RRepoGEO

REPOGEO REPORT · LITE

liquidos-ai/AutoAgents

Default branch main · commit 9781a48b · scanned 6/10/2026, 8:21:58 AM

GitHub: 670 stars · 74 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 liquidos-ai/AutoAgents, 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
    Strengthen the README's 'Overview' to explicitly differentiate from generic Rust frameworks

    Why:

    CURRENT
    AutoAgents is a modular, multi-agent framework for building intelligent systems in Rust.
    COPY-PASTE FIX
    AutoAgents is a modular, multi-agent framework for building intelligent systems in Rust. It provides a specialized, production-grade environment for orchestrating LLM-powered agents, distinct from general-purpose Rust web or async frameworks. AutoAgents focuses on type-safe agent models, structured tool calling, and configurable memory for robust AI applications.
  • mediumabout#2
    Enhance the repository description to explicitly mention LLM-powered agents

    Why:

    CURRENT
    A multi-agent framework written in Rust that enables you to build, deploy, and coordinate multiple intelligent agents
    COPY-PASTE FIX
    A production-grade multi-agent framework written in Rust for building, deploying, and coordinating LLM-powered intelligent agents.
  • lowtopics#3
    Expand repository topics with more specific Rust AI and agent system keywords

    Why:

    CURRENT
    agents, ai, ai-agents, ai-agents-framework, llm
    COPY-PASTE FIX
    agents, ai, ai-agents, ai-agents-framework, llm, rust-ai, rust-llm, multi-agent-systems, agent-orchestration

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 liquidos-ai/AutoAgents
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Actix-web
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Actix-web · recommended 1×
  2. Tokio · recommended 1×
  3. Bastion · recommended 1×
  4. Akka-rs · recommended 1×
  5. Rayon · recommended 1×
  • CATEGORY QUERY
    Looking for a Rust-based framework to build and coordinate multiple intelligent agents.
    you: not recommended
    AI recommended (in order):
    1. Actix-web
    2. Tokio
    3. Bastion
    4. Akka-rs
    5. Rayon

    AI recommended 5 alternatives but never named liquidos-ai/AutoAgents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust Rust frameworks for deploying production-grade AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. Actix-web (actix/actix-web)
    2. Axum (tokio-rs/axum)
    3. Warp (seanmonstar/warp)
    4. Rocket (SergioBenitez/Rocket)
    5. Tide (http-rs/tide)

    AI recommended 5 alternatives but never named liquidos-ai/AutoAgents. 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 liquidos-ai/AutoAgents?
    pass
    AI named liquidos-ai/AutoAgents explicitly

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

  • If a team adopts liquidos-ai/AutoAgents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named liquidos-ai/AutoAgents 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 liquidos-ai/AutoAgents solve, and who is the primary audience?
    pass
    AI named liquidos-ai/AutoAgents explicitly

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

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MARKDOWN (README)
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liquidos-ai/AutoAgents — 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