Open Source Python Software - Page 35
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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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FuzzBench
FuzzBench - Fuzzer benchmarking as a service
FuzzBench is a large-scale, open research platform developed by Google to evaluate and benchmark fuzzers — automated software testing tools that detect vulnerabilities through randomized input generation. It provides a standardized, reproducible environment for comparing the performance and effectiveness of different fuzzing algorithms on real-world software targets. FuzzBench integrates with the OSS-Fuzz infrastructure, allowing it to run experiments on authentic open source projects and collect meaningful data on crash discovery rates, code coverage, and bug-finding efficiency. The service includes an easy-to-use API for integrating custom fuzzers and an automated reporting system that generates detailed statistical analyses, comparative graphs, and significance testing. By running experiments at Google scale, FuzzBench ensures consistent, unbiased, and data-driven evaluations that support academic and industrial fuzzing research. -
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GHunt
Offensive Google framework
GHunt (v2) is an offensive Google framework, designed to evolve efficiently. It's currently focused on OSINT, but any use related with Google is possible. It will automatically use venvs to avoid dependency conflicts with other projects. First, launch the listener by doing ghunt login and choose between 1 of the 2 first methods. Put GHunt on listening mode (currently not compatible with docker) Paste base64-encoded cookies. Enter manually all cookies. The development of this extension has followed Firefox guidelines to use the Promise-based WebExtension/BrowserExt API being standardized by the W3 Browser Extensions group, and is using webextension-polyfill to provide cross-browser compatibility with no changes. -
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GLIDE (Text2Im)
GLIDE: a diffusion-based text-conditional image synthesis model
glide-text2im is an open source implementation of OpenAI’s GLIDE model, which generates photorealistic images from natural language text prompts. It demonstrates how diffusion-based generative models can be conditioned on text to produce highly detailed and coherent visual outputs. The repository provides both model code and pretrained checkpoints, making it possible for researchers and developers to experiment with text-to-image synthesis. GLIDE includes advanced techniques such as classifier-free guidance, which improves the quality and alignment of generated images with the input text. The project also offers sampling scripts and utilities for exploring how diffusion models can be applied to multimodal tasks. As one of the early diffusion-based text-to-image systems, glide-text2im laid important groundwork for later advances in generative AI research. -
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GLM-4
GLM-4 series: Open Multilingual Multimodal Chat LMs
GLM-4 is a family of open models from ZhipuAI that spans base, chat, and reasoning variants at both 32B and 9B scales, with long-context support and practical local-deployment options. The GLM-4-32B-0414 models are trained on ~15T high-quality data (including substantial synthetic reasoning data), then post-trained with preference alignment, rejection sampling, and reinforcement learning to improve instruction following, coding, function calling, and agent-style behaviors. The GLM-Z1-32B-0414 line adds deeper mathematical, coding, and logical reasoning via extended reinforcement learning and pairwise ranking feedback, while GLM-Z1-Rumination-32B-0414 introduces a “rumination” mode that performs longer, tool-using deep research for complex, open-ended tasks. A lightweight GLM-Z1-9B-0414 brings many of these techniques to a smaller model, targeting strong reasoning under tight resource budgets. -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference and more effective integration within agent frameworks. GLM-4.6 also enhances writing quality, producing outputs that better align with human preferences and role-playing scenarios. Benchmark evaluations demonstrate that it not only outperforms GLM-4.5 but also rivals leading global models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4. -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices. Once you've created an interface, you can permanently host it on Hugging Face. Hugging Face Spaces will host the interface on its servers and provide you with a link you can share. One of the best ways to share your machine learning model, API, or data science workflow with others is to create an interactive demo that allows your users or colleagues to try out the demo in their browsers. -
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HexStrike AI MCP Agents
HexStrike AI MCP Agents is an advanced MCP server
HexStrike AI is an MCP server that lets LLM agents autonomously operate a large catalog of offensive-security tools. Its goal is to bridge “language models” and practical pentest workflows—enumeration, exploitation, vulnerability discovery, and bug bounty reconnaissance—under safe, auditable controls. The server exposes typed tools and guardrails so agent prompts translate to concrete, parameterized actions rather than brittle shell strings. It ships with curated tool adapters, task orchestration, and guidance for connecting popular agent clients (Claude, GPT, Copilot) to a hardened execution environment. Documentation highlights the breadth of supported utilities and positions HexStrike as a research and red-team aid, not a point-and-click exploit kit. A public site and active repository activity signal an expanding community around autonomous security research agents. -
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HostHunter
OSINT reconnaissance tool for discovering hostnames from IP addresses
HostHunter is an open source reconnaissance tool designed to discover and extract hostnames associated with a large set of IPv4 or IPv6 addresses. It helps security professionals map IP addresses to virtual hostnames using a combination of OSINT data sources and active reconnaissance techniques. This approach enables users to identify hidden or additional services that may be hosted behind a single IP address. By correlating hostname information from certificates, APIs, HTTP headers, and other sources, the tool helps reveal the broader attack surface of an organization or infrastructure. HostHunter is commonly used in penetration testing, bug bounty reconnaissance, and security assessments where identifying virtual hosts is critical. HostHunter supports multiple output formats, making it easier to integrate the results into other security tools or workflows. -
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II Agent
A new open-source framework to build and deploy intelligent agents
II-Agent is an open-source intelligent assistant framework designed to automate complex workflows across multiple domains using large language models and external tools. The platform allows users to interact with multiple AI models within a single environment while connecting those models to external services and knowledge sources. Through a unified interface, users can switch between models, access specialized tools, and execute tasks that require information retrieval, code execution, or file analysis. The architecture focuses on transforming traditional software tools into autonomous assistants capable of completing tasks independently based on user instructions. II-Agent supports integration with modern AI services and can coordinate interactions between different models and capabilities within the same workflow. -
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IndexTTS2
Industrial-level controllable zero-shot text-to-speech system
IndexTTS is a modern, zero-shot text-to-speech (TTS) system engineered to deliver high-quality, natural-sounding speech synthesis with few requirements and strong voice-cloning capabilities. It builds on state-of-the-art models such as XTTS and other modern neural TTS backbones, improving them with a conformer-based speech conditional encoder and upgrading the decoder to a high-quality vocoder (BigVGAN2), leading to clearer and more natural audio output. The system supports zero-shot voice cloning — meaning it can mimic a target speaker’s voice from a short reference sample — making it versatile for multi-voice uses. Compared to many open-source TTS tools, IndexTTS emphasizes efficiency and controllability: it offers faster inference, simpler training pipelines, and controllable speech parameters (like duration, pitch, and prosody), which is critical for production use. -
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Intelligent stock analysis system
LLM-driven A/H/US stock intelligent analyzer
Intelligent stock analysis system is a Python-based smart stock analysis system that leverages large language models to automatically analyze selected equities across A-shares, Hong Kong stocks, and U.S. markets. It’s designed to produce a daily “decision dashboard” summarizing key insights such as core conclusions, precise entry/exit points, and checklists for potential trades, combining multi-dimensional technical analysis, market sentiment, chip distribution, and real-time price data. The system supports scheduled execution using GitHub Actions, enabling fully automated daily analysis and multi-channel notifications via platforms like Telegram, Enterprise WeChat, Feishu, email, and push services. Under the hood, it integrates multiple AI models (like Gemini and OpenAI-compatible models) and diverse market data sources (including AkShare, Tushare, and YFinance) to synthesize comprehensive reports. -
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Jarvis Python AI Assistant
Python AI assistant
Jarvis is a voice commanding assistant service in Python 3.8 It can recognize human speech, talk to user and execute basic commands. Opens a web page (e.g 'Jarvis open youtube') Play music in Youtube (e.g 'Jarvis play mozart') Increase/decrease the speakers master volume (also can set max/mute speakers volume) (e.g 'Jarvis volume up!') Opens libreoffice suite applications (calc, writer, impress) (e.g 'Jarvis open calc') Tells about something, by searching on the internet (e.g 'Jarvis tells me about oranges') Tells the weather for a place (e.g 'Jarvis tell_the_skills me the weather in London') Tells the current time and/or date (e.g 'Jarvis tell me time or date') Set an alarm (e.g 'Jarvis create a new alarm') Tells the internet speed (ping, uplink and downling) (e.g 'Jarvis tell_the_skills me the internet speed') Tells the internet availability (e.g 'Jarvis is the internet connection ok?') Tells the daily news (e.g 'Jarvis tell me today news') -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. Instead of relying purely on reward signals as in reinforcement learning agents, the models analyze contextual information and generate strategic actions based on the game environment. Performance is evaluated using a competitive ranking system that assigns models an ELO rating based on their results across matches against other models. -
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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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MiniCPM5-2B
SOTA on-device LLMs, small yet powerful
MiniCPM5-2B is a family of compact language models developed for local, on-device, and resource-constrained AI workloads. The current MiniCPM5 series includes dense 1B and 2B models designed to deliver strong capability with a small deployment footprint. MiniCPM5-2B provides a 131K-token context window and targets assistants, coding agents, reasoning, tool use, and other agentic tasks. The repository includes base, supervised fine-tuned, and deployment-ready model variants. Quantized releases are available in formats such as GGUF, MLX, GPTQ, and DSpark. It also provides fine-tuning, quantization, deployment, and agent-skill resources for adapting models to different environments. Earlier MiniCPM releases explore sparse attention, mixture-of-experts designs, long-context modeling, and efficient edge inference. -
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Moriarty Project
Web-based OSINT tool for investigating phone number information
Moriarty Project is an open source web-based investigation tool designed to gather publicly available information about phone numbers. It allows users to input a phone number and analyze various details related to that number through multiple investigation features. It performs information gathering by scraping data from online sources to retrieve insights such as owner information, spam risk, and related web references. Users can select specific investigation features to run individually or execute all available checks at once depending on their needs. Moriarty Project operates through a browser-based interface and includes multithreading improvements that help speed up the investigation process. Moriarty Project focuses strictly on OSINT-style information gathering and does not provide tracking, hacking, or harmful capabilities against phone number owners. It is fully open source, allowing users to inspect the code and understand how the data collection mechanisms work. -
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NormCap
OCR powered screen-capture tool to capture information
NormCap is a free, open-source screen capture application that extracts text and structured information from images using optical character recognition. Instead of saving conventional screenshots, it converts selected screen regions into editable text. Users can capture paragraphs, interface labels, documents, and other visible content with a simple selection tool. Its OCR processing operates locally, helping preserve privacy without relying on cloud services. NormCap includes different recognition modes for extracting plain text or structured information such as URLs and email addresses. Recognized content is automatically copied to the clipboard for immediate use. Available for Windows, macOS, and Linux, it provides a lightweight solution for turning visual information into reusable text. -
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ODM
A command line toolkit to generate maps, point clouds, 3D models
OpenDroneMap ODM is a command-line photogrammetry toolkit for converting aerial imagery into geospatial and 3D products. It can process photographs captured by drones, balloons, kites, and other aerial platforms. Outputs include classified point clouds, textured 3D models, georeferenced orthophotos, and digital elevation models. The processing pipeline integrates specialized open-source projects for reconstruction, meshing, geospatial conversion, and point-cloud processing. ODM runs on Windows, macOS, and Linux and is commonly deployed through Docker. GPU acceleration is available for selected feature-extraction workloads. NodeODM can expose the processing engine over a network for integration with other applications. -
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Odoo
Odoo. Open Source Apps To Grow Your Business
Odoo is a comprehensive suite of open source business applications designed to manage and streamline various organizational operations. It provides an integrated ecosystem of tools that cover core business functions such as CRM, accounting, eCommerce, inventory, HR, project management, and manufacturing. Each Odoo app can be deployed individually to meet specific business needs or combined to form a powerful all-in-one ERP system. The platform’s modular architecture allows businesses to scale easily as they grow, adapting to evolving requirements. Built with a web-based interface and a modern tech stack, Odoo emphasizes usability, flexibility, and seamless integration across departments. It is widely used by enterprises of all sizes, from startups to large corporations, for digital transformation and operational efficiency. -
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OpenKore
A free/open source client and automation tool for Ragnarok Online
OpenKore is a free and open source automation tool and custom client for the online game Ragnarok Online. It provides a flexible scripting system that allows users to automate gameplay, including navigation, combat, trading, and interaction with other players. The project supports plugins and modular extensions, enabling customization for different servers and play styles. OpenKore is cross-platform, running on Windows, Linux, and macOS, and is written primarily in Perl. With a strong community and extensive documentation, it is widely used by players and developers who want to explore automation, bot behavior, or server testing. The software emphasizes configurability, giving users control over character strategies and in-game decision-making. -
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OpenSRE
Build your own AI SRE agents. The open source toolkit for the AI era
OpenSRE is an open-source framework designed to build AI-powered Site Reliability Engineering agents that automate incident investigation and root cause analysis across modern cloud environments. It connects to observability tools, infrastructure systems, and communication platforms to gather logs, metrics, and traces in real time. When an alert is triggered, the system autonomously analyzes correlated signals, identifies anomalies, and generates structured investigation reports with probable causes and recommended actions. Its multi-agent architecture allows parallel reasoning across systems, mimicking how experienced SRE teams debug complex issues. The platform also incorporates memory and knowledge graph capabilities to learn from past incidents and improve future investigations. It is designed to run locally within an organization’s infrastructure, ensuring data privacy and compliance. -
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OpenViking
Context database designed specifically for AI Agents
OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences. -
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PSCoverDL
Small tool to download PS1/PS2 covers for DuckStation and PCSX2
PSCoverDL is a companion tool designed to automate the downloading and management of PlayStation game cover art for emulators, providing a user-friendly interface for populating game libraries with visuals. It allows users to select their emulator, specify directories, and automatically retrieve matching cover images based on game metadata. The tool supports multiple platforms and includes features such as multithreading for faster downloads and configuration files for customization. Its graphical interface simplifies the process, making it accessible even to non-technical users. It integrates directly with emulator cache files, ensuring accurate matching between games and covers. The system is designed for efficiency, reducing the manual effort required to curate large collections. Overall, pscoverdl complements the ps2-covers repository by providing automation and usability. -
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Qbot
AI-powered Quantitative Investment Research Platform
Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies — including supervised learning, reinforcement learning and multi-factor models — and offers a “model zoo” and example strategies to help users get started. For evaluation and analysis, Qbot integrates reporting and visualization (tearsheets, metrics) so you can compare performance across runs and inspect trade-level behavior. It supports multiple strategy runtimes and backtesting engines, is organized for extensibility (strategies live in a dedicated folder). -
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RF-DETR
RF-DETR is a real-time object detection and segmentation
RF-DETR is an open-source computer vision framework that implements a real-time object detection and instance segmentation model based on transformer architectures. Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. RF-DETR emphasizes strong performance across both accuracy and latency benchmarks, allowing developers to deploy high-quality detection models in applications that require immediate processing such as robotics, autonomous systems, and industrial inspection. The repository includes Python packages, training scripts, and model configurations that enable researchers and engineers to train and deploy detection models on custom datasets.
