RRepoGEO

REPOGEO REPORT · LITE

Arize-ai/openinference

Default branch main · commit 8ba01be4 · scanned 6/4/2026, 11:12:15 AM

GitHub: 1,006 stars · 248 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 Arize-ai/openinference, 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 paragraph to emphasize 'open standard'

    Why:

    CURRENT
    OpenInference is a set of conventions and plugins that is complimentary to OpenTelemetry to enable tracing of AI applications. OpenInference is natively supported by arize-phoenix, but can be used with any OpenTelemetry-compatible backend as well.
    COPY-PASTE FIX
    OpenInference is an **open, vendor-agnostic standard and specification for AI inference observability data**, built on OpenTelemetry. It provides a set of conventions and plugins to enable comprehensive tracing of AI applications, ensuring interoperability across various tools and backends. While natively supported by arize-phoenix, OpenInference is designed to be used with any OpenTelemetry-compatible backend.
  • hightopics#2
    Add specific topics for 'open standard' and 'specification'

    Why:

    CURRENT
    aiops, gemini, hacktoberfest, haystack, langchain, langraph, llamaindex, llmops, llms, mcp, openai, openai-agents, opentelemetry, pydantic-ai, smolagents, telemetry, tracing, vercel, vertex
    COPY-PASTE FIX
    aiops, ai-observability-standard, gemini, haystack, langchain, langraph, llamaindex, llmops, llms, mcp, openai, openai-agents, open-standard, opentelemetry, pydantic-ai, semantic-conventions, smolagents, specification, telemetry, tracing, vercel, vertex
  • mediumreadme#3
    Add a 'Why OpenInference?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why OpenInference? (vs. OpenTelemetry, LangChain, LlamaIndex)

OpenInference extends OpenTelemetry with specific semantic conventions for AI-related traces (inputs, outputs, model metadata) that are missing from general tracing standards. Unlike frameworks like LangChain or LlamaIndex, OpenInference focuses purely on standardizing observability data, providing a vendor-agnostic layer that complements these frameworks rather than replacing them.

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 Arize-ai/openinference
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenTelemetry
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenTelemetry · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Prometheus · recommended 1×
  5. Grafana · recommended 1×
  • CATEGORY QUERY
    How to trace and monitor LLM application performance using open standards?
    you: not recommended
    AI recommended (in order):
    1. OpenTelemetry
    2. LangChain
    3. LlamaIndex
    4. Prometheus
    5. Grafana
    6. Jaeger
    7. Elastic Stack
    8. Elasticsearch
    9. Kibana
    10. Beats
    11. Logstash
    12. SigNoz

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

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to add observability to my AI applications with OpenTelemetry?
    you: not recommended
    AI recommended (in order):
    1. OpenTelemetry SDKs
    2. Datadog
    3. New Relic
    4. Honeycomb
    5. Dynatrace
    6. Splunk Observability Cloud
    7. OpenTelemetry Collector (open-telemetry/opentelemetry-collector)
    8. Grafana (grafana/grafana)
    9. Prometheus (prometheus/prometheus)
    10. Loki (grafana/loki)
    11. Jaeger (jaegertracing/jaeger)
    12. Tempo (grafana/tempo)
    13. LangChain (langchain-ai/langchain)
    14. MLflow (mlflow/mlflow)

    AI recommended 14 alternatives but never named Arize-ai/openinference. 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 Arize-ai/openinference?
    pass
    AI named Arize-ai/openinference explicitly

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

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

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

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Arize-ai/openinference — 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