RRepoGEO

REPOGEO REPORT · LITE

Canner/wren-engine

Default branch main · commit bc2b06a1 · scanned 6/11/2026, 10:46:08 AM

GitHub: 660 stars · 201 forks

AI VISIBILITY SCORE
33 /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
2 / 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 Canner/wren-engine, 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
    Update repository description to clarify its purpose as an archived semantic layer

    Why:

    CURRENT
    This repository has been merged into Canner/WrenAI under the core/ directory
    COPY-PASTE FIX
    Archived: Wren Engine was the core semantic layer for AI agents, providing governed business data context. Now merged into Canner/WrenAI.
  • mediumtopics#2
    Refine repository topics to emphasize semantic layer for AI agents

    Why:

    CURRENT
    agent, agentic-ai, ai, business-intelligence, data, data-analysis, data-analytics, data-lake, data-warehouse, hacktoberfest, llm, mcp, mcp-server, semantic, semantic-layer, sql
    COPY-PASTE FIX
    agent, agentic-ai, ai, business-intelligence, llm, semantic, semantic-layer, data-context, data-governance, ai-agents
  • lowreadme#3
    Add a concise, explicit statement about its semantic layer role in the README's opening paragraph

    Why:

    CURRENT
    <p align="center">
  The open context engine for AI agents
</p>
    COPY-PASTE FIX
    <p align="center">
  The open context engine for AI agents: a semantic layer for governed business data.
</p>

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 Canner/wren-engine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Atlan
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Atlan · recommended 2×
  2. Looker · recommended 2×
  3. Dataform · recommended 2×
  4. dbt-labs/dbt-core · recommended 1×
  5. cube-js/cube · recommended 1×
  • CATEGORY QUERY
    Looking for a semantic layer to provide governed business data context to AI agents.
    you: not recommended
    AI recommended (in order):
    1. dbt Semantic Layer (dbt-labs/dbt-core)
    2. Cube.dev (cube-js/cube)
    3. Atlan
    4. Looker
    5. Metriql (metriql/metriql)
    6. Dataform

    AI recommended 6 alternatives but never named Canner/wren-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a tool to build a semantic data layer for LLM-powered business intelligence.
    you: not recommended
    AI recommended (in order):
    1. dbt
    2. Cube.js
    3. Looker
    4. Atlan
    5. Dataform
    6. Metriql

    AI recommended 6 alternatives but never named Canner/wren-engine. 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 Canner/wren-engine?
    pass
    AI named Canner/wren-engine explicitly

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

  • If a team adopts Canner/wren-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Canner/wren-engine 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 Canner/wren-engine solve, and who is the primary audience?
    pass
    AI did not name Canner/wren-engine — likely talking about a different project

    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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