RRepoGEO

REPOGEO REPORT · LITE

modelscope/ms-agent

Default branch main · commit f491aaae · scanned 5/20/2026, 1:57:09 PM

GitHub: 4,265 stars · 502 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
33 /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
2 / 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 modelscope/ms-agent, 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 H1 and introduction to highlight ModelScope integration

    Why:

    CURRENT
    <h1> MS-Agent: Lightweight Framework for Empowering Agents with Autonomous Exploration</h1> and MS-Agent is a lightweight framework designed to empower agents with autonomous exploration capabilities.
    COPY-PASTE FIX
    Replace the H1 with <h1> MS-Agent: Lightweight Framework for Autonomous Agents with ModelScope Multi-Modal Capabilities</h1> and update the first sentence of the introduction to MS-Agent is a lightweight framework designed to empower agents with autonomous exploration capabilities, leveraging the ModelScope ecosystem for diverse multi-modal (vision, speech, text) model integration.
  • mediumtopics#2
    Add specific topics related to ModelScope and multi-modal AI

    Why:

    CURRENT
    agentic-insight, agentic-search, chat-bot, code-generation, deep-research, memory
    COPY-PASTE FIX
    agentic-insight, agentic-search, chat-bot, code-generation, deep-research, memory, modelscope, multi-modal-ai, tool-calling-agents
  • lowabout#3
    Update repository description to reflect ModelScope integration

    Why:

    CURRENT
    MS-Agent: a lightweight framework to empower agentic execution of complex tasks
    COPY-PASTE FIX
    MS-Agent: a lightweight framework for autonomous agents, leveraging ModelScope for multi-modal capabilities and complex task execution.

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 modelscope/ms-agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. CrewAI · recommended 2×
  4. Haystack · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    How to build a lightweight AI agent framework for complex task execution?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Assistants API
    5. CrewAI
    6. LiteLLM
    7. FastAPI

    AI recommended 7 alternatives but never named modelscope/ms-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for autonomous AI agents needing memory and deep research capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. CrewAI
    6. Semantic Kernel

    AI recommended 6 alternatives but never named modelscope/ms-agent. 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 modelscope/ms-agent?
    pass
    AI did not name modelscope/ms-agent — likely talking about a different project

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

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

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

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modelscope/ms-agent — 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