REPOGEO REPORT · LITE

gradio-app/daggr

Default branch main · commit 73cca141 · scanned 6/5/2026, 8:27:00 AM

GitHub: 564 stars · 46 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 gradio-app/daggr, 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

gradio, workflow-orchestration, dag, machine-learning, ai-workflows, visual-programming, python

highreadme#2

Reposition the README's opening statement to emphasize visual orchestration for Gradio

Why:

CURRENT

<h3 align="center">
  <div style="display:flex;flex-direction:row;">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="daggr/assets/logo_dark.png">
      <source media="(prefers-color-scheme: light)" srcset="daggr/assets/logo_light.png">
      
    </picture>
    <p>DAG-based Gradio workflows!</p>
  </div>
</h3>

`daggr` is a Python library for building AI workflows that connect Gradio apps, ML models (through Hugging Face Inference Providers), and custom Python functions. It automatically generates a visual canvas for your workflow allowing you to inspect intermediate outputs, rerun any step any number of times, and preserves state for complex or long-running workflows. Daggr also tracks provenance: when you browse through previous results, it automatically restores the exact inputs that produced each output, and visually indicates which parts of your workflow are stale.

COPY-PASTE FIX

<h3 align="center">
  <div style="display:flex;flex-direction:row;">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="daggr/assets/logo_dark.png">
      <source media="(prefers-color-scheme: light)" srcset="daggr/assets/logo_light.png">
      
    </picture>
    <p>Visual DAG-based orchestration for Gradio workflows!</p>
  </div>
</h3>

`daggr` is a Python library for visually building and orchestrating complex AI workflows by connecting Gradio apps, ML models (through Hugging Face Inference Providers), and custom Python functions. It automatically generates an interactive visual canvas for your workflow, allowing you to inspect intermediate outputs, rerun any step any number of times, and preserves state for complex or long-running workflows. Daggr also tracks provenance: when you browse through previous results, it automatically restores the exact inputs that produced each output, and visually indicates which parts of your workflow are stale.

mediumhomepage#3
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
https://your-project-homepage.com
```

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 gradio-app/daggr

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Gradio Blocks

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Gradio Blocks · recommended 1×
LangChain · recommended 1×
Gradio `gr.Interface` · recommended 1×
FastAPI · recommended 1×
Flask · recommended 1×

CATEGORY QUERY
How can I chain multiple machine learning models and Gradio interfaces into a single application?
you: not recommended
AI recommended (in order):
1. Gradio Blocks
2. LangChain
3. Gradio `gr.Interface`
4. FastAPI
5. Flask
6. MLflow Projects
AI recommended 6 alternatives but never named gradio-app/daggr. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library to visually orchestrate and debug multi-step AI inference pipelines with state.
you: not recommended
AI recommended (in order):
1. LangChain Expression Language (LCEL) (langchain-ai/langchain)
2. LangServe (langchain-ai/langserve)
3. LangSmith (langchain-ai/langsmith)
4. Prefect (PrefectHQ/prefect)
5. Apache Airflow (apache/airflow)
6. Kedro (kedro-org/kedro)
7. Metaflow (Netflix/metaflow)
8. ZenML (zenml-io/zenml)
AI recommended 8 alternatives but never named gradio-app/daggr. 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 gradio-app/daggr?
pass
AI named gradio-app/daggr explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts gradio-app/daggr in production, what risks or prerequisites should they evaluate first?
pass
AI named gradio-app/daggr 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 gradio-app/daggr solve, and who is the primary audience?
pass
AI named gradio-app/daggr explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite