RRepoGEO

REPOGEO REPORT · LITE

uptrain-ai/uptrain

Default branch main · commit a31cc14e · scanned 7/1/2026, 5:31:23 PM

GitHub: 2,352 stars · 202 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 uptrain-ai/uptrain, 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's opening statement to clearly define UpTrain as an automated, open-source LLM evaluation and monitoring platform.

    Why:

    CURRENT
    **UpTrain** is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.
    COPY-PASTE FIX
    **UpTrain** is the open-source unified platform for **automated evaluation and monitoring of Generative AI applications**. It provides grades for 20+ preconfigured checks (covering language, code, embedding use-cases), performs root cause analysis on failure cases, and offers insights to resolve them, ensuring reliable LLM performance in production.
  • mediumtopics#2
    Add more specific topics to reinforce automated LLM evaluation and production use.

    Why:

    CURRENT
    autoevaluation, evaluation, experimentation, hallucination-detection, jailbreak-detection, llm-eval, llm-prompting, llm-test, llmops, machine-learning, monitoring, openai-evals, prompt-engineering, root-cause-analysis
    COPY-PASTE FIX
    autoevaluation, evaluation, experimentation, hallucination-detection, jailbreak-detection, llm-eval, llm-evaluation-platform, llm-prompting, llm-test, llmops, machine-learning, monitoring, openai-evals, prompt-engineering, root-cause-analysis, production-llm, ai-observability, automated-testing
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README.

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Clarify how UpTrain differs from human-in-the-loop evaluation services (e.g., Scale AI, Appen) and broader LLM development frameworks (e.g., LangChain, LangSmith), emphasizing its focus on automated, open-source evaluation and monitoring for production LLMs. Highlight UpTrain's unique combination of automated checks, root cause analysis, and unified platform approach.

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 uptrain-ai/uptrain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Scale AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Scale AI · recommended 1×
  2. Appen · recommended 1×
  3. Surge AI · recommended 1×
  4. explodinggradients/ragas · recommended 1×
  5. GPT-4 · recommended 1×
  • CATEGORY QUERY
    How to evaluate and improve generative AI application performance and detect hallucinations?
    you: not recommended
    AI recommended (in order):
    1. Scale AI
    2. Appen
    3. Surge AI
    4. RAGAS (explodinggradients/ragas)
    5. GPT-4
    6. Claude 3 Opus
    7. Gemini 1.5 Pro
    8. DeepEval (confident-ai/deepeval)
    9. LangChain Evaluation Module (langchain-ai/langchain)
    10. Fiddler AI Observability Platform
    11. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    12. Microsoft Guidance (microsoft/guidance)

    AI recommended 12 alternatives but never named uptrain-ai/uptrain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a platform for LLM testing, monitoring, and root cause analysis of prompt failures.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. Arize AI
    4. Weights & Biases
    5. W&B Prompts
    6. Helicone
    7. Deepchecks
    8. Galileo

    AI recommended 8 alternatives but never named uptrain-ai/uptrain. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 uptrain-ai/uptrain?
    pass
    AI named uptrain-ai/uptrain explicitly

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

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

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

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uptrain-ai/uptrain — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite