RRepoGEO

REPOGEO REPORT · LITE

AxDSan/mnemosyne

Default branch main · commit ad72b057 · scanned 6/10/2026, 10:01:43 PM

GitHub: 1,047 stars · 98 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface AxDSan/mnemosyne, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to clarify its specialized role for AI agents

    Why:

    CURRENT
    Mnemosyne is a universal, Hermes-first memory layer that works with any agent framework (Claude Code, Cursor, Codex, OpenWebUI, OpenClaw, or your own custom agent). One `pip install`, one SQLite database. No external services required.
    COPY-PASTE FIX
    Mnemosyne is a **specialized, zero-dependency AI memory system** designed for persistent, sub-millisecond context recall across **any AI agent framework** (Claude Code, Cursor, Codex, OpenWebUI, OpenClaw, or your own custom agent). It provides a universal, Hermes-first memory layer, powered by a single SQLite database with no external services required.
  • mediumtopics#2
    Add more specific topics to reinforce its function as an AI agent memory system

    Why:

    CURRENT
    agents, ai, hermes, hermes-agent, ml, nousresearch
    COPY-PASTE FIX
    agents, ai, hermes, hermes-agent, ml, nousresearch, llm-memory, agent-frameworks, context-management, sqlite-backed, zero-dependency
  • mediumabout#3
    Refine the repository description to emphasize its role in AI agent context management

    Why:

    CURRENT
    The Zero-Dependency, Sub-Millisecond AI Memory System for Hermes Agents and Everyone Else!
    COPY-PASTE FIX
    A zero-dependency, sub-millisecond AI memory system for Hermes Agents and all AI agent frameworks, designed for persistent context and efficient recall.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface AxDSan/mnemosyne
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python Dictionaries
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python Dictionaries · recommended 1×
  2. Python Lists · recommended 1×
  3. collections.deque · recommended 1×
  4. array.array · recommended 1×
  5. sqlite3 · recommended 1×
  • CATEGORY QUERY
    Looking for a sub-millisecond AI agent memory system with zero external dependencies.
    you: not recommended
    AI recommended (in order):
    1. Python Dictionaries
    2. Python Lists
    3. collections.deque
    4. array.array
    5. sqlite3
    6. ctypes
    7. Cython

    AI recommended 7 alternatives but never named AxDSan/mnemosyne. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some lightweight, local memory solutions for various AI agent frameworks?
    you: not recommended
    AI recommended (in order):
    1. SQLite
    2. DuckDB (duckdb/duckdb)
    3. ChromaDB (chroma-core/chroma)
    4. Faiss (facebookresearch/faiss)
    5. Redis (redis/redis)
    6. Pickle/JSON

    AI recommended 6 alternatives but never named AxDSan/mnemosyne. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of AxDSan/mnemosyne?
    pass
    AI named AxDSan/mnemosyne explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts AxDSan/mnemosyne in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AxDSan/mnemosyne explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo AxDSan/mnemosyne solve, and who is the primary audience?
    pass
    AI named AxDSan/mnemosyne explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite