AI Agents Tutorials

Agent Architecture: How to structure AI agents with tools and memory
Multimodal Data: Storing and retrieving both text and image embeddings
Tool Integration: Connecting agents to LanceDB for data access
Conversational Interfaces: Building chat-based user experiences
Production Deployment: Making agents ready for real-world use

Learn how to build sophisticated AI agents that leverage LanceDB’s multimodal capabilities for intelligent data retrieval and reasoning.

Agent Type	Description
Multimodal Recipe Agent View Tutorial	Build an AI agent that understands both text and images to help users find recipes. Uses PydanticAI with LanceDB for multimodal storage and retrieval, featuring semantic search, image similarity, and conversational interfaces.

What You’ll Learn

Semantic Search: Find relevant content using natural language queries
Image Understanding: Process and search through visual content
Tool-based Reasoning: Agents that can use multiple tools to solve complex problems
Conversation Memory: Maintain context across multiple interactions
Streamlit Integration: Build interactive web interfaces for your agents

💡 These tutorials build on LanceDB’s core capabilities to create intelligent, multimodal AI agents that can understand and reason about complex data.