RRepoGEO

REPOGEO REPORT · LITE

run-llama/LlamaIndexTS

Default branch main · commit ba24377d · scanned 5/15/2026, 12:12:12 AM

GitHub: 3,081 stars · 516 forks

AI VISIBILITY SCORE
70 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 run-llama/LlamaIndexTS, 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
  • highabout#1
    Update the repository description to reflect its deprecated status

    Why:

    CURRENT
    Data framework for your LLM applications. Focus on server side solution
    COPY-PASTE FIX
    Deprecated: A data framework for LLM applications, focusing on server-side solutions in TypeScript. No longer maintained.
  • mediumtopics#2
    Add 'deprecated' to the repository topics

    Why:

    COPY-PASTE FIX
    agent, chatbot, claude-ai, create-llama, deprecated, embedding, groq-ai, javascript, llama, llama-index, llama3, llamaindex, llm, node, nodejs, openai, react, typescript
  • mediumreadme#3
    Clarify the deprecation notice with a general migration path

    Why:

    CURRENT
    For LlamaCloud/LlamaParse usage, check out our docs: https://developers.llamaindex.ai/python/cloud/
    COPY-PASTE FIX
    For active development with LlamaIndex, please refer to the main Python project: https://www.llamaindex.ai/. For LlamaCloud/LlamaParse usage, check out our Python docs: https://developers.llamaindex.ai/python/cloud/

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
1 / 2
50% of queries surface run-llama/LlamaIndexTS
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
langchain-ai/langchainjs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchainjs · recommended 1×
  2. openai/openai-node · recommended 1×
  3. anthropics/anthropic-sdk-typescript · recommended 1×
  4. google/generative-ai-js · recommended 1×
  5. pinecone-io/pinecone-ts-client · recommended 1×
  • CATEGORY QUERY
    How to connect large language models with my custom data in a Node.js application?
    you: #2
    AI recommended (in order):
    1. LangChain.js (langchain-ai/langchainjs)
    2. LlamaIndex.TS (run-llama/LlamaIndexTS) ← you
    3. OpenAI Node.js Library (openai/openai-node)
    4. Anthropic AI SDK (anthropics/anthropic-sdk-typescript)
    5. Google Generative AI SDK (google/generative-ai-js)
    6. Pinecone (pinecone-io/pinecone-ts-client)
    7. Weaviate (weaviate/weaviate-ts-client)
    8. Chroma (chroma-core/chroma)
    9. Qdrant (qdrant/qdrant-js)
    10. Supabase Vector (supabase/supabase)
    11. Sentence Transformers
    12. Hugging Face Inference API
    13. FastAPI (tiangolo/fastapi)
    14. sentence-transformers library (UKPLab/sentence-transformers)
    15. transformers library (huggingface/transformers)
    16. OpenAI Embeddings API
    Show full AI answer
  • CATEGORY QUERY
    Looking for a TypeScript framework to build LLM applications that use proprietary data.
    you: not recommended
    AI recommended (in order):
    1. LangChain.js
    2. LlamaIndex.TS
    3. OpenAI SDK (TypeScript)
    4. Hugging Face Transformers.js
    5. Pinecone Node.js Client
    6. Weaviate Node.js Client
    7. Qdrant Node.js Client

    AI recommended 7 alternatives but never named run-llama/LlamaIndexTS. 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 run-llama/LlamaIndexTS?
    pass
    AI named run-llama/LlamaIndexTS 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/LlamaIndexTS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named run-llama/LlamaIndexTS 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/LlamaIndexTS solve, and who is the primary audience?
    pass
    AI named run-llama/LlamaIndexTS explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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