Open Source Python Software - Page 100
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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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abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
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agentic-stack
One brain, many harnesses. Portable .agent/ folder
agentic-stack is a framework or toolkit designed to build, orchestrate, and deploy AI agents in a structured and scalable way. It likely provides components for managing agent workflows, communication, and task execution across different systems. The project emphasizes modularity, enabling developers to assemble custom pipelines using various AI models, tools, and APIs. It may include abstractions for memory, planning, and tool usage, reflecting modern agentic AI design patterns. The stack is intended to accelerate development by providing reusable building blocks for complex AI systems. Overall, it represents an infrastructure layer for creating autonomous or semi-autonomous AI applications. -
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aisuite
Simple, unified interface to multiple Generative AI providers
Simple, unified interface to multiple Generative AI providers. aisuite makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, aisuite makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around Python client libraries and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focused on chat completions. We will expand it to cover more use cases in the near future. Currently supported providers are - OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace and Ollama. To maximize stability, aisuite uses either the HTTP endpoint or the SDK for making calls to the provider. -
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alfred-ai
The development of my ai assistant, Alfred
The development of my ai assistant, Alfred. -
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auto-sklearn
Automated machine learning with scikit-learn
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself and contains a multitude of improvements which speed up the fitting the AutoML system. auto-sklearn 2.0 works the same way as regular auto-sklearn. auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license. -
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auto-subtitle
Automatically generate and overlay subtitles for any video
auto-subtitle is a Python-based command-line tool that automatically generates and overlays subtitles on video files using AI-driven speech recognition. It combines FFmpeg with OpenAI’s Whisper model to transcribe spoken audio into text and synchronize it with video playback. The tool processes video input, extracts audio, and produces subtitle files that can be either exported separately or burned directly into the final video output. It supports multiple transcription models with varying accuracy and performance, allowing users to balance speed and quality depending on their needs. The system can also translate subtitles into English, enabling multilingual accessibility for video content. Once the required models are downloaded, it can operate offline, making it practical for local workflows. Designed for simplicity, it provides a streamlined way to automate subtitle creation without manual transcription effort. -
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autopep8
A tool that automatically formats Python code to conform to the PEP 8
autopep8 automatically formats Python code to conform to the PEP 8 style guide. It uses the pycodestyle utility to determine what parts of the code need to be formatted. autopep8 is capable of fixing most of the formatting issues that can be reported by pycodestyle. Correct deprecated or non-idiomatic Python code (via lib2to3). Use this for making Python 2.7 code more compatible with Python 3. Put a blank line between a class docstring and its first method declaration. Remove blank lines between a function declaration and its docstring. -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error. -
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autoresearch for AMD
AI agents running research on single-GPU nanochat training
autoresearch for AMD is a framework for autonomous scientific experimentation in machine learning, enabling AI agents to iteratively improve models through a continuous loop of hypothesis generation, experimentation, and evaluation. The system is built around a minimal structure that includes a data preparation module, a training script that can be modified, and a program specification that guides the agent’s decision-making process. During each iteration, the agent edits the training code, runs an experiment within a fixed time budget, evaluates performance metrics, and decides whether to retain or discard the changes. This loop allows the system to explore a wide range of architectural and hyperparameter configurations without human intervention. The framework emphasizes simplicity and reproducibility, ensuring that experiments are comparable and results are traceable over time. -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Make drawing and labeling bounding boxes easy as cake. This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have full control over your visualizations. -
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bilingual_book_maker
Make bilingual epub books Using AI translate
bilingual_book_maker is an AI-assisted translation tool for creating bilingual and multilingual versions of books and text files. It is designed to process formats such as EPUB, TXT, SRT, and PDF, then generate translated output that helps readers compare the original text with the target language. The project supports multiple AI providers and models, including OpenAI-compatible models and other translation backends through LiteLLM-style integrations. It is especially useful for public domain books, language learning, subtitle translation, and personal reading workflows. Users can run it from Python scripts or install it as a command-line package for repeated translation tasks. The repository also includes documentation, test books, prompt templates, and configuration options for customizing how translations are generated. -
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cheat.sh
The only cheat sheet you need
cheat.sh is a compact, network-accessible cheat-sheet service that serves concise examples and usage notes for hundreds of shell commands, programming languages, and tools via a simple HTTP interface. You can query it from the terminal (for example curl cht.sh/rsync or curl cheat.sh/ls) or browse the web front page; it also supports a shorthand hostname (cht.sh) and provides both online and standalone/local installation modes. The repository contains the server and client code, instructions to run a local standalone instance (including Python virtualenv setup), and tooling to fetch or maintain the upstream cheat-sheet data; installation documentation explains disk-space needs and dependency setup for offline use. Cheat.sh is intentionally minimal and scriptable, so it fits naturally into shells, CI scripts, editors, and quick lookups without leaving the terminal, while also offering ways to extend or host personal cheat sheets. -
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cloud-morph
Decentralize, Self-host Cloud Gaming/Application
cloud-morph is a cloud-based media processing service that enables real-time video and audio transformation using FFmpeg in scalable environments. It is designed to run as a backend service that processes media streams or files and applies transformations such as transcoding, filtering, and format conversion. The system supports API-driven workflows, allowing integration into web applications or automation pipelines. It leverages containerization and distributed architecture to handle workloads efficiently across multiple nodes. cloud-morph focuses on flexibility, enabling users to define custom processing pipelines for different media scenarios. It can be used for applications such as streaming, video processing, and content delivery optimization. Overall, it provides a scalable solution for handling multimedia transformations in cloud environments. -
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codex-orange-book
A Full-Link Guide to Using Codex from Installation to Real-World Cases
codex-orange-book is an unofficial open-source guide for learning and applying Codex in real software workflows. It is written as a full learning resource rather than a conventional software package. The guide covers installation, configuration, core concepts, standard workflows, practical examples, and extension paths. It explains Codex App, Codex CLI, Codex IDE Extension, Codex Web, cloud workflows, Skills, MCP, Git, GitHub, automation, and memory-related usage. It is aimed at developers, independent builders, AI tool users, and technical teams that want a structured way to adopt Codex. Overall, it functions as a practical handbook for moving from basic Codex setup to real project execution and review-ready deliverables. -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
Cognee implements scalable, modular data pipelines that allow for creating the LLM-enriched data layer using graph and vector stores. Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. Then, we establish graph topology and connect related knowledge clusters, enabling the LLM to "understand" the data. -
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d2l-zh
Chinese-language edition of Dive into Deep Learning
d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
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data-diff
Efficiently diff rows across two different databases
We're excited to announce the launch of a new open-source product, data-diff that makes comparing datasets across databases fast at any scale. data-diff automates data quality checks for data replication and migration. In modern data platforms, data is constantly moving between systems, and at the modern data volume and complexity, systems go out of sync all the time. Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates. -
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deepclaude
Use Claude Code's agent loop with DeepSeek V4 Pro, OpenRouter & more
deepclaude is a lightweight proxy tool that enables developers to run Claude Code’s autonomous coding agent loop using alternative AI backends like DeepSeek V4 Pro, OpenRouter, or other Anthropic-compatible models. It preserves the full Claude Code experience—including file editing, terminal execution, and multi-step agent workflows—while dramatically reducing operational costs. By swapping out the underlying model instead of the interface, deepclaude delivers the same familiar UX with significantly cheaper token pricing. The platform supports seamless backend switching in real time, allowing users to choose between cost efficiency and higher reasoning power when needed. It also includes built-in cost tracking and benchmarking tools to help developers monitor usage and optimize performance. Designed for flexibility and efficiency, deepclaude is ideal for developers who want powerful AI coding agents without the premium price tag. -
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discord.py-self
A fork of the popular discord.py for user accounts
discord.py-self is a Python API wrapper forked from discord.py and adapted for Discord user-account APIs. It provides an async-ready Python interface with familiar patterns for developers who know the original discord.py ecosystem. The library includes rate limit handling, performance-oriented implementation choices, upstream compatibility goals, and coverage for many user-specific Discord systems. It supports areas such as sessions, read states, relationships, connections, interactions, application management, and other account-level features. The project clearly warns that automating user accounts violates Discord’s Terms of Service and is not recommended. It is best viewed as a proof-of-concept or research-oriented fork rather than a safe replacement for official Discord bot libraries. -
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discover
Automation framework for reconnaissance and penetration testing tasks
Discover is a collection of custom Bash scripts designed to automate many common tasks involved in penetration testing workflows. The project brings together a variety of security testing functions into a single framework that simplifies reconnaissance, scanning, and enumeration processes. It provides a menu-driven interface that allows security professionals to quickly launch different tools and scripts without manually executing each command. The framework helps streamline activities such as information gathering, network scanning, and web application testing during security assessments. Discover also integrates with well-known security tools like Metasploit to generate malicious payloads and manage listeners for exploitation tasks. By organizing multiple security utilities and scripts into one environment, the project reduces repetitive manual steps and standardizes penetration testing workflows. The tool is commonly used in Kali Linux environments. -
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django-health-check
a pluggable app that runs a full check on the deployment
The primary intended use case is to monitor conditions via HTTP(S), with responses available in HTML and JSON formats. When you get back a response that includes one or more problems, you can then decide the appropriate course of action, which could include generating notifications and/or automating the replacement of a failing node with a new one. If you are monitoring health in a high-availability environment with a load balancer that returns responses from multiple nodes, please note that certain checks (e.g., disk and memory usage) will return responses specific to the node selected by the load balancer. -
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django-sspanel
Diango shadowsocks
Shadowsocks panel developed with diango. Smart subscription system , support ss/clash/clash premium version. Deep integration with transit tunnels , convenient and fast construction of transit tunnels d7e4380-6532-* Backend supports common protocols. Registration adopts the invitation system to bid farewell to bad users. Unified and perfect background management interface. Perfect commodity purchase logic. Alipay face-to-face payment module. Invitation rebate system. -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render through dm_control. Hardware rendering with a windowing system is supported via GLFW and GLEW. On Linux these can be installed using your distribution's package manager. "Headless" hardware rendering (i.e. without a windowing system such as X11) requires EXT_platform_device support in the EGL driver. While dm_control has been largely updated to use the pybind11-based bindings provided via the mujoco package, at this time it still relies on some legacy components that are automatically generated. -
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ety
A Python module to discover the etymology of words
ety is a Python library and command-line tool designed to explore and retrieve the etymological origins of words by analyzing linguistic data and relationships between languages. It allows users to query a word and obtain its historical roots, including intermediate forms across different languages and time periods. The tool can generate recursive etymology chains as well as tree structures that visually represent how a word evolved over time. It is built as both a reusable module and a CLI utility, making it suitable for integration into other applications or standalone use. The project relies on structured datasets that map relationships between words and languages, enabling systematic exploration of linguistic evolution. It is particularly useful for developers, linguists, and researchers interested in historical language patterns and word origins. -
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ex-skill
Distill your ex into an AI Skill
ex-skill is an experimental AI tooling project that allows users to transform personal memories, particularly past relationships, into interactive AI “skills” that replicate the communication style, personality, and behavioral patterns of a specific individual. The system works by ingesting various forms of personal data such as chat logs, social media content, photos, and user-provided descriptions, then structuring this information into a layered representation that combines memory and persona modeling. It is designed to run within Claude Code environments, where users can generate, manage, and interact with these personalized AI entities through command-based interfaces. The project emphasizes emotional realism by reconstructing conversational tone, habits, and contextual memories, enabling interactions that feel consistent with the original person.
