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Renamed async_credential to credential (#2648)

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Dmytro Struk
2025-12-07 17:21:18 -08:00
committed by GitHub
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parent eb06faea2d
commit cfcb71334a
64 changed files with 266 additions and 269 deletions
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python/samples/demos/workflow_evaluation/create_workflow.py
+126 -138
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@@ -21,7 +21,7 @@ WORKFLOW STRUCTURE (7 agents):
Agents:
1. Travel Agent - Main coordinator (no tools to avoid thread conflicts)
2. Hotel Search - Searches hotels with tools
3. Flight Search - Searches flights with tools
3. Flight Search - Searches flights with tools
4. Activity Search - Searches activities with tools
5. Booking Information Aggregation - Aggregates hotel & flight booking info
6. Booking Confirmation - Confirms bookings with tools
@@ -32,40 +32,37 @@ import asyncio
import os
from collections import defaultdict
from dotenv import load_dotenv
from agent_framework import (
AgentExecutorResponse,
AgentRunUpdateEvent,
AgentRunResponseUpdate,
ChatMessage,
Executor,
executor,
handler,
Role,
WorkflowContext,
WorkflowBuilder,
WorkflowOutputEvent,
)
from typing_extensions import Never
from agent_framework.azure import AzureAIClient
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from _tools import (
check_flight_availability,
check_hotel_availability,
confirm_booking,
get_flight_details,
get_hotel_details,
process_payment,
search_activities,
search_flights,
# Travel planning tools
search_hotels,
get_hotel_details,
search_flights,
get_flight_details,
search_activities,
confirm_booking,
check_hotel_availability,
check_flight_availability,
process_payment,
validate_payment_method,
)
from agent_framework import (
AgentExecutorResponse,
AgentRunResponseUpdate,
AgentRunUpdateEvent,
ChatMessage,
Executor,
Role,
WorkflowBuilder,
WorkflowContext,
WorkflowOutputEvent,
executor,
handler,
)
from agent_framework.azure import AzureAIClient
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import DefaultAzureCredential
from dotenv import load_dotenv
from typing_extensions import Never
load_dotenv()
@@ -78,7 +75,7 @@ async def start_executor(input: str, ctx: WorkflowContext[list[ChatMessage]]) ->
class ResearchLead(Executor):
"""Aggregates and summarizes travel planning findings from all specialized agents."""
def __init__(self, chat_client: AzureAIClient, id: str = "travel-planning-coordinator"):
# store=True to preserve conversation history for evaluation
self.agent = chat_client.create_agent(
@@ -92,58 +89,69 @@ class ResearchLead(Executor):
"Clearly indicate which information came from which agent. Do not use tools."
),
name="travel-planning-coordinator",
store=True
store=True,
)
super().__init__(id=id)
@handler
async def fan_in_handle(self, responses: list[AgentExecutorResponse], ctx: WorkflowContext[Never, str]) -> None:
user_query = responses[0].full_conversation[0].text
# Extract findings from all agent responses
agent_findings = self._extract_agent_findings(responses)
summary_text = "\n".join(agent_findings) if agent_findings else "No specific findings were provided by the agents."
summary_text = (
"\n".join(agent_findings) if agent_findings else "No specific findings were provided by the agents."
)
# Generate comprehensive travel plan summary
messages = [
ChatMessage(role=Role.SYSTEM, text="You are a travel planning coordinator. Summarize findings from multiple specialized travel agents and provide a clear, comprehensive travel plan based on the user's query."),
ChatMessage(role=Role.USER, text=f"Original query: {user_query}\n\nFindings from specialized travel agents:\n{summary_text}\n\nPlease provide a comprehensive travel plan based on these findings.")
ChatMessage(
role=Role.SYSTEM,
text="You are a travel planning coordinator. Summarize findings from multiple specialized travel agents and provide a clear, comprehensive travel plan based on the user's query.",
),
ChatMessage(
role=Role.USER,
text=f"Original query: {user_query}\n\nFindings from specialized travel agents:\n{summary_text}\n\nPlease provide a comprehensive travel plan based on these findings.",
),
]
try:
final_response = await self.agent.run(messages)
output_text = (final_response.messages[-1].text if final_response.messages and final_response.messages[-1].text
else f"Based on the available findings, here's your travel plan for '{user_query}': {summary_text}")
output_text = (
final_response.messages[-1].text
if final_response.messages and final_response.messages[-1].text
else f"Based on the available findings, here's your travel plan for '{user_query}': {summary_text}"
)
except Exception:
output_text = f"Based on the available findings, here's your travel plan for '{user_query}': {summary_text}"
await ctx.yield_output(output_text)
def _extract_agent_findings(self, responses: list[AgentExecutorResponse]) -> list[str]:
"""Extract findings from agent responses."""
agent_findings = []
for response in responses:
findings = []
if response.agent_run_response and response.agent_run_response.messages:
for msg in response.agent_run_response.messages:
if msg.role == Role.ASSISTANT and msg.text and msg.text.strip():
findings.append(msg.text.strip())
if findings:
combined_findings = " ".join(findings)
agent_findings.append(f"[{response.executor_id}]: {combined_findings}")
return agent_findings
async def run_workflow_with_response_tracking(query: str, chat_client: AzureAIClient | None = None) -> dict:
"""Run multi-agent workflow and track conversation IDs, response IDs, and interaction sequence.
Args:
query: The user query to process through the multi-agent workflow
chat_client: Optional AzureAIClient instance
Returns:
Dictionary containing interaction sequence, conversation/response IDs, and conversation analysis
"""
@@ -155,11 +163,11 @@ async def run_workflow_with_response_tracking(query: str, chat_client: AzureAICl
credential=credential,
api_version="2025-11-15-preview",
)
async with (
DefaultAzureCredential() as credential,
project_client,
AzureAIClient(project_client=project_client, async_credential=credential) as client
AzureAIClient(project_client=project_client, credential=credential) as client,
):
return await _run_workflow_with_client(query, client)
except Exception as e:
@@ -171,53 +179,46 @@ async def run_workflow_with_response_tracking(query: str, chat_client: AzureAICl
async def _run_workflow_with_client(query: str, chat_client: AzureAIClient) -> dict:
"""Execute workflow with given client and track all interactions."""
# Initialize tracking variables - use lists to track multiple responses per agent
conversation_ids = defaultdict(list)
response_ids = defaultdict(list)
workflow_output = None
# Create workflow components and keep agent references
# Pass project_client and credential to create separate client instances per agent
workflow, agent_map = await _create_workflow(
chat_client.project_client,
chat_client.credential
)
workflow, agent_map = await _create_workflow(chat_client.project_client, chat_client.credential)
# Process workflow events
events = workflow.run_stream(query)
workflow_output = await _process_workflow_events(events, conversation_ids, response_ids)
return {
"conversation_ids": dict(conversation_ids),
"response_ids": dict(response_ids),
"output": workflow_output,
"query": query
"query": query,
}
async def _create_workflow(project_client, credential):
"""Create the multi-agent travel planning workflow with specialized agents.
IMPORTANT: Each agent needs its own client instance because the V2 client stores
agent_name and agent_version as instance variables, causing all agents to share
the same agent identity if they share a client.
"""
# Create separate client for Final Coordinator
final_coordinator_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="final-coordinator"
project_client=project_client, credential=credential, agent_name="final-coordinator"
)
final_coordinator = ResearchLead(chat_client=final_coordinator_client, id="final-coordinator")
# Agent 1: Travel Request Handler (initial coordinator)
# Create separate client with unique agent_name
travel_request_handler_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="travel-request-handler"
project_client=project_client, credential=credential, agent_name="travel-request-handler"
)
travel_request_handler = travel_request_handler_client.create_agent(
id="travel-request-handler",
@@ -225,14 +226,12 @@ async def _create_workflow(project_client, credential):
"You receive user travel queries and relay them to specialized agents. Extract key information: destination, dates, budget, and preferences. Pass this information forward clearly to the next agents."
),
name="travel-request-handler",
store=True
store=True,
)
# Agent 2: Hotel Search Executor
hotel_search_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="hotel-search-agent"
project_client=project_client, credential=credential, agent_name="hotel-search-agent"
)
hotel_search_agent = hotel_search_client.create_agent(
id="hotel-search-agent",
@@ -241,14 +240,12 @@ async def _create_workflow(project_client, credential):
),
name="hotel-search-agent",
tools=[search_hotels, get_hotel_details, check_hotel_availability],
store=True
store=True,
)
# Agent 3: Flight Search Executor
flight_search_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="flight-search-agent"
project_client=project_client, credential=credential, agent_name="flight-search-agent"
)
flight_search_agent = flight_search_client.create_agent(
id="flight-search-agent",
@@ -257,14 +254,12 @@ async def _create_workflow(project_client, credential):
),
name="flight-search-agent",
tools=[search_flights, get_flight_details, check_flight_availability],
store=True
store=True,
)
# Agent 4: Activity Search Executor
activity_search_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="activity-search-agent"
project_client=project_client, credential=credential, agent_name="activity-search-agent"
)
activity_search_agent = activity_search_client.create_agent(
id="activity-search-agent",
@@ -273,14 +268,12 @@ async def _create_workflow(project_client, credential):
),
name="activity-search-agent",
tools=[search_activities],
store=True
store=True,
)
# Agent 5: Booking Confirmation Executor
booking_confirmation_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="booking-confirmation-agent"
project_client=project_client, credential=credential, agent_name="booking-confirmation-agent"
)
booking_confirmation_agent = booking_confirmation_client.create_agent(
id="booking-confirmation-agent",
@@ -289,14 +282,12 @@ async def _create_workflow(project_client, credential):
),
name="booking-confirmation-agent",
tools=[confirm_booking, check_hotel_availability, check_flight_availability],
store=True
store=True,
)
# Agent 6: Booking Payment Executor
booking_payment_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="booking-payment-agent"
project_client=project_client, credential=credential, agent_name="booking-payment-agent"
)
booking_payment_agent = booking_payment_client.create_agent(
id="booking-payment-agent",
@@ -305,14 +296,12 @@ async def _create_workflow(project_client, credential):
),
name="booking-payment-agent",
tools=[process_payment, validate_payment_method],
store=True
store=True,
)
# Agent 7: Booking Information Aggregation Executor
booking_info_client = AzureAIClient(
project_client=project_client,
async_credential=credential,
agent_name="booking-info-aggregation-agent"
project_client=project_client, credential=credential, agent_name="booking-info-aggregation-agent"
)
booking_info_aggregation_agent = booking_info_client.create_agent(
id="booking-info-aggregation-agent",
@@ -320,9 +309,9 @@ async def _create_workflow(project_client, credential):
"You aggregate hotel and flight search results. Receive options from search agents and organize them. Provide: top 2-3 hotel options with prices and top 2-3 flight options with prices in a structured format."
),
name="booking-info-aggregation-agent",
store=True
store=True,
)
# Build workflow with logical booking flow:
# 1. start_executor → travel_request_handler
# 2. travel_request_handler → hotel_search, flight_search, activity_search (fan-out)
@@ -331,19 +320,22 @@ async def _create_workflow(project_client, credential):
# 5. booking_info_aggregation → booking_confirmation
# 6. booking_confirmation → booking_payment
# 7. booking_info_aggregation, booking_payment, activity_search → final_coordinator (final aggregation, fan-in)
workflow = (WorkflowBuilder(name='Travel Planning Workflow')
.set_start_executor(start_executor)
.add_edge(start_executor, travel_request_handler)
.add_fan_out_edges(travel_request_handler, [hotel_search_agent, flight_search_agent, activity_search_agent])
.add_edge(hotel_search_agent, booking_info_aggregation_agent)
.add_edge(flight_search_agent, booking_info_aggregation_agent)
.add_edge(booking_info_aggregation_agent, booking_confirmation_agent)
.add_edge(booking_confirmation_agent, booking_payment_agent)
.add_fan_in_edges([booking_info_aggregation_agent, booking_payment_agent, activity_search_agent],
final_coordinator)
.build())
workflow = (
WorkflowBuilder(name="Travel Planning Workflow")
.set_start_executor(start_executor)
.add_edge(start_executor, travel_request_handler)
.add_fan_out_edges(travel_request_handler, [hotel_search_agent, flight_search_agent, activity_search_agent])
.add_edge(hotel_search_agent, booking_info_aggregation_agent)
.add_edge(flight_search_agent, booking_info_aggregation_agent)
.add_edge(booking_info_aggregation_agent, booking_confirmation_agent)
.add_edge(booking_confirmation_agent, booking_payment_agent)
.add_fan_in_edges(
[booking_info_aggregation_agent, booking_payment_agent, activity_search_agent], final_coordinator
)
.build()
)
# Return workflow and agent map for thread ID extraction
agent_map = {
"travel_request_handler": travel_request_handler,
@@ -355,14 +347,14 @@ async def _create_workflow(project_client, credential):
"booking-info-aggregation-agent": booking_info_aggregation_agent,
"final-coordinator": final_coordinator.agent,
}
return workflow, agent_map
async def _process_workflow_events(events, conversation_ids, response_ids):
"""Process workflow events and track interactions."""
workflow_output = None
async for event in events:
if isinstance(event, WorkflowOutputEvent):
workflow_output = event.data
@@ -370,12 +362,12 @@ async def _process_workflow_events(events, conversation_ids, response_ids):
try:
print(f"\nWorkflow Output: {event.data}\n")
except UnicodeEncodeError:
output_str = str(event.data).encode('ascii', 'replace').decode('ascii')
output_str = str(event.data).encode("ascii", "replace").decode("ascii")
print(f"\nWorkflow Output: {output_str}\n")
elif isinstance(event, AgentRunUpdateEvent):
_track_agent_ids(event, event.executor_id, response_ids, conversation_ids)
return workflow_output
@@ -384,21 +376,21 @@ def _track_agent_ids(event, agent, response_ids, conversation_ids):
if isinstance(event.data, AgentRunResponseUpdate):
# Check for conversation_id and response_id from raw_representation
# V2 API stores conversation_id directly on raw_representation (ChatResponseUpdate)
if hasattr(event.data, 'raw_representation') and event.data.raw_representation:
if hasattr(event.data, "raw_representation") and event.data.raw_representation:
raw = event.data.raw_representation
# Try conversation_id directly on raw representation
if hasattr(raw, 'conversation_id') and raw.conversation_id:
if hasattr(raw, "conversation_id") and raw.conversation_id:
# Only add if not already in the list
if raw.conversation_id not in conversation_ids[agent]:
conversation_ids[agent].append(raw.conversation_id)
# Extract response_id from the OpenAI event (available from first event)
if hasattr(raw, 'raw_representation') and raw.raw_representation:
if hasattr(raw, "raw_representation") and raw.raw_representation:
openai_event = raw.raw_representation
# Check if event has response object with id
if hasattr(openai_event, 'response') and hasattr(openai_event.response, 'id'):
if hasattr(openai_event, "response") and hasattr(openai_event.response, "id"):
# Only add if not already in the list
if openai_event.response.id not in response_ids[agent]:
response_ids[agent].append(openai_event.response.id)
@@ -406,7 +398,7 @@ def _track_agent_ids(event, agent, response_ids, conversation_ids):
async def create_and_run_workflow():
"""Run the workflow evaluation and display results.
Returns:
Dictionary containing agents data with conversation IDs, response IDs, and query information
"""
@@ -415,36 +407,32 @@ async def create_and_run_workflow():
"Find a budget hotel in Tokyo for January 5-10, 2026 under $150/night near Shibuya station, book activities including a sushi making class",
"Search for round-trip flights from Los Angeles to London departing March 20, 2026, returning March 27, 2026. Economy class, 2 passengers. Recommend tourist attractions and museums.",
]
query = example_queries[0]
print(f"Query: {query}\n")
result = await run_workflow_with_response_tracking(query)
# Create output data structure
output_data = {
"agents": {},
"query": result["query"],
"output": result.get("output", "")
}
output_data = {"agents": {}, "query": result["query"], "output": result.get("output", "")}
# Create agent-specific mappings - now with lists of IDs
all_agents = set(result["conversation_ids"].keys()) | set(result["response_ids"].keys())
for agent_name in all_agents:
output_data["agents"][agent_name] = {
"conversation_ids": result["conversation_ids"].get(agent_name, []),
"response_ids": result["response_ids"].get(agent_name, []),
"response_count": len(result["response_ids"].get(agent_name, []))
"response_count": len(result["response_ids"].get(agent_name, [])),
}
print(f"\nTotal agents tracked: {len(output_data['agents'])}")
# Print summary of multiple responses
print("\n=== Multi-Response Summary ===")
for agent_name, agent_data in output_data["agents"].items():
response_count = agent_data["response_count"]
print(f"{agent_name}: {response_count} response(s)")
return output_data