REPOGEO REPORT · LITE

run-llama/LlamaIndexTS

Default branch main · commit ba24377d · scanned 6/25/2026, 2:32:27 PM

GitHub: 3,078 stars · 521 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)

3 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 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

highreadme#1

Remove all deprecation indicators from README

Why:

CURRENT

> [!CAUTION]
> ## Deprecation Notice
> **This project is deprecated and no longer maintained.**
> 
> For LlamaCloud/LlamaParse usage, check out our docs: https://developers.llamaindex.ai/python/cloud/
> 
> Thank you to everyone who contributed to and used LlamaIndex.TS.

<p align="center">
  
</p>
<h1 align="center">LlamaIndex.TS (Deprecated)</h1>
<h3 align="center">
  Data framework for your LLM application.
</h3>

COPY-PASTE FIX

<p align="center">
  
</p>
<h1 align="center">LlamaIndex.TS</h1>
<h3 align="center">
  Data framework for your LLM application.
</h3>

mediumreadme#2

Refine the README's initial positioning statement

Why:

CURRENT

Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.

COPY-PASTE FIX

LlamaIndex.TS is a robust TypeScript framework designed for integrating custom data with Large Language Models (LLMs) in Node.js and other server-side JavaScript environments, enabling powerful Retrieval Augmented Generation (RAG) applications.

mediumtopics#3

Add specific RAG-related topics

Why:

CURRENT

agent, chatbot, claude-ai, create-llama, embedding, groq-ai, javascript, llama, llama-index, llama3, llamaindex, llm, node, nodejs, openai, react, typescript

COPY-PASTE FIX

agent, chatbot, claude-ai, create-llama, embedding, groq-ai, javascript, llama, llama-index, llama3, llamaindex, llm, node, nodejs, openai, react, typescript, rag, retrieval-augmented-generation

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/LlamaIndexTS

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchainjs

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchainjs · recommended 1×
run-llama/llama_index_ts · recommended 1×
pinecone-io/pinecone-ts-client · recommended 1×
chroma-core/chroma · recommended 1×
openai/openai-node · recommended 1×

CATEGORY QUERY
How to integrate custom data with LLMs in a Node.js server application?
you: not recommended
AI recommended (in order):
1. LangChain.js (langchain-ai/langchainjs)
2. LlamaIndex.TS (run-llama/llama_index_ts)
3. Pinecone (pinecone-io/pinecone-ts-client)
4. Chroma (chroma-core/chroma)
5. OpenAI API (openai/openai-node)
AI recommended 5 alternatives but never named run-llama/LlamaIndexTS. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best TypeScript frameworks for building LLM-powered chatbots with private data?
you: not recommended
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?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/run-llama/LlamaIndexTS"><img src="https://repogeo.com/badge/run-llama/LlamaIndexTS.svg" alt="RepoGEO" /></a>

Pro

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run-llama/LlamaIndexTS — 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