RRepoGEO

REPOGEO REPORT · LITE

ganarajpr/awesome-dspy

Default branch master · commit b8886c4a · scanned 6/6/2026, 7:17:32 AM

GitHub: 559 stars · 44 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 ganarajpr/awesome-dspy, 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
    awesome-list, dspy, llm, ai, generative-ai, language-models, machine-learning
  • highreadme#2
    Reposition README opening to clarify it's an awesome list

    Why:

    CURRENT
    A collection of awesome things regarding DSPy.

## DSPy - A library for compiling declarative language model calls into self-improving pipelines.
    COPY-PASTE FIX
    A curated collection of awesome resources, projects, and tools specifically for the DSPy framework.

## What is DSPy?
DSPy is a library for compiling declarative language model calls into self-improving pipelines.
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) to clarify usage rights for the awesome list itself.

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 ganarajpr/awesome-dspy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
kubeflow/pipelines
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. kubeflow/pipelines · recommended 1×
  2. mlflow/mlflow · recommended 1×
  3. ray-project/ray · recommended 1×
  4. prometheus/prometheus · recommended 1×
  5. grafana/grafana · recommended 1×
  • CATEGORY QUERY
    How to build robust, self-optimizing pipelines for large language models?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines (kubeflow/pipelines)
    2. MLflow (mlflow/mlflow)
    3. Ray Tune (ray-project/ray)
    4. Prometheus (prometheus/prometheus)
    5. Grafana (grafana/grafana)
    6. Arize AI
    7. WhyLabs AI
    8. Hugging Face Transformers (huggingface/transformers)
    9. Accelerate (huggingface/accelerate)
    10. Apache Airflow (apache/airflow)

    AI recommended 10 alternatives but never named ganarajpr/awesome-dspy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to structure and improve language model interactions declaratively.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Guidance
    4. Marvin
    5. Pydantic-LLM
    6. Instructor

    AI recommended 6 alternatives but never named ganarajpr/awesome-dspy. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 ganarajpr/awesome-dspy?
    pass
    AI did not name ganarajpr/awesome-dspy — 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 ganarajpr/awesome-dspy in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ganarajpr/awesome-dspy 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 ganarajpr/awesome-dspy solve, and who is the primary audience?
    pass
    AI named ganarajpr/awesome-dspy 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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ganarajpr/awesome-dspy — 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