RRepoGEO

REPOGEO REPORT · LITE

xataio/agent

Default branch main · commit 40d62625 · scanned 5/26/2026, 10:22:00 AM

GitHub: 1,072 stars · 79 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 xataio/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
  • highreadme#1
    Reposition the README's opening to clarify core purpose and avoid brand confusion

    Why:

    CURRENT
    Xata Agent is an open source agent that monitors your database, finds root causes of issues, and suggests fixes and improvements. It's like having a new SRE hire in your team, one with extensive experience in Postgres.
    COPY-PASTE FIX
    Xata Agent is an open source AI agent specifically for **PostgreSQL database observability and optimization**. It monitors your Postgres database, finds root causes of issues, and suggests fixes and improvements. This agent operates independently and is *not* a connector or gateway for the Xata cloud platform.
  • mediumreadme#2
    Convert the agent's self-introduction into a direct 'Key Capabilities' section

    Why:

    CURRENT
    > Hire me as your AI PostgreSQL expert. I can:
>
> - watch logs & metrics for potential issues.
> - proactively suggest configuration tuning for your database instance.
> - troubleshoot performance issues and make indexing suggestions.
> - troubleshoot common issues like high CPU, high memory usage, high connection count, etc.
> - and help you vacuum (your Postgres DB, not your room).
    COPY-PASTE FIX
    ## Key Capabilities
As your AI PostgreSQL expert, Xata Agent can:
-   Watch logs & metrics for potential issues.
-   Proactively suggest configuration tuning for your database instance.
-   Troubleshoot performance issues and make indexing suggestions.
-   Troubleshoot common issues like high CPU, high memory usage, high connection count, etc.
-   Help you vacuum your Postgres DB.
  • lowreadme#3
    Add a brief 'Why Xata Agent?' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    ## Why Xata Agent?
Unlike general-purpose monitoring tools, Xata Agent is an AI-native expert specifically trained for PostgreSQL. It goes beyond simple alerts to provide root cause analysis and actionable, proactive recommendations, acting as a dedicated SRE for your database.

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 xataio/agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Datadog
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Datadog · recommended 2×
  2. New Relic · recommended 2×
  3. Dynatrace · recommended 2×
  4. PG-Strom · recommended 1×
  5. prometheus/prometheus · recommended 1×
  • CATEGORY QUERY
    How can I use AI to monitor and troubleshoot PostgreSQL performance issues?
    you: not recommended
    AI recommended (in order):
    1. Datadog
    2. New Relic
    3. Dynatrace
    4. PG-Strom
    5. Prometheus (prometheus/prometheus)
    6. Grafana (grafana/grafana)
    7. scikit-learn (scikit-learn/scikit-learn)
    8. TensorFlow (tensorflow/tensorflow)
    9. Percona Monitoring and Management (PMM) (percona/pmm)
    10. Jupyter Notebooks (jupyter/notebook)
    11. Pandas (pandas-dev/pandas)
    12. NumPy (numpy/numpy)
    13. TimescaleDB (timescale/timescaledb)
    14. psycopg2 (psycopg/psycopg2)

    AI recommended 14 alternatives but never named xataio/agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What AI-powered tools help with proactive PostgreSQL database optimization and fixes?
    you: not recommended
    AI recommended (in order):
    1. Everest
    2. PG Analyze
    3. Datadog
    4. New Relic
    5. Dynatrace
    6. Pganalyze
    7. OpsDash

    AI recommended 7 alternatives but never named xataio/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
    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 xataio/agent?
    pass
    AI did not name xataio/agent — 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?

  • If a team adopts xataio/agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xataio/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 xataio/agent solve, and who is the primary audience?
    pass
    AI named xataio/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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite