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    Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.
    4
  • A
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    Provides a plug-and-play persistent memory layer for MCP-compatible AI assistants, enabling them to store, retrieve, and delete memories across multiple databases simultaneously using semantic vector search.
    15
    MIT
  • A
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    Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.
    3
    Apache 2.0
  • A
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    Enables interaction with KDB.AI through natural language for vector database operations, similarity searches, hybrid search, and advanced data analysis.
    1
    Apache 2.0
  • A
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    An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
    189
    MIT
  • A
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    Provides intelligent, persistent memory for AI assistants with semantic search, natural language queries, and OAuth-based team collaboration, enabling context-aware conversations across multiple clients.
    7
    Apache 2.0
  • A
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    MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.
    MIT
  • A
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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
    8
    MIT
  • A
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    A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.
    2
    Apache 2.0
  • A
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    A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.
    12
    MIT
  • A
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    RAGX MCP Server enables retrieval-augmented generation with document ingestion, hybrid search, and agentic answering using Claude, exposing tools for querying, searching, and managing documents.
    2
    MIT
  • A
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    MCP server for Qdrant vector database with local BERT embeddings. Enables semantic search and vector storage operations through natural language.
    MIT
  • A
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    Enables interaction with DataStax Astra DB through the Model Context Protocol. Provides database connectivity and operations for Astra DB instances via secure token-based authentication.
    1
    MIT