RRepoGEO

REPOGEO REPORT · LITE

InternLM/MindSearch

Default branch main · commit 7952c5f8 · scanned 5/18/2026, 2:52:03 AM

GitHub: 6,858 stars · 682 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
    Add a concise opening paragraph to the README

    Why:

    CURRENT
    The README currently starts with a logo, paper/demo links, and then a changelog, without an immediate, explicit summary paragraph.
    COPY-PASTE FIX
    Add the following text directly after the initial visual elements and language links, before the '## ✨ MindSearch' heading:

MindSearch is an LLM-based multi-agent framework designed for building custom web search engines, mimicking advanced platforms like Perplexity.ai Pro and SearchGPT. It provides a robust architecture for developing intelligent search experiences with advanced query capabilities.
  • mediumhomepage#2
    Add the project homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/InternLM/MindSearch
  • mediumcomparison#3
    Add a 'Differentiators & Use Cases' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example:

## 🚀 Differentiators & Use Cases
MindSearch provides a flexible, open-source framework for developers to build and customize their own LLM-based multi-agent web search engines. Unlike proprietary services like Perplexity.ai Pro or SearchGPT, MindSearch offers full control over agents, models, and search backends, making it ideal for research, enterprise knowledge retrieval, and specialized domain search applications.

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. Weaviate · recommended 2×
  3. Elasticsearch · recommended 2×
  4. OpenSearch · recommended 2×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How can I build a custom AI-powered web search engine using a multi-agent framework?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI
    3. Anthropic
    4. Google Gemini
    5. FAISS
    6. Pinecone
    7. Weaviate
    8. LlamaIndex
    9. Chroma
    10. Qdrant
    11. AutoGen
    12. Azure OpenAI
    13. Hugging Face
    14. Haystack
    15. Elasticsearch
    16. OpenSearch
    17. CrewAI
    18. GPT-4
    19. GPT-3.5 Turbo
    20. Claude 3 Opus
    21. Sonnet
    22. Gemini 1.5 Pro
    23. Flash
    24. Llama 3
    25. Mixtral
    26. Beautiful Soup
    27. Scrapy
    28. Playwright
    29. Selenium

    AI recommended 29 alternatives but never named InternLM/MindSearch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist for developing an LLM-driven web search experience with advanced query capabilities?
    you: not recommended
    AI recommended (in order):
    1. Elasticsearch
    2. Pinecone
    3. Weaviate
    4. Milvus
    5. OpenSearch
    6. Google Cloud Search
    7. Vertex AI
    8. Azure Cognitive Search
    9. Azure OpenAI Service
    10. Vespa.ai
    11. Algolia
    12. OpenAI GPT models
    13. Hugging Face Transformers
    14. Apache Solr

    AI recommended 14 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