Agentset.ai is an open-source, local-first semantic search and Retrieval-Augmented Generation (RAG) platform built for teams that want full control over their data. Instead of sending documents to third-party cloud services, you can index, search, and query your own text collections entirely on your own infrastructure. Agentset.ai turns scattered files, notes, and knowledge bases into a searchable, conversational knowledge layer that your applications and agents can use in real time. With Agentset.ai, you can plug modern embedding models into a fast local index, then use RAG to provide your LLMs with precise, document-grounded context. This dramatically improves answer accuracy, traceability, and compliance, while reducing hallucinations. The system is designed to be developer-friendly, with clear APIs, simple configuration, and flexible integration into existing backend services, chatbots, or internal tools. Because it is open source, Agentset.ai can be customized to your security, performance, and workflow requirements. You decide how data is stored, how models are deployed, and what retrieval strategies to use. Whether you are building internal knowledge assistants, developer copilots, or customer support tools, Agentset.ai gives you a robust semantic search and RAG backbone that stays close to your data and scales with your use cases.
Build an internal knowledge assistant that answers employee questions based on wikis, PDFs, and tickets stored on your own servers.
Power a developer copilot that retrieves relevant code snippets, design docs, and runbooks from your engineering knowledge base.
Create a customer support assistant that uses your product manuals, FAQs, and historical chats to generate accurate, source-linked responses.
Enable semantic search across research papers, reports, and notes for analysts who need fast, contextual insights from large archives.
Integrate RAG into existing chatbots so they can ground answers in up-to-date internal data without exposing it to third-party clouds.