RRepoGEO

REPOGEO REPORT · LITE

ysz/recursive-llm

Default branch main · commit cc7a8266 · scanned 6/17/2026, 3:02:09 PM

GitHub: 552 stars · 81 forks

AI VISIBILITY SCORE
35 /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
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 ysz/recursive-llm, 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
    Refine README's opening to highlight unique differentiation

    Why:

    CURRENT
    Python implementation of Recursive Language Models for processing unbounded context lengths.
    COPY-PASTE FIX
    Python implementation of Recursive Language Models (RLM) for processing unbounded context lengths. **Unlike traditional methods that struggle with token limits or context rot, RLM enables any LLM to handle 100k+ tokens by storing context as variables and allowing recursive exploration.**
  • mediumhomepage#2
    Add a homepage URL, linking to the associated paper

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/XXXX.XXXXX (replace with actual arXiv link)

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 ysz/recursive-llm
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. langchain-ai/langchain · recommended 2×
  3. huggingface/transformers · recommended 2×
  4. LangChain · recommended 1×
  5. UnstructuredHTMLLoader · recommended 1×
  • CATEGORY QUERY
    How to process extremely long documents with large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. UnstructuredHTMLLoader
    3. PyPDFLoader
    4. CSVLoader
    5. TextLoader
    6. RecursiveUrlLoader
    7. RecursiveCharacterTextSplitter
    8. Chroma
    9. FAISS
    10. Pinecone
    11. Weaviate
    12. Qdrant
    13. VectorStoreRetriever
    14. RetrievalQA
    15. LlamaIndex
    16. text-embedding-ada-002
    17. Cohere Embed v3
    18. Sentence-Transformers
    19. all-MiniLM-L6-v2
    20. Claude
    21. Claude 2.1
    22. Opus
    23. MapReduceDocumentsChain
    24. load_summarize_chain
    25. Gemini 1.5 Pro

    AI recommended 25 alternatives but never named ysz/recursive-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for methods to handle unbounded LLM context without hitting token limits or performance issues.
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Chroma (chroma-core/chroma)
    4. Qdrant (qdrant/qdrant)
    5. LangChain (langchain-ai/langchain)
    6. Hugging Face Transformers (huggingface/transformers)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)
    9. Hugging Face Transformers (huggingface/transformers)
    10. OpenAI Fine-tuning API

    AI recommended 10 alternatives but never named ysz/recursive-llm. 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 ysz/recursive-llm?
    pass
    AI named ysz/recursive-llm explicitly

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

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

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

Embed your GEO score

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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