RRepoGEO

REPOGEO REPORT · LITE

rlancemartin/auto-evaluator

Default branch main · commit 2d099b21 · scanned 5/26/2026, 9:07:47 AM

GitHub: 1,090 stars · 92 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 rlancemartin/auto-evaluator, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the Apache-2.0 license.
  • mediumreadme#2
    Enhance the README's opening sentence to highlight core differentiators

    Why:

    CURRENT
    This is a lightweight evaluation tool for question-answering using Langchain to:
    COPY-PASTE FIX
    This is a lightweight, highly configurable LLM-as-a-judge evaluation tool for question-answering using Langchain, designed to auto-generate test questions and apply custom evaluation criteria.

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 rlancemartin/auto-evaluator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
https://github.com/explodinggradients/ragas
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. https://github.com/explodinggradients/ragas · recommended 1×
  2. https://github.com/langchain-ai/langchain · recommended 1×
  3. https://github.com/confident-ai/deepeval · recommended 1×
  4. https://github.com/huggingface/evaluate · recommended 1×
  5. https://github.com/promptfoo/promptfoo · recommended 1×
  • CATEGORY QUERY
    How to automatically generate and evaluate question-answering performance for large language models?
    you: not recommended
    AI recommended (in order):
    1. Ragas (https://github.com/explodinggradients/ragas)
    2. LangChain Evaluation (https://github.com/langchain-ai/langchain)
    3. DeepEval (https://github.com/confident-ai/deepeval)
    4. Hugging Face Evaluate (https://github.com/huggingface/evaluate)
    5. Promptfoo (https://github.com/promptfoo/promptfoo)
    6. LlamaIndex Evaluation Modules (https://github.com/run-llama/llama_index)
    7. OpenAI Evals (https://github.com/openai/openai-evals)

    AI recommended 7 alternatives but never named rlancemartin/auto-evaluator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for automatically creating test questions and scoring responses from LLM-powered chatbots?
    you: not recommended
    AI recommended (in order):
    1. Humanloop
    2. Weights & Biases (W&B) Prompts
    3. LangChain
    4. OpenAI Evals
    5. Giskard
    6. Deepchecks

    AI recommended 6 alternatives but never named rlancemartin/auto-evaluator. 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 rlancemartin/auto-evaluator?
    pass
    AI named rlancemartin/auto-evaluator explicitly

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

  • If a team adopts rlancemartin/auto-evaluator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rlancemartin/auto-evaluator 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 rlancemartin/auto-evaluator solve, and who is the primary audience?
    pass
    AI named rlancemartin/auto-evaluator 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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rlancemartin/auto-evaluator — 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