Agentset

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Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed to help developers build production-grade AI chat and search applications. It handles the complex aspects of RAG, including document extraction, chunking, hybrid search, and agentic reasoning, ensuring that AI apps remain reliable even with large-scale data and high user traffic. The platform is model-agnostic, allowing users to choose their own LLMs, vector databases, and embedding models. It supports over 22 file formats and multimodal data like images and tables, providing cited answers out of the box for sectors like medical AI, legal tech, and enterprise search.
Open-source RAG infrastructure for building production-ready AI chat and search applications.
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agentset.ai/
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What is Agentset?

Open-source RAG infrastructure for building production-ready AI chat and search applications.

Agentset is an open-source RAG (Retrieval-Augmented Generation) infrastructure designed to help developers build production-grade AI chat and search applications. It handles the complex aspects of RAG, including document extraction, chunking, hybrid search, and agentic reasoning, ensuring that AI apps remain reliable even with large-scale data and high user traffic. The platform is model-agnostic, allowing users to choose their own LLMs, vector databases, and embedding models. It supports over 22 file formats and multimodal data like images and tables, providing cited answers out of the box for sectors like medical AI, legal tech, and enterprise search.

Core Features
Production-grade RAG infrastructure with hybrid search and reranking
Multimodal support for text, images, graphs, and tables
Model-agnostic architecture (choose your own LLM, Vector DB, and Embeddings)
Automatic source citations and metadata filtering
Developer-friendly SDKs and MCP server integration
Deep research and agentic reasoning capabilities
Popular Use Cases
  • Building research-grounded medical AI assistants
  • Creating enterprise search systems for complex legal or municipal documents
  • Replacing traditional keyword search with high-precision semantic search
  • Developing AI chatbots with reliable answers for customer support
Feature Comparison
A functional comparison based on maker input.
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Comparison details are provided for informational purposes and should be verified with the official website.
How to use
  • Developers can start by signing up and using the JavaScript or Python SDKs to upload documents in various formats. After ingestion
  • you can configure your preferred LLM and vector database. The platform then allows you to query your data via API to receive accurate answers with automatic citations
  • or integrate it into external apps using the Model Context Protocol (MCP) server.
Pricing
Agentset uses a freemium pricing model. Pricing and features may change over time.
Free
$0 USD
1,000 pages, 10,000 retrievals, and community support.
Pro
$49 USD per month
10,000 pages ($0.01 per additional page), unlimited retrievals, $100 per connector, and email support.
Enterprise
Custom
Unlimited pages/retrievals, self-hosted/BYOC options, SOC 2/HIPAA reports, and dedicated engineering support.
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