Comparing Agentic AI Frameworks for Java
summary
Compares three JVM frameworks by showing the exact same Spring Boot meal‑planner app implemented with Spring AI, LangChain4j and Embabel, measuring how each handles reasoning loops, tool calls, memory and orchestration. LangChain4j’s agentic module gives builder APIs and subagents, Spring AI uses advisor patterns and needs more boilerplate, Embabel provides planner‑style A planning and easy per‑agent LLM selection. Notes slower UX and higher token cost from multiple LLM calls and recommends set max iterations to control token spend*; MCP support is uneven and requires extra work.
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chapters (9)
- 0:00Introduction and speaker intro
- 1:00Basics of agentic AI frameworks
- 8:49Agentic AI systems and workflows
- 12:01Demo app user flow
- 14:50Spring AI overview and code
- 20:17LangChain4j overview and code
- 27:43Imbabura overview and code
- 31:36Comparison and takeaways
- 34:48Q&A and resources
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