RRepoGEO

REPOGEO REPORT · LITE

aryn-ai/sycamore

Default branch main · commit ea707714 · scanned 6/9/2026, 11:06:47 PM

GitHub: 602 stars · 68 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 aryn-ai/sycamore, 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 the README's opening to emphasize 'end-to-end RAG framework'

    Why:

    CURRENT
    Sycamore is an open source, AI-powered document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data.
    COPY-PASTE FIX
    Sycamore is an open source, **end-to-end Python framework for building Retrieval Augmented Generation (RAG) and other LLM-powered applications.** It provides an AI-powered document processing engine for ETL and analytics on unstructured data, enabling you to partition and enrich a wide range of document types.
  • mediumtopics#2
    Add more specific RAG and LLM application development topics

    Why:

    CURRENT
    ai, dataprep, etl, information-retrieval, llm, ml, nlp, opensearch, search, semantic-search
    COPY-PASTE FIX
    ai, dataprep, etl, information-retrieval, llm, ml, nlp, opensearch, search, semantic-search, retrieval-augmented-generation, rag-framework, llm-applications
  • lowcomparison#3
    Add a comparison section clarifying Sycamore's unique positioning

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Sycamore vs. X' or 'Why Sycamore?') that explicitly contrasts Sycamore's end-to-end RAG framework approach with specialized document parsing tools (like Unstructured.io or cloud Document AI services) and generic ETL platforms (like Airbyte), highlighting how Sycamore integrates these capabilities within a unified RAG development workflow.

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 aryn-ai/sycamore
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
airbytehq/airbyte
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. airbytehq/airbyte · recommended 1×
  2. apache/nifi · recommended 1×
  3. Unstructured-IO/unstructured · recommended 1×
  4. apache/tika · recommended 1×
  5. pandas-dev/pandas · recommended 1×
  • CATEGORY QUERY
    How to effectively prepare diverse unstructured data for RAG and LLM applications?
    you: not recommended
    AI recommended (in order):
    1. Airbyte (airbytehq/airbyte)
    2. Apache NiFi (apache/nifi)
    3. Unstructured.io (Unstructured-IO/unstructured)
    4. Apache Tika (apache/tika)
    5. Pandas (pandas-dev/pandas)
    6. spaCy (explosion/spaCy)
    7. NLTK (nltk/nltk)
    8. LangChain (langchain-ai/langchain)
    9. OpenAI Embeddings
    10. Hugging Face Sentence Transformers
    11. Hugging Face Transformers (huggingface/transformers)
    12. Elasticsearch (elastic/elasticsearch)
    13. Pinecone
    14. Weaviate (weaviate/weaviate)
    15. Qdrant (qdrant/qdrant)

    AI recommended 15 alternatives but never named aryn-ai/sycamore. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools accurately extract and chunk complex documents, including tables and images, for search?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Document Intelligence
    2. Google Cloud Document AI
    3. Amazon Textract
    4. Unstructured.io
    5. Nougat
    6. LayoutParser
    7. Tesseract OCR

    AI recommended 7 alternatives but never named aryn-ai/sycamore. 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 aryn-ai/sycamore?
    pass
    AI named aryn-ai/sycamore explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of aryn-ai/sycamore. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/aryn-ai/sycamore.svg)](https://repogeo.com/en/r/aryn-ai/sycamore)
HTML
<a href="https://repogeo.com/en/r/aryn-ai/sycamore"><img src="https://repogeo.com/badge/aryn-ai/sycamore.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

aryn-ai/sycamore — 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