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PMI

PMI

by yunzePlaud
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Python
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Alternatives to PMI

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    Related Servers

    • neurakeep

      dominiclachance
      A
      license
      Not graded
      quality
      B
      maintenance
      Provides a local-first, source-cited memory layer for AI agents, with MCP tools to search, read, explain sources, and propose/apply memory updates.
      75 npm
      13
      Apache 2.0

    • Talamus

      ampres-ai
      A
      license
      Not graded
      quality
      B
      maintenance
      Local-first, source-grounded memory for AI agents, with citations, bitemporal history, review-gated corrections, and MCP tools for search and recall.
      4
      Apache 2.0

    • Agent Memory Engine

      rgolabs
      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

    • Memory Cortex

      TYRANNOSAURX
      A
      license
      Not graded
      quality
      C
      maintenance
      Durable, inspectable memory for MCP agents. Preserves decisions, preferences, and project knowledge across sessions with full provenance and version history.
      3
      Apache 2.0

    • mnemochain

      Nulfied
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to store and recall an append-only, hash-chained memory with citation-backed semantic search, fact verification, contradiction detection, and replay/audit capabilities through local MCP tools.
      1
      AGPL 3.0

    • plainmem-mcp

      HarshShroff
      A
      license
      Not graded
      quality
      B
      maintenance
      Gives AI agents a long-term memory layer over plain Markdown notes, letting them run ranked, citable searches, record new facts, and inspect conflicts and stale entries. Results carry freshness labels, supersession history, and audit fields like search timestamp and no-match status so agents can prove a search actually ran.
      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

    B3.4/5.0

    Scored across 6 tools

    Disambiguation4/5

    Each tool has a distinct role: get_context assembles a full answer context, search is explicitly low-level, get fetches by typed id prefix, list browses, add_source ingests evidence, and propose is declared the sole mutation path. The only mild overlap is between get_context/search and search/list, but descriptions ('start here' vs 'low-level search') give clear guidance on when to use each.

    Naming Consistency5/5

    All six tools use a uniform memory_<verb>[_<noun>] snake_case pattern: get_context, search, get, add_source, propose, list. The shared prefix makes the tool family instantly recognizable and the verb choices are semantically consistent.

    Tool Count5/5

    Six tools is well-scoped for a memory store: one high-level reader, two low-level retrieval tools, one browse tool, one ingest tool, and one write path. No tool feels redundant or gratuitous at this count.

    Completeness4/5

    The surface covers the core lifecycle: ingest evidence (add_source), retrieve (get_context/search/get/list), and mutate via proposals, with versioning via origin_key and include_history. Minor gaps exist around explicit deletion/archival of memories or sources and any tooling to approve/reject proposals, though the latter may be intentionally user-driven.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources