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Add langchain4j-interop: Spring AI 2.0 and LangChain4j 1.22 side by side

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Same order assistant in both frameworks against a local fake OpenAI
server: starter bean collision, wire differences, tool failure and loop
bounds, observability meters, MCP-first tools, and a compile check of
the 2025 article's snippets and Maven coordinates.

Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01G8ikz8xdWuTP5yun8DZ1hk
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Claude
2026-10-11 10:09:45 +00:00
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@@ -23,5 +23,6 @@ Runnable companion code for the Spring AI articles on [ankurm.com](https://ankur
| [`providers/`](providers) | One ticket-summarising app on the real `OpenAiChatModel`, `AnthropicChatModel` and `GoogleGenAiChatModel` against a local server in all three wire formats: where each puts the system prompt and what it sends by default, portable versus provider-specific options (and the per-call `ChatOptions` that crashes two providers and silently resets the third), prompt caching, a cost table from the vendors' price sheets, and failover with the retry multiplication measured. No vendor API called; token counts and cache hits are simulated from documented rules; latency not measured. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Anthropic Claude vs OpenAI vs Gemini in Spring AI 2.0](https://ankurm.com/spring-ai-claude-vs-openai-vs-gemini-switch-providers-compare-cost/) |
| [`llm-gateway/`](llm-gateway) | A gateway service on Spring AI 2.0: model-hint routing, failover under the tool-calling advisor (a provider failing mid tool loop does not re-run the tool), one circuit breaker per provider, dollar caps that reserve before the call, a token-per-hour limiter, and a tenant-keyed semantic cache. Real OpenAI and Anthropic models against a local fake; no vendor API called. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [The LLM Gateway Pattern for Java Microservices](https://ankurm.com/llm-gateway-pattern-java-microservices/) |
| [`semantic-cache/`](semantic-cache) | A `CallAdvisor` that serves a stored answer when a new question embeds close to an old one, over a Redis Stack vector index and a real in-process embedding model (all-MiniLM-L6-v2). A labelled set of 30 intents x 4 phrasings plus 20 off-topic questions swept across thresholds (hit rate against wrong answers), 600 requests replayed with an illustrative cost model, and the traps reproduced: Spring AI's score is `(1 + cosine) / 2`, reset-password and reset-2FA are close enough to answer for each other, a tenant filter, expiry, and an index built for another embedding model. The chat model is a script that counts calls; one test uses a real local model. Spring Boot 4.1.1, Spring AI 2.0.1, Java 25. | [Semantic Caching for LLM Calls in Spring Boot with Redis Vector Search](https://ankurm.com/semantic-caching-for-llm-calls-in-spring-boot-with-redis-vector-search/) |
| [`langchain4j-interop/`](langchain4j-interop) | The same order assistant in Spring AI 2.0.1 and LangChain4j 1.22.0 in one Spring Boot 4.1.1 app, against a local fake OpenAI server: the bean collision when both OpenAI starters are present, the two requests compared on the wire, a throwing tool and a looping model (what each framework bounds), the meters each records, one MCP tool server used from both, and which lines of the 2025 version of the article still compile. Java 25. | [Spring AI vs LangChain4j in 2026: Is the Pairing Still Worth It?](https://ankurm.com/spring-ai-langchain4j-production-ai-microservices-java/) |
Upgrading from Spring AI 1.x: [migration guide](https://ankurm.com/spring-ai-1-to-2-migration-guide/).