Carlos

• Updated: July 11, 2025
• 4 min read

Mastering the Agent Communication Protocol (ACP) for Weather Agents

Mastering Agent Communication Protocol (ACP): A Comprehensive Guide to Building a Weather Agent

In the rapidly evolving landscape of artificial intelligence, seamless communication between AI agents, applications, and humans is paramount. The Agent Communication Protocol (ACP) stands as a pivotal open standard designed to bridge the gap between diverse AI systems, enabling them to collaborate effectively. This guide will walk you through setting up an ACP server and client to fetch weather data using the Open-Meteo API, offering insights into its benefits and potential applications.

Understanding the Significance of ACP

The ACP is more than just a communication protocol; it’s a solution to the fragmentation that plagues AI systems developed on varied frameworks. By providing a unified RESTful API, ACP facilitates multimodal communication, both synchronous and asynchronous messaging, and real-time streaming. It supports stateful and stateless agent interactions, allowing for the discovery of agents and execution of long-running tasks. For developers and tech enthusiasts interested in agent communication protocols and weather data integration, ACP offers a robust framework for innovation.

Setting Up the ACP Server

To begin, you’ll need to set up the ACP server. This involves creating a file named agent.py and importing necessary libraries. The OpenAI ChatGPT integration and other libraries such as httpx will be instrumental in fetching weather data for London. Here’s a step-by-step guide:

1. Install the required libraries using pip:

   pip install acp acp-sdk beeai-framework httpx

2. Set up the ACP server by importing the necessary modules:

   import asyncio
   from collections.abc import AsyncGenerator
   import httpx
   from acp_sdk.models import Message, MessagePart
   from acp_sdk.server import Context, RunYield, RunYieldResume, Server
   
   server = Server()

3. Create an asynchronous helper function to fetch London’s weather using the Open-Meteo API:

   async def get_london_weather() -> str:
       """Fetch current London weather from the free Open‑Meteo API."""
       params = {
           "latitude": 51.5072,  # London coordinates
           "longitude": -0.1276,
           "current_weather": True,
           "timezone": "Europe/London"
       }
       url = "https://api.open-meteo.com/v1/forecast"
       async with httpx.AsyncClient(timeout=10) as client:
           resp = await client.get(url, params=params)
           resp.raise_for_status()
           cw = resp.json()["current_weather"]
           return (f"Weather in London: {cw['temperature']} °C, "
                   f"wind {cw['windspeed']} km/h, code {cw['weathercode']}.")

Running the ACP Server

Once the server is set up, you can run the agent.py file to start the server. This will make the ACP agent available to handle requests at http://localhost:8000. To verify the server’s functionality, execute the following curl command in a new terminal:

curl http://localhost:8000/agents

If everything is working correctly, you’ll receive a JSON response listing your agent, confirming its availability and readiness to handle requests.

Creating the ACP Client

With the server up and running, it’s time to create an ACP client to interact with it. This client, set up in a file named client.py, uses the ACP SDK to connect to the locally running london_weather_agent via the ACP server. Here’s how you can set it up:

1. Import necessary modules:

   import asyncio
   from acp_sdk.client import Client
   from acp_sdk.models import Message, MessagePart

2. Create an asynchronous function to call the weather agent:

   async def call_london_weather_agent() -> None:
       async with Client(base_url="http://localhost:8000") as client:
           run = await client.run_sync(
               agent="london_weather_agent",
               input=[Message(parts=[MessagePart(content="Tell me the weather", content_type="text/plain")])],
           )
           print("Response from london_weather_agent:")
           for message in run.output:
               for part in message.parts:
                   print("-", part.content)

Benefits and Potential Applications of ACP

The implementation of ACP offers numerous benefits, particularly in enhancing the interoperability of AI systems. By facilitating seamless communication, ACP can be leveraged in various sectors, from AI revolution in marketing with UBOS to AI in stock market trading. Its ability to support both synchronous and asynchronous messaging makes it ideal for real-time applications, such as weather forecasting, where timely data is crucial.

Conclusion and Future Prospects

As we continue to explore the capabilities of ACP, its potential applications in technology and data communication become increasingly apparent. From enhancing AI-driven weather forecasting to revolutionizing marketing strategies, the possibilities are endless. For developers and tech enthusiasts, mastering ACP opens doors to innovative solutions and advancements in AI communication protocols.

For more insights into AI and its transformative impact on various industries, explore the UBOS platform overview and discover how UBOS is at the forefront of AI innovation.

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.