The MCP TypeScript Server Template is a production-grade framework for building powerful and scalable Model Context Protocol servers with TypeScript. It features built-in observability, declarative tooling, robust error handling, and a modular, DI-driven architecture. The template is designed to be AI-agent-friendly, providing detailed rules and guidance for developers to adhere to best practices. It enforces architectural principles like 'Logic Throws, Handler Catches' pattern, full-stack observability, declarative components, and dependency injection for decoupling. The project structure includes directories for configuration, container setup, server resources, services, storage, utilities, tests, and more. Configuration is done via environment variables, and key scripts are available for development, testing, and publishing to the MCP Registry.

DeepTutor is an AI-powered personalized learning assistant that offers a suite of modules for massive document knowledge Q&A, interactive learning visualization, knowledge reinforcement with practice exercise generation, deep research, and idea generation. The tool supports multi-agent collaboration, dynamic topic queues, and structured outputs for various tasks. It provides a unified system entry for activity tracking, knowledge base management, and system status monitoring. DeepTutor is designed to streamline learning and research processes by leveraging AI technologies and interactive features.

mcp-prompts is a Python library that provides a collection of prompts for generating creative writing ideas. It includes a variety of prompts such as story starters, character development, plot twists, and more. The library is designed to inspire writers and help them overcome writer's block by offering unique and engaging prompts to spark creativity. With mcp-prompts, users can access a wide range of writing prompts to kickstart their imagination and enhance their storytelling skills.

multi-agent-shogun is a system that runs multiple AI coding CLI instances simultaneously, orchestrating them like a feudal Japanese army. It supports Claude Code, OpenAI Codex, GitHub Copilot, and Kimi Code. The system allows you to command your AI army with zero coordination cost, enabling parallel execution, non-blocking workflow, cross-session memory, event-driven communication, and full transparency. It also features skills discovery, phone notifications, pane border task display, shout mode, and multi-CLI support.

MCP Fusion is a Model-View-Agent framework for the Model Context Protocol, providing structured perception for AI agents with validated data, domain rules, UI blocks, and action affordances in every response. It introduces the MVA pattern, where a Presenter layer sits between data and the AI agent, ensuring consistent, validated, contextually-rich data across the API surface. The tool facilitates schema validation, system rules, UI blocks, cognitive guardrails, and action affordances for domain entities. It offers tools for defining actions, prompts, middleware, error handling, type-safe clients, observability, streaming progress, and more, all integrated with the Model Context Protocol SDK and Zod for type safety and validation.

Antigravity Workflow System is a structured workflow framework for Agentic AI assistants, addressing core pain points of the Vibe Coding era. It enforces architecture design first, tackles issues like architecture drift, spaghetti code, context amnesia, and lack of planning. The system includes workflows like `/genesis`, `/scout`, `/design-system`, `/challenge`, `/blueprint`, `/forge`, `/change`, `/explore`, and `/craft`, emphasizing versioned architecture, deep thinking first, and filesystem as memory. It requires the Antigravity environment with `.agent/workflows/` support and Sequential Thinking MCP Server for deep reasoning. Users can invoke workflows using the Slash Protocol or Intent Protocol, with a defined project structure under `.agent/` directory. Contributions are encouraged, and the system is licensed under MIT.

AIO Sandbox is an all-in-one agent sandbox environment that combines Browser, Shell, File, MCP operations, and VSCode Server in a single Docker container. It provides a unified, secure execution environment for AI agents and developers, with features like unified file system, multiple interfaces, secure execution, zero configuration, and agent-ready MCP-compatible APIs. The tool allows users to run shell commands, perform file operations, automate browser tasks, and integrate with various development tools and services.

EasyClaw is a desktop application that simplifies the usage of OpenClaw, a powerful agent runtime, by providing a user-friendly interface for non-programmers. Users can write rules in plain language, configure multiple LLM providers and messaging channels, manage API keys, and interact with the agent through a local web panel. The application ensures data privacy by keeping all information on the user's machine and offers features like natural language rules, multi-provider LLM support, Gemini CLI OAuth, proxy support, messaging integration, token tracking, speech-to-text, file permissions control, and more. EasyClaw aims to lower the barrier of entry for utilizing OpenClaw by providing a user-friendly cockpit for managing the engine.

Lihil is a performant, productive, and professional web framework designed to make Python the mainstream programming language for web development. It is 100% test covered and strictly typed, offering fast performance, ergonomic API, and built-in solutions for common problems. Lihil is suitable for enterprise web development, delivering robust and scalable solutions with best practices in microservice architecture and related patterns. It features dependency injection, OpenAPI docs generation, error response generation, data validation, message system, testability, and strong support for AI features. Lihil is ASGI compatible and uses starlette as its ASGI toolkit, ensuring compatibility with starlette classes and middlewares. The framework follows semantic versioning and has a roadmap for future enhancements and features.

Cordum is a control plane for AI agents designed to close the Trust Gap by providing safety, observability, and control features. It allows teams to deploy autonomous agents with built-in governance mechanisms, including safety policies, workflow orchestration, job routing, observability, and human-in-the-loop approvals. The tool aims to address the challenges of deploying AI agents in production by offering visibility, safety rails, audit trails, and approval mechanisms for sensitive operations.

thepopebot is a self-evolving agent that operates through git commits, utilizing free cloud computing time from GitHub accounts. It modifies its own code through pull requests, ensuring auditability and reversibility. Users interact with the bot via web chat or Telegram, creating job branches that trigger Docker containers to perform tasks and open pull requests. Auto-merge handles the completion process, providing notifications upon task completion.

A swarm-native terminal platform designed to replace legacy terminal workflows for massive AI agent orchestration. `ft` is a full terminal platform for agent swarms with first-class observability, deterministic eventing, policy-gated automation, and machine-native control surfaces. It offers perfect observability, intelligent detection, event-driven automation, Robot Mode API, lexical + hybrid search, and a policy engine for safe multi-agent control. The platform is actively expanding with concepts learned from Ghostty and Zellij, purpose-built subsystems for agent swarms, and integrations from other projects like `/dp/asupersync`, `/dp/frankensqlite`, and `/frankentui`.

VT.ai is a multimodal AI platform that offers dynamic conversation routing with SemanticRouter, multi-modal interactions (text/image/audio), an assistant framework with code interpretation, real-time response streaming, cross-provider model switching, and local model support with Ollama integration. It supports various AI providers such as OpenAI, Anthropic, Google Gemini, Groq, Cohere, and OpenRouter, providing a wide range of core capabilities for AI orchestration.

nono is a secure, kernel-enforced capability shell for running AI agents and any POSIX style process. It leverages OS security primitives to create an environment where unauthorized operations are structurally impossible. It provides protections against destructive commands and securely stores API keys, tokens, and secrets. The tool is agent-agnostic, works with any AI agent or process, and blocks dangerous commands by default. It follows a capability-based security model with defense-in-depth, ensuring secure execution of commands and protecting sensitive data.

httpjail is a cross-platform tool designed for monitoring and restricting HTTP/HTTPS requests from processes using network isolation and transparent proxy interception. It provides process-level network isolation, HTTP/HTTPS interception with TLS certificate injection, script-based and JavaScript evaluation for custom request logic, request logging, default deny behavior, and zero-configuration setup. The tool operates on Linux and macOS, creating an isolated network environment for target processes and intercepting all HTTP/HTTPS traffic through a transparent proxy enforcing user-defined rules.

The MCP TypeScript Server Template is a production-grade framework for building powerful and scalable Model Context Protocol servers with TypeScript. It features built-in observability, declarative tooling, robust error handling, and a modular, DI-driven architecture. The template is designed to be AI-agent-friendly, providing detailed rules and guidance for developers to adhere to best practices. It enforces architectural principles like 'Logic Throws, Handler Catches' pattern, full-stack observability, declarative components, and dependency injection for decoupling. The project structure includes directories for configuration, container setup, server resources, services, storage, utilities, tests, and more. Configuration is done via environment variables, and key scripts are available for development, testing, and publishing to the MCP Registry.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.

PAR LLAMA is a Text UI application for managing and using LLMs, designed with Textual and Rich and PAR AI Core. It runs on major OS's including Windows, Windows WSL, Mac, and Linux. Supports Dark and Light mode, custom themes, and various workflows like Ollama chat, image chat, and OpenAI provider chat. Offers features like custom prompts, themes, environment variables configuration, and remote instance connection. Suitable for managing and using LLMs efficiently.

An efficient OCR engine for receipt image processing, providing a comprehensive solution for Optical Character Recognition (OCR) on receipt images. The repository includes a dedicated Tesseract OCR module and a general receipt processing package using LLMs. Users can extract structured data from receipts, configure environment variables for multiple LLM providers, process receipts using CLI or programmatically in Python, and run the OCR engine as a Docker web service. The project also offers direct OCR capabilities using Tesseract and provides troubleshooting tips, contribution guidelines, and license information under the MIT license.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository contains recipes for building and running containerized AI and LLM applications with Podman. It provides model servers that serve machine-learning models via an API, allowing developers to quickly prototype new AI applications locally. The recipes include components like model servers and AI applications for tasks such as chat, summarization, object detection, etc. Images for sample applications and models are available in `quay.io`, and bootable containers for AI training on Linux OS are enabled.

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.