RRepoGEO

REPOGEO REPORT · LITE

Agenta-AI/agenta

Default branch main · commit 75dc5e22 · scanned 5/16/2026, 7:51:07 AM

GitHub: 4,125 stars · 521 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Agenta-AI/agenta, 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
    Strengthen README's opening statement to emphasize end-to-end LLMOps platform

    Why:

    CURRENT
    Build reliable LLM applications faster with integrated prompt management, evaluation, and observability.
    COPY-PASTE FIX
    Agenta is the open-source, end-to-end LLMOps platform that unifies prompt engineering, experimentation, automated evaluation, and deployment for LLM applications, including built-in version control for all components.
  • mediumreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Insert specific license name(s) here, e.g., 'a custom license combining X and Y']. See the [LICENSE](LICENSE) file for details.
  • mediumcomparison#3
    Add a 'Why Agenta?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Agenta?
While tools like MLflow, LangChain, Phoenix, Weights & Biases, and Transformers offer valuable components for ML development, Agenta provides an **open-source, end-to-end LLMOps platform** that unifies prompt engineering, experimentation, automated evaluation, and deployment for LLM applications, including built-in version control for all components. Unlike individual libraries or point solutions, Agenta offers a holistic environment to build and manage reliable LLM applications from ideation to production.

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 Agenta-AI/agenta
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. Arize-AI/phoenix · recommended 1×
  4. wandb/wandb · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    What open-source platform helps manage, evaluate, and monitor my large language model applications?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. LangChain (langchain-ai/langchain)
    3. Phoenix (Arize-AI/phoenix)
    4. Weights & Biases (wandb/wandb)
    5. Transformers (huggingface/transformers)
    6. Datasets (huggingface/datasets)
    7. Evaluate (huggingface/evaluate)
    8. Deepchecks (deepchecks/deepchecks)

    AI recommended 8 alternatives but never named Agenta-AI/agenta. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a comprehensive tool for prompt experimentation and robust LLM evaluation workflows.
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases Prompts
    2. Arize AI
    3. LangChain
    4. OpenAI Evals
    5. Humanloop
    6. MLflow

    AI recommended 6 alternatives but never named Agenta-AI/agenta. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Agenta-AI/agenta?
    pass
    AI named Agenta-AI/agenta explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite