LlamaIndex vs Agentset: 2026 comparison

LlamaIndex and Agentset in brief

LlamaIndex and Agentset both help connect language models to documents, combining retrieval with answers linked to sources. LlamaIndex pairs an open framework with document extraction and structuring capabilities, including its commercial LlamaParse service. Agentset highlights document import, semantic search and a conversation interface you can test and share. Both provide an API and a free offering. The key distinction is how much of your project involves preparing and processing documents, compared with searching them and presenting answers to users.

LlamaIndex : LlamaIndex lets teams processing documents extract content, structure tables, and index documents for search. It also connects model responses to source documents and supports building agents that process documents.

Agentset : Agentset lets teams querying documents import and index them, search passages by meaning, and obtain answers with sources. It also supports querying document images and tables and filtering with metadata.

Both meet the same need, which makes the comparison useful. We compare them on 10 verifiable criteria, using the same data as their profiles, collected from official sites.

Comparison table

Criterion by criterion

For each criterion: why it matters, each one's value and who has the edge. A criterion without data on both sides is not decided.

Free tier

A free tier lets you test the tool on your own examples before paying anything. LlamaIndex : yes. Agentset : yes. A tie on this point.

Entry price

The first paid plan, per month, gives the minimum cost of regular use; higher plans cost more. LlamaIndex : $50 per month. Agentset : $49 per month. Edge: Agentset.

Public API

An API lets you connect the tool to your software, an automation tool or an AI agent. LlamaIndex : yes. Agentset : yes. A tie on this point.

Data export

Being able to get your content back avoids lock-in the day you want to switch tools. LlamaIndex : yes. Agentset : not disclosed. Cannot be decided: data missing on one side.

Data hosting

Where data is stored matters for GDPR and client data: the EU or local processing make compliance simpler. LlamaIndex : several regions. Agentset : not disclosed. Edge: LlamaIndex.

Interface languages

More interface languages make the tool easier to roll out across an international team. LlamaIndex : 1. Agentset : 1. A tie on this point.

Platforms

The more platforms (web, desktop, mobile, extension), the better the tool fits your daily routine. LlamaIndex : web browser and API. Agentset : web browser and API. A tie on this point.

Open source

Public code lets you audit the tool, self-host it and keep using it if the publisher changes course. LlamaIndex : yes. Agentset : yes. A tie on this point.

Track record

A tool launched several years ago has a longer history: less risk of sudden shutdown, more user feedback. LlamaIndex : launched in 2022. Agentset : launched in 2025. Edge: LlamaIndex.

Recognition

The number of reference community lists citing the tool shows how well known it is among practitioners. LlamaIndex : cited in 3 lists. Agentset : cited in 2 lists. Edge: LlamaIndex.

In charts

Our analysis

LlamaIndex suits projects that require you to build a document processing sequence: extracting complex content, turning tables into structured data, indexing documents and connecting model answers to source material. Its listed functions also include building agents that process documents. Agentset emphasizes importing and indexing material, semantic passage retrieval and answers with sources. Image and table queries, metadata filters and a shareable conversation interface extend that approach. Your decision therefore depends on the work you need to organize: a broader document processing chain, or the retrieval and question answering functions explicitly described for Agentset.

The LlamaIndex framework remains free and open source. Its listed USD 50 monthly price applies to LlamaParse Starter, a commercial service that includes 40,000 credits. Agentset’s hosted Pro plan costs USD 49 per month for 10,000 pages and unlimited searches, with connectors charged separately. Similar headline amounts do not mean equivalent coverage, because the services use different units and include different activities. LlamaIndex’s access and transparency score is 95, compared with 66 for Agentset. Those scores do not assess extraction quality, answer accuracy or the performance either system will achieve on your documents.

Both tools have open code, but their hosting disclosures differ. LlamaIndex lists mixed hosting, without explaining the distribution of processing in the supplied information. Agentset does not declare data hosting. LlamaParse lists exports in Markdown, plain text, JSON, XLSX, HTML tables and annotated PDF. Those formats belong specifically to LlamaParse and should not automatically be attributed to every LlamaIndex component. Agentset’s export options are unspecified. If retrieving structured results is essential to your project, the published LlamaParse formats offer concrete information, while Agentset requires further clarification before you can assess that aspect of the workflow.

Both products are available through the web and an API, and both list a single interface language without French. Platform availability alone therefore does little to distinguish their practical fit. LlamaIndex’s functions support a document chain involving extraction, structuring and indexing. Agentset includes testing and sharing a conversation, filtering by metadata and asking questions about images and tables within documents. The records contain no comparative results on representative files or questions. To judge suitability, you would need to relate these stated functions to your own source material, required outputs and intended way of presenting answers.

Depending on your situation

The right choice depends on use. For a few common situations, here are the criteria that matter and who wins (LlamaIndex value / Agentset value).

Personal use on a tight budget

Here, Agentset has the edge: it wins 1 of the 3 criteria that matter in this situation, namely free tier (yes / yes), entry price ($50 per month / $49 per month), and interface languages (1 / 1).

Business use with client data

Here, LlamaIndex has the edge: it wins 2 of the 3 criteria that matter in this situation, namely data hosting (several regions / not disclosed), data export (yes / not disclosed), and track record (launched in 2022 / launched in 2025).

Integration with your software

Neither stands out on the criteria for this situation (public api (yes / yes), open source (yes / yes), and platforms (web browser and API / web browser and API)): decide with a trial.

Which one to choose?

Choose LlamaIndex if…

• you value data hosting (several regions)
• you value track record (launched in 2022)
• you value recognition (cited in 3 lists)

LlamaIndex makes sense when your project involves extracting complex documents, structuring tables and building agents that process document content. LlamaParse’s published export formats may matter if you need several kinds of output. Keep the free framework separate from the paid parsing service in your assessment, and estimate that service using its credit allowance rather than assuming a page equivalent.

Choose Agentset if…

• you value entry price ($49 per month)

Agentset fits a need to import documents, retrieve relevant passages and share a conversation that provides sourced answers. Metadata filtering and questions about document images and tables may match specific consultation requirements. Its hosted plan gives a published page allowance and unlimited searches, while separate connector charges, unspecified exports and undeclared data hosting still need consideration.

Strengths and limits of each

LlamaIndex

• + Free tier to try it
• + Public API
• + Public source code

Agentset

• + Free tier to try it
• + Public API
• + Public source code
• − Data hosting not disclosed
• − Data export not documented

The best test is still yours: take three real tasks from your day and give them to both for a week.

What about the others?

If neither suits you, look at Pinecone, Weaviate, Chroma, Qdrant, and Gestell.

Related comparisons:

• LlamaIndex vs Pinecone

Conclusion

LlamaIndex is a logical fit for building a document processing chain, with LlamaParse supplying published structured export options. Agentset fits document consultation organized around questions, metadata filters and a shareable conversation. Their listed prices cover different services and units, while their scores do not measure answer quality. Base your choice on the documents you process, the outputs you need and the parts of the workflow you intend to host.