55 repositorios
Interfaces using the Model Context Protocol to expose system data and functions to AI models.
Distinct from AI Assistant Integrations: Specifically implements the Model Context Protocol for AI assistant data exposure, rather than general tool integration.
Explore 55 awesome GitHub repositories matching artificial intelligence & ml · Model Context Protocol Integrations. Refine with filters or upvote what's useful.
ECC es un framework de orquestación de agentes LLM y una suite de herramientas de IA multiplataforma diseñada para coordinar flujos de trabajo de múltiples modelos. Proporciona un sistema para gestionar roles de agentes especializados, habilidades reutilizables y planificación estructurada para ejecutar tareas complejas de desarrollo de software a través de diferentes editores de código impulsados por IA. El proyecto se distingue como un gestor de Protocolo de Contexto de Modelo, proporcionando una capa de configuración para integrar servidores externos y auditar la ejecución de herramientas. Además, implementa un sandbox de seguridad agentic que restringe el acceso a archivos confidenciales y escanea en busca de fugas de secretos para asegurar flujos de trabajo autónomos. El framework cubre amplias áreas de capacidad, incluyendo la automatización del flujo de trabajo de codificación de IA con barandillas de desarrollo impulsado por pruebas, optimización de costos de modelos a través de enrutamiento inteligente y gestión de memoria con estado aislado. También incluye herramientas para hacer cumplir los estándares de codificación específicos del lenguaje y gestionar los comportamientos de los agentes a través de varios entornos de desarrollo integrados. El sistema se gestiona a través de una interfaz de línea de comandos que maneja la instalación de herramientas, la reparación de configuración y la implementación de preajustes de herramientas.
Provides Model Context Protocol integrations to expose system data and functions to AI agents.
Mempalace is a local-first long-term memory store for large language models and AI agents. It provides a persistent storage system for verbatim conversation history and agent data, utilizing a local-first knowledge graph to track evolving entity relationships and timelines. The project implements a standardized memory protocol that allows external AI clients to read and write persistent memory via standard input and output. It features a hybrid semantic search engine that combines keyword boosting and reranking to find precise historical information across scoped categories. The system inclu
Implements the Model Context Protocol to expose memory structures and agent diaries to AI models.
LocalAI is a local generative AI platform and inference engine designed to host large language, vision, and audio models on private hardware. It functions as an API compatible gateway that mimics proprietary service endpoints, allowing existing third-party software to integrate with a self-hosted backend. The platform distinguishes itself as a distributed AI model orchestrator, capable of scaling inference across machine clusters using VRAM-aware routing and hardware coordination. It provides a unified interface for diverse open-source backends and supports self-hosted RAG infrastructure thro
Implements Model Context Protocol to expose system data and functions to AI models.
Roo-Code is an editor extension and AI agent orchestrator designed to automate software engineering tasks. It functions as an LLM-powered tool that generates source code from natural language descriptions and manages autonomous agents directly within the development environment. The system distinguishes itself through the use of role-based behavioral profiles, allowing the agent to switch between personas such as Architect or Debugger to align with different project phases. It also operates as a Model Context Protocol client, connecting to external servers to expand the data sources and tools
Uses the Model Context Protocol to connect agents to external servers for data retrieval and tool execution.
gbrain is an agent framework and retrieval-augmented generation system that combines a durable task queue, a git-synced vector store, and a knowledge graph engine. It provides a foundation for building AI agents that interact with structured knowledge bases using the Model Context Protocol. The system synchronizes markdown files from a git repository into a database for high-performance semantic retrieval and creates typed edges between data pages by extracting entity references and wikilinks. It uses a database-backed queue to execute persistent background jobs and tool loops, ensuring relia
Exposes internal tools and data to AI agents using the Model Context Protocol for direct interaction.
AgentMemory is a persistent knowledge store and memory server designed to provide AI coding agents with long-term memory. It functions as a knowledge graph engine and vector database store that saves and recalls project context, architectural decisions, and patterns across different sessions. The system distinguishes itself by using a tiered-memory consolidation pipeline that compresses raw observations into episodic, semantic, and procedural layers to optimize token usage. It employs a hybrid retrieval strategy combining keyword matching, vector embeddings, and graph traversal to surface rel
Provides standardized MCP tools that allow agents to explicitly save and analyze project patterns.
Lens is a multi-cluster management platform and desktop application for administering Kubernetes environments. It provides a graphical interface for deploying Helm charts, editing YAML manifests, and managing the lifecycle of pods and deployments. The project features an AI-powered cluster assistant that enables users to query cluster state, perform autonomous troubleshooting, and translate natural language requests into system commands. It also supports collaborative team access through shared spaces, utilizing encrypted cluster sharing and role-based access control to manage credentials and
Implements the Model Context Protocol to allow external AI agents to interact with cluster data.
Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level
Implements the Model Context Protocol to integrate external tool servers and registries for expanded AI capabilities.
This project is a static code analysis tool and local-first code indexer that builds a persistent dependency graph of functions, classes, and imports. It functions as an AI context optimizer and codebase dependency graph, designed to reduce token usage by providing AI assistants with only the most relevant code fragments and impact analysis for a given change. The system implements a Model Context Protocol server that exposes code intelligence and architectural graph queries to external AI coding tools. It distinguishes itself by computing the change blast radius and risk scores of modificati
Implements a Model Context Protocol server to deliver compact summaries and impact reports to AI coding assistants.
WeKnora is a multi-tenant retrieval-augmented generation (RAG) knowledge platform and autonomous AI agent framework. It transforms raw documents into queryable knowledge bases and integrates large language models with vector databases to provide grounded AI responses. The system also functions as a Model Context Protocol (MCP) tool server, exposing knowledge search and agentic capabilities to external AI clients. The platform distinguishes itself through an autonomous agent framework that utilizes iterative reasoning, tool calling, and web search to solve multi-step tasks. It implements a sta
Implements the Model Context Protocol to expose internal knowledge and agent tools to external AI clients.
Xpipe is a remote infrastructure management tool and cross-platform terminal orchestrator. It provides a centralized desktop interface for managing remote server connections, shell sessions, and secure tunneling. The system functions as a remote application gateway, streaming graphical applications to a local desktop via RDP, VNC, or X11. It also implements a Model Context Protocol server, which exposes server infrastructure and remote command execution capabilities to external AI agents. The tool covers several operational areas, including hierarchical connection management, remote file sys
Exposes server capabilities and remote command execution to AI agents using the Model Context Protocol.
OpenSandbox is a secure sandbox runtime and containerized code execution engine designed to run AI-generated code and scripts in isolated environments. It serves as a workload orchestrator that prevents host system contamination by utilizing kernel-level isolation to execute arbitrary commands and scripts. The project distinguishes itself by providing a model context server that bridges large language models to the sandbox for performing file operations and system commands. It also includes a remote GUI sandbox that supports browser automation and desktop interfaces via remote access protocol
Implements a bridge using the Model Context Protocol to expose sandbox operations and file systems to AI models.
MoviePilot is a self-hosted media orchestrator and NAS media library automator. It coordinates workflows between downloaders, metadata scrapers, and file systems to automate the discovery, downloading, renaming, and organization of movie and television content. The system functions as an LLM media management agent, allowing users to control subscriptions, searches, and file organization through conversational text commands. It also acts as a Model Context Protocol server, exposing internal media management tools via a standardized interface for external AI clients and agents. The project inc
Implements the Model Context Protocol to expose internal media management tools to external AI agents.
Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework
Establishes a standardized bridge between AI models and external services using the Model Context Protocol.
Higress es una API gateway nativa de IA y nativa de la nube que enruta, asegura y optimiza el tráfico entre clientes y servicios de grandes modelos de lenguaje. Funciona como un punto de entrada centralizado para microservicios, sirviendo tanto como controlador de ingreso (ingress) de Kubernetes como orquestador de puerta de enlace de IA. El proyecto se distingue por gestionar el tráfico a través de múltiples proveedores de IA utilizando un protocolo unificado, incorporando limitación de tasa consciente de tokens y almacenamiento en caché de respuestas para optimizar la inferencia del modelo. Coordina la comunicación entre modelos de IA y herramientas externas para proporcionar contexto y datos en tiempo real, al tiempo que aloja puntos finales de servidor para agentes de IA. Sus capacidades incluyen la aplicación de seguridad de API mediante firewalls de aplicaciones web, gestión automatizada de certificados TLS y descubrimiento dinámico de servicios. La puerta de enlace admite el procesamiento de solicitudes personalizadas a través de plugins de WebAssembly en sandbox que permiten la transformación del tráfico con recarga en caliente. El sistema implementa API de ingreso estandarizadas para gestionar el enrutamiento de red dentro de clústeres en contenedores con baja sobrecarga de recursos.
Implements Model Context Protocol integrations to expose real-time system data and functions to AI models.
k8sgpt es un conjunto de herramientas centradas en Kubernetes diseñadas para la depuración impulsada por IA, diagnósticos de clúster y auto-reparación. Funciona como un analizador y depurador automatizado que utiliza modelos de lenguaje grandes (LLM) para explicar errores de clúster, sugerir pasos de remediación e identificar fallos de recursos. El proyecto se distingue por un framework de análisis extensible que admite plugins de diagnóstico personalizados y un servidor de Model Context Protocol, que expone los diagnósticos del clúster como herramientas para asistentes de IA. Incluye un agente de auto-reparación capaz de generar y aplicar automáticamente correcciones para anomalías detectadas, así como middleware de anonimización de datos para enmascarar información sensible antes de que se transmita a proveedores de IA externos. El conjunto de herramientas cubre una amplia gama de capacidades operativas, incluyendo monitoreo continuo de salud mediante un operador, auditoría de cumplimiento frente a motores de políticas y orquestación multi-clúster para identificar patrones de fallo generalizados. También proporciona características de observabilidad como exportación de resultados de diagnóstico, integración de métricas de observabilidad y resolución de problemas de fallos en pods.
Implements the Model Context Protocol to expose cluster diagnostic functions to AI assistants.
jcode es un framework para desarrollar agentes de codificación de IA autónomos que automatizan tareas de desarrollo de software. Funciona como un orquestador de agentes, tiempo de ejecución de herramientas y motor de memoria semántica, permitiendo la creación de agentes que pueden modificar código, ejecutar pruebas e iterar sobre su propia funcionalidad. El proyecto se distingue por su uso de enjambres de agentes recursivos, donde una jerarquía de agentes colaboradores puede generar agentes hijos para descomponer tareas complejas. Implementa un sistema de memoria semántica que combina la recuperación basada en vectores con el mapeo de relaciones basado en grafos para mantener el contexto a través de las sesiones. Para gestionar el riesgo, el sistema utiliza una gobernanza de acciones escalonada que requiere aprobación humana para operaciones sensibles y aísla las actividades de los agentes dentro de worktrees de git separados. El framework incluye un kit de herramientas de automatización de navegador completo para interactuar con páginas web, extraer instantáneas del DOM y capturar capturas de pantalla. También implementa el Model Context Protocol para integrar herramientas y datos externos, y admite recarga en caliente de binarios para actualizar el servidor sin perder conexiones de red activas. El sistema proporciona una interfaz de línea de comandos para gestionar las memorias de los agentes e incluye herramientas de auditoría para rastrear el progreso del plan y visualizar la topología del enjambre de agentes.
Implements the Model Context Protocol to wrap third-party data and external tool servers into local traits.
Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario
Implements the Model Context Protocol to connect AI assistants to external binaries and HTTP services.
Horizon es un sistema de agregación de noticias impulsado por IA diseñado para construir tuberías personalizadas que obtienen, filtran y enriquecen información de diversas fuentes web. Utiliza modelos de lenguaje de gran tamaño para automatizar el filtrado de información, puntuando el contenido para eliminar el ruido y resaltar historias de alto valor. El sistema integra el Protocolo de Contexto de Modelo (Model Context Protocol) para exponer las etapas de la tubería como herramientas para asistentes de IA externos. Emplea un adaptador unificado para estandarizar diversos proveedores de modelos de IA para tareas consistentes de puntuación y resumen de contenido. La tubería agrega datos de feeds RSS, plataformas sociales, kits de herramientas financieras y repositorios de código. Gestiona el contenido mediante deduplicación, filtrado de categorías basado en cuotas y enriquecimiento contextual antes de entregar resúmenes multilingües por correo electrónico, webhooks o despliegue de sitio estático. Los flujos de trabajo se orquestan a través de automatización en la nube recurrente para gestionar la recolección y entrega programada de información procesada.
Exposes pipeline stages as tools for external AI assistants using the Model Context Protocol.
Civitai is a platform for generative media creation and AI model distribution. It provides a centralized service for producing images, videos, audio, and music, while serving as a repository where users can share, discover, and browse custom model weights and fine-tuned adaptations. The platform distinguishes itself through a provider-agnostic orchestration layer that manages multi-step generation pipelines and complex workflows across different backends. It integrates with autonomous AI agents and editors via the Model Context Protocol, allowing external tools to access generation pipelines
Implements the Model Context Protocol to expose platform operations to AI-aware editors and chat clients.