Showing 4 open source projects for "sql performance monitor"

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    DB MCP Server

    DB MCP Server

    A powerful multi-database server implementing the MCP

    The DB MCP Server is a powerful multi-database server implementing the Model Context Protocol (MCP) to provide AI assistants with structured access to databases. Built on the FreePeak/cortex framework, it enables execution of SQL queries, transaction management, schema exploration, and performance analysis across different database systems through a unified interface. ​
    Downloads: 0 This Week
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  • 2
    Memobase

    Memobase

    Fast backend for long-term AI user memory via structured profiles

    ...Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline structures to deliver memory that reflects user context efficiently and meaningfully. The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks.
    Downloads: 2 This Week
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  • 3
    MCP Toolbox for Databases

    MCP Toolbox for Databases

    Open source MCP server that exposes database tools for AI agents

    ...It also supports observability through built-in metrics and tracing capabilities, allowing developers to monitor how tools are used and debug interactions.
    Downloads: 3 This Week
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  • 4
    Acontext

    Acontext

    Context data platform for building observable, self-learning AI agents

    ...Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable.
    Downloads: 0 This Week
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