RRepoGEO

REPOGEO REPORT · LITE

nageoffer/ragent

Default branch main · commit 5857e097 · scanned 6/24/2026, 12:27:07 AM

GitHub: 2,846 stars · 572 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 nageoffer/ragent, 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's primary tagline to reflect core product

    Why:

    CURRENT
    <p align="center"> <strong>后端程序员转型 AI 工程师的第一站</strong><br/> </p>
    COPY-PASTE FIX
    <p align="center"> <strong>企业级 Agentic RAG 智能体平台</strong><br/> </p>
  • mediumreadme#2
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 Ragent AI 对比其他框架 (e.g., LangChain, LlamaIndex)

Ragent AI 专注于提供一个轻量级、模块化、可扩展的框架，特别为构建 RAG 驱动的 AI 智能体设计，与通用型 LLM 编排框架相比，Ragent 更侧重于企业级生产落地场景的完整工程实现。
  • mediumtopics#3
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    agent, agentic-rag, ai, llm, mcp, rag, springai
    COPY-PASTE FIX
    agent, agentic-rag, ai, llm, mcp, rag, springai, enterprise-rag, llm-orchestration, ai-agent-framework, tool-calling

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 nageoffer/ragent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. MLflow · recommended 1×
  3. Kubernetes · recommended 1×
  4. Pinecone · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How can I build an enterprise-level agentic RAG system with full lifecycle management?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. MLflow
    3. Kubernetes
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. Qdrant
    8. LlamaIndex
    9. Microsoft Azure AI Studio
    10. Azure Machine Learning
    11. Semantic Kernel
    12. Azure AI Search
    13. Google Cloud Vertex AI
    14. PaLM
    15. Gemini
    16. Google Cloud Vector Search
    17. AWS Bedrock
    18. AWS SageMaker
    19. Amazon OpenSearch Service
    20. Amazon Aurora
    21. Hugging Face Ecosystem
    22. Hugging Face Transformers
    23. TRL (Transformer Reinforcement Learning)
    24. Hugging Face Hub
    25. Hugging Face Inference Endpoints

    AI recommended 25 alternatives but never named nageoffer/ragent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks support building LLM agents with multi-path retrieval, intent recognition, and tool calling?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    4. Haystack (deepset-ai/haystack)
    5. CrewAI (joaomdmoura/crewai)
    6. AutoGen (microsoft/autogen)

    AI recommended 6 alternatives but never named nageoffer/ragent. 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 nageoffer/ragent?
    pass
    AI named nageoffer/ragent explicitly

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

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

Subscribe to Pro for deep diagnoses

nageoffer/ragent — 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