RRepoGEO

REPOGEO REPORT · LITE

dust-tt/dust

Default branch main · commit a6cef8e6 · scanned 6/24/2026, 6:26:33 AM

GitHub: 1,391 stars · 288 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 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
    Expand README's opening to clearly position Dust as an integrated AI agent platform

    Why:

    CURRENT
    ## Dust

Custom AI agent platform to speed up your work.
    COPY-PASTE FIX
    ## Dust: An Integrated Platform for Building and Deploying Production-Grade AI Agents

Dust provides a full-lifecycle platform for developers and teams to build, deploy, and manage reliable, production-grade AI assistants that connect to internal data and tools. Orchestrate complex LLM workflows with a developer-centric experience.
  • hightopics#2
    Update topics to better reflect platform identity and avoid miscategorization

    Why:

    CURRENT
    agents, large-language-models, llm, rust
    COPY-PASTE FIX
    agents, large-language-models, llm, ai-agents, llm-platform, ai-platform
  • mediumcomparison#3
    Add a 'Compared to X' section in the README

    Why:

    COPY-PASTE FIX
    ### Compared to LangChain, LlamaIndex, and other LLM frameworks

Dust is an integrated, full-lifecycle platform designed for building, deploying, and monitoring production-grade LLM applications, offering a developer-centric experience and visual orchestration beyond what typical libraries provide.

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
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. Microsoft Azure AI Studio · recommended 1×
  4. Google Cloud Vertex AI · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    What platforms allow me to build and manage custom large language model agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Microsoft Azure AI Studio
    4. Google Cloud Vertex AI
    5. OpenAI Assistants API
    6. Hugging Face Agents (huggingface/transformers)

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for a robust platform to develop and deploy custom AI agents using Rust.
    you: not recommended
    AI recommended (in order):
    1. Candle (huggingface/candle)
    2. tch-rs (LaurentMazare/tch-rs)
    3. tract (sonos/tract)
    4. burn (burn-rs/burn)
    5. ONNX Runtime (microsoft/onnxruntime-rs)
    6. WebAssembly

    AI recommended 6 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?

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dust-tt/dust — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite