Awesome GitHub RepositoriesModel Context Protocol Integrations

Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines. The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from

This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizi

Firecrawl is a web data extraction platform designed to convert unstructured web content into clean, LLM-ready formats like markdown or JSON. It functions as an autonomous web crawler and scraper, capable of mapping entire domains, performing recursive navigation, and executing complex data gathering tasks. By leveraging headless browser orchestration, the system handles dynamic, JavaScript-heavy pages to ensure comprehensive data capture. The platform distinguishes itself through its focus on agentic workflows, providing a programmatic interface that allows autonomous agents to perform live

Graphify is a knowledge retrieval system that transforms directories of source code and documentation into structured, queryable project maps. It utilizes a code-to-graph parser to extract technical metadata and system connectivity, converting a mix of code, SQL schemas, and documentation into a unified graph structure. The project distinguishes itself by integrating these knowledge graphs with AI coding assistants through a Model Context Protocol server and dedicated tool hooks. This allows AI agents to perform lookups and impact analysis on node neighbors and shortest paths to understand ho

This project is a high-performance headless browser engine designed for scalable web automation, data extraction, and AI agent integration. It provides a specialized environment that allows autonomous agents and testing frameworks to interact with web content through standardized remote control protocols. By executing pages in a lightweight, headless state, the engine minimizes resource consumption while maintaining the ability to perform complex navigation and dynamic content rendering. The platform distinguishes itself through deep integration with AI-centric communication layers and advanc

Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large language models. It functions as an automated evaluation pipeline that systematically adjusts prompt structure, constraints, and clarity to improve the accuracy and consistency of model outputs. The system distinguishes itself through a model-agnostic interface that standardizes communication across different artificial intelligence providers. It incorporates a versioned asset management system to track prompt history, enabling developers to maintain consistency and perform r

This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

Superset is an agentic development environment designed to orchestrate autonomous AI coding agents. It functions as a workspace where multiple command-line based agents can run in parallel, utilizing a persistent terminal multiplexer to maintain long-lived shell sessions and state. The project distinguishes itself through the use of Git worktrees to provide physical directory isolation for each task, preventing merge conflicts during concurrent agent operations. It incorporates a Model Context Protocol client to extend agent capabilities via external tools and data, while keeping execution en

This project functions as a Model Context Protocol server and a multi-agent orchestration framework designed to bridge large language models with external data sources and specialized engineering tools. It provides a structured environment for automating software development workflows, enabling models to interact directly with codebases and remote services to perform complex tasks. The system distinguishes itself through a multi-agent orchestration layer that coordinates autonomous assistants to manage shared objectives and multi-step workflows. By utilizing structured task decomposition and

Nezha is a multi-server infrastructure monitor and website uptime monitor that provides a centralized dashboard for tracking real-time resource utilization and system health. It functions as a protocol server and alerting engine, utilizing remote agents to collect telemetry data across multiple operating systems. The system distinguishes itself with a web-based remote administration interface, allowing users to execute maintenance commands and manage scheduled tasks on remote hosts via a browser-based terminal. It also integrates a Model Context Protocol server to provide a secure HTTP entry

BrowserOS is an AI agent browser orchestrator and automation framework designed to manage browser state and execute complex web workflows. It functions as a local AI browser assistant and a Model Context Protocol controller, enabling the control of browser tabs, windows, and navigation through programmable AI agents and standardized context protocols. The system distinguishes itself through a graph-based visual workflow builder for creating repeatable automation sequences and the use of markdown-based files to define agent personalities and task recipes. It supports multi-provider orchestrati

Intel XPU LLM Acceleration Library is a toolkit designed to accelerate large language model inference and finetuning on Intel CPUs, GPUs, and NPUs. It provides a distributed inference engine for scaling models across multiple accelerators, a multimodal model runtime for vision and speech tasks, and a low-bit model quantization tool for converting weights into INT4, FP8, and GGUF formats. The project features a parameter-efficient finetuning framework that enables model adaptation using QLoRA and DPO on Intel hardware. It distinguishes itself by providing specialized optimizations for Intel XP

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and

Steampipe is a cloud infrastructure query engine and API-to-SQL mapper that translates REST and GraphQL API responses into relational rows and columns. It allows for the retrieval and joining of real-time data from multiple cloud service providers using a relational database interface. The project functions as a PostgreSQL foreign data wrapper and an SQLite API extension, mapping external API endpoints to virtual tables. This enables the use of standard SQL to query live cloud services and aggregate data from different providers and service accounts into a single unified dataset. The system

Coroot is an observability platform and Kubernetes performance monitor that utilizes eBPF to automatically collect metrics, logs, and traces without requiring manual code instrumentation. It functions as an OpenTelemetry trace analyzer and an LLM observability gateway, exposing system health data to large language models through the Model Context Protocol. The platform differentiates itself by combining automated root cause analysis and AI-driven diagnostics to investigate performance regressions. It also includes a cloud cost monitoring tool that attributes infrastructure spending to specifi

code2prompt is a codebase-to-prompt converter and LLM context generator that transforms source code and directory structures into formatted text blocks for large language models. It functions as both a utility for generating prompts and an AI agent context server that exposes codebase files and metadata to coding assistants via a standardized server protocol. The tool distinguishes itself through git-aware capabilities, integrating commit messages and branch diffs to provide version control context for AI-generated code changes. It also utilizes the Model Context Protocol to allow external AI

This project is an AI-powered code reviewer and static analysis server that identifies low-quality files and generates automated critiques. It functions as an automated quality scoring tool that evaluates source code structure and complexity through local parsing. The system utilizes a standardized context protocol to stream analysis results to AI agents and editors. It integrates large language models to produce automated reviews and suggestions for improvement based on quantitative quality metrics. The tool includes a weight-based scoring engine and an asynchronous analysis pipeline for pr

Kimi is a terminal-based AI agent that autonomously plans and executes software development tasks by reading, editing, and running code. It operates as an intelligent command-line agent that breaks down high-level goals into sequences of shell commands and code edits, carrying them out without manual step-by-step guidance. The agent can run in an interactive loop, switch to a shell mode for direct terminal command execution, and operate in non-interactive or one-shot modes suitable for scripting. The project distinguishes itself through multiple integration and execution modes. It can run as