RRepoGEO

REPOGEO REPORT · LITE

THUDM/AgentBench

Default branch main · commit d1e4a10d · scanned 5/22/2026, 7:23:14 AM

GitHub: 3,443 stars · 255 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 THUDM/AgentBench, 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 an explicit introductory sentence to the README

    Why:

    CURRENT
    The README currently starts with the title and then immediately discusses the 'FC (Function Calling)' version.
    COPY-PASTE FIX
    Insert the following sentence immediately after the `# AgentBench` title and before the leaderboard/social links: "AgentBench is a comprehensive and challenging benchmark suite designed to rigorously evaluate the reasoning, planning, and tool-use capabilities of large language model (LLM) agents across diverse interactive environments."
  • hightopics#2
    Add more specific topics related to benchmarking and evaluation

    Why:

    CURRENT
    chatgpt, gpt-4, llm, llm-agent
    COPY-PASTE FIX
    chatgpt, gpt-4, llm, llm-agent, llm-benchmark, agent-benchmark, llm-evaluation, agent-evaluation
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://docs.google.com/spreadsheets/d/e/2PACX-1vRR3Wl7wsCgHpwUw1_eUXW_fptAPLL3FkhnW_rua0O1Ji_GIVrpTjY5LaKAhwO-WeARjnY_KNw0SYNJ/pubhtml

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 THUDM/AgentBench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
alfworld/alfworld
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. alfworld/alfworld · recommended 1×
  2. web-arena-benchmark/web-arena · recommended 1×
  3. stanford-futuredata/MiniWoB · recommended 1×
  4. allenai/scienceworld · recommended 1×
  5. MineDojo/Voyager · recommended 1×
  • CATEGORY QUERY
    What are the best benchmarks for assessing LLM agent capabilities in complex environments?
    you: not recommended
    AI recommended (in order):
    1. ALFWorld (alfworld/alfworld)
    2. WebArena (web-arena-benchmark/web-arena)
    3. MiniWoB++ (stanford-futuredata/MiniWoB)
    4. ScienceWorld (allenai/scienceworld)
    5. Voyager (MineDojo/Voyager)
    6. HotpotQA
    7. BabyAI (mila-iqia/babyai)

    AI recommended 7 alternatives but never named THUDM/AgentBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to evaluate and compare large language models for agentic function-calling tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation
    2. LangSmith
    3. OpenAI Evals
    4. Ragas

    AI recommended 4 alternatives but never named THUDM/AgentBench. 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 THUDM/AgentBench?
    pass
    AI named THUDM/AgentBench explicitly

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

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

    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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THUDM/AgentBench — 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