RRepoGEO

REPOGEO REPORT · LITE

dbos-inc/dbos-transact-py

Default branch main · commit d230506c · scanned 5/13/2026, 6:36:44 PM

GitHub: 1,348 stars · 66 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 dbos-inc/dbos-transact-py, 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 paragraph to clearly state its category and core value

    Why:

    CURRENT
    DBOS provides lightweight durable workflows built on top of Postgres.
    COPY-PASTE FIX
    DBOS is a Python workflow orchestration engine that provides lightweight, durable workflows built on top of Postgres. It offers a simpler, database-backed alternative to complex systems, enabling reliable, ACID-compliant transactions and fault-tolerant execution for microservices and long-running processes.
  • mediumreadme#2
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## DBOS vs. Other Workflow Engines (Temporal, Cadence, Airflow)' that explicitly outlines DBOS's core differentiator: 'ACID transactional guarantees across multiple services and databases within a serverless environment, managed through a Python SDK with decorators.'
  • lowabout#3
    Enhance the repository description

    Why:

    CURRENT
    Database-Backed Durable Python Workflows
    COPY-PASTE FIX
    DBOS: A Python workflow orchestration engine for durable, ACID-compliant transactions and fault-tolerant microservices, backed by Postgres.

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 dbos-inc/dbos-transact-py
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Temporal
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Temporal · recommended 2×
  2. Cadence · recommended 2×
  3. Prefect · recommended 2×
  4. Apache Airflow · recommended 2×
  5. Luigi · recommended 1×
  • CATEGORY QUERY
    Python library for building fault-tolerant workflows that persist state in a database.
    you: not recommended
    AI recommended (in order):
    1. Temporal
    2. Cadence
    3. Prefect
    4. Apache Airflow
    5. Luigi
    6. Celery

    AI recommended 6 alternatives but never named dbos-inc/dbos-transact-py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to orchestrate complex Python microservices with durable execution and reliable failure handling?
    you: not recommended
    AI recommended (in order):
    1. Temporal
    2. Cadence
    3. Apache Airflow
    4. AWS Step Functions
    5. Prefect
    6. Zeebe

    AI recommended 6 alternatives but never named dbos-inc/dbos-transact-py. 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 dbos-inc/dbos-transact-py?
    pass
    AI did not name dbos-inc/dbos-transact-py — 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 dbos-inc/dbos-transact-py in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dbos-inc/dbos-transact-py 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 dbos-inc/dbos-transact-py solve, and who is the primary audience?
    pass
    AI named dbos-inc/dbos-transact-py explicitly

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

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dbos-inc/dbos-transact-py — 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