RRepoGEO

REPOGEO REPORT · LITE

Datus-ai/Datus-agent

Default branch main · commit 33adf9fd · scanned 5/20/2026, 4:32:04 PM

GitHub: 1,248 stars · 196 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 Datus-ai/Datus-agent, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    data-engineering, sql-generation, ai-agent, natural-language-to-sql, semantic-layer, data-context, llm-agent, cli-tool, modern-data-stack
  • highreadme#2
    Reposition README's opening to emphasize core function as a CLI SQL client

    Why:

    CURRENT
    Datus is an open-source data engineering agent that builds evolvable context for your data system — turning natural language into accurate SQL through domain-aware reasoning, semantic search, and continuous learning.
    COPY-PASTE FIX
    Datus is an open-source **AI-native data engineering agent** and CLI SQL client for the modern data stack. It transforms natural language into accurate SQL by building **evolvable context** for your data system through domain-aware reasoning, semantic search, and continuous learning.
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    ## License
Datus is released under [License Name(s) or description of terms]. Please see the LICENSE file for full details.

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 Datus-ai/Datus-agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Google Cloud Vertex AI · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. Microsoft Azure OpenAI Service · recommended 1×
  5. dataherald/dataherald · recommended 1×
  • CATEGORY QUERY
    How can I use an AI agent to generate accurate SQL queries from natural language questions?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google Cloud Vertex AI
    3. Hugging Face Transformers (huggingface/transformers)
    4. Microsoft Azure OpenAI Service
    5. Dataherald (dataherald/dataherald)
    6. Vanna.ai (vanna-ai/vanna)
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)

    AI recommended 8 alternatives but never named Datus-ai/Datus-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source data engineering tools build evolvable context and semantic models for data systems?
    you: not recommended
    AI recommended (in order):
    1. Apache Atlas
    2. Amundsen
    3. DataHub
    4. OpenMetadata
    5. DBT
    6. Apache Flink

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

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 Datus-ai/Datus-agent?
    pass
    AI named Datus-ai/Datus-agent explicitly

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

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

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

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Datus-ai/Datus-agent — 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