RRepoGEO

REPOGEO REPORT · LITE

mlfoundations/evalchemy

Default branch main · commit 6ed67415 · scanned 6/16/2026, 8:22:47 AM

GitHub: 597 stars · 83 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mlfoundations/evalchemy, 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:

    COPY-PASTE FIX
    llm-evaluation, language-models, machine-learning, nlp, benchmarks, generative-ai, evaluation-harness, llmops
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file in the repository root, e.g., MIT or Apache-2.0, and ensure its content is a standard SPDX template. If a custom license is intended, add a clear statement to the README about the applicable license terms.)
  • mediumreadme#3
    Strengthen the README's opening sentence to highlight core value

    Why:

    CURRENT
    > A unified and easy-to-use toolkit for evaluating post-trained language models
    COPY-PASTE FIX
    > Evalchemy is a comprehensive, extensible toolkit for orchestrating, tracking, and analyzing large language model evaluations across diverse models, datasets, and evaluation paradigms, building on LM-Eval-Harness.

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 mlfoundations/evalchemy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Evals
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Evals · recommended 2×
  2. EleutherAI/lm-evaluation-harness · recommended 1×
  3. Hugging Face Evaluate · recommended 1×
  4. BigCode/bigcode-evaluation-harness · recommended 1×
  5. LightEval · recommended 1×
  • CATEGORY QUERY
    Need a toolkit for evaluating post-trained language model performance across various benchmarks.
    you: not recommended
    AI recommended (in order):
    1. EleutherAI/lm-evaluation-harness (EleutherAI/lm-evaluation-harness)
    2. Hugging Face Evaluate
    3. OpenAI Evals
    4. BigCode/bigcode-evaluation-harness (BigCode/bigcode-evaluation-harness)
    5. LightEval

    AI recommended 5 alternatives but never named mlfoundations/evalchemy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an automated system to compare different large language models via API.
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases Prompts
    2. Arize AI
    3. Phoenix
    4. LangChain
    5. OpenAI Evals
    6. Humanloop
    7. MLflow

    AI recommended 7 alternatives but never named mlfoundations/evalchemy. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 mlfoundations/evalchemy?
    pass
    AI named mlfoundations/evalchemy explicitly

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

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

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

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mlfoundations/evalchemy — 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