Skip to main content
Glama
andrewpetecoleman-cloud

agentmemo-mcp

by andrewpetecoleman-cloud
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

Related Servers

Alternatives to agentmemo-mcp

No user-submitted related servers found.

    Related Servers

    • kontexta

      safiyu
      A
      license
      A
      quality
      A
      maintenance
      A local-first MCP server that gives AI coding agents persistent memory and controlled commands. Features a git-backed markdown knowledge vault with FTS5 search, surgical section edits, token-aware context budgeting, and a sandboxed command engine with human approval gates. Works with Claude Code, Cursor, Copilot, Gemini, and more.
      4
      58
      249 npm
      1
      Apache 2.0
    • dakera-mcp by Dakera-AI

      dakera-mcpofficial

      Dakera-AI
      F
      license
      A
      quality
      B
      maintenance
      Self-hosted MCP-native agent memory server. Gives AI agents persistent, decay-weighted memory via 83 MCP tools — no cloud, full control. RocksDB+HNSW backend. Works with Claude Code, Cursor, and any MCP-compatible agent.
      14
      8
      -

    • Kronvex

      Daftgoldens
      A
      license
      A
      quality
      D
      maintenance
      Persistent memory API for AI agents — store, recall, and inject semantically-searchable context across sessions. EU-hosted, GDPR-compliant. Supports Claude, Cursor, Cline, and any MCP-compatible client.
      4
      2
      MIT

    • mind-mem

      star-ga
      A
      license
      Not graded
      quality
      A
      maintenance
      Persistent, auditable memory for AI agents. Hybrid BM25 + vector recall with 18 MCP tools, adaptive block metadata (A-MEM), intent-aware routing, contradiction detection, and governance workflows. Zero external dependencies. Drop-in memory for Claude Code and any MCP-compatible agent.
      327 PyPI
      17
      Apache 2.0
    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

    A3.7/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: check_approval and request_approval handle approval workflows, while forget, list_memories, recall, and remember manage memory operations. The descriptions make it easy to differentiate between approval and memory tools, and within each category, the actions are well-defined (e.g., recall vs. remember).

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (e.g., check_approval, list_memories, request_approval) with no deviations in style or casing. This uniformity makes the tool set predictable and easy for an agent to navigate, enhancing usability.

    Tool Count5/5

    With 6 tools, the server is well-scoped for its memory and approval management domain. Each tool serves a specific, necessary function without redundancy, making the count appropriate and efficient for handling core workflows like storing, retrieving, and managing memories, along with human-in-the-loop approvals.

    Completeness5/5

    The tool set provides complete coverage for the memory and approval domain: it includes create (remember), read (recall, list_memories), delete (forget), and approval lifecycle (request_approval, check_approval). There are no obvious gaps, ensuring agents can perform all essential operations without dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources