RRepoGEO

REPOGEO REPORT · LITE

run-llama/llama-hub

Default branch main · commit b476d3bd · scanned 5/29/2026, 5:03:10 PM

GitHub: 3,475 stars · 718 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 run-llama/llama-hub, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, llamaindex, langchain, data-loaders, data-ingestion, ai, nlp, archived
  • highabout#2
    Update the repository description to reflect its archived status

    Why:

    CURRENT
    A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain
    COPY-PASTE FIX
    ARCHIVED: Community-contributed data loaders for LLMs (formerly LlamaHub). Integrations are now in the core LlamaIndex repo. See llamahub.ai for details.
  • mediumreadme#3
    Insert an explicit statement about the repository's archival role in the README

    Why:

    COPY-PASTE FIX
    (Insert this sentence immediately after the existing CAUTION block in the README) This repository now serves as a historical archive for community-contributed data loaders and tools that were formerly part of LlamaHub, with all active integrations migrated to the core `llama-index` Python repository.

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 run-llama/llama-hub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Airbyte · recommended 1×
  4. Apache NiFi · recommended 1×
  5. Unstructured.io · recommended 1×
  • CATEGORY QUERY
    How can I easily ingest diverse data sources for my large language model applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Airbyte
    4. Apache NiFi
    5. Unstructured.io
    6. Meltano

    AI recommended 6 alternatives but never named run-llama/llama-hub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What community-contributed tools are available for integrating LLMs with external data services?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. OpenAI Functions
    5. Instructor
    6. Semantic Kernel

    AI recommended 6 alternatives but never named run-llama/llama-hub. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 run-llama/llama-hub?
    pass
    AI named run-llama/llama-hub explicitly

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

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

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

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MARKDOWN (README)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite