RRepoGEO

REPOGEO REPORT · LITE

sierra-research/tau-bench

Default branch main · commit 59a200c6 · scanned 5/8/2026, 3:28:08 PM

GitHub: 1,213 stars · 195 forks

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 sierra-research/tau-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
  • highabout#1
    Update About section (description & homepage) to redirect to τ³-bench

    Why:

    CURRENT
    Description: "Code and Data for Tau-Bench", Homepage: (none)
    COPY-PASTE FIX
    Description: "Legacy code and data for τ-bench. Please use τ³-bench for the latest benchmark for tool-agent-user interaction.", Homepage: "https://github.com/sierra-research/tau3-bench"
  • highreadme#2
    Update README H1 to immediately signal deprecation and redirect

    Why:

    CURRENT
    # τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
    COPY-PASTE FIX
    # τ-bench (Legacy): A Benchmark for Tool-Agent-User Interaction in Real-World Domains (Please use τ³-bench)
  • mediumtopics#3
    Add specific topics for LLM agent benchmarking

    Why:

    COPY-PASTE FIX
    llm-benchmark, language-agents, tool-use, api-tools, conversational-ai, agent-evaluation, multi-step-reasoning, 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/tau-bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain Evaluation
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain Evaluation · recommended 1×
  2. LlamaIndex Evaluation · recommended 1×
  3. Scale AI · recommended 1×
  4. Appen · recommended 1×
  5. Surge AI · recommended 1×
  • CATEGORY QUERY
    How to benchmark language agents that interact with users and external API tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation
    2. LlamaIndex Evaluation
    3. Scale AI
    4. Appen
    5. Surge AI
    6. MLflow
    7. HELM
    8. EleutherAI's LM Evaluation Harness

    AI recommended 8 alternatives but never named sierra-research/tau-bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What benchmarks exist for evaluating conversational AI agents in real-world, dynamic domains like banking?
    you: not recommended
    AI recommended (in order):
    1. GLUE (General Language Understanding Evaluation)
    2. SuperGLUE
    3. SQuAD (Stanford Question Answering Dataset)
    4. Natural Questions
    5. ATIS (Airline Travel Information System)
    6. SNIPS
    7. MultiWOZ (Multi-Domain Wizard-of-Oz)
    8. DSTC (Dialogue System Technology Challenges)
    9. PARADISE (PARAdigm for DIalogue System Evaluation)
    10. NIST (National Institute of Standards and Technology) Human Evaluation Guidelines

    AI recommended 10 alternatives but never named sierra-research/tau-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
    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 sierra-research/tau-bench?
    pass
    AI named sierra-research/tau-bench explicitly

    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/tau-bench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sierra-research/tau-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/tau-bench solve, and who is the primary audience?
    pass
    AI named sierra-research/tau-bench explicitly

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

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sierra-research/tau-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