Overview
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.
Pricing
Freemium
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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.
Features
Local-first semantic search engine
Open-source RAG infrastructure
Private, on-premise data processing
Flexible embedding model integration
Document-grounded conversational answers
Developer-friendly APIs and SDKs
Configurable indexing and retrieval
Scalable architecture for large datasets
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FAQ
What is Agentset.ai and how is it different from regular search?
Agentset.ai is an open-source local semantic search and RAG platform. Unlike keyword-based search, it uses vector embeddings to understand meaning, then combines retrieval with large language models to generate context-aware, document-grounded answers.
Can I run Agentset.ai entirely on-premise and keep my data private?
Yes. Agentset.ai is designed for local and on-premise deployments, so you can process, index, and query your data within your own infrastructure without sending it to third-party cloud services.
What models and data sources does Agentset.ai support?
Agentset.ai is model-agnostic and can integrate with various embedding and LLM providers, including local and cloud models. It can index text from files, knowledge bases, APIs, and other structured or unstructured sources via its ingest pipelines.
Do I need to pay to use Agentset.ai?
Agentset.ai is open source, so you can use and self-host it under its license terms. However, you may still incur costs from infrastructure, model providers, or managed services you choose to use alongside it.
How hard is it to integrate Agentset.ai with my existing applications?
Agentset.ai offers developer-friendly APIs and configuration, making it straightforward to plug into backend services, chat interfaces, or custom agents. Most teams can start with basic indexing and RAG queries in a short setup time.