RRepoGEO

REPOGEO REPORT · LITE

princeton-nlp/WebShop

Default branch master · commit 64fa2a5c · scanned 6/2/2026, 2:23:19 AM

GitHub: 545 stars · 99 forks

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 princeton-nlp/WebShop, 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 to clearly state project type

    Why:

    CURRENT
    # 🛒 WebShop
    COPY-PASTE FIX
    # 🛒 WebShop: A Realistic Web-Based Simulation Environment for Grounded Language Agents
  • mediumtopics#2
    Add more specific topics to refine categorization

    Why:

    CURRENT
    decision-making, language, language-grounding, ml, nlp, rl, rl-environment, shopping, sim-to-real, web-based
    COPY-PASTE FIX
    decision-making, language, language-grounding, ml, nlp, rl, rl-environment, shopping, sim-to-real, web-based, language-agents, e-commerce-environment, web-interaction, llm-evaluation, agent-training
  • lowreadme#3
    Clarify the initial README paragraph's description of the environment

    Why:

    CURRENT
    Implementation of the WebShop environment and search agents for the paper:
    
    **WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents**  
    Shunyu Yao*, Howard Chen*, John Yang, Karthik Narasimhan
    COPY-PASTE FIX
    This repository provides the WebShop environment, a simulated e-commerce website designed for training and evaluating grounded language agents on complex, real-world web interaction tasks. It includes code for reproducing results from the paper:
    
    **WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents**  
    Shunyu Yao*, Howard Chen*, John Yang, Karthik Narasimhan

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 princeton-nlp/WebShop
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
google-research/recsim
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. google-research/recsim · recommended 1×
  2. openai/gym · recommended 1×
  3. Microsoft's Personalizer (Azure Cognitive Services) · recommended 1×
  4. RUCAIBox/RecBole · recommended 1×
  5. lyst/lightfm · recommended 1×
  • CATEGORY QUERY
    What are good reinforcement learning environments for training agents on e-commerce websites?
    you: not recommended
    AI recommended (in order):
    1. RecSim by Google Research (google-research/recsim)
    2. OpenAI Gym (openai/gym)
    3. Microsoft's Personalizer (Azure Cognitive Services)
    4. RecBole (RUCAIBox/RecBole)
    5. LightFM (lyst/lightfm)
    6. Selenium (SeleniumHQ/selenium)
    7. Playwright (microsoft/playwright)

    AI recommended 7 alternatives but never named princeton-nlp/WebShop. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a realistic web-based simulation for training grounded language agents to interact with shopping sites.
    you: not recommended
    AI recommended (in order):
    1. MiniWoB++
    2. Playwright
    3. Selenium
    4. WebArena
    5. Puppeteer

    AI recommended 5 alternatives but never named princeton-nlp/WebShop. 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 princeton-nlp/WebShop?
    pass
    AI named princeton-nlp/WebShop explicitly

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

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

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

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princeton-nlp/WebShop — 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