chore: import upstream snapshot with attribution

python/samples/04-hosting/a2a/a2a_server.py

@@ -0,0 +1,124 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import argparse
+import os
+import sys
+
+import uvicorn
+from a2a.server.request_handlers import DefaultRequestHandler
+from a2a.server.routes import create_agent_card_routes, create_jsonrpc_routes
+from a2a.server.tasks import InMemoryTaskStore
+from agent_definitions import AGENT_CARD_FACTORIES, AGENT_FACTORIES  # pyrefly: ignore[missing-import]
+from agent_framework.a2a import A2AExecutor
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from starlette.applications import Starlette
+
+# Load environment variables from .env file
+load_dotenv()
+
+"""
+A2A Server Sample — Host an Agent Framework agent as an A2A endpoint
+
+This sample creates a Python-based A2A-compliant server that wraps an Agent
+Framework agent.  The server uses the a2a-sdk's Starlette application to handle
+JSON-RPC requests and serves the AgentCard at /.well-known/agent.json.
+
+Three agent types are available:
+  - invoice   — Answers invoice queries using mock data and function tools.
+  - policy    — Returns a fixed policy response.
+  - logistics — Returns a fixed logistics response.
+
+Usage:
+  uv run python a2a_server.py --agent-type policy --port 5001
+  uv run python a2a_server.py --agent-type invoice --port 5000
+  uv run python a2a_server.py --agent-type logistics --port 5002
+
+Environment variables:
+  FOUNDRY_PROJECT_ENDPOINT              — Your Azure AI Foundry project endpoint
+  FOUNDRY_MODEL — Model deployment name (e.g. gpt-4o)
+"""
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="A2A Agent Server")
+    parser.add_argument(
+        "--agent-type",
+        choices=["invoice", "policy", "logistics"],
+        default="policy",
+        help="Type of agent to host (default: policy)",
+    )
+    parser.add_argument(
+        "--host",
+        default="localhost",
+        help="Host to bind to (default: localhost)",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=5001,
+        help="Port to listen on (default: 5001)",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    # Validate environment
+    project_endpoint = os.getenv("FOUNDRY_PROJECT_ENDPOINT")
+    model = os.getenv("FOUNDRY_MODEL")
+
+    if not project_endpoint:
+        print("Error: FOUNDRY_PROJECT_ENDPOINT environment variable is not set.")
+        sys.exit(1)
+    if not model:
+        print("Error: FOUNDRY_MODEL environment variable is not set.")
+        sys.exit(1)
+
+    # Create the LLM client
+    credential = AzureCliCredential()
+    client = FoundryChatClient(
+        project_endpoint=project_endpoint,
+        model=model,
+        credential=credential,
+    )
+
+    # Create the Agent Framework agent for the chosen type
+    agent_factory = AGENT_FACTORIES[args.agent_type]
+    agent = agent_factory(client)
+
+    # Build the A2A server components
+    url = f"http://{args.host}:{args.port}/"
+    agent_card = AGENT_CARD_FACTORIES[args.agent_type](url)
+    executor = A2AExecutor(agent, stream=True)
+    task_store = InMemoryTaskStore()
+    request_handler = DefaultRequestHandler(
+        agent_executor=executor,
+        task_store=task_store,
+        agent_card=agent_card,
+    )
+
+    app = Starlette(
+        routes=[
+            *create_agent_card_routes(agent_card),
+            *create_jsonrpc_routes(request_handler, "/"),
+        ]
+    )
+
+    print(f"Starting A2A server: {agent_card.name}")
+    print(f"  Agent type : {args.agent_type}")
+    print(f"  Listening  : {url}")
+    print(f"  Agent card : {url}.well-known/agent.json")
+    print()
+
+    uvicorn.run(
+        app,
+        host=args.host,
+        port=args.port,
+    )
+
+
+if __name__ == "__main__":
+    main()