RRepoGEO

REPOGEO REPORT · LITE

JudgmentLabs/judgeval

Default branch main · commit 47df3495 · scanned 5/15/2026, 3:37:37 PM

GitHub: 1,033 stars · 93 forks

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 JudgmentLabs/judgeval, 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
  • mediumabout#1
    Refine the 'About' description for clarity on its 'stack' nature

    Why:

    CURRENT
    The Continuous-Improvement Stack for Agents. Our environment data and evals power agent improvement and monitoring.
    COPY-PASTE FIX
    The Continuous-Improvement Stack for Agents: an open-source Python SDK for agent evaluation, tracing, and monitoring, enabling data-backed improvement of LLM-powered applications.
  • lowtopics#2
    Add more specific topics related to production LLM and agent frameworks

    Why:

    CURRENT
    agent, agentic-ai, agents, grpo, langchain, langgraph, llama-index, llm, llm-evaluation, llm-observability, open-source, openai, prompt-engineering, reinforcement-learning, rl
    COPY-PASTE FIX
    agent, agentic-ai, agents, llm-evaluation, llm-observability, llm-ops, agent-framework, production-llm, langchain, langgraph, llama-index, llm, open-source, openai, prompt-engineering, reinforcement-learning, rl, grpo

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 JudgmentLabs/judgeval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenTelemetry
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenTelemetry · recommended 2×
  2. LangChain · recommended 1×
  3. Datadog · recommended 1×
  4. PostgreSQL · recommended 1×
  5. Amazon S3 · recommended 1×
  • CATEGORY QUERY
    How to continuously evaluate and improve LLM agent performance using production data?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenTelemetry
    3. Datadog
    4. PostgreSQL
    5. Amazon S3
    6. Google Cloud Storage
    7. Azure Blob Storage
    8. MLflow
    9. Galileo (by Arize AI)
    10. Humanloop
    11. Weights & Biases
    12. DVC (Data Version Control)
    13. Kubeflow

    AI recommended 13 alternatives but never named JudgmentLabs/judgeval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source tools for tracing and debugging failures in LLM-powered agent applications.
    you: not recommended
    AI recommended (in order):
    1. LangChain Plus (LangSmith)
    2. OpenTelemetry
    3. WandB (Weights & Biases) Prompts
    4. Helicone (helicone/helicone)
    5. Phoenix (by Arize AI) (Arize-AI/phoenix)
    6. LlamaIndex Observability (with LlamaCloud/LlamaParse)

    AI recommended 6 alternatives but never named JudgmentLabs/judgeval. 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 JudgmentLabs/judgeval?
    pass
    AI named JudgmentLabs/judgeval explicitly

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

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

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

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JudgmentLabs/judgeval — 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