# Uthereal Cortex SDK vs LlamaIndex

> Comparing @uthereal-sdk/cortex with LlamaIndex for document question answering: who owns ingestion, indexing and evaluation, and how claim-level citations with PDF page evidence differ from source nodes.

- Canonical page: https://uthereal.ai/developers/vs-llamaindex
- Last updated: 2026-09-18
- Product: Uthereal Cortex (sovereign enterprise AI, Swiss/EU data residency)
- npm package: `@uthereal-sdk/cortex` (https://www.npmjs.com/package/@uthereal-sdk/cortex)
- Repository: https://github.com/Uthereal-Labs/Uthereal-Cortex-SDK

## Short answer

Short answer: choose LlamaIndex when you want to own ingestion, indexing and evaluation, typically in Python. Choose @uthereal-sdk/cortex when you want cited answers in a TypeScript product without operating an index, with claim-level references and highlighted PDF pages returned by the API.

LlamaIndex is an excellent toolkit for people whose job is retrieval. Most teams shipping a knowledge product do not want that job; they want answers their users can check.

## Different scopes

- LlamaIndex - connectors, node parsers, indexes, query engines, evaluators. Deep control over retrieval.
- Uthereal Cortex SDK - a hosted retrieval and answer service with React components for citations and PDF evidence.
- Both answer questions over documents. Only one of them hands you the index to operate.

## Source nodes versus claim-level citations

A list of retrieved nodes shows what the system looked at. It does not show which sentence supports which statement. Cortex returns the answer decomposed into claims, each bound to a reference with a document, page and span, and resolves that reference to a rendered page with the passage highlighted.

> That mapping is why an editor, auditor or clinician will accept the output. It is also the part that is hardest to retrofit onto a home-grown pipeline.

## What operations look like

- Ingestion - LlamaIndex: your pipeline and schedule. Cortex: upload to the agent.
- Re-indexing - LlamaIndex: on every chunking or embedding change. Cortex: none.
- Evaluation - LlamaIndex: you build and maintain the eval set. Cortex: retrieval quality is the service's responsibility.
- Residency - LlamaIndex: wherever you host it. Cortex: EU or Swiss resident, zero retention, no training on your content.

## How to choose

- Pick LlamaIndex for research, custom retrieval strategies and Python-centred stacks
- Pick Cortex for TypeScript products where provenance is a requirement and time to launch matters
- Use the rag endpoint to keep your query engine and swap only the evidence source

## Frequently asked questions

### Is Uthereal Cortex a LlamaIndex alternative?

For document question answering, yes. LlamaIndex is a data framework: ingestion, node parsing, indexing, query engines and evaluation, run by you. @uthereal-sdk/cortex is a hosted service returning cited answers, so there is no index to build or maintain.

### LlamaIndex returns source nodes. Is that the same as citations?

No. Source nodes tell you which chunks were retrieved. A citation ties one claim in the answer to one passage, with the document, page and span, so a reader can verify that specific statement. The second is what regulated users ask for.

### Which is better for a large, changing corpus?

With LlamaIndex you own the re-ingestion pipeline and its cost every time chunking or the embedding model changes. With Cortex, uploading or removing a document is the whole operation.

### Can I keep LlamaIndex and use Cortex for retrieval?

Yes. Call the rag endpoint for ranked JSON evidence and use it as a retriever inside your existing query engine.

### Is TypeScript a constraint?

The SDK is TypeScript-first and ESM, running on Node.js 22+, Deno 2, Bun, Cloudflare Workers and browsers. LlamaIndex is strongest in Python. For a TypeScript product, the SDK avoids a second runtime in your stack.

## Related pages

- [Cortex vs LangChain](https://uthereal.ai/developers/vs-langchain): Framework or hosted retrieval.
- [Do you need a vector database?](https://uthereal.ai/developers/vector-database-alternative): What an index costs you.
- [Build vs framework vs SDK](https://uthereal.ai/developers/rag-sdk-comparison): The full cost picture.
- [SDK overview](https://uthereal.ai/developers/sdk): Setup and API reference.

## Next steps

- Create an agent and a scoped server key: https://agent.uthereal.ai
- Full SDK guide (HTML): https://uthereal.ai/developers/sdk
- Full SDK guide (Markdown): https://uthereal.ai/developers/sdk.md
- AI index for this site: https://uthereal.ai/llms.txt and https://uthereal.ai/llms-full.txt
- Help: sdk@uthereal.ai