StackA2A
media-contentOfficialjava

Content Writer (Official Sample)

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by A2A Project

Official A2A java sample agent: Content Writer

1,329 starsUpdated 2026-02-22apache-2.0
Quality Score59/100
Community
70
Freshness
100
Official
100
Skills
10
Protocol
30
🔒 Security
20

Getting Started

1Clone the repository
$ git clone https://github.com/a2aproject/a2a-samples
2Navigate to the project
$ cd a2a-samples/samples/java/agents/content_writer
3Install dependencies
$ mvn install
4Run the agent
$ mvn exec:java

README

Content Writer Agent

This sample agent can be used to generate an engaging piece of content given a content outline. This agent is written using Quarkus LangChain4j and makes use of the A2A Java SDK.

Prerequisites

  • Java 17 or higher
  • Access to an LLM and API Key

Running the Sample

  1. Navigate to the content_writer sample directory:

    cd samples/java/agents/content_writer
    
  2. Create a .env file in the content_writer directory as follows:

    cp .env.example .env
    

    Then update the .env file to specify your Google AI Studio API Key (note that no quotes are needed below):

    QUARKUS_LANGCHAIN4J_AI_GEMINI_API_KEY=your_api_key_here
    
  3. Run the Content Writer Agent

    NOTE: By default, the agent will start on port 10002. To override this, add the -Dquarkus.http.port=YOUR_PORT option at the end of the command below.

    mvn quarkus:dev
    
  4. In a separate terminal, run the A2A client and use it to send a message to the agent:

    # Connect to the agent (specify the agent URL with correct port)
    cd samples/python/hosts/cli
    uv run . --agent http://localhost:10002
    
    # If you changed the port when starting the agent, use that port instead
    # uv run . --agent http://localhost:YOUR_PORT
    
  5. To make use of this agent in a content creation multi-agent system, check out the content_creation sample.

Disclaimer

Important: The sample code provided is for demonstration purposes and illustrates the mechanics of the Agent-to-Agent (A2A) protocol. When building production applications, it is critical to treat any agent operating outside of your direct control as a potentially untrusted entity.

All data received from an external agent—including but not limited to its AgentCard, messages, artifacts, and task statuses—should be handled as untrusted input. For example, a malicious agent could provide an AgentCard containing crafted data in its fields (e.g., description, name, skills.description). If this data is used without sanitization to construct prompts for a Large Language Model (LLM), it could expose your application to prompt injection attacks. Failure to properly validate and sanitize this data before use can introduce security vulnerabilities into your application.

Developers are responsible for implementing appropriate security measures, such as input validation and secure handling of credentials to protect their systems and users.

Capabilities

StreamingPush NotificationsMulti-TurnAuth: none
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