Skip to main content
Glama

Related Servers

Alternatives to M3 Memory

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      D
      maintenance
      Local-first AI memory layer with hybrid retrieval and brain-inspired namespaces. Enables agents to save, search, and manage memories directly via MCP tools.
      0
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Local-first persistent memory for AI agents via MCP, enabling semantic search and memory sharing across agents with zero cloud cost and full privacy.
      11 npm
      1
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Embedded memory and retrieval engine for AI agents, providing local-first memory with MCP support for multi-agent access control.
      3
      48 PyPI
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Local-first memory layer for AI agents. Compresses context with a local LLM and serves hybrid search over MCP.
      623 npm
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Local-first persistent memory for coding agents and MCP clients. It keeps important project context across sessions and reduces wasted tokens by retrieving only relevant memories instead of replaying unnecessary history.
      8
      MIT
    • A
      license
      Not graded
      quality
      B
      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.
      392 npm
      MIT

    TDQS

    B3/5.0

    Scored across 20 tools

    Disambiguation2/5

    Several meta-tools (tools_list_domains, m3_help_capabilities, m3_index, tools_load_domain, and m3_call) all occupy the discovery/loading/calling space, making their boundaries unclear at a glance. While the core memory/chatlog/files tools are mostly distinct, an agent could easily pick the wrong meta-tool for a given task.

    Naming Consistency4/5

    The vast majority of tools follow a consistent `{domain}_{action}` snake_case pattern, e.g. memory_get, chatlog_write, files_search, task_list. The m3_* and tools_* meta-tools deviate slightly but still use lowercase underscore naming and are readable.

    Tool Count3/5

    At 20 tools, the set sits in the 16-25 range that feels heavy, especially with five meta-tools that could be consolidated. However, the multi-domain scope (memory, chatlog, files, agents, tasks, admin, etc.) provides some justification for the count.

    Completeness4/5

    Core memory workflows are well covered with get, search, write, and supersede, and chatlog/files have solid read/search/status surfaces. Minor gaps remain, such as no direct memory_delete or explicit entity/conversation/admin tools, though the m3_index/m3_call meta-layer can reach those catalog tools if needed.

    Maintenance

    ActivityActive
    ResponsivenessResponsive