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    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
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    A local-first MCP server for durable agent memory using SQLite and FTS5, enabling knowledge graph storage, search, and recall for AI agents.
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    A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.
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    Provides comprehensive A-share (Chinese stock market) data including stock information, historical prices, financial reports, macroeconomic indicators, technical analysis, and valuation metrics through the free Baostock data source.
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    MIT
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    A memory MCP server with a dual-storage system using ChromaDB and NetworkX DiGraph, enabling efficient data management and integration with IDEs like Cursor and VSCode for enhanced research and note organization.
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    A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep
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    MIT
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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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    Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.
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    Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.
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    MIT