RRepoGEO

REPOGEO REPORT · LITE

akitaonrails/ai-memory

Default branch main · commit 2922635a · scanned 6/17/2026, 5:36:33 AM

GitHub: 711 stars · 72 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 akitaonrails/ai-memory, 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
  • hightopics#1
    Add relevant topics for discoverability

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-memory, long-term-memory, ai-agents, coding-assistants, agent-handoff, context-management, rust, cli-tool
  • highreadme#2
    Clarify unique value proposition in README opening

    Why:

    CURRENT
    > Long-term memory for AI coding agents. Quit Claude Code mid-task, start OpenAI Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions.
    COPY-PASTE FIX
    > Long-term memory for AI coding agents. Quit Claude Code mid-task, start OpenAI Codex in the same directory, continue without re-explaining the architecture, the failed approaches, or the open questions. This project provides a lightweight, vendor-agnostic persistence layer specifically designed to maintain context and facilitate seamless handoff between different AI coding models and CLIs.
  • mediumhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/akitaonrails/ai-memory

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 akitaonrails/ai-memory
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. Continue · recommended 1×
  3. Cursor · recommended 1×
  4. Codeium · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How to maintain context and long-term memory for AI coding assistants across sessions?
    you: not recommended
    AI recommended (in order):
    1. Continue
    2. Cursor
    3. Codeium
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. Qdrant
    8. LangChain
    9. Confluence
    10. Notion
    11. LlamaIndex

    AI recommended 11 alternatives but never named akitaonrails/ai-memory. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to persist AI agent work context when switching between different models or vendors?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Pinecone
    3. Chroma (chroma-ai/chroma)
    4. Weaviate (weaviate/weaviate)
    5. LlamaIndex (run-llama/llama_index)
    6. MongoDB (mongodb/mongo)
    7. PostgreSQL (postgres/postgres)
    8. Elasticsearch (elastic/elasticsearch)
    9. Redis (redis/redis)
    10. Faiss (facebookresearch/faiss)
    11. AWS S3
    12. Google Cloud Storage
    13. MySQL (mysql/mysql-server)
    14. Pickle
    15. JSON
    16. MLflow (mlflow/mlflow)

    AI recommended 16 alternatives but never named akitaonrails/ai-memory. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 akitaonrails/ai-memory?
    pass
    AI named akitaonrails/ai-memory explicitly

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

  • If a team adopts akitaonrails/ai-memory in production, what risks or prerequisites should they evaluate first?
    pass
    AI named akitaonrails/ai-memory 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 akitaonrails/ai-memory solve, and who is the primary audience?
    pass
    AI named akitaonrails/ai-memory explicitly

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

Embed your GEO score

Drop this badge into the README of akitaonrails/ai-memory. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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