REPOGEO REPORT · LITE

BUAADreamer/EasyRAG

Default branch master · commit a87b6852 · scanned 5/29/2026, 5:23:08 PM

GitHub: 632 stars · 77 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 BUAADreamer/EasyRAG, 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

Rephrase the 'Overview' section's opening sentence for direct value proposition

Why:

CURRENT

This paper presents EasyRAG, a simple, lightweight, and efficient retrieval-augmented generation framework for automated network operations. Our framework has three advantages.

COPY-PASTE FIX

EasyRAG is a simple, lightweight, and highly efficient retrieval-augmented generation (RAG) framework specifically designed for automated network operations. It delivers accurate question answering, simple deployment without fine-tuning, and efficient inference, making it an ideal solution for developers and researchers.

mediumcomparison#2

Add a 'Comparison with Alternatives' section to README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

While frameworks like LangChain and LlamaIndex offer extensive modularity and integrations, EasyRAG prioritizes extreme simplicity, lightweight deployment, and efficient inference specifically for automated network operations. Our framework requires no fine-tuning and minimal VRAM, making it exceptionally easy to deploy and scale for specific use cases where speed and ease of use are paramount.

lowabout#3

Refine repository description for more keyword density

Why:

CURRENT

Easy-to-Use RAG Framework; CCF AIOps International Challenge 2024 Top3 Solution; CCF AIOps 国际挑战赛 2024 季军方案

COPY-PASTE FIX

Easy-to-Use, Lightweight, and Efficient RAG Framework for Automated Network Operations. Top3 Solution in CCF AIOps International Challenge 2024; CCF AIOps 国际挑战赛 2024 季军方案.

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 BUAADreamer/EasyRAG

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
FAISS · recommended 2×
LlamaIndex · recommended 2×
Haystack · recommended 2×
Pinecone · recommended 1×

CATEGORY QUERY
Seeking an efficient retrieval-augmented generation framework for automated network operations.
you: not recommended
AI recommended (in order):
1. LangChain
2. Pinecone
3. Weaviate
4. Chroma
5. FAISS
6. LlamaIndex
7. Haystack
8. BM25
9. DPR
10. Sentence Transformers
11. RAGatouille
12. OpenAI Assistants API
13. PyTorch
14. TensorFlow
15. Hugging Face Transformers
16. ScaNN
17. PostgreSQL
18. pgvector
19. Elasticsearch
AI recommended 19 alternatives but never named BUAADreamer/EasyRAG. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are simple, lightweight RAG frameworks for accurate question answering?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. Haystack
3. LangChain
4. Ragas
5. Sentence-Transformers
6. FAISS
7. Annoy
AI recommended 7 alternatives but never named BUAADreamer/EasyRAG. 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 BUAADreamer/EasyRAG?
pass
AI named BUAADreamer/EasyRAG explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts BUAADreamer/EasyRAG in production, what risks or prerequisites should they evaluate first?
pass
AI named BUAADreamer/EasyRAG 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 BUAADreamer/EasyRAG solve, and who is the primary audience?
pass
AI named BUAADreamer/EasyRAG 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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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/BUAADreamer/EasyRAG.svg)](https://repogeo.com/en/r/BUAADreamer/EasyRAG)

HTML

<a href="https://repogeo.com/en/r/BUAADreamer/EasyRAG"><img src="https://repogeo.com/badge/BUAADreamer/EasyRAG.svg" alt="RepoGEO" /></a>

Pro

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BUAADreamer/EasyRAG — 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