RRepoGEO

REPOGEO REPORT · LITE

lmnr-ai/lmnr

Default branch main · commit f9f49549 · scanned 6/21/2026, 5:46:55 AM

GitHub: 3,020 stars · 210 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 lmnr-ai/lmnr, 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
  • highabout#1
    Refine 'About' description for precise positioning

    Why:

    CURRENT
    Laminar - open-source observability platform purpose-built for AI agents. YC S24.
    COPY-PASTE FIX
    Laminar: The open-source, self-hosted observability platform purpose-built for AI agents, offering comprehensive tracing, evals, and monitoring to understand, debug, and optimize agent performance. YC S24.
  • highreadme#2
    Strengthen README H1 and opening paragraph

    Why:

    CURRENT
    # Laminar
Laminar is an open-source observability platform purpose-built for AI agents.
    COPY-PASTE FIX
    # Laminar
Laminar is the open-source, self-hosted observability platform purpose-built specifically for AI agents. It provides comprehensive tracing, evaluation (evals), and monitoring capabilities to understand, debug, and optimize your agent's performance in production.
  • mediumtopics#3
    Add 'debugging' to repository topics

    Why:

    CURRENT
    agent-observability, agents, ai, ai-observability, aiops, analytics, developer-tools, evals, evaluation, llm-evaluation, llm-observability, llmops, monitoring, observability, open-source, rust, rust-lang, self-hosted, ts, typescript
    COPY-PASTE FIX
    agent-observability, agents, ai, ai-observability, aiops, analytics, debugging, developer-tools, evals, evaluation, llm-evaluation, llm-observability, llmops, monitoring, observability, open-source, rust, rust-lang, self-hosted, ts, typescript

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 lmnr-ai/lmnr
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langsmith-sdk
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langsmith-sdk · recommended 1×
  2. openreplay/openreplay · recommended 1×
  3. Arize-AI/phoenix · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. grafana/grafana · recommended 1×
  • CATEGORY QUERY
    What are the best open-source platforms for monitoring and debugging AI agent interactions with LLMs?
    you: not recommended
    AI recommended (in order):
    1. LangSmith (langchain-ai/langsmith-sdk)
    2. OpenReplay (openreplay/openreplay)
    3. Phoenix (Arize-AI/phoenix)
    4. MLflow (mlflow/mlflow)
    5. Grafana (grafana/grafana)
    6. Prometheus (prometheus/prometheus)
    7. Elasticsearch (elastic/elasticsearch)
    8. Logstash (elastic/logstash)
    9. Kibana (elastic/kibana)

    AI recommended 9 alternatives but never named lmnr-ai/lmnr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform LLM evaluations and analyze AI agent performance using a self-hosted solution?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases (W&B) Local
    3. Arize AI (Self-Hosted/On-Premise)
    4. Streamlit
    5. Gradio
    6. Plotly
    7. Matplotlib
    8. PostgreSQL
    9. SQLite
    10. Hugging Face `evaluate` library
    11. Prometheus
    12. Grafana

    AI recommended 12 alternatives but never named lmnr-ai/lmnr. 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 lmnr-ai/lmnr?
    pass
    AI named lmnr-ai/lmnr explicitly

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

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

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

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lmnr-ai/lmnr — 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