RRepoGEO

REPOGEO REPORT · LITE

OpenBMB/UltraRAG

Default branch main · commit 909a2345 · scanned 5/23/2026, 5:37:02 AM

GitHub: 5,552 stars · 421 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 OpenBMB/UltraRAG, 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 highlight unique value

    Why:

    CURRENT
    <h3 align="center">Less Code, Lower Barrier, Faster Deployment</h3>
    COPY-PASTE FIX
    <h3 align="center">UltraRAG: Low-Code Multi-Agent RAG with Visible Reasoning for Complex Pipelines</h3>
  • mediumreadme#2
    Add a 'Why UltraRAG?' section highlighting core differentiators

    Why:

    COPY-PASTE FIX
    **Why UltraRAG?** UltraRAG stands out by offering a low-code framework for building complex, multi-agent RAG pipelines. It emphasizes transparent, visible reasoning steps, moving beyond 'black box' development to provide clear logic for every decision in your RAG system, especially for multimodal and advanced queries.
  • mediumtopics#3
    Add specific topics for multi-agent and visible reasoning

    Why:

    CURRENT
    deepseek, demo, easy, embedding, flask, gpt, huggingface-transformers, llm, mcp, multimodal, openai, qwen, rag, sentence-transformers, ui, vllm, vlm
    COPY-PASTE FIX
    deepseek, demo, easy, embedding, flask, gpt, huggingface-transformers, llm, mcp, multimodal, openai, qwen, rag, sentence-transformers, ui, vllm, vlm, multi-agent-rag, reasoning-engine, explainable-ai

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 OpenBMB/UltraRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Gradio · recommended 1×
  5. FlowiseAI · recommended 1×
  • CATEGORY QUERY
    What are some low-code frameworks for building advanced RAG pipelines easily?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Gradio
    5. FlowiseAI
    6. Dify

    AI recommended 6 alternatives but never named OpenBMB/UltraRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I build complex multimodal RAG systems with clear, visible reasoning steps?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. OpenAI API
    6. Google Cloud Vertex AI

    AI recommended 6 alternatives but never named OpenBMB/UltraRAG. 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 OpenBMB/UltraRAG?
    pass
    AI named OpenBMB/UltraRAG explicitly

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

  • If a team adopts OpenBMB/UltraRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenBMB/UltraRAG 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 OpenBMB/UltraRAG solve, and who is the primary audience?
    pass
    AI named OpenBMB/UltraRAG explicitly

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

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OpenBMB/UltraRAG — 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