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memento-mcp

by lfrmonteiro99
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  • Memento

    veerps57
    A
    license
    Not graded
    quality
    C
    maintenance
    Memento is a local-first, LLM-agnostic memory layer. It runs an MCP server over a single SQLite file on your machine, so any MCP-capable AI assistant — Claude Desktop, Claude Code, Cursor, GitHub Copilot, Cline, OpenCode, Aider, a custom agent — can read and write durable, structured memory about you, your work, and your decisions.
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  • Memento

    JoyciAkira
    A
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    A
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    Memento is a local-first, open-source MCP middleware that gives AI agents persistent memory, proactive goal enforcement, and autonomous intelligence using a SQLite temporal graph with Reciprocal Rank Fusion retrieval.
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  • memento-context

    FranBarInstance
    A
    license
    A
    quality
    D
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    A Model Context Protocol server that gives AI assistants persistent, scoped memory by saving and retrieving short notes (mementos) across sessions and projects, enabling dynamic learning and context injection without heavy dependencies.
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  • coster

    savai15
    A
    license
    Not graded
    quality
    A
    maintenance
    MCP server that provides offline-first, persistent memory for AI coding assistants. It captures project decisions and conventions into a local SQLite database and syncs them to tool-specific memory files like CLAUDE.md and AGENTS.md.
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  • Mono Memory MCP

    potato-castle
    A
    license
    Not graded
    quality
    D
    maintenance
    A self-hosted MCP server that provides AI assistants with a shared, persistent SQLite-backed memory for storing and retrieving project context, decisions, and discoveries. It enables cross-session continuity and team-wide knowledge sharing to keep AI coding tools aligned and informed.
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  • apex-memory

    JJerryChoi
    A
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    Not graded
    quality
    C
    maintenance
    Persistent memory for AI coding agents. Enables agents to save and recall decisions, patterns, bugs, and context across sessions via an MCP server with local SQLite storage.
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Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.5/5.0

Scored across 19 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: decisions_log for ADR entry, pitfalls_log for problem tracking, and memory_* tools covering CRUD, search, graph, linking, analytics, etc. No two tools overlap in functionality.

Naming Consistency4/5

Most tools use the 'memory_' prefix with a verb or noun (e.g., memory_store, memory_search), but decisions_log and pitfalls_log break the prefix pattern. The naming is mostly consistent and intuitive.

Tool Count5/5

19 tools is well-scoped for a knowledge management server. The count covers all necessary operations without being excessive, and each tool contributes a unique, valuable function.

Completeness5/5

The tool surface is comprehensive: store, retrieve, update, delete, search, list, graph traversal, import/export, dedup, compression, pinning, linking/unlinking, timelines, decisions, pitfalls, and analytics. No obvious gaps.

Maintenance

ActivityInactive
ResponsivenessResponsive

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