RRepoGEO

REPOGEO REPORT · LITE

dbos-inc/dbos-transact-py

Default branch main · commit 3df88c4b · scanned 6/24/2026, 5:32:12 AM

GitHub: 1,434 stars · 75 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
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 README intro to explicitly state category and differentiate

    Why:

    CURRENT
    DBOS provides lightweight durable workflows built on top of Postgres. Instead of managing your own workflow orchestrator or task queue system, you can use DBOS to add durable workflows and queues to your program in just a few lines of code.
    COPY-PASTE FIX
    DBOS Transact is a lightweight, database-backed durable workflow engine for Python, offering a simpler, transactional alternative to systems like Temporal or Airflow for building fault-tolerant microservices and reliable task orchestration. Instead of managing your own workflow orchestrator or task queue system, you can use DBOS to add durable workflows and queues to your program in just a few lines of code.
  • mediumtopics#2
    Expand topics with more specific terms from competitor categories

    Why:

    CURRENT
    agentic-workflow, cronjob-scheduler, dbos, durable-execution, microservice-orchestration, microservices-orchestration, orchestration, postgresql, python, sqlite, workflow-engine, workflows
    COPY-PASTE FIX
    agentic-workflow, cronjob-scheduler, dbos, durable-execution, fault-tolerance, microservice-orchestration, microservices-orchestration, orchestration, postgresql, python, sqlite, task-queue, distributed-transactions, workflow-engine, workflows
  • lowreadme#3
    Add a 'Why DBOS?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why DBOS? (Compared to X, Y, Z)' or '## DBOS vs. Traditional Workflow Engines', detailing its unique transactional approach and lightweight nature compared to systems like Temporal, Airflow, or Celery, and emphasizing the 'unified, distributed transactional view over app logic and data'.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Temporal · recommended 1×
  2. Cadence · recommended 1×
  3. AWS Step Functions · recommended 1×
  4. Apache Airflow · recommended 1×
  5. Celery · recommended 1×
  • CATEGORY QUERY
    How to build fault-tolerant Python microservices with durable execution?
    you: not recommended
    AI recommended (in order):
    1. Temporal
    2. Cadence
    3. AWS Step Functions
    4. Apache Airflow
    5. Celery
    6. Durable Functions (Azure)
    7. Prefect

    AI recommended 7 alternatives but never named dbos-inc/dbos-transact-py. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a lightweight Python workflow engine for reliable task orchestration using a database?
    you: not recommended
    AI recommended (in order):
    1. Prefect (PrefectHQ/prefect)
    2. Apache Airflow (apache/airflow)
    3. Luigi (spotify/luigi)
    4. Bonobo (bonobo-project/bonobo)
    5. Dagster (dagster-io/dagster)

    AI recommended 5 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 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?

  • 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 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?

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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