"Robot Framework" matching MCP servers:

Enables RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.

A pluggable, observable modular RAG service framework that exposes tool interfaces via the MCP protocol, enabling AI assistants like Copilot and Claude to directly invoke knowledge retrieval and reasoning capabilities.

A pluggable and observable Retrieval-Augmented Generation framework that exposes hybrid search and document management tools via the Model Context Protocol. It features a complete ingestion pipeline with multi-modal support, automated evaluation using Ragas, and a Streamlit dashboard for real-time tracking.

A pluggable RAG framework that exposes hybrid search, ingestion, and evaluation tools via the Model Context Protocol, enabling AI assistants like Copilot and Claude to query knowledge bases directly.

A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

A pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.

A fully self-hosted MCP server that integrates the Mem0 framework to provide persistent memory capabilities for AI assistants using local models and vector storage. It enables users to store, search, and manage contextual information across conversations through a Docker-based deployment.

A generic Model Context Protocol framework for building AI-powered applications that provides standardized ways to create MCP servers and clients for integrating LLMs with support for Ollama and Supabase.

An Agent Framework Documentation server that enables AI agents to efficiently retrieve information from documentation databases using hybrid semantic and keyword search for seamless agent integration.

Enables AI assistants to create and query multi-dimensional vector indexes from structured data files using Superlinked's vector search framework, supporting semantic search, recency, numerical, and categorical filtering.