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Profiling AI: LangChain4j and Spring AI

Agentic applications introduce unique profiling challenges. Beyond standard CPU usage, performance and costs are determined by the complex structure of prompts, RAG retrievers, and tool calls that remain hidden behind framework abstractions.

To avoid vendor lock-in, most developers use frameworks like LangChain4j or Spring AI. JProfiler’s AI probe provides deep visibility into these frameworks.

This screencast walks through profiling a LangChain4j customer support agent, showing how the AI probe visualizes prompt compositions, isolates token-heavy outliers, and projects resource costs directly onto the recorded call tree.