The first of its kind promptable speech language model
Open-source RAG infrastructure that survives production workloads. Upload documents, query via API, get answers with sources. Hybrid search, multimodal, complex reasoning - all included. Model-agnostic. Used by 1,500+ teams in medical AI, legal tech, enterprise search.
Agentset is designed for production conditions with hybrid search, reranking, agentic reasoning, and automatic citations. The platform has been tested on 6B+ tokens and 9M+ pages, ensuring reliability as requirements and data complexity grow.
Agentset automatically cites sources for all answers, supports multimodal content (images, graphs, tables), includes metadata filtering for precise retrieval, and benchmarks at industry-leading accuracy on MultiHopQA and FinanceBench. This allows users to inspect answer provenance and verify reliability.
Agentset natively supports 22+ file formats (PDF, DOCX, XLSX, PPTX, HTML, CSV, images, emails, and more) and handles multimodal content. The platform automatically chunks and processes documents at scale, with pricing that scales from 1,000 pages (Free) to millions of pages (Enterprise).
Agentset offers JavaScript and Python SDKs, AI SDK integration, MCP server support for external applications, and model-agnostic architecture that works with any LLM, embedding model, or vector database. Developers can integrate in hours rather than weeks.
Agentset provides production-ready RAG infrastructure with advanced retrieval, high-resolution document parsing, reranking, and agentic reasoning built-in. Developers can ship accurate AI-powered search in minutes using TypeScript or Python SDKs, avoiding the 3+ month iteration cycle of building RAG from scratch.