RRepoGEO

REPOGEO REPORT · LITE

Zleap-AI/SAG

Default branch main · commit 29b62d56 · scanned 5/21/2026, 5:58:07 AM

GitHub: 1,130 stars · 23 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 Zleap-AI/SAG, 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
    Add a concise English introductory sentence at the top of README.md

    Why:

    COPY-PASTE FIX
    Add this line immediately after the H1/subtitle and before any Chinese text or images: 'SAG is a SQL-driven RAG engine that automatically builds knowledge graphs during querying, eliminating the need for pre-maintained KGs.'
  • mediumtopics#2
    Add 'sql' and 'graph-database' to repository topics

    Why:

    CURRENT
    ai, data-engineering, fastapi, graphrag, information-retrieval, knowledge-base, knowledge-graph, knowledge-graphs, llm, machine-learning, nextjs, python, rag, retrieval-augmented-generation, vector-search
    COPY-PASTE FIX
    ai, data-engineering, fastapi, graphrag, information-retrieval, knowledge-base, knowledge-graph, knowledge-graphs, llm, machine-learning, nextjs, python, rag, retrieval-augmented-generation, vector-search, sql, graph-database
  • mediumreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with Alternatives' or 'Why SAG?' that highlights how SAG's SQL-driven, dynamic knowledge graph approach differs from traditional graph databases (which require pre-built KGs) and general RAG frameworks (which may lack dynamic KG capabilities).

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 Zleap-AI/SAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TypeDB
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TypeDB · recommended 2×
  2. LlamaIndex · recommended 2×
  3. LangChain · recommended 2×
  4. ArangoDB · recommended 2×
  5. Amazon Neptune · recommended 2×
  • CATEGORY QUERY
    Need a RAG engine that dynamically constructs knowledge graphs from text during queries.
    you: not recommended
    AI recommended (in order):
    1. Neo4j with GenAI Extensions
    2. TypeDB
    3. LlamaIndex
    4. LangChain
    5. ArangoDB
    6. Amazon Neptune

    AI recommended 6 alternatives but never named Zleap-AI/SAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a RAG system using an event-centric model for on-demand knowledge graph generation.
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. TypeDB
    5. Amazon Neptune
    6. ArangoDB
    7. Memgraph
    8. DGL
    9. PyG
    10. Pinecone
    11. Weaviate

    AI recommended 11 alternatives but never named Zleap-AI/SAG. 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 Zleap-AI/SAG?
    pass
    AI named Zleap-AI/SAG explicitly

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

  • If a team adopts Zleap-AI/SAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Zleap-AI/SAG 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 Zleap-AI/SAG solve, and who is the primary audience?
    pass
    AI named Zleap-AI/SAG 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 Zleap-AI/SAG. 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/Zleap-AI/SAG.svg)](https://repogeo.com/en/r/Zleap-AI/SAG)
HTML
<a href="https://repogeo.com/en/r/Zleap-AI/SAG"><img src="https://repogeo.com/badge/Zleap-AI/SAG.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Zleap-AI/SAG — 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