# Agentset

> Build document search and AI question-answering applications with ingestion, retrieval, and citations.

Agentset provides infrastructure for developers building search and question-answering features over their own data. It combines document ingestion, extraction, chunking, and retrieval, including material stored in images, graphs, and tables. Generated answers can cite their sources, and metadata filters let an application limit the information used for a request.

JavaScript and Python SDKs support uploading documents in a range of formats. Developers can choose their vector database, embedding model, and language model, connect through an MCP server, or integrate with an existing AI application stack. Shareable chat previews help gather feedback before embedding the experience in a product or expanding it to a larger document collection.

## Listing details

- Canonical BetaNeon page: https://betaneon.com/products/agentset
- Official website: https://agentset.ai/
- Category: Artificial intelligence / AI document understanding
- Status: Launched
- Topics: AI, Developer tools, Feedback, SDK, Search
- Listing updated: 2026-10-30T16:49:34.757Z
- Information source: https://agentset.ai/

This is a product introduction, not an endorsement. Confirm current availability, features, and prices on the official website. Community upvotes on BetaNeon are not star ratings.