RRepoGEO

REPOGEO REPORT · LITE

lmnr-ai/lmnr

Default branch main · commit f02af8e5 · scanned 5/11/2026, 2:56:39 AM

GitHub: 2,864 stars · 195 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 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
  • highreadme#1
    Reposition the README H1 to explicitly state the project's category and purpose

    Why:

    CURRENT
    # Laminar
    COPY-PASTE FIX
    # Laminar: Open-Source Observability Platform for AI Agents & LLM Applications
  • mediumcomparison#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled "## Comparison to Alternatives" that briefly outlines how Laminar differs from key competitors like LangSmith, Helicone, Weights & Biases, Phoenix, and MLflow, focusing on aspects like open-source nature, self-hosting, and specific AI agent features.
  • lowreadme#3
    Expand and complete the 'Getting Started' section in the README

    Why:

    CURRENT
    ## Getting start
    COPY-PASTE FIX
    Ensure the "## Getting Started" section in the README is comprehensive, providing clear, copy-pasteable instructions for installation and initial setup, including any necessary code snippets or configuration steps.

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 Plus (now LangSmith)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain Plus (now LangSmith) · recommended 1×
  2. OpenTelemetry · recommended 1×
  3. Datadog · recommended 1×
  4. Weights & Biases (W&B) Prompts · recommended 1×
  5. Helicone · recommended 1×
  • CATEGORY QUERY
    How to get comprehensive observability for my AI agents and LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain Plus (now LangSmith)
    2. OpenTelemetry
    3. Datadog
    4. Weights & Biases (W&B) Prompts
    5. Helicone
    6. Grafana + Prometheus

    AI recommended 6 alternatives but never named lmnr-ai/lmnr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source platform for AI agent evaluation, monitoring, and data analysis.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Phoenix (Arize-AI/phoenix)
    3. MLflow (mlflow/mlflow)
    4. wandb (wandb/wandb)
    5. Ragas (explodinggradients/ragas)
    6. LlamaIndex (run-llama/llama_index)

    AI recommended 6 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?

Embed your GEO score

Drop this badge into the README of lmnr-ai/lmnr. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/lmnr-ai/lmnr.svg)](https://repogeo.com/en/r/lmnr-ai/lmnr)
HTML
<a href="https://repogeo.com/en/r/lmnr-ai/lmnr"><img src="https://repogeo.com/badge/lmnr-ai/lmnr.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

lmnr-ai/lmnr — 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