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"Memory and Database Solutions for AI Systems" matching MCP servers:

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    An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.
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    MIT
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    Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.
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    Apache 2.0
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    Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.
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    Apache 2.0
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    Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.
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    MIT
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    Analyzes Microsoft Sentinel solutions from GitHub repositories to map data connectors to Log Analytics tables and query security content like detections and playbooks. It provides instant access to the official Content Hub or private repositories through a high-performance pre-built index.
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    Submarket-level US residential rental intelligence for AI agents. Search, compare, rank, and analyze rent data, trends, vacancy, affordability, and days on market across 1,000+ named submarkets in the 20+ largest US metros. ZIP-level and metro-level queries included. Always current, always expanding. Free tier available.
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    MIT
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    Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.
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    MIT
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    An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.
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    Provides a natural language interface for querying and managing PostgreSQL, MySQL, MariaDB, MSSQL, and SQLite databases using the Model Context Protocol. Users can explore database schemas and visualize query results through an integrated web dashboard.
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    MIT
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    Provides universal database operations for AI assistants through MCP, supporting 40+ databases including PostgreSQL, MySQL, MongoDB, Redis, and SQLite with built-in introspection tools for schema exploration.
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    MIT
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    Enables AI assistants to interact with PostgreSQL databases through query execution and schema inspection, supporting multiple schemas for customer data, document management, loan systems, and asset leasing.
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    Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic
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    Two-layer memory for AI agents. Episodes compress into identity. The only MCP memory server with an immune system. Patterns earn permanence through evidence, false knowledge gets caught and demoted, and stale information fades — so your agent's memory gets smarter over time, not just bigger. Zero dependencies. 5 tools. Works with any MCP client.
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    MIT