RRepoGEO

REPOGEO REPORT · LITE

abshkbh/arrakis

Default branch main · commit 87723149 · scanned 6/16/2026, 11:02:18 AM

GitHub: 816 stars · 81 forks

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 abshkbh/arrakis, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, sandboxing, microvm, code-execution, security, backtracking, agent-workflows, python-sdk, rest-api
  • highreadme#2
    Reposition the core value proposition immediately after the H1

    Why:

    CURRENT
    [](./LICENSE)

## Introduction ##

AI agents can generate malicious or buggy code that can attack the host system its run on.
    COPY-PASTE FIX
    Arrakis is a fully customizable and self-hosted sandboxing solution for secure AI agent code execution and computer use, featuring out-of-the-box support for backtracking via snapshot-and-restore. 

[](./LICENSE)

## Introduction ##

AI agents can generate malicious or buggy code that can attack the host system its run on.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Link to project website or documentation, e.g., https://arrakis.dev or your documentation site]

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 abshkbh/arrakis
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
gVisor
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. gVisor · recommended 1×
  2. Kata Containers · recommended 1×
  3. Firecracker · recommended 1×
  4. Docker with AppArmor/SELinux Profiles · recommended 1×
  5. NSjail · recommended 1×
  • CATEGORY QUERY
    How to securely run untrusted AI agent code in an isolated environment?
    you: not recommended
    AI recommended (in order):
    1. gVisor
    2. Kata Containers
    3. Firecracker
    4. Docker with AppArmor/SELinux Profiles
    5. NSjail
    6. WebAssembly (Wasm) with WASI (WebAssembly System Interface)
    7. Wasmtime
    8. Wasmer

    AI recommended 8 alternatives but never named abshkbh/arrakis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a self-hosted solution for testing multi-step AI agent workflows with state snapshots?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Pytest (pytest-dev/pytest)
    3. Unittest
    4. MLflow (mlflow/mlflow)
    5. Prefect (PrefectHQ/prefect)
    6. Apache Airflow (apache/airflow)
    7. Pickle
    8. JSON
    9. YAML
    10. Docker
    11. Kubernetes

    AI recommended 11 alternatives but never named abshkbh/arrakis. 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 abshkbh/arrakis?
    pass
    AI named abshkbh/arrakis explicitly

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

  • If a team adopts abshkbh/arrakis in production, what risks or prerequisites should they evaluate first?
    pass
    AI named abshkbh/arrakis 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 abshkbh/arrakis solve, and who is the primary audience?
    pass
    AI named abshkbh/arrakis 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 abshkbh/arrakis. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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abshkbh/arrakis — 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