Add a blog post about multi-language agent collaboration with A2A

Author: fjuma

_posts/2025-09-10-quarkus-a2a-multi-agent-content-creation.adoc

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+---
+layout: post
+title: 'Multi-Language Agent Collaboration and Interoperability with A2A'
+date: 2025-09-10
+tags: ai a2a
+synopsis: Let's learn how to build a multi-agent system where agents written in different languages and with different LLM frameworks can seamlessly collaborate using A2A.
+author: fjuma
+---
+:imagesdir: /assets/images/posts/quarkus-a2a-multi-agent-content-creation
+
+Building a multi-agent system can involve using different languages to meet specific needs. The https://a2a-protocol.org/latest/[Agent2Agent (A2A) protocol] is an open standard that enables AI agents to communicate and collaborate with one another, regardless of each agent's underlying technology stack.
+
+In this post, we'll see how to create a multi-agent system, where agents written in Java, Python, and TypeScript work together to accomplish a goal: content creation. The multi-agent system uses A2A for communication between the AI agents.
+
+image::ContentCreationDiagram.png[scaledwidth=100%]
+
+== Content Creation Sample
+
+We're going to do a deep dive into the https://github.com/a2aproject/a2a-samples/tree/main/samples/python/hosts/content_creation[Content Creation] sample from the https://github.com/a2aproject/a2a-samples[a2a-samples] project.
+
+This sample showcases a content creation pipeline with a Host agent that acts as the central orchestrator,
+dynamically routing requests to a set of specialized agents.
+
+=== Agents
+
+Here's a quick overview of all the agents in our multi-agent system:
+
+[cols="a,a,a,a",options=header]
+|===
+| Agent | Role | Technology Stack | Description
+
+| *Host*
+| A2A Client
+| Python, Google ADK, A2A Python SDK
+| Serves as the central orchestrator, routing requests to the appropriate agent based on the task at hand.
+
+| *Content Planner*
+| A2A Server
+| Python, Google ADK, A2A Python SDK
+| Receives a high-level content request and creates a detailed content outline.
+
+| *Content Writer*
+| A2A Server
+| Java, Quarkus LangChain4j, A2A Java SDK
+| Generates engaging content from a content outline.
+
+| *Content Editor*
+| A2A Server
+| TypeScript, Genkit, A2A JS SDK
+| Proofreads and polishes the given content.
+|===
+
+Notice that the agents are written in different programming languages and make use of different LLM frameworks.
+
+=== Handling a Content Creation Request
+
+Upon receiving a content creation request from the user, the Host agent breaks down the content creation task
+into a few different sub-tasks: planning, writing, and editing.
+
+==== Dynamic Agent Selection
+The Host agent uses an LLM and the agent cards from our specialized A2A server agents to determine which agent to assign a particular sub-task to. For example, let's take a look at the agent card for our Content Writer agent:
+
+[source,json]
+----
+{
+  "name": "Content Writer Agent",
+  "description": "An agent that can write a comprehensive and engaging piece of content based on the provided outline and high-level description of the content",
+  "url": "http://localhost:10002",
+  "version": "1.0.0",
+  "documentationUrl": "http://example.com/docs",
+  "capabilities": {
+    "streaming": true,
+    "pushNotifications": false,
+    "stateTransitionHistory": false
+  },
+  "defaultInputModes": [
+    "text"
+  ],
+  "defaultOutputModes": [
+    "text"
+  ],
+  "skills": [
+    {
+      "id": "writer",
+      "name": "Writes content using an outline",
+      "description": "Writes content using a given outline and high-level description of the content",
+      "tags": [
+        "writer"
+      ],
+      "examples": [
+        "Write a short, upbeat, and encouraging twitter post about learning Java. Base your writing on the given outline."
+      ]
+    }
+  ],
+  "supportsAuthenticatedExtendedCard": false,
+  "additionalInterfaces": [
+    {
+      "transport": "JSONRPC",
+      "url": "http://localhost:10002"
+    }
+  ],
+  "preferredTransport": "JSONRPC",
+  "protocolVersion": "0.3.0"
+}
+----
+
+[NOTE]
+====
+The agent card for an A2A server agent can be found using its Well-Known URI, e.g., for the Content Writer agent, we can fetch its agent card using http://localhost:10002/.well-known/agent-card.json.
+====
+
+The description and skills specified in the Content Writer's agent card allow the Host agent's LLM to determine that the writing sub-task should be sent to the Content Writer agent.
+
+==== Agent Communication
+
+The Host agent communicates with each A2A server agent using an A2A client. Notice that an A2A client
+does not need to be written in the same programming language as an A2A server since all that matters is
+that both are using the A2A protocol to communicate with each other.
+
+Later on in this post, we'll see how we can easily swap out the TypeScript Content Editor agent for
+an equivalent agent written in Java, highlighting the flexibility and interoperability that's made
+possible by the A2A protocol.
+
+=== Running the Agents
+
+Let's get this multi-agent system running locally.
+
+==== Content Planner
+
+In a terminal:
+
+[source,shell]
+----
+cd samples/python/agents/content_planner
+----
+
+Follow the instructions in the `content_planner` https://github.com/a2aproject/a2a-samples/blob/main/samples/python/agents/content_planner/README.md[README.md] to start the Content Planner agent.
+
+==== Content Writer
+
+In a terminal:
+
+[source,shell]
+----
+cd samples/java/agents/content_writer
+----
+
+Follow the instructions in the `content_writer` https://github.com/a2aproject/a2a-samples/blob/main/samples/java/agents/content_writer/README.md[README.md] to start the Content Writer agent.
+
+==== Content Editor
+
+In a terminal:
+
+[source,shell]
+----
+cd samples/js/src/agents/content-editor
+----
+
+Follow the instructions in the `content-editor` https://github.com/a2aproject/a2a-samples/blob/main/samples/js/src/agents/content-editor/README.md[README.md] to start the Content Editor agent.
+
+==== Host
+
+In a terminal:
+
+[source,shell]
+----
+cd samples/python/hosts/content_creation
+uv run .
+----
+
+[NOTE]
+====
+As mentioned in the agent `README.md` files, don't forget to create a `.env` file for each agent with your
+`GOOGLE_API_KEY`. This is needed since the agents in this sample make use of Gemini. You can create a
+Google AI Studio API Key for free https://aistudio.google.com/[here].
+====
+
+=== Access the Content Creation Application
+
+Now that all of our agents are up and running, from your browser, navigate to http://localhost:8083.
+
+Try asking questions like:
+
+* Create a short, concise LinkedIn post about getting started with the Agent2Agent protocol
+* Create a short, upbeat X post about Quarkus LangChain4j
+
+image::UI.png[scaledwidth=100%]
+
+=== Swap Out an Agent
+
+One of the most powerful features of the A2A protocol is its interoperability. Let's see this in
+action by swapping out the TypeScript-based Content Editor agent for an equivalent agent written
+in Java.
+
+image::ContentCreationSwapped.png[scaledwidth=100%]
+
+First, stop the Host agent and the Content Editor agent.
+
+==== Start the Java Content Editor Agent
+
+In a terminal:
+
+[source,java]
+----
+cd samples/java/agents/content_editor
+----
+
+Follow the instructions in the `content_editor` https://github.com/a2aproject/a2a-samples/blob/main/samples/java/agents/content_editor/README.md[README.md] to start the Content Editor agent.
+
+==== Start the Host Agent
+
+In a terminal:
+
+[source,shell]
+----
+cd samples/python/hosts/content_creation
+uv run .
+----
+
+==== Access the Content Creation Application
+
+From your browser, navigate to http://localhost:8083 and try asking some questions.
+
+This time, the Host agent will seamlessly use the Java-based Content Editor agent for editing
+content instead of the TypeScript-based Content Editor agent. This flexibility is made possible
+because the A2A protocol is language-agnostic. This can be really useful for prototyping agents
+in one language to get things up and running quickly and then migrating to another language for
+a production environment.
+
+== Conclusion
+
+In this post, we saw how to use the A2A protocol to enable agents written in different programming
+languages and with different LLM frameworks to collaborate seamlessly to accomplish a goal. We also
+saw how easy it is to swap out one of the agents for an equivalent agent written in a different language.
+
+=== Further Reading
+
+* https://github.com/a2aproject/a2a-samples/blob/main/samples/python/hosts/content_creation/README.md[Content Creation Sample]
+* https://github.com/a2aproject/a2a-samples/tree/main/samples/java/agents[A2A Java SDK Samples]
+* https://github.com/a2aproject/a2a-java/blob/main/README.md[A2A Java SDK Documentation]
+* https://a2a-protocol.org/latest/specification/[A2A Specification]
+
+