quarkus-workshop-langchain4j
Verified Safeby quarkusio
Overview
A workshop demonstrating how to build AI-infused applications and agentic systems with Quarkus and LangChain4j, specifically focusing on a car management system that uses AI agents for tasks like cleaning, maintenance, and disposition decisions, including communication with remote agents via the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol.
Installation
cd section-2/step-04/remote-a2a-agent && ./mvnw quarkus:devEnvironment Variables
- OPENAI_API_KEY
Security Notes
The system implements `InputGuardrails` to detect prompt injection using a separate AI service, which enhances security. API keys are handled via environment variables. Network interactions include calls to external weather APIs (`api.open-meteo.com`) and internal A2A communication, which are standard for distributed systems but require careful configuration and trust boundaries. The inherent risk of LLM interpreting prompts for tool execution is present, though mitigated by guardrails and structured tool definitions.
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