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content-creationOfficialpython

Content Planner (Official Sample)

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by A2A Project

Official A2A python sample agent: Content Planner

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/python/agents/content_planner
3Install dependencies
$ pip install -r requirements.txt
4Run the agent
$ python main.py

README

Content Planner Agent

Given a high-level description of the content that's needed, this sample agent creates a detailed content outline. This agent is written using the Google Agent Development Kit (ADK) and the Python A2A SDK.

Prerequisites

  • Python 3.10 or higher
  • UV
  • Access to an LLM and API Key

Running the Sample

  1. Navigate to the samples directory:

    cd samples/python/agents/content_planner
    
  2. Create an environment file with your API key:

    echo "GOOGLE_API_KEY=your_api_key_here" > .env
    
  3. Run the agent:

    NOTE: By default, the agent will start on port 10001. To override this, add the --port=YOUR_PORT option at the end of the command below.

    uv run .
    
  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:10001
    
    # 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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