RRepoGEO

REPOGEO REPORT · LITE

dust-tt/dust

Default branch main · commit 41a0c5ca · scanned 5/13/2026, 7:31:27 PM

GitHub: 1,348 stars · 263 forks

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 dust-tt/dust, 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 opening to emphasize "production-grade platform" and "Rust-based agents"

    Why:

    CURRENT
    ## Dust

Custom AI agent platform to speed up your work.

Check out our user guides and developer platform
    COPY-PASTE FIX
    ## Dust: The Production-Grade Platform for Building and Deploying Custom AI Agents with Structured Workflows

Dust is a Rust-based, production-grade platform designed to help developers build, deploy, and observe custom AI agents with structured workflows. It provides the engine for managing the full lifecycle of your LLM applications.

Check out our user guides and developer platform
  • mediumtopics#2
    Add more specific topics to clarify platform and workflow nature

    Why:

    CURRENT
    agents, large-language-models, llm, rust
    COPY-PASTE FIX
    agents, large-language-models, llm, rust, ai-platform, llm-orchestration, workflow-engine, ai-agents
  • lowreadme#3
    Add a brief "Why Dust?" or "Differentiators" section to the README

    Why:

    COPY-PASTE FIX
    ### Why Dust?

Dust stands out as a production-grade engine for building, deploying, and observing LLM applications, emphasizing structured workflows and agents for a more integrated lifecycle management than typical LLM orchestration libraries or raw APIs.

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 dust-tt/dust
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Google Cloud AI Platform / Vertex AI · recommended 1×
  3. Microsoft Azure AI Platform · recommended 1×
  4. Hugging Face Transformers / Inference API · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    What are the best platforms for building and deploying custom AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google Cloud AI Platform / Vertex AI
    3. Microsoft Azure AI Platform
    4. Hugging Face Transformers / Inference API
    5. LangChain
    6. AWS SageMaker

    AI recommended 6 alternatives but never named dust-tt/dust. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to develop custom LLM-powered agents using a Rust-based framework?
    you: not recommended
    AI recommended (in order):
    1. llm-chain
    2. candle
    3. llm
    4. Tonic
    5. reqwest
    6. OpenAI API
    7. Tokio
    8. async-std

    AI recommended 8 alternatives but never named dust-tt/dust. 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 dust-tt/dust?
    pass
    AI named dust-tt/dust explicitly

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

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

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/dust-tt/dust.svg)](https://repogeo.com/en/r/dust-tt/dust)
HTML
<a href="https://repogeo.com/en/r/dust-tt/dust"><img src="https://repogeo.com/badge/dust-tt/dust.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

dust-tt/dust — 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