RRepoGEO

REPOGEO REPORT · LITE

AgentOps-AI/agentops

Default branch main · commit a855a92d · scanned 5/27/2026, 1:51:50 PM

GitHub: 5,580 stars · 587 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 AgentOps-AI/agentops, 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 README's core value proposition to be the very first text

    Why:

    CURRENT
    The current README starts with visual elements and a less direct `<em>` tag before the main textual explanation.
    COPY-PASTE FIX
    AgentOps is the open-source observability and devtool platform specifically designed for building, evaluating, and monitoring AI agents from prototype to production.
  • mediumreadme#2
    Add a direct comparison/differentiation statement to README

    Why:

    COPY-PASTE FIX
    Add a sentence or short paragraph early in the README, e.g., 'Unlike general-purpose monitoring solutions or broader LLM development platforms, AgentOps is purpose-built for the unique challenges of AI agent observability, offering deep insights into agent behavior, performance, and costs.'
  • lowtopics#3
    Expand topics with problem-solution keywords

    Why:

    CURRENT
    agent, agentops, agents-sdk, ai, anthropic, autogen, cost-estimation, crewai, evals, evaluation-metrics, groq, langchain, llm, mistral, ollama, openai, openai-agents
    COPY-PASTE FIX
    agent, agentops, agents-sdk, ai, anthropic, autogen, cost-estimation, crewai, evals, evaluation-metrics, groq, langchain, llm, mistral, ollama, openai, openai-agents, ai-agent-monitoring, agent-observability, llm-observability, agent-debugging, production-ai-agents

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 AgentOps-AI/agentops
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Arize AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Arize AI · recommended 1×
  2. whylabs/whylogs · recommended 1×
  3. Datadog · recommended 1×
  4. prometheus/prometheus · recommended 1×
  5. grafana/grafana · recommended 1×
  • CATEGORY QUERY
    How can I monitor the performance and behavior of my AI agents in production?
    you: not recommended
    AI recommended (in order):
    1. Arize AI
    2. WhyLabs (whylabs/whylogs)
    3. Datadog
    4. Prometheus (prometheus/prometheus)
    5. Grafana (grafana/grafana)
    6. Weights & Biases (W&B)
    7. Seldon Deploy

    AI recommended 7 alternatives but never named AgentOps-AI/agentops. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help track LLM costs and benchmark AI agent performance during development?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangSmith
    3. Phoenix (Arize-AI/phoenix)
    4. W&B Prompts (wandb/wandb)
    5. OpenAI API Usage Dashboard
    6. Helicone (helicone/helicone)
    7. MLflow (mlflow/mlflow)
    8. DeepEval (confident-ai/deepeval)

    AI recommended 8 alternatives but never named AgentOps-AI/agentops. 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 AgentOps-AI/agentops?
    pass
    AI named AgentOps-AI/agentops explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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