A2A

A2A is the protocol-facing surface for one AgentOS entity to discover and call another. This lesson covers both sides: serving Agents and Teams under the /a2a namespace, and consuming those endpoints with Agno's first-party A2AClient. It finishes with a three-server topology in which one Agent calls two specialist Agents over A2A.

Files

File	What it teaches
`basic.py`	Expose one persistent Agent with `a2a_interface=True`.
`client.py`	Send, stream, preserve multi-turn context, and handle an unavailable server with `A2AClient`.
`agent_card.py`	Read stable Agent card identity and endpoint fields with the sync and async client APIs.
`team.py`	Expose, discover, and call a Team under `/a2a/teams`.
`multi_agent/weather_agent.py`	Serve the OpenWeather-backed specialist on port 7782.
`multi_agent/airbnb_agent.py`	Serve the OpenBNB MCP-backed specialist on port 7783.
`multi_agent/trip_planning_a2a_client.py`	Use async A2A client tools to orchestrate both specialists, then expose the planner on port 7779.

Prerequisites

Install the demo environment and export an OpenAI key:

bash

./scripts/demo_setup.sh
export OPENAI_API_KEY=...

The demo environment includes the agno[a2a] extra. The multi-agent example has two additional requirements:

weather_agent.py needs OPENWEATHER_API_KEY.
airbnb_agent.py needs Node.js, npx, and internet access so it can run @openbnb/mcp-server-airbnb.

client.py and agent_card.py do not call a model directly, but both require basic.py to be running.

Serve and call an Agent

Start the server:

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/basic.py

Then use the first-party client and card reader:

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/client.py
.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/agent_card.py

a2a_interface=True exposes all Agents, Teams, and Workflows registered with that AgentOS. Use an explicit interface such as interfaces=[A2A(agents=[public_agent])] when only selected entities should be served or the interface needs custom tags.

The modern Agent routes are:

Operation	Route
Discover	`GET /a2a/agents/{id}/.well-known/agent-card.json`
Send	`POST /a2a/agents/{id}/v1/message:send`
Stream	`POST /a2a/agents/{id}/v1/message:stream`
Get task	`POST /a2a/agents/{id}/v1/tasks:get`
Cancel task	`POST /a2a/agents/{id}/v1/tasks:cancel`

For the REST-style first-party client, pass the entity root as base_url, for example:

python

client = A2AClient(
    "http://127.0.0.1:7779/a2a/agents/a2a-assistant"
)

send_message() and stream_message() are asynchronous. A completed TaskResult already exposes the response as .content; no manual JSON-RPC unwrapping is needed. Pass a returned .context_id to the next call to keep the same AgentOS session. Card discovery has both a synchronous get_agent_card() method and an asynchronous aget_agent_card() method.

Serve and call a Team

Stop basic.py, because standalone A2A examples share port 7779, then start the Team:

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/team.py

In another terminal:

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/team.py --demo

Teams use the same protocol shape under their own namespace:

GET /a2a/teams/{id}/.well-known/agent-card.json
POST /a2a/teams/{id}/v1/message:send
POST /a2a/teams/{id}/v1/message:stream
POST /a2a/teams/{id}/v1/tasks:get
POST /a2a/teams/{id}/v1/tasks:cancel

Run the multi-agent topology

Start the services in this order, one terminal per command:

bash

export OPENWEATHER_API_KEY=...
.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/multi_agent/weather_agent.py

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/multi_agent/airbnb_agent.py

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/multi_agent/trip_planning_a2a_client.py

The topology is:

Service	Port	Entity root
Trip planner	7779	`/a2a/agents/trip-planner`
Weather specialist	7782	`/a2a/agents/weather-agent`
Airbnb specialist	7783	`/a2a/agents/airbnb-agent`

With all three servers running, call the planner from a fourth terminal:

bash

.venvs/demo/bin/python cookbook/05_agent_os/15_a2a/multi_agent/trip_planning_a2a_client.py --demo

The planner's two async tools use A2AClient, check the downstream task status, and consume TaskResult.content. The weather and Airbnb Agents remain independently discoverable and callable.

Choosing an A2A entry point

Use a2a_interface=True or A2A(...) in this lesson to serve a local AgentOS entity over A2A.
Use A2AClient in this lesson for direct protocol calls, task metadata, streaming events, and explicit context threading.
Use RemoteAgent(protocol="a2a") in 20_remote when a remote A2A entity should behave like a composable Agent inside another Agent or Team.