RRepoGEO

REPOGEO REPORT · LITE

SuanmoSuanyangTechnology/MemoryBear

Default branch main · commit 798ce0ba · scanned 5/21/2026, 6:32:17 AM

GitHub: 3,926 stars · 235 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
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 SuanmoSuanyangTechnology/MemoryBear, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify target audience and use case in README overview

    Why:

    CURRENT
    MemoryBear is a next-generation AI memory system developed by RedBear AI. Its core breakthrough lies in moving beyond the limitations of traditional "static knowledge storage". Inspired by the cognitive mechanisms of biological brains, MemoryBear builds an intelligent knowledge-processing framework that spans the full lifecycle of **perception → extraction → association → forgetting**.
    COPY-PASTE FIX
    MemoryBear is a next-generation AI memory system designed to equip **AI agents and large language models (LLMs)** with human-like memory capabilities. Developed by RedBear AI, its core breakthrough lies in moving beyond the limitations of traditional "static knowledge storage". Inspired by the cognitive mechanisms of biological brains, MemoryBear builds an intelligent knowledge-processing framework that spans the full lifecycle of **perception → extraction → association → forgetting**.
  • mediumreadme#2
    Add a section comparing MemoryBear to traditional AI memory approaches

    Why:

    COPY-PASTE FIX
    ## Why MemoryBear? A Cognitive Leap Beyond Traditional AI Memory

Unlike traditional memory tools that treat knowledge as static data or simple retrieval systems, MemoryBear offers a dynamic, biologically-inspired approach to AI memory management, encompassing perception, extraction, association, and forgetting.

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 SuanmoSuanyangTechnology/MemoryBear
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Pinecone · recommended 2×
  4. Weaviate · recommended 2×
  5. Redis · recommended 2×
  • CATEGORY QUERY
    How can I give my AI agents a more human-like, associative memory system?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pinecone
    4. Weaviate
    5. Redis
    6. Faiss
    7. Neo4j

    AI recommended 7 alternatives but never named SuanmoSuanyangTechnology/MemoryBear. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help AI models manage knowledge dynamically, including forgetting and association?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Neo4j
    3. LlamaIndex
    4. Weaviate
    5. ArangoDB
    6. Redis
    7. Pinecone
    8. Milvus
    9. Qdrant

    AI recommended 9 alternatives but never named SuanmoSuanyangTechnology/MemoryBear. 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 SuanmoSuanyangTechnology/MemoryBear?
    pass
    AI did not name SuanmoSuanyangTechnology/MemoryBear — 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 SuanmoSuanyangTechnology/MemoryBear in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SuanmoSuanyangTechnology/MemoryBear 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 SuanmoSuanyangTechnology/MemoryBear solve, and who is the primary audience?
    pass
    AI named SuanmoSuanyangTechnology/MemoryBear 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 SuanmoSuanyangTechnology/MemoryBear. 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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SuanmoSuanyangTechnology/MemoryBear — 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