RRepoGEO

REPOGEO REPORT · LITE

Victorwz/LongMem

Default branch main · commit b7f3c6b8 · scanned 6/2/2026, 11:58:20 AM

GitHub: 826 stars · 74 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Victorwz/LongMem, 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 opening to clarify its research nature

    Why:

    CURRENT
    # LongMem
    
    Official implementation of our paper "Augmenting Language Models with Long-Term Memory".
    COPY-PASTE FIX
    # LongMem
    
    This repository provides the official research implementation of our NeurIPS 2023 paper, "Augmenting Language Models with Long-Term Memory," focusing on a novel memory augmentation technique rather than a general-purpose LLM orchestration framework.
  • mediumtopics#2
    Add more specific topics to highlight research and conference affiliation

    Why:

    CURRENT
    large-language-models, long-context-modeling, long-term-memory
    COPY-PASTE FIX
    large-language-models, long-context-modeling, long-term-memory, llm-memory-augmentation, neurips-2023, research-implementation
  • lowreadme#3
    Add a section to the README distinguishing LongMem from general tools

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, '## Scope and Audience', with the content: 'LongMem is designed as a research implementation to demonstrate a specific memory augmentation technique for LLMs, as detailed in our NeurIPS 2023 paper. It is distinct from general-purpose LLM orchestration frameworks (like LangChain) or vector databases (like Pinecone, Chroma, FAISS, LlamaIndex), which offer broader tooling for LLM applications.'

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 Victorwz/LongMem
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. Chroma · recommended 2×
  4. FAISS · recommended 2×
  5. LlamaIndex · recommended 2×
  • CATEGORY QUERY
    Seeking solutions to augment large language models with external memory for long contexts.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Chroma
    4. FAISS
    5. LlamaIndex
    6. Haystack
    7. Elasticsearch
    8. Weaviate
    9. Milvus
    10. Qdrant
    11. OpenAI Embeddings
    12. Sentence Transformers
    13. Retrieval-Augmented Language Model (REALM)
    14. RAG
    15. Neo4j
    16. Amazon Neptune

    AI recommended 16 alternatives but never named Victorwz/LongMem. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement long-term memory mechanisms for LLMs to handle extensive conversational histories?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pinecone
    3. Chroma
    4. Weaviate
    5. LlamaIndex
    6. FAISS
    7. Milvus
    8. Qdrant
    9. Redis
    10. RediSearch
    11. Redis Stack
    12. Elasticsearch
    13. DynamoDB
    14. PostgreSQL
    15. pgvector
    16. MongoDB

    AI recommended 16 alternatives but never named Victorwz/LongMem. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 Victorwz/LongMem?
    pass
    AI named Victorwz/LongMem explicitly

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

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

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

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Victorwz/LongMem — 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