The Azure AI Travel Agents sample is an end-to-end reference implementation of a conversational agent that helps users plan trips by generating personalized travel itineraries, sourcing flight and hotel options, and answering travel-related questions. Built on the Azure AI Agent framework, it integrates OpenAI’s GPT models for natural language understanding and generation, uses Azure Functions for hosting skills such as weather lookup, and connects to external APIs for real-time booking information. Developers can run the sample locally or deploy it to Azure, extend existing skills or add new ones for currency conversion, local attraction recommendations, or travel alerts. This sample highlights how to orchestrate multiple AI-powered skills and manage context state across turns, enabling a robust, scalable travel assistant solution.

Layra is designed to simplify developing LLM-powered agents by providing a modular architecture that integrates with various tools and memory stores. It features a planner that breaks down tasks into subgoals, a memory module for storing conversation and context, and a plugin system to connect external APIs or custom functions. Layra also supports orchestrating multiple agent instances to collaborate on complex workflows, enabling parallel execution and task delegation. With clear abstractions for tools, memory, and policy definitions, developers can rapidly prototype and deploy intelligent agents for customer support, data analysis, RAG, and more. It is framework-agnostic toward modeling backends, supporting OpenAI, Hugging Face, and local LLMs.

Joylive Agent offers a modular, plugin-based architecture tailored for building sophisticated AI agents. It provides seamless integration with LLMs such as OpenAI GPT, configurable memory backends for session persistence, and a toolkit manager to expose external APIs or custom functions as agent capabilities. The framework also includes built-in chain-of-thought orchestration, multi-turn dialogue management, and a RESTful server for easy deployment. Its Java core ensures enterprise-grade stability, allowing teams to rapidly prototype, extend, and scale intelligent assistants across various use cases.