RRepoGEO

REPOGEO REPORT · LITE

Alibaba-NLP/ZeroSearch

Default branch main · commit 67de5228 · scanned 5/16/2026, 10:07:54 PM

GitHub: 1,270 stars · 116 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 Alibaba-NLP/ZeroSearch, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, search, retrieval, reinforcement-learning, rlhf, simulation, policy-models, nlp, generative-ai
  • highreadme#2
    Add a concise positioning statement to the README's introduction

    Why:

    COPY-PASTE FIX
    ZeroSearch provides a novel framework and policy models to train Large Language Models (LLMs) to *simulate* search and generate search-aware responses *internally*, without requiring external API calls or actual web browsing.
  • mediumabout#3
    Expand the repository's 'About' description

    Why:

    CURRENT
    ZeroSearch: Incentivize the Search Capability of LLMs without Searching
    COPY-PASTE FIX
    ZeroSearch is a framework and collection of policy models designed to incentivize and enhance the search-like capabilities of Large Language Models (LLMs) through simulation and reinforcement learning, enabling them to generate search-aware responses without requiring external search APIs or actual web browsing.

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 Alibaba-NLP/ZeroSearch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 1×
  2. Mistral · recommended 1×
  3. Gemma · recommended 1×
  4. chroma-core/chroma · recommended 1×
  5. facebookresearch/faiss · recommended 1×
  • CATEGORY QUERY
    How can I improve LLM search-like capabilities without needing external API calls?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mistral
    3. Gemma
    4. Chroma (chroma-core/chroma)
    5. FAISS (facebookresearch/faiss)
    6. LanceDB (lancedb/lancedb)
    7. Weaviate (weaviate/weaviate)
    8. Sentence-Transformers (UKPLab/sentence-transformers)
    9. OpenAI's embedding models
    10. vLLM (vllm-project/vllm)
    11. Neo4j Desktop
    12. RDFox
    13. Hugging Face Transformers library (huggingface/transformers)

    AI recommended 13 alternatives but never named Alibaba-NLP/ZeroSearch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help LLMs generate search-aware responses without actual web browsing?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. RAGatouille
    5. DSPy
    6. Sentence-Transformers

    AI recommended 6 alternatives but never named Alibaba-NLP/ZeroSearch. 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 Alibaba-NLP/ZeroSearch?
    pass
    AI named Alibaba-NLP/ZeroSearch explicitly

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

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