Agentset

Categories: Coding & Developer Tools, Chatbots & Assistants, Productivity | Pricing: Freemium | Official Website ↗

Agentset provides infrastructure for developers to build reliable AI chat and search applications using RAG, supporting various data types.

Agentset is an infrastructure platform designed for developers to build production-ready Retrieval Augmented Generation (RAG) applications. It enables the creation of AI chat and search functionalities within products, focusing on delivering reliable answers even with large document sets and complex data. The platform aims to simplify the development process by abstracting away the complexities of RAG pipelines, allowing developers to deploy AI applications quickly without needing deep RAG expertise. The platform supports multimodal data, including images, graphs, and tables, alongside text, and automatically cites sources for answers. It offers features like metadata filtering, customizable chat interfaces for feedback, and supports over 22 file formats for data ingestion. Agentset provides JavaScript and Python SDKs, integrates with AI SDKs, and is model-agnostic, allowing users to select their preferred vector database, embedding model, and LLM. It also offers deployment options including managed and self-hosted infrastructure.

Key Features

Agentic Search
Extraction
Chunking
Retrieval
Accurate Answers
Multimodal Support
Citations
Filters

Pros

Delivers reliable answers on complex data without RAG expertise
Supports multimodal data including images, graphs, and tables
Automatically cites sources for answers
Offers flexible model and database choices
Provides SDKs for easy integration and data ingestion

Cons

Custom formats and self-hosted deployment only available on Enterprise plan
Connectors and custom integrations are not included in the Free plan
Email support is not available on the Free plan
Additional pages and connectors incur extra costs on the Pro plan
Requires developer knowledge to implement

Use Cases

Powering Q&A inside products
Building AI-powered search for legal corpuses
Creating AI assistants for medical research
Developing content search for municipalities
Integrating AI search into existing applications

Best For

Developers building AI chat applications
Developers building AI search applications
Companies needing reliable RAG systems for large document sets
Organizations requiring multimodal AI capabilities

Integrations: AI SDK, xAI Grok, Google AI, Anthropic, OpenAI, Azure, Cohere, Pinecone, Qdrant, Qwen

Platforms: Web

Watch demo on YouTube ↗

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