REPOGEO REPORT · LITE

yifanfeng97/Hyper-Extract

Default branch main · commit 3fb2f383 · scanned 6/14/2026, 10:01:58 PM

GitHub: 1,081 stars · 126 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 yifanfeng97/Hyper-Extract, 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 paragraph to clarify text-based focus

Why:

CURRENT

Hyper-Extract is an intelligent, LLM-powered knowledge extraction and evolution framework. It radically simplifies transforming highly unstructured texts into persistent, predictable, and strongly-typed **Knowledge Abstracts**. It effortlessly extracts information into a wide spectrum of formats—ranging from simple **Collections** (Lists/Sets) and **Pydantic Models**, to complex **Knowledge Graphs**, **Hypergraphs**, and even **Spatio-Temporal Graphs**.

COPY-PASTE FIX

Hyper-Extract is an intelligent, LLM-powered framework specifically designed for **text-based knowledge extraction**. It radically simplifies transforming highly unstructured **text documents** into persistent, predictable, and strongly-typed **Knowledge Abstracts**, effortlessly extracting information into a wide spectrum of formats—from simple Collections and Pydantic Models to complex Knowledge Graphs, Hypergraphs, and Spatio-Temporal Graphs. Unlike tools for image or sensor data analysis, Hyper-Extract focuses exclusively on deriving structured insights from textual content.

mediumtopics#2

Add more specific topics to emphasize unique extraction capabilities

Why:

CURRENT

ai, ai-agents, cli, hypergraph, information-extraction, knowledge, knowledge-graph, llm, python, rag

COPY-PASTE FIX

ai, ai-agents, cli, hypergraph, information-extraction, knowledge, knowledge-graph, llm, python, rag, text-extraction, llm-extraction, spatio-temporal-graphs, knowledge-abstracts

lowreadme#3

Add a statement to the README clarifying the project's license

Why:

COPY-PASTE FIX

This project is released under a custom license. Please refer to the `LICENSE` file for specific terms and conditions.

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 yifanfeng97/Hyper-Extract

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

spaCy

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

spaCy · recommended 2×
Hugging Face Transformers · recommended 1×
OpenAI GPT-3.5 / GPT-4 · recommended 1×
Google Cloud Document AI · recommended 1×
Amazon Textract · recommended 1×

CATEGORY QUERY
How to extract structured knowledge from large unstructured text documents using AI?
you: not recommended
AI recommended (in order):
1. spaCy
2. Hugging Face Transformers
3. OpenAI GPT-3.5 / GPT-4
4. Google Cloud Document AI
5. Amazon Textract
6. Prodigy
7. Rasa
AI recommended 7 alternatives but never named yifanfeng97/Hyper-Extract. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Python library for building knowledge graphs and hypergraphs from raw text with LLMs?
you: not recommended
AI recommended (in order):
1. Haystack
2. LlamaIndex
3. spaCy
4. GraphRAG
5. NetworkX
6. LangChain
7. RDFLib
AI recommended 7 alternatives but never named yifanfeng97/Hyper-Extract. 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 yifanfeng97/Hyper-Extract?
pass
AI named yifanfeng97/Hyper-Extract explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yifanfeng97/Hyper-Extract in production, what risks or prerequisites should they evaluate first?
pass
AI named yifanfeng97/Hyper-Extract 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 yifanfeng97/Hyper-Extract solve, and who is the primary audience?
pass
AI named yifanfeng97/Hyper-Extract 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 yifanfeng97/Hyper-Extract. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/yifanfeng97/Hyper-Extract"><img src="https://repogeo.com/badge/yifanfeng97/Hyper-Extract.svg" alt="RepoGEO" /></a>

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yifanfeng97/Hyper-Extract — 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