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Build Frontier RAG Apps

Ground AI agents in your knowledge base, minimize hallucinations, and impress out of the box.

Agentset Platform Interface
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Features

Production-ready at scale

Reliable Accuracy

Agentset incorporates best-in-class RAG techniques such as hybrid search and reranking to get the highest result accuracy before you doing any customizations.

Deep Research

Agentset has built-in support for deep research, taking longer to return a result but reviews more sources and returns in-depth answers with greater context.

Citations

Agentset automatically cites the sources of your answers, allowing your users to inspect it.

Filters

Agentset supports metadata filtering, allowing you to base answers on a subset of the data.

Preview Links

Agentset allows you to capture external feedback quickly using a customizable chat interface.

Developer Experience

Built for developers

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File Formats

Developer-first APIs to upload your data with 22+ file formats supported.

import { Agentset } from "agentset";  const agentset = new Agentset({ apiKey: "agentset_xxx" }); const namespace = agentset.namespace("ns_1234");  const ingestJob = await namespace.ingestion.create({  payload: {  type: "FILE",  fileUrl: "https://example.com/document.pdf",  fileName: "my-document.pdf"  },  config: {  metadata: {  foo: "bar"  }  } });

MCP Server

Bring your knowledge base to external applications through our MCP server.

AI SDK Integration

You can use Agentset with the AI SDK, making it easy to integrate into your own applications.

Model Agnostic

Agentset lets you select your own vector database, embedding model, and LLM.

Frequently asked questions

Your questions answered.
What is RAG?
RAG stands for Retrieval Augmented Generation. It's a method that lets AIs give better answers by first looking up relevant information from documents, then using that information to generate responses.
What is Agentset?
Agentset is an RAG-as-a-service platform that allows you to build high-quality RAG applications without the hassle of building and scaling the infrastructure.
How is this different from LangChain or LllamaIndex?
LangChain and LlamaIndex typically take weeks of implementation time to get an agent working, we abstract all the details and get you powerful agentic RAG working quickly
Can I self-host Agentset?
Yes, Agentset is open-source so you're able to host it and scale it in house. Our hosted solution gives you one less thing to worry about.
How is Agentset different from other agent building tools?
Agentset is optimized for documents. This focus allows us to get excellent results out of the box with minimal configuration.