What it does
Agentset describes itself as infrastructure for developers building production RAG applications that power search and question answering inside their products. Instead of assembling and maintaining a retrieval pipeline, developers upload documents with JavaScript or Python SDKs and query them, with agentic reasoning, hybrid search and reranking, and automatic source citations built in. The page lists support for more than 22 file formats, multimodal content such as images, graphs and tables, metadata filtering, and preview links with a customizable chat interface for collecting feedback. It is model agnostic, letting you choose the vector database, embedding model and LLM, and it offers an MCP server and an AI SDK integration. The page offers a login and a scheduled demo. It does not state prices; a separate pricing page is linked.
Capabilities
- JavaScript and Python SDKs for uploading documents in 22+ file formats
- Hybrid search with reranking and agentic reasoning
- Automatic citations of sources
- Metadata filtering and support for images, graphs and tables
- Choice of your own vector database, embedding model and LLM
- MCP server and AI SDK integration
When to use it
- You are adding question answering or search over your own documents to a product
- You need answers with cited sources that users can inspect
- You want to avoid building and maintaining your own ingestion and retrieval pipeline
Alternatives to explore
- Ragie
- Vectara
- Graphlit
- Credal
