quarkusio/quarkus-workshop-langchain4j

Description and use

**Brief Summary (2‑3 sentences)**
The **quarkusio/quarkus‑workshop‑langchain4j** repository is a Quarkus‑based tutorial that demonstrates how to compose isolated prompts, tools, and memory into reusable LangChain4j agent workflows. It showcases patterns for coordinating multi‑agent pipelines, adding tool‑use steps, and standardising agent state, making it a practical entry point for teams that want to prototype AI‑driven orchestration on the JVM.

**Value**
By turning ad‑hoc prompt calls into repeatable, composable agents, the project accelerates the development of robust AI/ML back‑ends, reduces duplication, and provides a clear blueprint for integrating external tools (e.g., search, databases) into LangChain4j‑powered services.

**Practical Adoption Path**

1. **Explore the workshop** – clone the repo and run the provided Quarkus examples locally to understand the core concepts (prompt chaining, tool adapters, memory handling).
2. **Prototype a workflow** – replace the sample prompts/tools with your own domain logic, leveraging the same Quarkus extensions and LangChain4j APIs.
3. **Integrate into your service** – embed the generated agent code into an existing Quarkus microservice, expose it via the built‑in REST/GraphQL endpoints, and iterate on the workflow using the same development cycle.
4. **Validate and test** – add unit/integration tests for each agent step, and use Quarkus DevMode for rapid feedback before promoting to a CI pipeline.

**Production Readiness**
The project is **medium‑ready**: it is actively maintained (last update 2026‑05‑11), written in Java, and already has a modest community (≈ 83 stars, 65 forks). It is suitable for prototypes, internal tooling, or as a reference implementation, but production use should include a formal security review, license verification, and dependency hygiene (e.g., checking for vulnerable transitive libraries) before deployment.

Use in a workflow

quarkusio/quarkus-workshop-langchain4j helps turn isolated prompts and tools into repeatable agent workflows.

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.