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Answer-first summary52/100 GEO

LlamaIndex

Data framework for LLM applications.

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Your Citability Score

52/100
Identity75/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
  • Identity: Add: Founder / Team, Social links.
  • Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
  • Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
  • Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
  • Classification: Add: At least 2 tags.

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Frequently asked questions

What is LlamaIndex?
Data framework for LLM applications.
What does LlamaIndex do?
Data framework for connecting custom data sources to large language models with advanced indexing and retrieval.
Who is LlamaIndex for?
Developers and data/ML teams building production LLM applications over private or enterprise data.
Is LlamaIndex verified?
LlamaIndex is listed on CitableHub with a citability score of 52/100, computed from verifiable profile evidence.
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LlamaIndex

Invited

Data framework for LLM applications.

AI FrameworksCH-VER-967279Listed September 11, 2026
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AI-Extractable Summary
What:Data framework for LLM applications.
For whom:Developers and data/ML teams building production LLM applications over private or enterprise data
Key outcome:Teams build LLM applications that reliably retrieve and reason over private data, reducing time to production for RAG and agent workflows
Category:AI Frameworks

Structured for AI systems to extract and cite.

Citability Score

52/100
75
Identity
45
Evidence
15
Trust
50
Freshness
80
Classification
57
Impressions
0
Clicks
0
Likes
0
GQI Earned

Citable Outcome

Teams build LLM applications that reliably retrieve and reason over private data, reducing time to production for RAG and agent workflows.

About

Data framework for connecting custom data sources to large language models with advanced indexing and retrieval.

Target Audience: Developers and data/ML teams building production LLM applications over private or enterprise data.
Not ideal for: Non-technical users or teams looking for a no-code chatbot builder or a fully packaged end-user application.

What makes it different

  • Specialized for indexing, retrieving, and querying unstructured and structured data across many sources
  • Composable abstractions for building RAG pipelines, agents, and custom retrieval workflows
  • Strong support for data connectors and document ingestion from diverse enterprise systems
  • Flexible enough for low-level control while still providing higher-level building blocks for rapid prototyping

Tags & Classification

retrieval augmented generationenterprise searchdocument question answeringagentic workflowsknowledge base chatbots
ai engineersml engineersdata engineersbackend developers
softwarehealthcarefinancial serviceslegal
Platform: FrameworkModel: Open Source

Links & Transparency

Website

Cite this Project

BibTeX
@misc{citablehub_llamaindex,
  title = {LlamaIndex},
  url = {https://citablehub.com/p/llamaindex},
  note = {Listed September 11, 2026. CitableHub ID: CH-VER-967279},
  year = {2026}
}
APA
LlamaIndex. (2026). CitableHub Software Index. https://citablehub.com/p/llamaindex.
MLA
"LlamaIndex." CitableHub, 2026, https://citablehub.com/p/llamaindex.