RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/auto-evaluator

Default branch main · commit 8a31f910 · scanned 6/4/2026, 12:23:27 PM

GitHub: 782 stars · 102 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 langchain-ai/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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    Automate evaluation of LLM question-answering systems by auto-generating test sets and grading chain performance using LLMs.
  • mediumreadme#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, for example: 'This project is licensed under [Specific License Name(s) or description of custom license]. See the LICENSE file for details.'

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 langchain-ai/auto-evaluator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. confident-ai/deepeval · recommended 2×
  3. Amazon Mechanical Turk · recommended 1×
  4. Scale AI · recommended 1×
  5. Appen · recommended 1×
  • CATEGORY QUERY
    How to systematically evaluate the quality of LLM document question-answering systems and improve them?
    you: not recommended
    AI recommended (in order):
    1. Amazon Mechanical Turk
    2. Scale AI
    3. Appen
    4. Ragas (explodinggradients/raga)
    5. LangChain Evaluation Module (langchain-ai/langchain)
    6. DeepEval (confident-ai/deepeval)
    7. Hugging Face Evaluate Library (huggingface/evaluate)
    8. Sentence-BERT (UKPLab/sentence-transformers)
    9. OpenAI Embeddings
    10. Surge AI
    11. LangSmith
    12. Argilla (argilla-io/argilla)
    13. LlamaIndex's Sentence Splitter (run-llama/llama_index)
    14. OpenAI's `text-embedding-3-large`
    15. Cohere Embed
    16. HyDE (Hypothetical Document Embedding)
    17. RAG-Fusion
    18. Cohere Rerank
    19. bge-reranker (BAAI-DMR/bge-reranker)
    20. Pydantic (pydantic/pydantic)

    AI recommended 20 alternatives but never named langchain-ai/auto-evaluator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help auto-generate test sets and grade LLM question-answering chain performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation (langchain-ai/langchain)
    2. Ragas (explodinggradients/ragas)
    3. DeepEval (confident-ai/deepeval)
    4. Arize AI (Phoenix) (Arize-AI/phoenix)
    5. Galileo (Galileo Evaluate)
    6. OpenAI Evals (openai/evals)
    7. Humanloop

    AI recommended 7 alternatives but never named langchain-ai/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 langchain-ai/auto-evaluator?
    pass
    AI named langchain-ai/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 langchain-ai/auto-evaluator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/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 langchain-ai/auto-evaluator solve, and who is the primary audience?
    pass
    AI named langchain-ai/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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MARKDOWN (README)
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langchain-ai/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