RRepoGEO

REPOGEO REPORT · LITE

IAAR-Shanghai/Awesome-AI-Memory

Default branch main · commit 92348b86 · scanned 6/15/2026, 3:47:40 AM

GitHub: 984 stars · 92 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 IAAR-Shanghai/Awesome-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
  • highreadme#1
    Reposition the README's introduction to explicitly state it's a curated knowledge base

    Why:

    CURRENT
    Large Language Models (LLMs) have rapidly evolved into powerful general-purpose reasoning and generation engines. Nevertheless, despite their continuously advancing capabilities, LLMs remain fundamentally constrained by a critical limitation: the finite length of their context window.
    COPY-PASTE FIX
    Awesome-AI-Memory is a comprehensive, curated knowledge base and resource list dedicated to AI memory for LLMs and agents, covering long-term memory, reasoning, retrieval, and memory-native system design. Large Language Models (LLMs) have rapidly evolved into powerful general-purpose reasoning and generation engines. Nevertheless, despite their continuously advancing capabilities, LLMs remain fundamentally constrained by a critical limitation: the finite length of their context window.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/IAAR-Shanghai/Awesome-AI-Memory
  • lowtopics#3
    Add 'awesome-list' to the repository topics

    Why:

    CURRENT
    agent-memory, ai-memory, ai-memory-system, awesome-ai-memory, continual-learning, llm-memory, long-term-memory, memory-augmented-models, memory-systems, rag, reasoning-over-time
    COPY-PASTE FIX
    agent-memory, ai-memory, ai-memory-system, awesome-ai-memory, continual-learning, llm-memory, long-term-memory, memory-augmented-models, memory-systems, rag, reasoning-over-time, awesome-list

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 IAAR-Shanghai/Awesome-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. PostgreSQL · recommended 2×
  3. LangChain · recommended 1×
  4. Chroma · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How to implement long-term memory solutions for LLMs to overcome context window limits?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Chroma
    4. Weaviate
    5. LlamaIndex
    6. Milvus
    7. Qdrant
    8. FAISS
    9. Redis
    10. RediSearch
    11. PostgreSQL
    12. pgvector
    13. Cassandra
    14. Astra DB

    AI recommended 14 alternatives but never named IAAR-Shanghai/Awesome-AI-Memory. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for designing AI agents with persistent, retrievable memory capabilities?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Qdrant (qdrant/qdrant)
    4. Chroma (chroma-core/chroma)
    5. Milvus (milvus-io/milvus)
    6. Neo4j (neo4j/neo4j)
    7. Amazon Neptune
    8. TypeDB (vaticle/typedb)
    9. PostgreSQL
    10. MySQL
    11. SQLite
    12. Redis (redis/redis)
    13. Memcached
    14. MongoDB (mongodb/mongo)
    15. Couchbase (couchbase/couchbase-server)

    AI recommended 15 alternatives but never named IAAR-Shanghai/Awesome-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 IAAR-Shanghai/Awesome-AI-Memory?
    pass
    AI did not name IAAR-Shanghai/Awesome-AI-Memory — 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 IAAR-Shanghai/Awesome-AI-Memory in production, what risks or prerequisites should they evaluate first?
    pass
    AI named IAAR-Shanghai/Awesome-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 IAAR-Shanghai/Awesome-AI-Memory solve, and who is the primary audience?
    pass
    AI named IAAR-Shanghai/Awesome-AI-Memory explicitly

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

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IAAR-Shanghai/Awesome-AI-Memory — 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