates, ticker updates, etc. Utilizes WebSockets when possible, but can also poll data via REST endpoints if a WebSocket is not provided. Create a FeedHandler object and add subscriptions. For the various data channels that an exchange supports, you can supply callbacks for data events, or use provided backends to handle the data for you. Start the feed handler and you're done! Cryptofeed also provides a synthetic NBBO (National Best Bid/Offer) feed that aggregates the best bids and asks from the user specified feeds. Cryptofeed supports backend callbacks that will write directly to storage or other interfaces. Cryptofeed can be installed from PyPi. (It's recommended that you install in a virtual environment of your choosing). -
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Data Contract CLI
Enforce Data Contracts
Data Contract CLI is an open-source command-line tool and Python library for creating, validating, testing, importing, exporting, and enforcing data contracts. It uses YAML-based contract files to define the structure, meaning, quality rules, service levels, and connection details for a data product. The tool can connect to real data sources and check whether the actual dataset matches the schema, constraints, and quality expectations described in the contract. It supports both the Data Contract Specification and the Open Data Contract Standard, making it useful for teams standardizing data governance across different platforms. It can run locally, inside CI/CD pipelines, through Docker, or directly from Python code. Overall, it helps data producers and consumers treat data products more like APIs, with explicit expectations, automated checks, and clearer accountability. -
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Deep-Learning-with-PyTorch-Tutorials
Deep Learning and PyTorch Introduction Video Tutorial with Source Code
Deep-Learning-with-PyTorch-Tutorials is a companion repository for an introductory deep learning course built around PyTorch. It provides source code, notebooks, and presentation materials for a practical video-based learning path. The lessons begin with PyTorch setup, tensors, indexing, mathematical operations, gradients, and basic optimization. They then move into neural networks, logistic regression, multilayer perceptrons, CNNs, ResNet, RNNs, LSTMs, autoencoders, VAEs, GANs, graph convolutional networks, and transfer learning. The repository is designed for learners who want to connect deep learning concepts with executable examples. Overall, it is a structured PyTorch practice resource for beginners and early deep learning practitioners.
