RRepoGEO

REPOGEO REPORT · LITE

THUDM/AgentTuning

Default branch main · commit e33a45d7 · scanned 6/24/2026, 1:43:00 PM

GitHub: 1,499 stars · 106 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agents, instruction-tuning, large-language-models, agent-abilities, generalization, agent-lm, thudm, agentinstruct
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    (Create a LICENSE file in the root of the repository. A common choice for research projects is Apache-2.0 or MIT, but choose the license that best fits THUDM's policy for open-source projects.)
  • mediumreadme#3
    Explicitly state AgentTuning's core differentiator in the README introduction

    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 the pioneering approach to instruction-tune Large Language Models (LLMs) specifically for generalized agent abilities, leveraging interaction trajectories across diverse agent tasks. Unlike general agent frameworks (e.g., LangChain, LlamaIndex) or prompting strategies, AgentTuning focuses on *how* to train LLMs to become robust, general-purpose agents through supervised fine-tuning with our high-quality AgentInstruct dataset. This enables LLMs to generalize effectively on unseen agent tasks while maintaining strong general language abilities.

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-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. Significant-Gravitas/AutoGPT · recommended 1×
  4. yoheinakajima/babyagi · recommended 1×
  5. OpenAI Function Calling · recommended 1×
  • CATEGORY QUERY
    How can I improve my large language model's ability to act as a general agent?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. AutoGPT (Significant-Gravitas/AutoGPT)
    4. BabyAGI (yoheinakajima/babyagi)
    5. OpenAI Function Calling
    6. Toolformer
    7. Hugging Face Transformers Agents (huggingface/transformers)

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

    Show full AI answer
  • CATEGORY QUERY
    What techniques exist for instruction-tuning large language models to generalize across diverse agent tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's InstructGPT/ChatGPT
    2. Hugging Face's TRL (huggingface/trl)
    3. DeepMind's Sparrow/Chinchilla
    4. Anthropic's Constitutional AI
    5. Axolotl (OpenAccessAICollective/axolotl)
    6. LoRAX (predibase/lorax)
    7. FLAN
    8. T0
    9. Alpaca/Vicuna/Llama-2-Chat
    10. PandaLM (PandaLM/PandaLM)
    11. OpenAI's Function Calling API
    12. LangChain (langchain-ai/langchain)
    13. LlamaIndex (run-llama/llama_index)
    14. ToolFormer
    15. PaLM-2
    16. GPT-4
    17. FAISS (facebookresearch/faiss)
    18. Weaviate (weaviate/weaviate)
    19. Pinecone
    20. Qdrant (qdrant/qdrant)

    AI recommended 20 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 did not name THUDM/AgentTuning — 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 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?

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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