Open Source Linux Artificial Intelligence Software - Page 21
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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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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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Copperhead
Hardware as fast as software
Copperhead is an AI product-development agent for designing, documenting, and validating real printed circuit boards from natural-language requirements. It works directly with existing KiCad repositories instead of generating isolated mockups or diagrams. A full creation workflow can turn a product brief into specifications, architecture, component selection, schematics, an initial PCB layout, Gerber files, firmware scaffolding, and a development plan. The agent can also modify existing designs through natural-language change requests. It reads and edits real KiCad schematic and PCB files while maintaining Markdown design documents as persistent project memory. Changes are propagated across related artifacts so documentation and hardware files remain synchronized. Copperhead validates its work by running KiCad ERC and DRC checks, although the project is still in an early development stage. -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Gemini-API
Reverse-engineered Python API for Google Gemini web app
Gemini-API is a community-created asynchronous Python wrapper for the web interface of Google’s Gemini models (formerly Bard). It is the result of reverse-engineering the Gemini web app and exposing its functionality through a programmatic API. This enables developers to incorporate Gemini into Python applications, scripts, bots, or tools without relying solely on official SDKs. The wrapper supports streaming responses, model selection, and handling of the web-based authentication/session mechanisms used by Google’s interface. While the project offers a powerful integration, users should note that the API is reverse-engineered (not officially supported by Google) and may face changes or rate-limits. The project is licensed under AGPL-3.0, emphasizing the “open” nature but also requiring derivative works to remain open. It has a strong community following and active discussions/issue tracking around model support, error handling, and new features. -
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Hindsight
Hindsight: Agent Memory That Learns
Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works. Instead of relying solely on vector similarity or basic retrieval techniques, Hindsight organizes information into distinct categories such as facts, experiences, beliefs, and observations, allowing agents to differentiate between raw data and inferred knowledge. The system operates through three core mechanisms—retain, recall, and reflect—which respectively handle storing information, retrieving relevant context, and generating new insights based on accumulated experience. -
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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. Deploy to the cloud or on-premise. -
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LlamaParse
Parse files for optimal RAG
LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers. -
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MCP Grafana
MCP server for Grafana
The Grafana MCP Server is a Model Context Protocol (MCP) server designed to provide access to Grafana instances and their surrounding ecosystems. It enables seamless integration with Grafana's visualization and monitoring capabilities. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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MONAI
AI Toolkit for Healthcare Imaging
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm. MONAI is an open-source project. It is built on top of PyTorch and is released under the Apache 2.0 license. Aiming to capture best practices of AI development for healthcare researchers, with an immediate focus on medical imaging. Providing user-comprehensible error messages and easy to program API interfaces. Provides reproducibility of research experiments for comparisons against state-of-the-art implementations. -
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NanoClaw
A lightweight alternative to Clawdbot / OpenClaw
Nanoclaw is a lightweight, security-focused personal agent runtime designed as a slimmer alternative to larger “personal assistant” agent stacks, with an emphasis on being easy to audit and safe by default. It runs agent execution inside Apple containers to provide strong isolation boundaries, so individual chats and actions can be sandboxed with tighter filesystem and process separation than a typical single-process bot. The project connects directly to WhatsApp, letting you deploy an assistant that can chat in a familiar interface while still supporting real agent behaviors instead of simple call-and-response prompts. It includes memory so the assistant can retain important context across interactions, enabling more consistent follow-through on ongoing tasks. It also supports scheduled jobs, making it suitable for recurring reminders, periodic automations, and timed workflows without needing an external orchestrator. -
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Nimbalyst
Run multiple Codex and Claude Code AI sessions
Crystal is an open-source project focused on building a lightweight and flexible system for managing structured data, workflows, or automation pipelines, typically oriented toward developer productivity and extensible backend tooling. It is designed with modularity in mind, allowing developers to define reusable components and compose them into larger workflows that can adapt to different use cases. The project emphasizes simplicity and clarity, making it easier to understand and extend compared to heavier enterprise frameworks. Crystal often leverages modern programming practices and clean architecture principles to ensure maintainability and scalability as projects grow. It can be used as a foundation for building internal tools, automation systems, or data processing pipelines, depending on how developers configure its components. The system is particularly useful for teams that want control over their infrastructure without relying on overly complex or opinionated platforms. -
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Obot MCP Gateway
Hosting, Registry, Gateway, and Chat Client
Obot is an open-source platform built to help organizations adopt and operate Model Context Protocol (MCP) capabilities in a centralized, production-friendly way. It combines multiple MCP building blocks into one system, including hosting for MCP servers, a registry for discovery, a gateway layer to route access, and a standards-compliant chat client experience. The project is aimed at solving common enterprise rollout problems such as reliably hosting servers for internal and external users, curating “approved” MCP servers for employees to find, and enforcing authentication, access control, and auditable activity. It also supports building richer agents and chatbots that can leverage MCP servers while keeping operations manageable for IT and platform teams. The platform is designed to work with a variety of workflows and clients, so MCP servers managed inside Obot can be used by automation/agent frameworks as well as popular chat clients that speak MCP. -
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OpenPose
Real-time multi-person keypoint detection library for body, face, etc.
OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. It is authored by Ginés Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Yaadhav Raaj, Hanbyul Joo, and Yaser Sheikh. It is maintained by Ginés Hidalgo and Yaadhav Raaj. OpenPose would not be possible without the CMU Panoptic Studio dataset. We would also like to thank all the people who has helped OpenPose in any way. 15, 18 or 25-keypoint body/foot keypoint estimation, including 6 foot keypoints. Runtime invariant to number of detected people. 2x21-keypoint hand keypoint estimation. Runtime depends on number of detected people. 70-keypoint face keypoint estimation. Runtime depends on number of detected people. Input: Image, video, webcam, Flir/Point Grey, IP camera, and support to add your own custom input source (e.g., depth camera). -
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Plugins Quickstart
Get a ChatGPT plugin up and running in under 5 minutes
plugins-quickstart is a starter project created by OpenAI to help developers build and deploy ChatGPT plugins quickly. It provides a minimal but complete example of how to structure a plugin, implement an API, and define the necessary configuration files. The repository demonstrates how a plugin can be served, authenticated, and integrated with ChatGPT for real-world use. By including both the backend code and plugin manifest, it guides developers through the end-to-end development workflow. This makes it a useful resource for those experimenting with extending ChatGPT capabilities or adding custom functionality to their own workflows. Designed to be simple and approachable, plugins-quickstart allows developers to learn plugin mechanics without dealing with unnecessary complexity. -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task. -
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Qwen3-VL
Qwen3-VL, the multimodal large language model series by Alibaba Cloud
Qwen3-VL is the latest multimodal large language model series from Alibaba Cloud’s Qwen team, designed to integrate advanced vision and language understanding. It represents a major upgrade in the Qwen lineup, with stronger text generation, deeper visual reasoning, and expanded multimodal comprehension. The model supports dense and Mixture-of-Experts (MoE) architectures, making it scalable from edge devices to cloud deployments, and is available in both instruction-tuned and reasoning-enhanced variants. Qwen3-VL is built for complex tasks such as GUI automation, multimodal coding (converting images or videos into HTML, CSS, JS, or Draw.io diagrams), long-context reasoning with support up to 1M tokens, and comprehensive video understanding. It also brings advanced perception capabilities, including spatial grounding, object recognition, OCR across 32 languages, and robust handling of challenging inputs like low-light or distorted text. -
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Recogito JS
A JavaScript library for text annotation
A JavaScript library for text annotation. Use it to add annotation functionality to a web page, or as a toolbox for building your own, completely custom annotation apps. Try the online demo or see the API reference. -
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Telegram Bot SDK
Telegram Bot API PHP SDK to build Telegram Bots
Telegram Bot SDK lets you develop Telegram Bots in PHP easily! Supports Laravel out of the box. Telegram Bot API is an HTTP-based interface created for developers keen on building bots for Telegram. Complete API Methods Support. Laravel Support out of the box! Clean, Highly Documented & Industry Standard Code. PSR Standards, popular & widely used. Powered by Laravel Collection API for Response Objects. Events/Plugin Support to Extend Features Conversational Support. Well-designed, tested & built. Active support from the author as well as the community. If you're using this SDK with Laravel, the SDK will self-register its ServiceProvider and Facade using Laravel's auto-discovery. If you're using this SDK to build your Telegram Bots, We'd love to know and share the bot with the world. -
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Telegram.Bot
.NET Client for Telegram Bot API
Telegram.Bot is the most popular .NET Client for Telegram Bot API. The Bot API is an HTTP-based interface created for developers keen on building bots for Telegram. Check Bots: An introduction for developers to understand what a Telegram bot is and what it can do. All Bot API methods are already documented by Telegram but this book covers all you need to know to create a chatbot in .NET. There are also many concrete examples written in C#. The guides here can even be useful to bot developers using other languages/platforms as it shows best practices in developing Telegram chatbots with examples. This project is fully tested using Unit tests and Systems Integration tests before each release. In fact, our test cases are self-documenting and serve as examples for Bot API methods. Once you learn the basics of Telegram chatbots, you will be able to easily understand the code in examples and use it in your own bot program. -
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Translate-Subtitle-File
Subtitle Creation Assistant
Subtitle group machine translation assistant - [Function 1: Translate subtitle file] .srt .ass .vtt [Function 2: Voice to text] (Drag in video or audio to recognize subtitles) (The latest version v4.1.0 Update time 2021 2 May 23) 12 translation service providers can be configured, such as Google, Baidu, Tencent, Caiyun, IBM, Azure, Amazon, etc. (6 voice service providers can be configured: Alibaba Cloud, Xunfei, Tencent Cloud, IBM, Azure, Amazon ) Advantages: 1. You can use multiple service providers, 2. You can configure your own API Key to use your own account's free quota, such as Tencent's free translation quota of 5 million characters per month, IBM's 500-minute speech-to-text free quota (tern. best The domain name has expired and I don't want to renew it.) Azure speech-to-text and DeepL free version have problems, it is normal to not use it, please wait for the next version to fix. Machine translation of subtitle files, use machine translation to process files. -
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VGGFace2
VGGFace2 Dataset for Face Recognition
VGGFace2 is a large-scale face recognition dataset developed to support research on facial recognition across variations in pose, age, illumination, and identity. It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities, making it suitable for benchmarking and large-scale model training. Alongside the dataset, the repository provides pre-trained models based on ResNet-50 and SE-ResNet-50 architectures, trained with both MS-Celeb-1M pretraining and fine-tuning on VGGFace2. These models achieve strong verification performance on benchmarks such as IJB-B and include variants with lower-dimensional embeddings for compact feature representation. The project also includes preprocessing tools, face detection scripts, and etc. -
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VoxCPM2
Tokenizer-Free TTS for Multilingual Speech Generation
VoxCPM2 is an advanced open-source text-to-speech system that redefines speech synthesis by eliminating traditional tokenization and instead generating continuous speech representations through a diffusion-based autoregressive architecture. Built on top of the MiniCPM model family, it enables highly natural, expressive, and context-aware speech generation that adapts tone, emotion, and pacing directly from input text. The system is trained on massive multilingual datasets, enabling support for dozens of languages and dialects while maintaining high fidelity and realism in generated audio. VoxCPM stands out for its ability to perform voice cloning with minimal input, capturing not only the speaker’s timbre but also nuanced features such as rhythm, accent, and emotional delivery. It also introduces voice design capabilities, allowing users to generate entirely new voices from natural language descriptions without requiring reference audio. -
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sag
Like the macOS say command, but with a modern voice
sag is a command-line text-to-speech utility inspired by the macOS say command but powered by modern ElevenLabs voice synthesis technology. The project allows users to stream synthesized speech directly to speakers, save audio files, or list and manage available voices through a lightweight terminal interface. Designed for speed and convenience, sag supports voice selection, playback rate adjustments, output format inference, and configurable API endpoints for flexible deployment. It integrates naturally into shell scripts, automation pipelines, and AI workflows where high-quality voice synthesis is required without complex setup. The tool emphasizes a familiar UNIX-style experience while significantly improving realism and expressiveness compared to traditional system speech engines. Its developer-focused architecture and Homebrew installation support make it especially appealing for terminal-centric users and automation-heavy environments.
