If beneath a rock and you’re employed with AI, you’ve in all probability heard about Agent2Agent (A2A) Protocol, “an open customary designed to allow communication and collaboration between AI Agents”. It’s nonetheless fairly new, however it’s already getting a variety of buzz. Because it performs so properly with MCP (which appears to be like prefer it’s changing into the business’s customary), A2A is shaping as much as be the go-to customary for multi-agent communication within the business.
When Google first dropped the Protocol Specification, my first response was mainly: “Okay, cool… however what am I imagined to do with this?” Fortunately, this week they launched the official Python SDK for the protocol, so now it lastly speaks a language I perceive.
On this article we’re going to dive into how the protocol really units up communication between brokers and shoppers. Spoiler: it’s all in a task-oriented method. To make issues much less summary, let’s construct slightly toy instance collectively.
Communication between the Occasion Detector Agent and an A2A Consumer
In our methods we have now an Occasion Detector AI Agent (chargeable for detecting occasions) and an Alert AI Agent (chargeable for alerting the consumer of the occasions). Since I’m specializing in the A2A protocol right here, each brokers are mocked as easy Python strategies that return strings. However in actual life you possibly can construct your brokers with any framework you want (LangGraph, Google ADK, CrewAI and so forth).
We have now three characters in our system, the consumer, the Occasion Agent and the Alert Agent. All of them talk utilizing Messages. A Message represents a single flip of communication within the A2A protocol. We wrap the brokers into A2A Servers. The servers expose an HTTP endpoint implementing the protocol. Every A2A Server has Occasion queues that act as a buffer between the agent’s asynchronous execution and the server’s response dealing with.
The A2A Consumer initiates communication, and if two brokers want to speak an A2A Server can even play the position of a A2A Consumer. The diagram beneath exhibits how a Consumer and a Server talk throughout the protocol.
The EventQueue shops Messages, Duties, TaskStatusUpdateEvent, TaskArtifactUpdateEvent, A2AError and JSONRPCError objects. The Process may be an important object to grasp how one can construct multi-agent methods with A2A. In line with the A2A documentation:
- When a shopper sends a message to an agent, the agent may decide that fulfilling the request requires a stateful job to be accomplished (e.g., “generate a report,” “ebook a flight,” “reply a query”).
- Every job has a singular ID outlined by the agent and progresses via an outlined lifecycle (e.g.,
submitted,working,input-required,accomplished,failed). - Duties are stateful and may contain a number of exchanges (messages) between the shopper and the server.
Consider a Process as one thing in your multi-agent system that has a clear and distinctive aim. We have now two duties in our system:
- Detect an occasion
- Alert the consumer
Every Agent does its personal factor (job). Let’s construct the A2A Server for the Occasion Agent so issues turn out to be extra tangible.
Constructing the A2A Server for the Occasion Agent
First up: the Agent Card. The Agent Card is JSON doc used to get to know different brokers out there. :
- Server’s id
- Capabilities
- Expertise
- Service endpoint
- URL
- How shoppers ought to authenticate and work together with the agent
Let’s first outline the Agent Card for the Occasion Detector AI Agent (I’ve outlined the abilities primarily based on this example from Google):
agent_card = AgentCard(
title='Occasion Detection Agent',
description='Detects related occasions and alerts the consumer',
url='http://localhost:10008/',
model='1.0.0',
defaultInputModes=['text'],
defaultOutputModes=['text'],
capabilities=AgentCapabilities(streaming=False),
authentication={ "schemes": ["basic"] },
expertise=[
AgentSkill(
id='detect_events',
name='Detect Events',
description='Detects events and alert the user',
tags=['event'],
),
],
)You possibly can be taught extra concerning the Agent Card object construction right here: https://google.github.io/A2A/specification/#55-agentcard-object-structure
The agent itself will really be a Uvicorn server, so let’s construct the important() technique to get it up and working. All of the request shall be dealt with by the DefaultRequestHandler of the a2a-python SDK. The handler wants a TaskStore to retailer the Duties and an AgentExecutor which has the implementation of the core logic of the agent (we’ll construct the EventAgentExecutor in a minute).
The final part of the important() technique is the A2AStarletteApplication, which is the Starlette utility that implements the A2A protocol server endpoints. We have to present the Agent Card and the DefaultRequestHandler to initialize it. Now the final step is to run the app utilizing uvicorn. Right here is the total code of the important() technique:
import click on
import uvicorn
from a2a.sorts import (
AgentCard, AgentCapabilities, AgentSkill
)
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.duties import InMemoryTaskStore
from a2a.server.apps import A2AStarletteApplication
@click on.command()
@click on.possibility('--host', default='localhost')
@click on.possibility('--port', default=10008)
def important(host: str, port: int):
agent_executor = EventAgentExecutor()
agent_card = AgentCard(
title='Occasion Detection Agent',
description='Detects related occasions and alerts the consumer',
url='http://localhost:10008/',
model='1.0.0',
defaultInputModes=['text'],
defaultOutputModes=['text'],
capabilities=AgentCapabilities(streaming=False),
authentication={ "schemes": ["basic"] },
expertise=[ AgentSkill( id='detect_events', name='Detect Events', description='Detects events and alert the user', tags=['event'],
),
],
)
request_handler = DefaultRequestHandler(
agent_executor=agent_executor,
task_store=InMemoryTaskStore()
)
a2a_app = A2AStarletteApplication(
agent_card=agent_card,
http_handler=request_handler
)
uvicorn.run(a2a_app.construct(), host=host, port=port)Creating the EventAgentExecutor
Now it’s time to construct the core of our agent and at last see how one can use the Duties to make the brokers work together with one another. The EventAgentExecutor class inherits from AgentExecutor interface and thus we have to implement the execute() and the cancel() strategies. Each take a RequestContext and an EventQueue object as parameters. The RequestContext holds details about the present request being processed by the server and the EventQueue acts as a buffer between the agent’s asynchronous execution and the server’s response dealing with.
Our agent will simply test if the string “occasion” is within the message the consumer have despatched (KISS ✨). If the “occasion” is there then we must always name the Alert Agent. We’ll try this by sending a Message to this different Alert agent. That is the Direct Configuration technique, that means we’ll configure the agent with a URL to fetch the Agent Card of the Alert Agent. To try this our Occasion Agent will act like a A2A Consumer.
Let’s construct the Executor step-by-step. First let’s create the primary Process (the duty to detect the occasions). We have to instantiate a TaskUpdater object (a helper class for brokers to publish updates to a job’s occasion queue), then submit the duty and announce we’re engaged on it with the start_work() technique:
from a2a.server.agent_execution import AgentExecutor
class EventAgentExecutor(AgentExecutor):
async def execute(self, context: RequestContext, event_queue: EventQueue):
task_updater = TaskUpdater(event_queue, context.task_id, context.context_id)
task_updater.submit()
task_updater.start_work()The message the consumer will ship to the agent will seem like this:
send_message_payload = {
'message': {
'position': 'consumer',
'elements': [{'type': 'text', 'text': f'it has an event!'}],
'messageId': uuid4().hex,
}
}A Half represents a definite piece of content material inside a Message, representing exportable content material as both TextPart, FilePart, or DataPart. We’ll use a TextPart so we have to unwrap it within the executor:
from a2a.server.agent_execution import AgentExecutor
class EventAgentExecutor(AgentExecutor):
async def execute(self, context: RequestContext, event_queue: EventQueue):
task_updater = TaskUpdater(event_queue, context.task_id, context.context_id)
task_updater.submit()
task_updater.start_work()
await asyncio.sleep(1) #let's fake we're really doing one thing
user_message = context.message.elements[0].root.textual content # unwraping the TextPartTime to create the tremendous superior logic of our agent. If the message doesn’t have the string “occasion” we don’t must name the Alert Agent and the duty is completed:
from a2a.server.agent_execution import AgentExecutor
class EventAgentExecutor(AgentExecutor):
async def execute(self, context: RequestContext, event_queue: EventQueue):
task_updater = TaskUpdater(event_queue, context.task_id, context.context_id)
task_updater.submit()
task_updater.start_work()
await asyncio.sleep(1) #let's fake we're really doing one thing
user_message = context.message.elements[0].root.textual content # unwraping the TextPart
if "occasion" not in user_message:
task_updater.update_status(
TaskState.accomplished,
message=task_updater.new_agent_message(elements=[TextPart(text=f"No event detected")]),
)Creating an A2A Consumer for the Consumer
Let’s create an A2A Consumer so we will check the agent as it’s. The shopper makes use of the get_client_from_agent_card_url() technique from A2AClient class to (guess what) get the agent card. Then we wrap the message in a SendMessageRequest object and ship it to the agent utilizing the send_message() technique of the shopper. Right here is the total code:
import httpx
import asyncio
from a2a.shopper import A2AClient
from a2a.sorts import SendMessageRequest, MessageSendParams
from uuid import uuid4
from pprint import pprint
async def important():
send_message_payload = {
'message': {
'position': 'consumer',
'elements': [{'type': 'text', 'text': f'nothing happening here'}],
'messageId': uuid4().hex,
}
}
async with httpx.AsyncClient() as httpx_client:
shopper = await A2AClient.get_client_from_agent_card_url(
httpx_client, 'http://localhost:10008'
)
request = SendMessageRequest(
params=MessageSendParams(**send_message_payload)
)
response = await shopper.send_message(request)
pprint(response.model_dump(mode='json', exclude_none=True))
if __name__ == "__main__":
asyncio.run(important())That is what occurs within the terminal that’s working the EventAgent server:
And that is the message the shopper sees:
The duty to detect the occasion was created and no occasion was detected, good! However the entire level of A2A is to make Brokers talk with one another, so let’s make the Occasion Agent discuss to the Alert Agent.
Making the Occasion Agent discuss to the Alert Agent
To make the Occasion Agent discuss to the Alert Agent the Occasion Agent will act as a shopper as effectively:
from a2a.server.agent_execution import AgentExecutor
ALERT_AGENT_URL = "http://localhost:10009/"
class EventAgentExecutor(AgentExecutor):
async def execute(self, context: RequestContext, event_queue: EventQueue):
task_updater = TaskUpdater(event_queue, context.task_id, context.context_id)
task_updater.submit()
task_updater.start_work()
await asyncio.sleep(1) #let's fake we're really doing one thing
user_message = context.message.elements[0].root.textual content # unwraping the TextPart
if "occasion" not in user_message:
task_updater.update_status(
TaskState.accomplished,
message=task_updater.new_agent_message(elements=[TextPart(text=f"No event detected")]),
)
else:
alert_message = task_updater.new_agent_message(elements=[TextPart(text="Event detected!")])
send_alert_payload = SendMessageRequest(
params=MessageSendParams(
message=alert_message
)
)
async with httpx.AsyncClient() as shopper:
alert_agent = A2AClient(httpx_client=shopper, url=ALERT_AGENT_URL)
response = await alert_agent.send_message(send_alert_payload)
if hasattr(response.root, "consequence"):
alert_task = response.root.consequence
# Polling till the duty is completed
whereas alert_task.standing.state not in (
TaskState.accomplished, TaskState.failed, TaskState.canceled, TaskState.rejected
):
await asyncio.sleep(0.5)
get_resp = await alert_agent.get_task(
GetTaskRequest(params=TaskQueryParams(id=alert_task.id))
)
if isinstance(get_resp.root, GetTaskSuccessResponse):
alert_task = get_resp.root.consequence
else:
break
# Full the unique job
if alert_task.standing.state == TaskState.accomplished:
task_updater.update_status(
TaskState.accomplished,
message=task_updater.new_agent_message(elements=[TextPart(text="Event detected and alert sent!")]),
)
else:
task_updater.update_status(
TaskState.failed,
message=task_updater.new_agent_message(elements=[TextPart(text=f"Failed to send alert: {alert_task.status.state}")]),
)
else:
task_updater.update_status(
TaskState.failed,
message=task_updater.new_agent_message(elements=[TextPart(text=f"Failed to create alert task")]),
)We name the Alert Agent simply as we referred to as the Occasion Agent because the consumer, and when the Alert Agent job is completed, we full the unique Occasion Agent job. Let’s name the Occasion Agent once more however this time with an occasion:
The sweetness right here is that we merely referred to as the Alert Agent and we don’t must know something about the way it alerts the consumer. We simply ship a message to it and look ahead to it to complete.
The Alert Agent is tremendous just like the Occasion Agent. You possibly can test the entire code right here: https://github.com/dmesquita/multi-agent-communication-a2a-python
Last Ideas
Understanding how one can construct multi-agent methods with A2A may be daunting at first, however ultimately you simply ship messages to let the brokers do their factor. All it’s good to do to combine you brokers with A2A is to create a category with the agent’s logic that inherit from the AgentExecutor and run the agent as a server.
I hope this text have helped you in your A2A journey, thanks for studying!
References
[1] Padgham, Lin, and Michael Winikoff. Growing clever agent methods: A sensible information. John Wiley & Sons, 2005.
[2] https://github.com/google/a2a-python
[3] https://github.com/google/a2a-python/tree/main/examples/google_adk
[4] https://developers.googleblog.com/en/agents-adk-agent-engine-a2a-enhancements-google-io/
