RRepoGEO

REPOGEO REPORT · LITE

alexzhang13/rlm

Default branch main · commit 8467a580 · scanned 5/19/2026, 2:52:12 PM

GitHub: 4,268 stars · 748 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
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 alexzhang13/rlm, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    recursive-language-models, rlm, llm-inference, long-context, language-models, ai-research, python
  • highreadme#2
    Add a direct positioning statement to the README's opening

    Why:

    COPY-PASTE FIX
    Add this sentence *before* the '## Overview' section: "This library provides a novel inference engine for language models to programmatically handle near-infinite length contexts through recursion, offering a distinct paradigm from traditional LLM orchestration."
  • mediumcomparison#3
    Clarify RLM's differentiator against common LLM frameworks in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled "RLM vs. Traditional LLM Frameworks" or integrate a paragraph into the "Overview" that explains: "Unlike frameworks focused on prompt chaining or retrieval augmentation (e.g., LangChain, LlamaIndex), RLM empowers the language model itself to recursively examine, decompose, and call itself over its input, enabling true programmatic interaction with extremely long contexts."

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 alexzhang13/rlm
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. Haystack · recommended 2×
  4. spaCy · recommended 2×
  5. OpenAI's Function Calling · recommended 1×
  • CATEGORY QUERY
    How to enable language models to process extremely long contexts recursively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI's Function Calling
    5. nltk
    6. spaCy
    7. Microsoft's Semantic Kernel
    8. Sentence Transformers
    9. FAISS
    10. Pinecone

    AI recommended 10 alternatives but never named alexzhang13/rlm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library for language models to programmatically decompose and interact with input context.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. OpenAI Python Library
    5. Hugging Face Transformers
    6. NLTK
    7. spaCy

    AI recommended 7 alternatives but never named alexzhang13/rlm. 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 alexzhang13/rlm?
    pass
    AI named alexzhang13/rlm explicitly

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

  • If a team adopts alexzhang13/rlm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alexzhang13/rlm 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 alexzhang13/rlm solve, and who is the primary audience?
    pass
    AI named alexzhang13/rlm explicitly

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

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alexzhang13/rlm — 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