REPOGEO REPORT · LITE

hatchet-dev/hatchet

Default branch main · commit 91933012 · scanned 6/18/2026, 3:31:20 AM

GitHub: 7,383 stars · 420 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

40 /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

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 hatchet-dev/hatchet, 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 core value proposition to the top of the README

Why:

CURRENT

The README excerpt shows the core value proposition `### An orchestration engine for background tasks, AI agents, and durable workflows` appearing after a large `div align="center">` block containing a logo and multiple links.

COPY-PASTE FIX

Move the line `An orchestration engine for background tasks, AI agents, and durable workflows` to be the very first textual content in the README, immediately after any necessary badges, and elevate it to an H1 (`# An orchestration engine...`).

mediumtopics#2

Expand repository topics with more specific keywords

Why:

CURRENT

concurrency, dag, distributed, distributed-systems, durable-execution, event-driven, fastapi, golang, nodejs, python, queue, typescript, workflow-engine

COPY-PASTE FIX

concurrency, dag, distributed, distributed-systems, durable-execution, event-driven, fastapi, golang, nodejs, python, queue, typescript, workflow-engine, workflow-orchestration, distributed-task-queue, stateful-workflows, microservices-orchestration, background-jobs, task-orchestration

lowcomparison#3

Add a dedicated comparison section to the README

Why:

COPY-PASTE FIX

Add a new section to the README titled 'Hatchet vs. Alternatives' or 'Why Hatchet?', explicitly comparing Hatchet to common tools like Temporal, Prefect, Airflow, and Celery, highlighting its unique benefits and use cases.

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 hatchet-dev/hatchet

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Apache Airflow

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Apache Airflow · recommended 2×
AWS Step Functions · recommended 2×
Temporal.io · recommended 1×
Prefect · recommended 1×
Celery · recommended 1×

CATEGORY QUERY
How to build robust, durable workflows for background tasks in Python?
you: not recommended
AI recommended (in order):
1. Temporal.io
2. Prefect
3. Apache Airflow
4. Celery
5. AWS Step Functions
6. Azure Durable Functions
7. Luigi
AI recommended 7 alternatives but never named hatchet-dev/hatchet. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good alternatives to simple message queues for complex distributed workflows?
you: not recommended
AI recommended (in order):
1. Apache Kafka
2. Kafka Streams
3. KSQL DB
4. Temporal
5. Cadence
6. Apache Airflow
7. AWS Step Functions
8. Azure Logic Apps
9. Google Cloud Workflows
10. Camunda Platform
11. Netflix Conductor
AI recommended 11 alternatives but never named hatchet-dev/hatchet. 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 hatchet-dev/hatchet?
pass
AI named hatchet-dev/hatchet explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts hatchet-dev/hatchet in production, what risks or prerequisites should they evaluate first?
pass
AI named hatchet-dev/hatchet 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 hatchet-dev/hatchet solve, and who is the primary audience?
pass
AI named hatchet-dev/hatchet explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of hatchet-dev/hatchet. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/hatchet-dev/hatchet.svg)](https://repogeo.com/en/r/hatchet-dev/hatchet)

HTML

<a href="https://repogeo.com/en/r/hatchet-dev/hatchet"><img src="https://repogeo.com/badge/hatchet-dev/hatchet.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite