Skip to main content
Glama
OpenAgentHQ

localmem-mcp

by OpenAgentHQ
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to localmem-mcp

No user-submitted related servers found.

    Related Servers

    • ClawMem MCP Server

      BFLabsAI
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI agents to maintain persistent, local memory with retrieval-augmented search, knowledge graphs, and context surfacing, without any cloud dependencies.
      52 npm
      MIT

    • localmind

      sahinsamrat75
      A
      license
      Not graded
      quality
      B
      maintenance
      Gives AI agents a private, on-device persistent memory using semantic search over stored facts, preferences, and decisions. It enables agents to remember information, recall it by meaning even without keyword overlap, delete memories, and list them by tag.
      10 npm
      4
      MIT

    • code-recall

      AbianS
      A
      license
      Not graded
      quality
      C
      maintenance
      Gives AI coding agents persistent memory by storing observations, decisions, and learnings in a local SQLite database with vector search, full-text search, and a rules engine.
      4
      MIT

    • polarmem

      m1kuno
      A
      license
      Not graded
      quality
      C
      maintenance
      A local-first memory engine for AI agents that stores embeddings in polar-quantized form in a single SQLite file, enabling private, compact, and human-like recall.
      MIT

    • engram-mcp

      Cartisien
      A
      license
      A
      quality
      C
      maintenance
      Persistent semantic memory for AI agents. SQLite-backed, local-first, zero config. Semantic search via Ollama embeddings with keyword fallback. Tools: remember, recall, history, forget, stats.
      17
      37 npm
      1
      MIT
    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

    A4.3/5.0

    Scored across 8 tools

    Disambiguation4/5

    Each tool targets a distinct operation (store, search, recall by id, list, update, delete, bulk delete, stats), and descriptions clearly separate search from recall and list. The only mild overlap is recall_memory's 'most recent memories' versus list_memories in chronological order, but the descriptions give enough guidance to choose correctly.

    Naming Consistency4/5

    Most tools follow a consistent verb_noun pattern (store_memory, search_memory, update_memory, forget_memory), and all names are lowercase snake_case. memory_stats breaks the pattern by using a noun_noun form rather than a verb like get_stats, and singular/plural alternates between memory and memories.

    Tool Count5/5

    Eight tools is well-scoped for a memory store: create, read (three retrieval modes), update, delete (single and bulk), and stats. Each tool earns its place without redundancy or bloat.

    Completeness5/5

    The surface covers the full memory lifecycle: store, retrieve by meaning/id/browse, update, delete, bulk prune, and inspect storage. There are no obvious dead ends; even bulk deletion is safely gated by requiring a filter.

    Maintenance

    ActivityMaintained
    ResponsivenessWithin a week

    Resources