RRepoGEO

REPOGEO REPORT · LITE

algorithmicsuperintelligence/optillm

Default branch main · commit df018d64 · scanned 5/12/2026, 5:51:44 PM

GitHub: 3,653 stars · 289 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 algorithmicsuperintelligence/optillm, 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 H1 to specify category

    Why:

    CURRENT
    # OptiLLM
    COPY-PASTE FIX
    # OptiLLM: An OpenAI API-Compatible Inference Optimization Proxy
  • mediumabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://optillm.ai (or your project's dedicated landing page)
  • mediumreadme#3
    Refine the README's opening sentence to emphasize its role as an inference-time layer

    Why:

    CURRENT
    OptiLLM** is an OpenAI API-compatible optimizing inference proxy that implements 20+ state-of-the-art techniques to dramatically improve LLM accuracy and performance on reasoning tasks - without requiring any model training or fine-tuning.
    COPY-PASTE FIX
    OptiLLM is a **drop-in inference proxy** for Large Language Models, compatible with the OpenAI API, designed to dramatically improve LLM accuracy and performance on reasoning tasks *at inference time* without requiring model training or fine-tuning.

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 algorithmicsuperintelligence/optillm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 1×
  2. weaviate/weaviate · recommended 1×
  3. chroma-core/chroma · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I significantly improve large language model reasoning accuracy without fine-tuning models?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. ChromaDB (chroma-core/chroma)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)

    AI recommended 5 alternatives but never named algorithmicsuperintelligence/optillm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an API-compatible inference proxy to boost LLM accuracy using advanced techniques.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. LangChain
    3. LlamaIndex
    4. Vellum
    5. Helicone
    6. LiteLLM

    AI recommended 6 alternatives but never named algorithmicsuperintelligence/optillm. 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 algorithmicsuperintelligence/optillm?
    pass
    AI named algorithmicsuperintelligence/optillm explicitly

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

  • If a team adopts algorithmicsuperintelligence/optillm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named algorithmicsuperintelligence/optillm 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 algorithmicsuperintelligence/optillm solve, and who is the primary audience?
    pass
    AI named algorithmicsuperintelligence/optillm 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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MARKDOWN (README)
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algorithmicsuperintelligence/optillm — 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