mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Giles Odigwe
68357b0250
- Fix non-streaming empty response by accumulating intermediate WORKING status updates and flushing them when an empty terminal event arrives - Fix sample agent_executor.py to enqueue Task before status events (required by v1.0 ActiveTask validation) - Fix create_jsonrpc_routes() calls to include required rpc_url param - Fix TYPE_CHECKING imports in sample agent_definitions.py - Add tests for non-streaming content accumulation behavior Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import argparse
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import os
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import sys
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import uvicorn
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from a2a.server.request_handlers import DefaultRequestHandler
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from a2a.server.routes import create_agent_card_routes, create_jsonrpc_routes
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from a2a.server.tasks import InMemoryTaskStore
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from agent_definitions import AGENT_CARD_FACTORIES, AGENT_FACTORIES
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from agent_executor import AgentFrameworkExecutor
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from starlette.applications import Starlette
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# Load environment variables from .env file
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load_dotenv()
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"""
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A2A Server Sample — Host an Agent Framework agent as an A2A endpoint
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This sample creates a Python-based A2A-compliant server that wraps an Agent
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Framework agent. The server uses the a2a-sdk's Starlette application to handle
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JSON-RPC requests and serves the AgentCard at /.well-known/agent.json.
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Three agent types are available:
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- invoice — Answers invoice queries using mock data and function tools.
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- policy — Returns a fixed policy response.
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- logistics — Returns a fixed logistics response.
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Usage:
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uv run python a2a_server.py --agent-type policy --port 5001
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uv run python a2a_server.py --agent-type invoice --port 5000
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uv run python a2a_server.py --agent-type logistics --port 5002
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Environment variables:
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FOUNDRY_PROJECT_ENDPOINT — Your Azure AI Foundry project endpoint
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FOUNDRY_MODEL — Model deployment name (e.g. gpt-4o)
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"""
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="A2A Agent Server")
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parser.add_argument(
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"--agent-type",
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choices=["invoice", "policy", "logistics"],
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default="policy",
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help="Type of agent to host (default: policy)",
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)
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parser.add_argument(
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"--host",
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default="localhost",
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help="Host to bind to (default: localhost)",
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)
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parser.add_argument(
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"--port",
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type=int,
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default=5001,
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help="Port to listen on (default: 5001)",
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)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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# Validate environment
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project_endpoint = os.getenv("FOUNDRY_PROJECT_ENDPOINT")
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model = os.getenv("FOUNDRY_MODEL")
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if not project_endpoint:
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print("Error: FOUNDRY_PROJECT_ENDPOINT environment variable is not set.")
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sys.exit(1)
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if not model:
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print("Error: FOUNDRY_MODEL environment variable is not set.")
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sys.exit(1)
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# Create the LLM client
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credential = AzureCliCredential()
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client = FoundryChatClient(
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project_endpoint=project_endpoint,
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model=model,
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credential=credential,
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)
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# Create the Agent Framework agent for the chosen type
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agent_factory = AGENT_FACTORIES[args.agent_type]
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agent = agent_factory(client)
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# Build the A2A server components
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url = f"http://{args.host}:{args.port}/"
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agent_card = AGENT_CARD_FACTORIES[args.agent_type](url)
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executor = AgentFrameworkExecutor(agent)
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task_store = InMemoryTaskStore()
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request_handler = DefaultRequestHandler(
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agent_executor=executor,
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task_store=task_store,
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agent_card=agent_card,
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)
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app = Starlette(
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routes=[
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*create_agent_card_routes(agent_card),
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*create_jsonrpc_routes(request_handler, "/"),
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]
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)
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print(f"Starting A2A server: {agent_card.name}")
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print(f" Agent type : {args.agent_type}")
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print(f" Listening : {url}")
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print(f" Agent card : {url}.well-known/agent.json")
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print()
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uvicorn.run(
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app,
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host=args.host,
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port=args.port,
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)
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if __name__ == "__main__":
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main()
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