RRepoGEO

REPOGEO REPORT · LITE

nageoffer/ragent

Default branch main · commit 3f42acf6 · scanned 5/13/2026, 1:23:08 PM

GitHub: 2,040 stars · 395 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 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 main heading to emphasize 'Enterprise-grade Agentic RAG Platform'

    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>Ragent AI: 企业级 Agentic RAG 智能体平台</strong><br/><strong>后端程序员转型 AI 工程师的第一站</strong></p>
  • mediumreadme#2
    Add an inline comparison section to the README

    Why:

    CURRENT
    The README links to '为什么不用 Spring AI / Langchain4j?' but does not provide an inline comparison.
    COPY-PASTE FIX
    Add a new section, e.g., `## 🆚 Ragent AI 对比主流框架` (Ragent AI vs. Mainstream Frameworks), with 2-3 bullet points or a short paragraph summarizing key differentiators from frameworks like LangChain and LlamaIndex.
  • lowreadme#3
    Add a concise 'Key Features' list near the top of the README

    Why:

    CURRENT
    Features are described in paragraphs under '什么是 Ragent AI？' but not as a top-level bulleted list.
    COPY-PASTE FIX
    Add a new section `## ✨ 核心特性` (Core Features) or integrate a bulleted list of 3-5 key features (e.g., Multi-path retrieval, Intent recognition, MCP integration, Model engine, Production-ready engineering) immediately after the main project description.

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-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. microsoft/semantic-kernel · recommended 1×
  4. Pinecone · recommended 1×
  5. weaviate/weaviate · recommended 1×
  • CATEGORY QUERY
    How to build an enterprise-grade RAG system with advanced agentic capabilities and tool integration?
    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. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. Qdrant (qdrant/qdrant)
    7. Chroma (chroma-core/chroma)
    8. OpenAI GPT-4
    9. GPT-3.5 Turbo
    10. Anthropic Claude 3
    11. Azure OpenAI Service
    12. Google Gemini
    13. OpenAPI
    14. Swagger
    15. Zapier NLA
    16. LangSmith
    17. Weights & Biases (wandb/wandb)
    18. OpenTelemetry
    19. Kubernetes (kubernetes/kubernetes)
    20. EKS
    21. AKS
    22. GKE
    23. AWS Lambda
    24. Azure Functions
    25. Google Cloud Functions
    26. Docker (docker/docker-ce)
    27. OpenAI's `text-embedding-ada-002`
    28. Cohere Embed
    29. Datadog
    30. New Relic
    31. Grafana Tempo (grafana/tempo)

    AI recommended 31 alternatives but never named nageoffer/ragent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to implement intelligent agents with multi-path retrieval and robust tool calling.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. AutoGen (microsoft/autogen)
    5. CrewAI
    6. DSPy

    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