Open Source Python Software - Page 90
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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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Chroma MCP
A Model Context Protocol (MCP) server implementation
Chroma MCP Server is an implementation of the Model Context Protocol (MCP) designed to integrate large language model (LLM) applications with external data sources or tools. It offers a standardized framework to seamlessly provide LLMs with the context they require for effective operation. -
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CineCLI
CineCLI is a cross-platform command-line movie browser
CineCLI is a command-line utility designed to help movie lovers quickly browse, search, and access film information from the terminal without needing a graphical interface. It connects to popular online movie databases to fetch metadata such as titles, release dates, ratings, genres, casts, posters, and plot summaries, presenting all of that in a concise, text-friendly format suitable for terminals or scripts. Users can search by keyword, year, or exact title and then drill into detailed views for individual films, making it useful for creating watchlists, learning more about films before watching, or integrating movie lookup into shell workflows. CineCLI also supports paginated results and filters so users can navigate large search outputs without overwhelming their screens. Because it runs entirely from the command line, it’s ideal for developers, movie enthusiasts in headless environments, or anyone who prefers text-based tools over web browsers. -
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Claude Code Projects Index
An index of my Claude Code related repos
Claude Code Projects Index is a curated directory of projects, tools, and resources built around Claude Code and related AI development ecosystems. It functions as a centralized index that helps developers discover useful repositories, workflows, and integrations. The project is organized to make navigation easy, grouping resources by categories such as tooling, frameworks, and use cases. It is particularly valuable for developers exploring the Claude ecosystem and looking for inspiration or best practices. The repository is continuously updated, reflecting the evolving landscape of AI-assisted development. It also serves as a knowledge-sharing platform, highlighting innovative approaches and implementations. Overall, it acts as a discovery hub that accelerates learning and adoption of AI development tools. -
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Claude Code Tools
Practical productivity tools for Claude Code, Codex-CLI
Claude Code Tools is an open-source collection of command-line utilities and productivity plugins designed to enhance developer workflows when using AI coding agents such as Claude Code and Codex-CLI. The project focuses on solving common problems encountered in AI-assisted development environments, including managing session history, automating terminal interactions, and maintaining context across multiple coding sessions. It includes tools that allow developers to search conversation logs quickly, manage environment variables securely, and execute interactive terminal workflows that AI agents can control. Some components enable Claude Code to interact with terminal multiplexers such as tmux so that it can run programs, debug applications, and interact with scripts that require user input. The toolkit also provides safety mechanisms that prevent potentially dangerous shell commands from being executed automatically by AI agents. -
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Claude for Legal
A suite of plugins for legal workflows
Claude for Legal is a suite of reference agents, skills, and connectors built to support common legal workflows with Claude. It is designed for in-house teams, law firms, clinics, and legal students who need structured assistance across commercial, corporate, employment, privacy, product, litigation, regulatory, AI governance, and IP work. The project can run as a Claude plugin or through Claude’s Managed Agents API, giving teams flexibility in how they deploy the same prompts and skills. Its workflows include contract review, NDA triage, diligence review, DSAR response, employment policy drafting, trademark screening, regulatory monitoring, and litigation support. The repository emphasizes attorney oversight, source attribution, jurisdiction awareness, conservative legal assumptions, and approval gates before anything is filed, sent, or relied on. -
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Claude-Red
claude-red is a curated library of offensive security skills
Claude Red is a curated collection of offensive-security skill files for Claude's skill system. Each skill provides structured domain guidance for a specific area of authorized security testing or research. The library covers web applications, identity, Active Directory, wireless systems, cloud environments, mobile platforms, embedded devices, reconnaissance, exploit research, and fuzzing. Skills load on demand from conversational triggers instead of occupying context continuously. Users can install the complete collection or select individual categories. The project is intended for legitimate red-team engagements, bug-bounty triage, CTF practice, operator training, and security research. Its modular structure makes the collection expandable as new methodologies and categories are added. -
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Click
Python composable command line interface toolkit
Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary. It’s the “Command Line Interface Creation Kit”. It’s highly configurable but comes with sensible defaults out of the box. It aims to make the process of writing command line tools quick and fun while also preventing any frustration caused by the inability to implement an intended CLI API. Click in three points, arbitrary nesting of commands, automatic help page generation, supports lazy loading of subcommands at runtime. Comes with useful common helpers (getting terminal dimensions, ANSI colors, fetching direct keyboard input, screen clearing, finding config paths, launching apps and editors, etc.). Click actually implements its own parsing of arguments and does not use optparse or argparse following the optparse parsing behavior. Click is designed to be fun and customizable but not overly flexible. -
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ClusterFuzz
Scalable fuzzing infrastructure
ClusterFuzz is a scalable fuzzing infrastructure that finds security and stability issues in software. Google uses ClusterFuzz to fuzz all Google products and as the fuzzing backend for OSS-Fuzz. ClusterFuzz provides many features which help seamlessly integrate fuzzing into a software project's development process. Can run on any size cluster (e.g. OSS-Fuzz instance runs on 100,000 VMs). Fully automatic bug filing, triage and closing for various issue trackers (e.g. Monorail, Jira). Supports multiple coverage guided fuzzing engines (libFuzzer, AFL, AFL++ and Honggfuzz) for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. -
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Code-Mode
Plug-and-play library to enable agents to call MCP and UTCP tools
Code-Mode is a plug-and-play library that lets AI agents call tools by executing TypeScript (or via a Python wrapper) instead of making many individual function calls. Its core philosophy is that language models are very good at writing code, so rather than exposing hundreds of separate tool endpoints, you give the model a single “code execution” tool that has access to your full toolkit through code. This approach can dramatically reduce the number of tool-call iterations needed in complex workflows, turning multi-step call chains into a single code execution with internal branching and loops. The repository contains both TypeScript and Python libraries, plus a code-mode-mcp component for integrating with MCP and UTCP ecosystems. Benchmarks in the README highlight improvements in latency and token cost for scenarios involving multiple tools, showing that code execution often outperforms traditional JSON-based function calling. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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ComfyUI-WanVideoWrapper
ComfyUI wrapper nodes for WanVideo and related models
The ComfyUI-WanVideoWrapper project is a custom node extension for ComfyUI that enables advanced video generation workflows using WanVideo diffusion models. It acts as a standalone wrapper layer that allows developers and creators to integrate experimental features and models without modifying the core ComfyUI codebase. This design makes it easier to rapidly test new capabilities such as text-to-video and image-to-video generation while avoiding compatibility issues with the main framework. The project supports complex node-based pipelines where users can control sampling, conditioning, and frame continuity across generated sequences. It also enables extended video generation by linking outputs between iterations, allowing for longer and more coherent animations. Additionally, the wrapper often includes optimizations for performance, such as low VRAM configurations and multi-stage sampling strategies. -
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CommanderPi
Easy RaspberryPi4 GUI system managment
Easy Raspberry Pi 4 GUI system manager. -
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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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Context Engineering
A frontier, first-principles handbook
Context Engineering is a comprehensive, open-source project serving as a first-principles handbook for the emerging discipline of context design and optimization in AI. Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. It takes inspiration from thought leaders like Andrej Karpathy and bridges theory with practical examples, offering structured guidance on context orchestration, memory, retrieval, and state control within AI workflows. With extensive materials drawn from research, surveys, and visual explanations, the project acts as both a learning resource and a reference for practitioners looking to improve model behavior by engineering richer inputs. -
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Cortex Analyzers
Cortex Analyzers Repository
Analyzers can be written in any programming language supported by Linux such as Python, Ruby, Perl, etc. Refer to the How to Write and Submit an Analyzer page for details on how to write and submit one. -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. The materials are written for developers but remain friendly to newcomers, with clear setup instructions and minimal boilerplate. Because the repo is live and maintained, lessons are updated as the SDK and models evolve, and issues are used to track fixes, clarifications, and new modules. -
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D4RL
Collection of reference environments, offline reinforcement learning
D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm without the need to collect fresh transitions, which accelerates experimentation and comparison. The API is based on Gymnasium (via gym.make) and each environment also exposes a method get_dataset() that returns the offline data to learn from. The repository emphasizes open science, reproducibility, and benchmarking at scale, making it easier to compare algorithms on equal footing. -
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DNF
Package manager based on libdnf and libsolv. Replaces YUM
DNF (Dandified YUM) is the next-generation package manager for RPM-based distributions, replacing the traditional YUM tool. It utilizes modern libraries like libsolv and librepo to provide efficient dependency resolution and package management. DNF offers a more robust and user-friendly experience, with enhanced performance and a cleaner codebase. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities. -
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Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates topics in one place, learners can move linearly or jump into specific areas as needed during projects. The notes also highlight common pitfalls and good practices, which helps beginners adopt professional habits early. It’s a living resource that many students consult when revising fundamentals or exploring adjacent tools in the ecosystem. -
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Datadog Agent
Datadog Agent Version 5
Datadog Agent is an open-source agent for collecting metrics, logs, and traces from servers, applications, and services, sending data to Datadog for analysis. -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep integration with the Hugging Face Hub, allowing you to easily load and share a dataset with the wider NLP community. There are currently over 2658 datasets, and more than 34 metrics available. Datasets naturally frees the user from RAM memory limitation, all datasets are memory-mapped using an efficient zero-serialization cost backend (Apache Arrow). Smart caching: never wait for your data to process several times. -
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Datasette
An open source multi-tool for exploring and publishing data
Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size, analyze and explore it, and publish it as an interactive website and accompanying API. Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world. It is part of a wider ecosystem of tools and plugins dedicated to making working with structured data as productive as possible. Try a demo and explore 33,000 power plants around the world, then take a look at some other examples of Datasette in action. Then read how to get started with Datasette, subscribe to the monthly-ish newsletter and consider signing up for office hours for an in-person conversation about the project. -
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Dear ImGui Bundle
Dear ImGui Bundle: easily create ImGui applications in Python and C++
Dear ImGui Bundle is a bundle for Dear ImGui, including various powerful libraries from its ecosystem. It enables to easily create ImGui applications in C++ and Python, under Windows, macOS, and Linux. It is aimed at application developers, researchers, and beginner developers who want to quickly get started. -
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DecryptLogin
Python library providing APIs for automated website login workflows
DecryptLogin is a Python library designed to simplify automated login processes for many popular websites by providing ready-to-use APIs that simulate authentication behavior. It focuses on implementing login mechanisms through HTTP requests, allowing developers to programmatically authenticate with supported services without manually replicating complex login flows. It includes modules that handle different authentication modes such as PC login, mobile login, and QR code login depending on what the target platform supports. DecryptLogin supports a wide variety of online services and platforms, including social media sites, developer platforms, cloud services, and other web portals. Developers can integrate these login routines into automation scripts, crawlers, or data collection tools that require authenticated sessions. It also provides example utilities and automation scripts demonstrating how the login APIs can be used in practical scenarios.
