RRepoGEO

REPOGEO REPORT · LITE

langwatch/scenario

Default branch main · commit 21226c0f · scanned 6/16/2026, 1:36:42 AM

GitHub: 897 stars · 65 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 langwatch/scenario, 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 the README H1 to specify category

    Why:

    CURRENT
    # Scenario
    COPY-PASTE FIX
    # Scenario: Agent Testing Framework for LLM Agents & Agentic Codebases
  • mediumtopics#2
    Add more specific topics for LLM agent testing

    Why:

    CURRENT
    agent-simulations, agent-testing, ai-testing, javascript-library, python-library
    COPY-PASTE FIX
    agent-simulations, agent-testing, ai-testing, javascript-library, python-library, llm-agent-testing, multi-turn-evaluation, agentic-ai
  • lowreadme#3
    Refine the README's opening paragraph for clarity and unique value

    Why:

    CURRENT
    Scenario is an Agent Testing Framework based on simulations, it can: - Test real agent behavior by simulating users in different scenarios and edge cases...
    COPY-PASTE FIX
    Scenario is an open-source **Agent Testing Framework** designed specifically for **LLM agents and agentic codebases**. It enables robust evaluation of real agent behavior by simulating users in diverse scenarios and edge cases, offering powerful multi-turn control and agnostic integration with any LLM eval framework.

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 langwatch/scenario
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Botium Box/Botium Core
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Botium Box/Botium Core · recommended 1×
  2. Rasa X/Rasa Enterprise · recommended 1×
  3. Microsoft Bot Framework Emulator · recommended 1×
  4. NLU-Dev-Tools · recommended 1×
  5. NLUTest · recommended 1×
  • CATEGORY QUERY
    How can I effectively test my AI agent's behavior with simulated user interactions?
    you: not recommended
    AI recommended (in order):
    1. Botium Box/Botium Core
    2. Rasa X/Rasa Enterprise
    3. Microsoft Bot Framework Emulator
    4. NLU-Dev-Tools
    5. NLUTest
    6. Python
    7. `requests`
    8. Apache JMeter
    9. Locust
    10. Playwright
    11. Selenium

    AI recommended 11 alternatives but never named langwatch/scenario. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for evaluating multi-turn AI agent conversations in Python or JavaScript?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Rasa (RasaHQ/rasa)
    4. DeepEval (confident-ai/deepeval)
    5. Humanloop (humanloop/humanloop-python)
    6. Promptfoo (promptfoo/promptfoo)
    7. OpenAI Evals (openai/evals)

    AI recommended 7 alternatives but never named langwatch/scenario. 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 langwatch/scenario?
    pass
    AI named langwatch/scenario explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of langwatch/scenario. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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Pro

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langwatch/scenario — 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