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"""
Airbnb Agent
============
Demonstrates airbnb agent.
"""
from textwrap import dedent
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.os import AgentOS
from agno.tools.mcp import MCPTools
# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------
airbnb_agent = Agent(
id="airbnb-search-agent",
name="Airbnb Search Agent",
description="A specialized agent for finding and detailing Airbnb listings using the OpenBNB MCP server.",
model=OpenAIChat(id="gpt-4o"),
tools=[MCPTools("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")],
instructions=dedent("""
You are an expert travel assistant.
Use the 'airbnb_search' tool to find properties based on location, dates, and people.
For detailed listing information, use 'airbnb_listing_details'.
Always provide location, price, and a link in your final response.
"""),
markdown=False,
)
agent_os = AgentOS(
id="airbnb-agent-os",
description="An AgentOS serving specialized Agent for Airbnb search",
agents=[
airbnb_agent,
],
a2a_interface=True,
)
app = agent_os.get_app()
# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
"""Run your AgentOS.
You can run the Agent via A2A protocol:
POST http://localhost:7774/agents/{id}/v1/message:send
For streaming responses:
POST http://localhost:7774/agents/{id}/v1/message:stream
Retrieve the agent card at:
GET http://localhost:7774/agents/{id}/.well-known/agent-card.json
"""
agent_os.serve(app="airbnb_agent:app", port=7774, reload=True)
Run the Example
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# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/05_agent_os/interfaces/a2a/multi_agent_a2a
# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate
python airbnb_agent.py