RRepoGEO

REPOGEO REPORT · LITE

aiming-lab/SimpleMem

Default branch main · commit 94ef7d76 · scanned 5/12/2026, 8:52:07 PM

GitHub: 3,246 stars · 334 forks

AI VISIBILITY SCORE
28 /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
2 / 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 aiming-lab/SimpleMem, 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 README's opening to clarify LLM agent focus

    Why:

    CURRENT
    <small>Store, compress, and retrieve long-term memories with semantic lossless compression. Now with multimodal support for text, image, audio & video. Works across Claude, Cursor, LM Studio, and more.</small>
    COPY-PASTE FIX
    SimpleMem is a Python library providing efficient, lifelong memory for LLM agents, handling both text and multimodal data. It enables agents to store, compress, and retrieve long-term memories with semantic lossless compression, supporting text, image, audio, and video across platforms like Claude, Cursor, and LM Studio.
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

SimpleMem differentiates itself from general-purpose vector databases (like Pinecone, Weaviate, Qdrant) and comprehensive LLM frameworks (like LangChain, LlamaIndex) by focusing specifically on efficient, lifelong, semantic lossless memory compression and retrieval *for LLM agents*. While vector databases provide storage, SimpleMem integrates advanced compression and multimodal handling tailored for agent memory, offering a more specialized and integrated solution for persistent agent knowledge.
  • lowhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://aiming-lab.github.io/SimpleMem

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 aiming-lab/SimpleMem
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. Weaviate · recommended 2×
  3. Qdrant · recommended 2×
  4. Chroma · recommended 2×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How to implement efficient lifelong memory for LLM agents across text and multimodal data?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. Qdrant
    5. LlamaIndex
    6. MemoryGPT
    7. Faiss
    8. Annoy
    9. Chroma

    AI recommended 9 alternatives but never named aiming-lab/SimpleMem. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide semantic lossless compression and retrieval for multimodal LLM agent memories?
    you: not recommended
    AI recommended (in order):
    1. Weaviate
    2. Pinecone
    3. Qdrant
    4. Milvus
    5. Chroma

    AI recommended 5 alternatives but never named aiming-lab/SimpleMem. 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 aiming-lab/SimpleMem?
    pass
    AI did not name aiming-lab/SimpleMem — likely talking about a different project

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

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

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

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aiming-lab/SimpleMem — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite