RRepoGEO

REPOGEO REPORT · LITE

THUDM/AgentTuning

Default branch main · commit e33a45d7 · scanned 5/14/2026, 2:32:45 AM

GitHub: 1,488 stars · 105 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 THUDM/AgentTuning, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, or a custom license if applicable).
  • highreadme#2
    Reposition the README's opening paragraph to clarify its unique focus

    Why:

    CURRENT
    AgentTuning represents the very first attempt to instruction-tune LLMs using interaction trajectories across multiple agent tasks. Evaluation results indicate that AgentTuning enables the agent capabilities of LLMs with robust generalization on unseen agent tasks while remaining strong in general language abilities. We have open-sourced the AgentInstruct dataset and AgentLM.
    COPY-PASTE FIX
    AgentTuning is a pioneering instruction-tuning framework and dataset specifically designed to enhance Large Language Models' (LLMs) generalized agent abilities. Unlike general instruction-tuning methods or broad agent frameworks, AgentTuning focuses on robust generalization across diverse, unseen agent tasks by leveraging interaction trajectories. We open-source the AgentInstruct dataset and AgentLM to empower researchers and developers.

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/AgentTuning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. AutoGPT · recommended 1×
  4. BabyAGI · recommended 1×
  5. OpenAI Function Calling · recommended 1×
  • CATEGORY QUERY
    How can I enhance my large language model's ability to act as a generalized agent?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. OpenAI Function Calling
    6. Google Gemini Function Calling
    7. Hugging Face Transformers Agents
    8. Semantic Kernel

    AI recommended 8 alternatives but never named THUDM/AgentTuning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for instruction-tuning LLMs to perform diverse agent tasks robustly?
    you: not recommended
    AI recommended (in order):
    1. Alpaca
    2. FLAN
    3. Dolly 2.0
    4. ShareGPT
    5. OpenAssistant Conversations Dataset
    6. ToolBench
    7. Gorilla
    8. Reinforcement Learning from Human Feedback
    9. GPT-4

    AI recommended 9 alternatives but never named THUDM/AgentTuning. 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/AgentTuning?
    pass
    AI named THUDM/AgentTuning 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/AgentTuning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named THUDM/AgentTuning 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/AgentTuning solve, and who is the primary audience?
    pass
    AI named THUDM/AgentTuning 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/AgentTuning — 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