Problem solved

Organizations and developers often struggle to build RAG systems that can handle complex, multi-faceted queries that require reasoning and tool use. Existing RAG solutions may lack agentic capabilities, limited search methods, or multimodal support, and many commercial platforms are closed-source and expensive. Agentset solves this by providing an open-source, production-ready RAG platform with built-in agentic reasoning, hybrid search, and multimodal support, enabling the development of sophisticated retrieval-augmented agents that can understand and answer complex queries by retrieving and reasoning over diverse data sources.

Use cases

• Building enterprise knowledge base Q&A systems that can answer complex questions using internal documents.
• Creating customer support bots that retrieve product information and reason over it to provide accurate answers.
• Developing research assistants that can search through academic papers, including figures and tables, and synthesize findings.
• Enabling multimodal search over corporate assets, such as PDFs with embedded images or diagrams.
• Automating document-heavy workflows where agents need to gather information from multiple sources and execute tasks.

Audience

• Developers
• ML Engineers
• Data Scientists
• Enterprise

Pros

• Open-source and self-hostable, offering full control and customization.
• Built-in agentic reasoning for complex query handling.
• Hybrid search combining keyword and vector search for better retrieval accuracy.
• Multimodal support enables handling of diverse data types.
• Production-ready with scalability and reliability in mind.

Cons

• May have a steep learning curve for setup and configuration.
• Documentation might be limited, given the early stage of the project.
• Requires self-hosting infrastructure, which could be resource-intensive.