uLoopMCP is a Unity integration tool designed to let AI drive your Unity project forward with minimal human intervention. It provides a 'self-hosted development loop' where an AI can compile, run tests, inspect logs, and fix issues using tools like compile, run-tests, get-logs, and clear-console. It also allows AI to operate the Unity Editor itself—creating objects, calling menu items, inspecting scenes, and refining UI layouts from screenshots via tools like execute-dynamic-code, execute-menu-item, and capture-window. The tool enables AI-driven development loops to run autonomously inside existing Unity projects.

The 'claude-code-tools' repository provides productivity tools for Claude Code, Codex-CLI, and similar CLI coding agents. It includes CLI commands, skills, agents, hooks, and plugins for various tasks. The tools cover functionalities like session search, terminal automation, encrypted backup and sync, safe inspection of .env files, safety hooks, voice feedback, session chain repair, conversion between markdown and Google Docs, and CSV to Google Sheets and vice versa. The repository architecture consists of Python CLI, Rust TUI for search, and Node.js for action menus.

LEANN is an innovative vector database that democratizes personal AI, transforming your laptop into a powerful RAG system that can index and search through millions of documents using 97% less storage than traditional solutions without accuracy loss. It achieves this through graph-based selective recomputation and high-degree preserving pruning, computing embeddings on-demand instead of storing them all. LEANN allows semantic search of file system, emails, browser history, chat history, codebase, or external knowledge bases on your laptop with zero cloud costs and complete privacy. It is a drop-in semantic search MCP service fully compatible with Claude Code, enabling intelligent retrieval without changing your workflow.

Forge is a powerful open-source tool for building modern web applications. It provides a simple and intuitive interface for developers to quickly scaffold and deploy projects. With Forge, you can easily create custom components, manage dependencies, and streamline your development workflow. Whether you are a beginner or an experienced developer, Forge offers a flexible and efficient solution for your web development needs.

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with Large Language Models (LLMs) to provide accurate question-answering capabilities. It offers a streamlined RAG workflow for businesses of all sizes, enabling them to extract knowledge from unstructured data in various formats, including Word documents, slides, Excel files, images, and more. RAGFlow's key features include deep document understanding, template-based chunking, grounded citations with reduced hallucinations, compatibility with heterogeneous data sources, and an automated and effortless RAG workflow. It supports multiple recall paired with fused re-ranking, configurable LLMs and embedding models, and intuitive APIs for seamless integration with business applications.

PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. It is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. The tool provides secure and isolated operations in a sandboxed Docker environment, fully autonomous AI-powered agent for penetration testing steps, a suite of 20+ professional security tools, smart memory system for storing research results, web intelligence for gathering information, integration with external search systems, team delegation system, comprehensive monitoring and reporting, modern interface, API integration, persistent storage, scalable architecture, self-hosted solution, flexible authentication, and quick deployment through Docker Compose.

iloom is a tool designed to streamline AI-assisted development by focusing on maintaining alignment between human developers and AI agents. It treats context as a first-class concern, persisting AI reasoning in issue comments rather than temporary chats. The tool allows users to collaborate with AI agents in an isolated environment, switch between complex features without losing context, document AI decisions publicly, and capture key insights and lessons learned from AI sessions. iloom is not just a tool for managing git worktrees, but a control plane for maintaining alignment between users and their AI assistants.

Factorio Learning Environment is an open source framework designed for developing and evaluating LLM agents in the game of Factorio. It provides two settings: Lab-play with structured tasks and Open-play for building large factories. Results show limitations in spatial reasoning and automation strategies. Agents interact with the environment through code synthesis, observation, action, and feedback. Tools are provided for game actions and state representation. Agents operate in episodes with observation, planning, and action execution. Tasks specify agent goals and are implemented in JSON files. The project structure includes directories for agents, environment, cluster, data, docs, eval, and more. A database is used for checkpointing agent steps. Benchmarks show performance metrics for different configurations.

The docs-mcp-server repository contains the server-side code for the documentation management system. It provides functionalities for managing, storing, and retrieving documentation files. Users can upload, update, and delete documents through the server. The server also supports user authentication and authorization to ensure secure access to the documentation system. Additionally, the server includes APIs for integrating with other systems and tools, making it a versatile solution for managing documentation in various projects and organizations.

The MCP LLMS-TXT Documentation Server is an open-source server that provides developers full control over tools used by applications like Cursor, Windsurf, and Claude Code/Desktop. It allows users to create a user-defined list of `llms.txt` files and use a `fetch_docs` tool to read URLs within these files, enabling auditing of tool calls and context returned. The server supports various applications and provides a way to connect to them, configure rules, and test tool calls for tasks related to documentation retrieval and processing.

AutoAgent is a fully-automated and zero-code framework that enables users to create and deploy LLM agents through natural language alone. It is a top performer on the GAIA Benchmark, equipped with a native self-managing vector database, and allows for easy creation of tools, agents, and workflows without any coding. AutoAgent seamlessly integrates with a wide range of LLMs and supports both function-calling and ReAct interaction modes. It is designed to be dynamic, extensible, customized, and lightweight, serving as a personal AI assistant.

Aidermacs is an AI pair programming tool for Emacs that integrates Aider, a powerful open-source AI pair programming tool. It provides top performance on the SWE Bench, support for multi-file edits, real-time file synchronization, and broad language support. Aidermacs delivers an Emacs-centric experience with features like intelligent model selection, flexible terminal backend support, smarter syntax highlighting, enhanced file management, and streamlined transient menus. It thrives on community involvement, encouraging contributions, issue reporting, idea sharing, and documentation improvement.

Golf is a simple command-line tool for calculating the distance between two geographic coordinates. It uses the Haversine formula to accurately determine the distance between two points on the Earth's surface. This tool is useful for developers working on location-based applications or projects that require distance calculations. With Golf, users can easily input latitude and longitude coordinates and get the precise distance in kilometers or miles. The tool is lightweight, easy to use, and can be integrated into various programming workflows.

AgenticSeek is a voice-enabled AI assistant powered by DeepSeek R1 agents, offering a fully local alternative to cloud-based AI services. It allows users to interact with their filesystem, code in multiple languages, and perform various tasks autonomously. The tool is equipped with memory to remember user preferences and past conversations, and it can divide tasks among multiple agents for efficient execution. AgenticSeek prioritizes privacy by running entirely on the user's hardware without sending data to the cloud.

MCP for Unity is a tool that acts as a bridge, enabling AI assistants to interact with the Unity Editor via a local MCP Client. Users can instruct their LLM to manage assets, scenes, scripts, and automate tasks within Unity. The tool offers natural language control, powerful tools for asset management, scene manipulation, and automation of workflows. It is extensible and designed to work with various MCP Clients, providing a range of functions for precise text edits, script management, GameObject operations, and more.

ctx is a tool designed to automatically generate organized context files from code files, GitHub repositories, Git commits, web pages, and plain text. It aims to efficiently provide necessary context to AI language models like ChatGPT and Claude, enabling users to streamline code refactoring, multiple iteration development, documentation generation, and seamless AI integration. With ctx, users can create structured markdown documents, save context files, and serve context through an MCP server for real-time assistance. The tool simplifies the process of sharing project information with AI assistants, making AI conversations smarter and easier.

uLoopMCP is a Unity integration tool designed to let AI drive your Unity project forward with minimal human intervention. It provides a 'self-hosted development loop' where an AI can compile, run tests, inspect logs, and fix issues using tools like compile, run-tests, get-logs, and clear-console. It also allows AI to operate the Unity Editor itself—creating objects, calling menu items, inspecting scenes, and refining UI layouts from screenshots via tools like execute-dynamic-code, execute-menu-item, and capture-window. The tool enables AI-driven development loops to run autonomously inside existing Unity projects.

Dash AI is an open-source coding assistant for Flutter developers. It is designed to not only write code but also run and debug it, allowing it to assist beyond code completion and automate routine tasks. Dash AI is powered by Gemini, integrated with the Dart Analyzer, and specifically tailored for Flutter engineers. The vision for Dash AI is to create a single-command assistant that can automate tedious development tasks, enabling developers to focus on creativity and innovation. It aims to assist with the entire process of engineering a feature for an app, from breaking down the task into steps to generating exploratory tests and iterating on the code until the feature is complete. To achieve this vision, Dash AI is working on providing LLMs with the same access and information that human developers have, including full contextual knowledge, the latest syntax and dependencies data, and the ability to write, run, and debug code. Dash AI welcomes contributions from the community, including feature requests, issue fixes, and participation in discussions. The project is committed to building a coding assistant that empowers all Flutter developers.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It facilitates communication with the Ollama server and provides models for deployment. The tool requires Java 11 or higher and can be installed locally or via Docker. Users can integrate Ollama4j into Maven projects by adding the specified dependency. The tool offers API specifications and supports various development tasks such as building, running unit tests, and integration tests. Releases are automated through GitHub Actions CI workflow. Areas of improvement include adhering to Java naming conventions, updating deprecated code, implementing logging, using lombok, and enhancing request body creation. Contributions to the project are encouraged, whether reporting bugs, suggesting enhancements, or contributing code.

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

Lightning Lab is a public template for artificial intelligence and machine learning research projects using Lightning AI's PyTorch Lightning. It provides a structured project layout with modules for command line interface, experiment utilities, Lightning Module and Trainer, data acquisition and preprocessing, model serving APIs, project configurations, training checkpoints, technical documentation, logs, notebooks for data analysis, requirements management, testing, and packaging. The template simplifies the setup of deep learning projects and offers extras for different domains like vision, text, audio, reinforcement learning, and forecasting.

Magic_Words is a repository containing code for the paper 'What's the Magic Word? A Control Theory of LLM Prompting'. It implements greedy back generation and greedy coordinate gradient (GCG) to find optimal control prompts (magic words). Users can set up a virtual environment, install the package and dependencies, and run example scripts for pointwise control and optimizing prompts for datasets. The repository provides scripts for finding optimal control prompts for question-answer pairs and dataset optimization using the GCG algorithm.

This repository contains separate packages for Grafana LLM Plugin and the @grafana/llm package for interfacing with it. The packages are tightly coupled and developed together with identical dependencies. The repository provides instructions for developing the packages, including backend and frontend development, testing, and release processes.

This repository provides a guide for setting up tools for crewAI agents to enhance functionality. It offers steps to equip agents with ready-to-use tools and create custom ones. Tools are expected to return strings for generating responses. Users can create tools by subclassing BaseTool or using the tool decorator. Contributions are welcome to enrich the toolset, and guidelines are provided for contributing. The development setup includes installing dependencies, activating virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. The goal is to empower AI solutions through advanced tooling.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.