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Alternatives to mcp-memory

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP-compatible AI coding tools to store and recall persistent memories across sessions and projects, with keyword and semantic search backed by a local SQLite database.
      752 npm
      70
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to store and recall durable facts, decisions, and lessons over MCP using hybrid semantic and keyword search, typed memories, graph relations, sessions, and time-travel queries. Runs locally with no server process or API key, persisting everything to files that both agents and a CLI can share.
      10 npm
      MIT
    • A
      license
      A
      quality
      D
      maintenance
      A lightweight, local-first MCP server and CLI that helps developers and AI coding agents store and retrieve lessons learned during development, using SQLite and context-aware filtering.
      6
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Provides persistent, searchable memory for AI agents across any MCP-compatible client, storing project context, user preferences, and session learnings locally in SQLite with tools to save, retrieve, search, and manage them.
      12
      4 npm
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to store, search, and recall persistent project and cross-project memories locally over MCP, keeping architectural decisions, conventions, and error patterns available across sessions. Also optionally exposes a task server for autonomous task execution driven by that same memory store.
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to read and write persistent, local-first memory over MCP, storing decisions, preferences, error patterns, research, and cross-project guidelines in SQLite with semantic search. Agents can recall relevant context before tasks and store new learnings mid-session, keeping knowledge across sessions and projects without any external API calls.
      -

    TDQS

    A3.9/5.0

    Scored across 6 tools

    Disambiguation4/5

    Each tool targets a distinct operation: search (find), get (read by ID), add (create), archive (soft delete), supersede (atomic replace), feedback (rate). The main adjacency is archive vs supersede, since supersede also archives, but the descriptions clarify the atomic-replace semantics. Minor overlap only, so an agent can reliably disambiguate.

    Naming Consistency5/5

    Every tool follows a strict memory_<verb> pattern (memory_search, memory_get, memory_add, memory_archive, memory_supersede, memory_feedback). Verb style is consistent and snake_case is used throughout with no deviations.

    Tool Count5/5

    Six tools is well-scoped for a memory store, covering the full lifecycle without redundancy. Each tool earns its place and no thin or bloated surface is apparent.

    Completeness4/5

    The surface covers the full lifecycle: search/list, get, add, archive (soft delete), supersede (update via atomic replace), and feedback. Coverage is strong, with only minor gaps such as hard deletion or bulk operations that agents can work around.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues