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Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts - MCP Implementation

Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts

Build lightning-fast agentic apps with Quarkus + MCP - seamless multi-server orchestration, LangChain4j smarts, and the raw energy of modern Java. Game on.

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About Quarkus + MCP = Agentic

What is Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts?

Agentic is a solution combining Quarkus, a high-performance Java framework, with the Model Context Protocol (MCP) from LangChain4j. This integration enables rapid orchestration of AI workflows and decision-making processes. It leverages Quarkus's low latency and MCP's modular architecture to create scalable, intelligent applications that execute complex tasks with minimal setup overhead.

How to Use Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts?

  1. Install Dependencies: Configure project dependencies for Quarkus, LangChain4j, and required MCP clients via Maven.
  2. Setup API Credentials: Store OpenAI and other service API keys in environment variables or configuration files.
  3. Run Development Mode: Execute `./mvnw compile quarkus:dev` to start a hot-reload development server with live coding capabilities.
  4. Test Workflows: Use the Dev UI (http://localhost:8080/q/dev) to simulate AI-driven tasks and validate orchestration logic.

Quarkus + MCP = Agentic Features

Key Features of Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts?

  • Sub-Second Latency: Native image compilation reduces startup times by 90% compared to traditional Java deployments.
  • AI Workflow Composition: Chain multiple LLMs, data sources, and tools declaratively using MCP's standardized interface.
  • Observability: Integrated metrics and tracing via Quarkus extensions for real-time performance monitoring.
  • Environment Portability: Deploy as lightweight containers or standalone executables without runtime dependencies.

Use Cases of Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts?

  • Intelligent Automation: Automate customer service workflows with contextual understanding from multiple data sources.
  • Real-Time Analytics: Power dashboard applications that combine live data streams with predictive AI models.
  • Smart Document Processing: Extract structured data from unstructured documents using LLM-driven parsing logic.
  • Edge-AI Applications: Deploy low-latency AI services directly on IoT devices using native compilation.

Quarkus + MCP = Agentic FAQ

FAQ from Quarkus + MCP = Agentic: Hyperfast Orchestration & AI-Driven Smarts?

How do I troubleshoot dependency conflicts?

Use `mvn dependency:tree` to identify version mismatches. Prioritize LangChain4j and Quarkus BOMs to maintain compatibility.

Can I customize the AI decision flow?

Yes, MCP's plugin architecture allows injecting custom tool implementations and modifying execution pipelines through extension points.

What monitoring tools are supported?

Out-of-the-box support for Prometheus and Grafana. OpenTelemetry integration is available for distributed tracing across microservices.

How is security handled in production?

API keys are encrypted via Vault integration, and native images can be secured with GraalVM's enterprise security features (commercial).

Content

Quarkus + MCP = Agentic

This project uses Quarkus, the Supersonic Subatomic Java Framework and the Model Context Protocol to implement a simple agentic app using multiple MCP servers and Quarkus + LangChain4j.

If you want to learn more about Quarkus, please visit its website: https://quarkus.io/ .

Running the application in dev mode

You'll need node and npm installed (this is used to start mcp services). Follow the recommended way to install for your system.

You will also need a container environment available (e.g. Podman or Docker) if you want to see built-in telemetry, which you can access once the app is up by going to the Dev UI and finding the Grafana link. If you don't have a container environment, comment out the part in application.properties dealing with telemetry.

Create a directory called playground at the root folder of your clone if you wish to use the filesystem MCP server (or change the name in application.properties to some other name, but the directory must exist)

Several of the MCP services require API keys. Here are links to get the keys:

Once you have all that, the easiest way is to create a file called .env in your clone (this file is listed in .gitignore so won't be pushed to GitHub if you fork this repo and make the file). The .env file should look like:

quarkus.langchain4j.mcp.bravesearch.environment.BRAVE_API_KEY=<YOUR BRAVE API KEY HERE>
quarkus.langchain4j.mcp.googlemaps.environment.GOOGLE_MAPS_API_KEY=<YOUR GMAPS API KEY HERE>
quarkus.langchain4j.mcp.slack.environment.SLACK_BOT_TOKEN=<YOUR SLACK BOT TOKEN HERE>
quarkus.langchain4j.mcp.slack.environment.SLACK_TEAM_ID=<YOUR SLACK TEAM ID HERE>
quarkus.langchain4j.openai.api-key=<YOUR OPENAI API KEY HERE>

These variables will automatically be included when you run Quarkus in Dev mode. You can also put them directly in application.properties but be careful not to check them into a public source repository!

For production use, these should obviously be treated differently, stored in secure places like vaults or kubernetes Secrets, and injected as environment variables at runtime.

But for testing, you can run your application in dev mode that enables live coding using:

./mvnw compile quarkus:dev

In Dev mode, you can use the Dev UI to chat with the LLM you've configured by going to "Extensions" and clicking "Chat" to chat. You'll find the system message pre-filled in from the content from Bot.java

NOTE: Quarkus now ships with a Dev UI, which is available in dev mode only at http://localhost:8080/q/dev/.

Testing the app

There is a simple frontend application to test the assistant - access http://localhost:8080 and you should see:

landing page

Click on the chat box icon at the lower left and issue some sample prompts to see how it uses agent reasoning to invoke the various tools:

My name is John Doe. I am a member of a team of 2 myself and Daniel Jane. I like Asian food, while Daniel is on a strict gluten-free diet.

Please find one good restaurant in Atlanta, GA with the highest rating that meets
the team's dietary needs and preferences. Then, invite the team to a lunch
at 12pm next Wednesday using the slack channel "#lunchtime".
In your message, include the name and description of the restaurant, the time and
date of the lunch, and driving directions from Georgia World Congress Center.
Also create an ICS calendar file for me to use in my calendar in the
"playground/calendar" directory.`

And some simpler follow-up prompts like:

  • What was the reasoning you used to arrive at that recommendation?
  • How did you choose the restaurant?
  • What actions did you take for each step and which tools did you use?
  • Why did you search for gluten-free restaurants?
  • What did you remember about each person on the team?

In Dev mode, you can also use the Dev UI to chat with the LLM you've configured by going to "Extensions" and clicking "Chat" to chat. You'll find the system message pre-filled in from the content from Bot.java

NOTE: Quarkus now ships with a Dev UI, which is available in dev mode only at http://localhost:8080/q/dev/.

Packaging and running the application

The application can be packaged using:

./mvnw package

It produces the quarkus-run.jar file in the target/quarkus-app/ directory. Be aware that it’s not an über-jar as the dependencies are copied into the target/quarkus-app/lib/ directory.

The application is now runnable using java -jar target/quarkus-app/quarkus-run.jar.

If you want to build an über-jar , execute the following command:

./mvnw package -Dquarkus.package.type=uber-jar

The application, packaged as an über-jar , is now runnable using java -jar target/*-runner.jar.

Creating a native executable

You can create a native executable using:

./mvnw package -Dnative

Or, if you don't have GraalVM installed, you can run the native executable build in a container using:

./mvnw package -Dnative -Dquarkus.native.container-build=true

You can then execute your native executable with: ./target/research-1.0-SNAPSHOT-runner

If you want to learn more about building native executables, please consult https://quarkus.io/guides/maven-tooling.

Related Guides

  • LangChain4j Model Context Protocol client (guide): Provides the Model Context Protocol client-side implementation for LangChain4j
  • LangChain4j OpenAI (guide): Provides the basic integration with LangChain4j

Related MCP Servers & Clients