RRepoGEO

REPOGEO REPORT · LITE

0xPlaygrounds/rig

Default branch main · commit 586f543e · scanned 6/18/2026, 3:22:14 AM

GitHub: 7,663 stars · 855 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 0xPlaygrounds/rig, 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
    Add a clear, concise H1 statement to the README's opening

    Why:

    CURRENT
    The README starts with a logo, links, and a "star the repo" message, followed by a warning, before the "What is Rig?" section.
    COPY-PASTE FIX
    Insert `<h1>Rig: Build modular and scalable LLM Applications in Rust</h1>` immediately after the initial badges/logo block and before the "Docs / Website" line.
  • mediumreadme#2
    Clarify the 'What is Rig?' section in the README

    Why:

    CURRENT
    The README has a `## What is Rig?` heading, but the content is not provided in the excerpt.
    COPY-PASTE FIX
    Ensure the content under `## What is Rig?` clearly and immediately defines Rig as a framework for building LLM applications and agents in Rust, emphasizing "modular" and "scalable" aspects. For example: "Rig is a Rust framework designed to simplify the development of modular and scalable Large Language Model (LLM) applications and AI agents. It provides the tools and abstractions needed to build robust, production-ready LLM systems."
  • mediumreadme#3
    Add a 'Why Rig?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., `## Why Rig?` or `## Comparison to other Rust AI tools`, that highlights Rig's differentiators as an LLM *application framework* compared to lower-level libraries like `llm-chain`, `candle`, or general-purpose HTTP clients like `reqwest`.

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 0xPlaygrounds/rig
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
reqwest
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. reqwest · recommended 2×
  2. llm · recommended 1×
  3. Tokio · recommended 1×
  4. Axum · recommended 1×
  5. Serde · recommended 1×
  • CATEGORY QUERY
    How can I build scalable and modular large language model applications with Rust?
    you: not recommended
    AI recommended (in order):
    1. llm
    2. Tokio
    3. Axum
    4. Serde
    5. reqwest
    6. SQLx
    7. OpenDAL

    AI recommended 7 alternatives but never named 0xPlaygrounds/rig. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Rust frameworks for developing AI agents and automating LLM workflows?
    you: not recommended
    AI recommended (in order):
    1. llm-chain
    2. candle
    3. tract
    4. reqwest
    5. OpenAI
    6. Anthropic
    7. Google Gemini
    8. tokio
    9. serde

    AI recommended 9 alternatives but never named 0xPlaygrounds/rig. 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 0xPlaygrounds/rig?
    pass
    AI named 0xPlaygrounds/rig explicitly

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

  • If a team adopts 0xPlaygrounds/rig in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 0xPlaygrounds/rig 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 0xPlaygrounds/rig solve, and who is the primary audience?
    pass
    AI named 0xPlaygrounds/rig 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 0xPlaygrounds/rig. 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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0xPlaygrounds/rig — 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
0xPlaygrounds/rig — RepoGEO report