Agentset

Agentset provides infrastructure for developers building production-ready RAG (Retrieval-Augmented Generation) applications, powering search and Q&A inside their products. Unlike typical RAG systems that work well in demos but struggle with real users and large document sets, Agentset is designed for production conditions, delivering reliable answers as data volume, usage, and complexity scale without requiring developers to build or maintain their RAG pipeline from scratch.

• Accurate Answers delivers superb accuracy on your data before any customizations, with industry-setting benchmarks for MultiHopQA and FinanceBench evaluations.

• Multimodal Support natively works with images, graphs, and tables just like text, allowing you to answer questions from every part of your knowledge base.

• Automatic Citations cites the sources of your answers automatically, allowing users to inspect and verify the information provided.

• Metadata Filtering supports filtering to base answers on a subset of your data, enabling more targeted and relevant responses.

• JavaScript and Python SDKs allow you to upload your data with 22+ file formats supported including PDF, DOCX, XLSX, PPTX, HTML, CSV, and more.

• MCP Server Integration brings your knowledge base to external applications through the Model Context Protocol server.

• AI SDK Integration makes it easy to integrate Agentset into your own applications using the Vercel AI SDK.

• Model Agnostic Architecture lets you select your own vector database, embedding model, and LLM from providers like OpenAI, Anthropic, Google AI, Mistral, DeepSeek, Pinecone, and Qdrant.

• Preview Links allow you to capture external feedback quickly using a customizable chat interface.

• Hybrid Search and Reranking provides precision retrieval with agentic reasoning built-in for minimal setup.

To get started, sign up for a free account, upload your documents through the dashboard or SDK, and begin querying your knowledge base immediately. The platform handles document parsing, chunking, embedding, and retrieval automatically, so you can focus on building features for your users.