RRepoGEO

REPOGEO REPORT · LITE

InternLM/MindSearch

Default branch main · commit 7952c5f8 · scanned 6/29/2026, 8:27:14 AM

GitHub: 6,883 stars · 687 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 InternLM/MindSearch, 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 opening sentence

    Why:

    CURRENT
    ## ✨ MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
    COPY-PASTE FIX
    ## ✨ MindSearch: An LLM-based Multi-agent Framework for Web Search Engines

MindSearch provides a comprehensive framework for building sophisticated AI-driven web search experiences, mimicking human minds to elicit deep search capabilities, similar to Perplexity.ai Pro and SearchGPT.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/InternLM/MindSearch
  • lowcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, 'Why MindSearch?' or 'Comparison with Alternatives', that explicitly contrasts MindSearch with general LLM frameworks (e.g., LangChain, LlamaIndex) and traditional search tools (e.g., Elasticsearch), highlighting its unique focus as an LLM-based multi-agent framework for web search engines.

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 InternLM/MindSearch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude API · recommended 1×
  • CATEGORY QUERY
    How can I build a web search engine using large language models and multi-agent systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI API
    4. Anthropic Claude API
    5. Google Gemini API
    6. Elasticsearch
    7. OpenSearch
    8. Pinecone
    9. Weaviate
    10. Scrapy
    11. Playwright
    12. Faiss
    13. Hnswlib
    14. Ray
    15. Dask
    16. FastAPI
    17. Express.js

    AI recommended 17 alternatives but never named InternLM/MindSearch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to develop a sophisticated AI-driven web search experience.
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch (elastic/elasticsearch)
    2. Kibana (elastic/kibana)
    3. React (facebook/react)
    4. Vue (vuejs/core)
    5. Angular (angular/angular)
    6. Apache Solr (apache/solr)
    7. Haystack (deepset-ai/haystack)
    8. PostgreSQL
    9. Weaviate (weaviate/weaviate)
    10. Pinecone
    11. Algolia

    AI recommended 11 alternatives but never named InternLM/MindSearch. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 InternLM/MindSearch?
    pass
    AI named InternLM/MindSearch explicitly

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

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

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

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