RRepoGEO

REPOGEO REPORT · LITE

OpenBMB/AgentCPM

Default branch main · commit 4a43561e · scanned 6/15/2026, 12:22:27 PM

GitHub: 801 stars · 70 forks

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 OpenBMB/AgentCPM, 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

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

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    A relevant project or documentation URL.

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 OpenBMB/AgentCPM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. Weights & Biases · recommended 1×
  3. mlflow/mlflow · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. LangSmith · recommended 1×
  • CATEGORY QUERY
    What infrastructure helps train and evaluate various large language model agents effectively?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow (mlflow/mlflow)
    3. LangChain (langchain-ai/langchain)
    4. LangSmith
    5. Hugging Face Transformers (huggingface/transformers)
    6. Accelerate (huggingface/accelerate)
    7. Datasets (huggingface/datasets)
    8. Ray (ray-project/ray)
    9. Ray Tune (ray-project/ray)
    10. Ray RLlib (ray-project/ray)
    11. TensorBoard (tensorflow/tensorboard)
    12. DVC (iterative/dvc)

    AI recommended 12 alternatives but never named OpenBMB/AgentCPM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to develop and benchmark small, on-device LLM agents for complex tasks.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Transformers
    5. MLflow
    6. LiteLLM

    AI recommended 6 alternatives but never named OpenBMB/AgentCPM. 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 OpenBMB/AgentCPM?
    pass
    AI did not name OpenBMB/AgentCPM — 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 OpenBMB/AgentCPM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenBMB/AgentCPM 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 OpenBMB/AgentCPM solve, and who is the primary audience?
    pass
    AI named OpenBMB/AgentCPM explicitly

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

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OpenBMB/AgentCPM — 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