RRepoGEO

REPOGEO REPORT · LITE

ucbepic/docetl

Default branch main · commit 6cae9531 · scanned 5/8/2026, 5:52:17 PM

GitHub: 3,746 stars · 396 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 ucbepic/docetl, 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
    Reposition README opening to emphasize agentic LLM-powered ETL

    Why:

    CURRENT
    DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers:
1. An interactive UI playground for iterative prompt engineering and pipeline development
2. A Python package for running production pipelines from the command line or Python code
    COPY-PASTE FIX
    DocETL is a system for agentic LLM-powered data processing and ETL, specifically designed for complex document processing pipelines. It offers both an interactive UI playground for iterative prompt engineering and pipeline development, and a Python package for running production pipelines from the command line or Python code.
  • mediumreadme#2
    Add a 'Why DocETL?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why DocETL?
While general-purpose LLM frameworks like LangChain and LlamaIndex offer broad capabilities, DocETL specializes in the **Extract, Transform, Load (ETL) phase for documents**. We provide a robust, agentic pipeline specifically for preparing unstructured data for RAG and LLM applications, with a unique focus on an interactive development playground.
  • lowtopics#3
    Add more specific topics related to agentic LLMs and interactive development

    Why:

    CURRENT
    agents, data, data-pipelines, document-analysis, document-processing, elt, etl, llm, python, semantic-data, unstructured-data, unstructured-data-analysis, workflow
    COPY-PASTE FIX
    agents, data, data-pipelines, document-analysis, document-processing, elt, etl, llm, python, semantic-data, unstructured-data, unstructured-data-analysis, workflow, llm-agents, prompt-engineering, interactive-development, rag

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 ucbepic/docetl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Unstructured.io · recommended 1×
  5. SpaCy · recommended 1×
  • CATEGORY QUERY
    How to build LLM-powered data processing pipelines for complex unstructured documents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Unstructured.io
    5. SpaCy
    6. NLTK
    7. Apache Tika

    AI recommended 7 alternatives but never named ucbepic/docetl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python framework for agentic document ETL with an interactive development playground.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. DSPy
    5. Rasa

    AI recommended 5 alternatives but never named ucbepic/docetl. 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 ucbepic/docetl?
    pass
    AI named ucbepic/docetl explicitly

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

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