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Agentset

Freemium
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Inputs: text, file, imageOutputs: text
Type
Saas
Company
Agentset

About Agentset

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.

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.

Key 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

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

Key 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

Pros & Cons

Pros
  • Open-source nature allows full customization and no vendor lock-in
  • Production-ready scalability for high traffic and large datasets
  • Model-agnostic flexibility supports preferred AI stack
  • Multimodal support broadens applicability to images and tables
  • Built-in citations enhance trust and verifiability
  • Handles complex RAG aspects out of the box
Cons
  • Requires developer expertise for setup and integration
  • Self-hosting demands infrastructure management
  • Freemium model details on hosted version unclear
  • Focused primarily on RAG, less for non-retrieval AI tasks
  • Limited documentation visibility from provided info

Best For

Building research-grounded medical AI assistantsCreating enterprise search systems for complex legal or municipal documentsReplacing traditional keyword search with high-precision semantic searchDeveloping AI chatbots with reliable answers for customer support

Alternatives to Agentset

FAQ

Is Agentset free to use?
Yes, it is open-source and available on GitHub, with a freemium pricing model likely for any hosted services.
What file formats does it support?
Over 22 file formats, including support for multimodal data like images and tables.
Is it compatible with my own models?
Yes, it is fully model-agnostic, allowing custom LLMs, vector databases, and embedding models.
Does it provide source citations?
Yes, cited answers are provided out of the box for transparency.
What makes it production-grade?
It handles large-scale data, high traffic, and complex RAG components like hybrid search and agentic reasoning reliably.
Where can I find the source code?
The GitHub repository is at https://github.com/agentset-ai/agentset.