RRepoGEO

REPOGEO REPORT · LITE

sierra-research/tau2-bench

Default branch main · commit 6899b47c · scanned 5/21/2026, 8:46:52 AM

GitHub: 1,210 stars · 313 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
27 /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
1 / 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 sierra-research/tau2-bench, 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 introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add a paragraph after the H1: "τ-Bench is a comprehensive open-source benchmark designed to evaluate the performance of LLM-powered conversational agents in real-world, interactive scenarios. It focuses on tool-agent-user interaction, including multimodal, knowledge-aware, and full-duplex voice capabilities, providing a robust framework for assessing agent effectiveness beyond simple text-based tasks."
  • hightopics#2
    Refine and expand repository topics for better categorization

    Why:

    CURRENT
    ai, benchmark, conversational-agents, language-model-agent, llm
    COPY-PASTE FIX
    llm-agents, agent-evaluation, multimodal-ai, knowledge-retrieval, conversational-ai, benchmark, ai
  • mediumabout#3
    Enhance the repository description to highlight key features

    Why:

    CURRENT
    τ-Bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
    COPY-PASTE FIX
    τ-Bench: A comprehensive benchmark for multimodal, knowledge-aware, and full-duplex voice evaluation of LLM-powered conversational agents in real-world domains.

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 sierra-research/tau2-bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
UserTesting.com
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. UserTesting.com · recommended 1×
  2. Optimizely · recommended 1×
  3. Google Optimize · recommended 1×
  4. VWO · recommended 1×
  5. Split.io · recommended 1×
  • CATEGORY QUERY
    How to evaluate the real-world performance of LLM-powered conversational agents?
    you: not recommended
    AI recommended (in order):
    1. UserTesting.com
    2. Optimizely
    3. Google Optimize
    4. VWO
    5. Split.io
    6. Google Forms
    7. SurveyMonkey
    8. Hugging Face Transformers
    9. Datadog
    10. New Relic
    11. Grafana
    12. Mixpanel
    13. Amplitude
    14. Elasticsearch
    15. Splunk
    16. LangChain
    17. LlamaIndex
    18. DeepEval
    19. Ragas
    20. Garak
    21. Microsoft's Responsible AI Toolkit
    22. Google Sheets
    23. Excel
    24. Label Studio

    AI recommended 24 alternatives but never named sierra-research/tau2-bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a benchmark for multimodal and knowledge-aware AI agent evaluation.
    you: not recommended
    AI recommended (in order):
    1. MM-Vet (Multimodal-Vet)
    2. MMMU (Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark)
    3. OK-VQA (Outside Knowledge VQA)
    4. AQuA-RAD (Abstractive Question Answering for Radiology)
    5. WebSRC (Web-based Structured Reasoning and Comprehension)
    6. ScienceQA
    7. MME (Multimodal Evaluation)

    AI recommended 7 alternatives but never named sierra-research/tau2-bench. 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 sierra-research/tau2-bench?
    pass
    AI did not name sierra-research/tau2-bench — likely talking about a different project

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

  • If a team adopts sierra-research/tau2-bench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sierra-research/tau2-bench 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 sierra-research/tau2-bench solve, and who is the primary audience?
    pass
    AI did not name sierra-research/tau2-bench — likely talking about a different project

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

Embed your GEO score

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sierra-research/tau2-bench — 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