RRepoGEO

REPOGEO REPORT · LITE

caspianmoon/memoripy

Default branch master · commit ffa11daf · scanned 6/1/2026, 4:42:46 PM

GitHub: 689 stars · 61 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 caspianmoon/memoripy, 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
    Explicitly state the project's core purpose and target audience in the README's first paragraph

    Why:

    CURRENT
    Memoripy is a Python library designed to manage and retrieve context-aware memory interactions using both short-term and long-term storage. It supports AI-driven applications requiring memory management, with compatibility for OpenAI, Azure OpenAI, OpenRouter and Ollama APIs.
    COPY-PASTE FIX
    Memoripy is a robust Python library providing an **AI memory layer** with short-term and long-term storage, semantic clustering, and optional memory decay. It's specifically designed for **LLM agents and context-aware AI applications** that need advanced memory management, offering compatibility with OpenAI, Azure OpenAI, OpenRouter, and Ollama APIs.
  • mediumtopics#2
    Expand repository topics with more specific AI memory and LLM agent terms

    Why:

    CURRENT
    ai, llm, memory, memory-management
    COPY-PASTE FIX
    ai, llm, memory, memory-management, llm-agents, semantic-memory, context-aware, vector-embeddings, python-library, memory-layer
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/caspianmoon/memoripy

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 caspianmoon/memoripy
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. Pinecone · recommended 2×
  3. Weaviate · recommended 2×
  4. LlamaIndex · recommended 2×
  5. Faiss · recommended 2×
  • CATEGORY QUERY
    How to implement a memory system for LLM agents with semantic clustering and decay?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Weaviate
    4. Milvus
    5. ChromaDB
    6. LlamaIndex
    7. Faiss
    8. Annoy
    9. Hnswlib
    10. PostgreSQL
    11. pgvector
    12. MongoDB
    13. Redis
    14. OpenAI's `text-embedding-ada-002`
    15. Sentence Transformers
    16. Redis Stack's `RedisSearch`
    17. MemoryGPT
    18. Haystack
    19. PineconeDocumentStore
    20. WeaviateDocumentStore
    21. InMemoryDocumentStore

    AI recommended 21 alternatives but never named caspianmoon/memoripy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for managing contextual long-term and short-term memory in AI applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Faiss
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. Redis

    AI recommended 8 alternatives but never named caspianmoon/memoripy. 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 caspianmoon/memoripy?
    pass
    AI did not name caspianmoon/memoripy — 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 caspianmoon/memoripy in production, what risks or prerequisites should they evaluate first?
    pass
    AI named caspianmoon/memoripy 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 caspianmoon/memoripy solve, and who is the primary audience?
    pass
    AI named caspianmoon/memoripy 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 caspianmoon/memoripy. 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite