Python: [BREAKING] Types API Review improvements (#3647)

* Replace Role and FinishReason classes with NewType + Literal

- Remove EnumLike metaclass from _types.py
- Replace Role class with NewType('Role', str) + RoleLiteral
- Replace FinishReason class with NewType('FinishReason', str) + FinishReasonLiteral
- Update all usages across codebase to use string literals
- Remove .value access patterns (direct string comparison now works)
- Add backward compatibility for legacy dict serialization format
- Update tests to reflect new string-based types

Addresses #3591, #3615

* Simplify ChatResponse and AgentResponse type hints (#3592)

- Remove overloads from ChatResponse.__init__
- Remove text parameter from ChatResponse.__init__
- Remove | dict[str, Any] from finish_reason and usage_details params
- Remove **kwargs from AgentResponse.__init__
- Both now accept ChatMessage | Sequence[ChatMessage] | None for messages
- Update docstrings and examples to reflect changes
- Fix tests that were using removed kwargs
- Fix Role type hint usage in ag-ui utils

* Remove text parameter from ChatResponseUpdate and AgentResponseUpdate (#3597)

- Remove text parameter from ChatResponseUpdate.__init__
- Remove text parameter from AgentResponseUpdate.__init__
- Remove **kwargs from both update classes
- Simplify contents parameter type to Sequence[Content] | None
- Update all usages to use contents=[Content.from_text(...)] pattern
- Fix imports in test files
- Update docstrings and examples

* Rename from_chat_response_updates to from_updates (#3593)

- ChatResponse.from_chat_response_updates → ChatResponse.from_updates
- ChatResponse.from_chat_response_generator → ChatResponse.from_update_generator
- AgentResponse.from_agent_run_response_updates → AgentResponse.from_updates

* Remove try_parse_value method from ChatResponse and AgentResponse (#3595)

- Remove try_parse_value method from ChatResponse
- Remove try_parse_value method from AgentResponse
- Remove try_parse_value calls from from_updates and from_update_generator methods
- Update samples to use try/except with response.value instead
- Update tests to use response.value pattern
- Users should now use response.value with try/except for safe parsing

* Add agent_id to AgentResponse and clarify author_name documentation (#3596)

- Add agent_id parameter to AgentResponse class
- Document that author_name is on ChatMessage objects, not responses
- Update ChatResponse docstring with author_name note
- Update AgentResponse docstring with author_name note

* Simplify ChatMessage.__init__ signature (#3618)

- Make contents a positional argument accepting Sequence[Content | str]
- Auto-convert strings in contents to TextContent
- Remove overloads, keep text kwarg for backward compatibility with serialization
- Update _parse_content_list to handle string items
- Update all usages across codebase to use new format: ChatMessage("role", ["text"])

* Allow Content as input on run and get_response

- Update prepare_messages and normalize_messages to accept Content
- Update type signatures in _agents.py and _clients.py
- Add tests for Content input handling

* Fix ChatMessage usage across packages and samples

Update all remaining ChatMessage(role=..., text=...) to use new
ChatMessage('role', ['text']) signature.

* Fix Role string usage and response format parsing

- Fix redis provider: remove .value access on string literals
- Fix durabletask ensure_response_format: set _response_format before accessing .value

* Fix ollama .value and ai_model_id issues, handle None in content list

- Fix ollama _chat_client: remove .value on string literals
- Fix ollama _chat_client: rename ai_model_id to model_id
- Fix _parse_content_list: skip None values gracefully

* Fix A2AAgent type signature to include Content

* Fix Role/FinishReason NewType dict annotations and improve test coverage to 95%

* Fix mypy errors for Role/FinishReason NewType usage

* Fix Role.TOOL and Role.ASSISTANT usage in _orchestrator_helpers.py

* Fix Role NewType usage in durabletask _models.py

python/samples/getting_started/agents/anthropic/anthropic_basic.py

@@ -4,8 +4,8 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework.anthropic import AnthropicClient
 from agent_framework import tool
+from agent_framework.anthropic import AnthropicClient
 
 """
 Anthropic Chat Agent Example
@@ -13,6 +13,7 @@ Anthropic Chat Agent Example
 This sample demonstrates using Anthropic with an agent and a single custom tool.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_basic.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent Basic Example
@@ -16,6 +16,7 @@ This sample demonstrates basic usage of AzureAIProjectAgentProvider.
 Shows both streaming and non-streaming responses with function tools.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_provider_methods.py

@@ -5,12 +5,12 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.ai.projects.aio import AIProjectClient
 from azure.ai.projects.models import AgentReference, PromptAgentDefinition
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Project Agent Provider Methods Example
@@ -26,6 +26,7 @@ with different configurations, which is efficient for multi-agent scenarios.
 Each method returns a ChatAgent that can be used for conversations.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_use_latest_version.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent Latest Version Example
@@ -17,6 +17,7 @@ instead of creating a new agent version on each instantiation. The first call cr
 while subsequent calls with `get_agent()` reuse the latest agent version.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_with_code_interpreter_file_generation.py

@@ -5,7 +5,6 @@ import asyncio
 from agent_framework import (
     AgentResponseUpdate,
     HostedCodeInterpreterTool,
-    tool,
 )
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.identity.aio import AzureCliCredential

python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_conversation.py

@@ -4,11 +4,11 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent Existing Conversation Example
@@ -16,6 +16,7 @@ Azure AI Agent Existing Conversation Example
 This sample demonstrates usage of AzureAIProjectAgentProvider with existing conversation created on service side.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIProjectAgentProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating Azure AI Agents with explicit configuration
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai/azure_ai_with_hosted_mcp.py

@@ -25,10 +25,10 @@ async def handle_approvals_without_thread(query: str, agent: "AgentProtocol") ->
                 f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
                 f" with arguments: {user_input_needed.function_call.arguments}"
             )
-            new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
+            new_inputs.append(ChatMessage("assistant", [user_input_needed]))
             user_approval = input("Approve function call? (y/n): ")
             new_inputs.append(
-                ChatMessage(role="user", contents=[user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
+                ChatMessage("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
             )
 
         result = await agent.run(new_inputs, store=False)

python/samples/getting_started/agents/azure_ai/azure_ai_with_response_format.py

@@ -41,12 +41,13 @@ async def main() -> None:
         print(f"User: {query}")
         result = await agent.run(query)
 
-        if release_brief := result.try_parse_value(ReleaseBrief):
+        try:
+            release_brief = result.value
             print("Agent:")
             print(f"Feature: {release_brief.feature}")
             print(f"Benefit: {release_brief.benefit}")
             print(f"Launch date: {release_brief.launch_date}")
-        else:
+        except Exception:
             print(f"Failed to parse response: {result.text}")
 
 

python/samples/getting_started/agents/azure_ai_agent/azure_ai_basic.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent Basic Example
@@ -16,6 +16,7 @@ This sample demonstrates basic usage of AzureAIAgentsProvider to create agents w
 lifecycle management. Shows both streaming and non-streaming responses with function tools.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai_agent/azure_ai_provider_methods.py

@@ -5,11 +5,11 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.ai.agents.aio import AgentsClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent Provider Methods Example
@@ -20,6 +20,7 @@ This sample demonstrates the methods available on the AzureAIAgentsProvider clas
 - as_agent(): Wrap an SDK Agent object without making HTTP calls
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_code_interpreter_file_generation.py

@@ -7,7 +7,6 @@ from agent_framework import (
     AgentResponseUpdate,
     HostedCodeInterpreterTool,
     HostedFileContent,
-    tool,
 )
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.ai.agents.aio import AgentsClient

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_existing_thread.py

@@ -5,11 +5,11 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.ai.agents.aio import AgentsClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent with Existing Thread Example
@@ -18,6 +18,7 @@ This sample demonstrates working with pre-existing conversation threads
 by providing thread IDs for thread reuse patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating Azure AI Agents with explicit configuration
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_function_tools.py

@@ -5,10 +5,10 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Agent with Function Tools Example
@@ -17,6 +17,7 @@ This sample demonstrates function tool integration with Azure AI Agents,
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -26,6 +27,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_multiple_tools.py

@@ -34,9 +34,9 @@ To set up Bing Grounding:
 4. Copy the connection ID and set it as the BING_CONNECTION_ID environment variable
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
-
 def get_time() -> str:
     """Get the current UTC time."""
     current_time = datetime.now(timezone.utc)

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_response_format.py

@@ -56,13 +56,14 @@ async def main() -> None:
 
         result1 = await agent.run(query1)
 
-        if weather := result1.try_parse_value(WeatherInfo):
+        try:
+            weather = result1.value
             print("Agent:")
             print(f"  Location: {weather.location}")
             print(f"  Temperature: {weather.temperature}")
             print(f"  Conditions: {weather.conditions}")
             print(f"  Recommendation: {weather.recommendation}")
-        else:
+        except Exception:
             print(f"Failed to parse response: {result1.text}")
 
         # Request 2: Override response_format at runtime with CityInfo
@@ -72,12 +73,13 @@ async def main() -> None:
 
         result2 = await agent.run(query2, options={"response_format": CityInfo})
 
-        if city := result2.try_parse_value(CityInfo):
+        try:
+            city = result2.value
             print("Agent:")
             print(f"  City: {city.city_name}")
             print(f"  Population: {city.population}")
             print(f"  Country: {city.country}")
-        else:
+        except Exception:
             print(f"Failed to parse response: {result2.text}")
 
 

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread
-from agent_framework import tool
+from agent_framework import AgentThread, tool
 from agent_framework.azure import AzureAIAgentsProvider
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
@@ -17,6 +16,7 @@ This sample demonstrates thread management with Azure AI Agents, comparing
 automatic thread creation with explicit thread management for persistent context.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_assistants_basic.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Assistants Basic Example
@@ -16,6 +16,7 @@ This sample demonstrates basic usage of AzureOpenAIAssistantsClient with automat
 assistant lifecycle management, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_assistants_with_existing_assistant.py

@@ -5,8 +5,7 @@ import os
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential, get_bearer_token_provider
 from openai import AsyncAzureOpenAI
@@ -19,6 +18,7 @@ This sample demonstrates working with pre-existing Azure OpenAI Assistants
 using existing assistant IDs rather than creating new ones.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_assistants_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Assistants with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating Azure OpenAI Assistants with explicit configur
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_assistants_with_function_tools.py

@@ -5,8 +5,7 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -18,6 +17,7 @@ This sample demonstrates function tool integration with Azure OpenAI Assistants,
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -27,6 +27,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/azure_openai/azure_assistants_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread, ChatAgent
-from agent_framework import tool
+from agent_framework import AgentThread, ChatAgent, tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -17,6 +16,7 @@ This sample demonstrates thread management with Azure OpenAI Assistants, compari
 automatic thread creation with explicit thread management for persistent context.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_chat_client_basic.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Chat Client Basic Example
@@ -16,6 +16,7 @@ This sample demonstrates basic usage of AzureOpenAIChatClient for direct chat-ba
 interactions, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_chat_client_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Chat Client with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating Azure OpenAI Chat Client with explicit configu
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_chat_client_with_function_tools.py

@@ -5,8 +5,7 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -18,6 +17,7 @@ This sample demonstrates function tool integration with Azure OpenAI Chat Client
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -27,6 +27,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/azure_openai/azure_chat_client_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread, ChatAgent, ChatMessageStore
-from agent_framework import tool
+from agent_framework import AgentThread, ChatAgent, ChatMessageStore, tool
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -17,6 +16,7 @@ This sample demonstrates thread management with Azure OpenAI Chat Client, compar
 automatic thread creation with explicit thread management for persistent context.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_responses_client_basic.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Responses Client Basic Example
@@ -16,6 +16,7 @@ This sample demonstrates basic usage of AzureOpenAIResponsesClient for structure
 response generation, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_responses_client_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure OpenAI Responses Client with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating Azure OpenAI Responses Client with explicit co
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/azure_openai/azure_responses_client_with_function_tools.py

@@ -5,8 +5,7 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -18,6 +17,7 @@ This sample demonstrates function tool integration with Azure OpenAI Responses C
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -27,6 +27,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/azure_openai/azure_responses_client_with_hosted_mcp.py

@@ -30,10 +30,10 @@ async def handle_approvals_without_thread(query: str, agent: "AgentProtocol"):
                 f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
                 f" with arguments: {user_input_needed.function_call.arguments}"
             )
-            new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
+            new_inputs.append(ChatMessage("assistant", [user_input_needed]))
             user_approval = input("Approve function call? (y/n): ")
             new_inputs.append(
-                ChatMessage(role="user", contents=[user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
+                ChatMessage("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
             )
 
         result = await agent.run(new_inputs)
@@ -71,7 +71,7 @@ async def handle_approvals_with_thread_streaming(query: str, agent: "AgentProtoc
     new_input_added = True
     while new_input_added:
         new_input_added = False
-        new_input.append(ChatMessage(role="user", text=query))
+        new_input.append(ChatMessage("user", [query]))
         async for update in agent.run_stream(new_input, thread=thread, store=True):
             if update.user_input_requests:
                 for user_input_needed in update.user_input_requests:

python/samples/getting_started/agents/azure_openai/azure_responses_client_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread, ChatAgent
-from agent_framework import tool
+from agent_framework import AgentThread, ChatAgent, tool
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
@@ -17,6 +16,7 @@ This sample demonstrates thread management with Azure OpenAI Responses Client, c
 automatic thread creation with explicit thread management for persistent context.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/custom/custom_agent.py

@@ -11,8 +11,6 @@ from agent_framework import (
     BaseAgent,
     ChatMessage,
     Content,
-    Role,
-    tool,
 )
 
 """
@@ -77,8 +75,8 @@ class EchoAgent(BaseAgent):
 
         if not normalized_messages:
             response_message = ChatMessage(
-                role=Role.ASSISTANT,
-                contents=[Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")],
+                "assistant",
+                [Content.from_text(text="Hello! I'm a custom echo agent. Send me a message and I'll echo it back.")],
             )
         else:
             # For simplicity, echo the last user message
@@ -88,7 +86,7 @@ class EchoAgent(BaseAgent):
             else:
                 echo_text = f"{self.echo_prefix}[Non-text message received]"
 
-            response_message = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(text=echo_text)])
+            response_message = ChatMessage("assistant", [Content.from_text(text=echo_text)])
 
         # Notify the thread of new messages if provided
         if thread is not None:
@@ -134,7 +132,7 @@ class EchoAgent(BaseAgent):
 
             yield AgentResponseUpdate(
                 contents=[Content.from_text(text=chunk_text)],
-                role=Role.ASSISTANT,
+                role="assistant",
             )
 
             # Small delay to simulate streaming
@@ -142,7 +140,7 @@ class EchoAgent(BaseAgent):
 
         # Notify the thread of the complete response if provided
         if thread is not None:
-            complete_response = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(text=response_text)])
+            complete_response = ChatMessage("assistant", [Content.from_text(text=response_text)])
             await self._notify_thread_of_new_messages(thread, normalized_messages, complete_response)
 
 

python/samples/getting_started/agents/custom/custom_chat_client.py

@@ -12,10 +12,8 @@ from agent_framework import (
     ChatResponse,
     ChatResponseUpdate,
     Content,
-    Role,
     use_chat_middleware,
     use_function_invocation,
-    tool,
 )
 from agent_framework._clients import TOptions_co
 
@@ -68,7 +66,7 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
             # Echo the last user message
             last_user_message = None
             for message in reversed(messages):
-                if message.role == Role.USER:
+                if message.role == "user":
                     last_user_message = message
                     break
 
@@ -77,7 +75,7 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
             else:
                 response_text = f"{self.prefix} [No text message found]"
 
-        response_message = ChatMessage(role=Role.ASSISTANT, contents=[Content.from_text(text=response_text)])
+        response_message = ChatMessage("assistant", [Content.from_text(text=response_text)])
 
         return ChatResponse(
             messages=[response_message],
@@ -104,7 +102,7 @@ class EchoingChatClient(BaseChatClient[TOptions_co], Generic[TOptions_co]):
             for char in response_text:
                 yield ChatResponseUpdate(
                     contents=[Content.from_text(text=char)],
-                    role=Role.ASSISTANT,
+                    role="assistant",
                     response_id=f"echo-stream-resp-{random.randint(1000, 9999)}",
                     model_id="echo-model-v1",
                 )

python/samples/getting_started/agents/ollama/ollama_agent_basic.py

@@ -3,8 +3,8 @@
 import asyncio
 from datetime import datetime
 
-from agent_framework.ollama import OllamaChatClient
 from agent_framework import tool
+from agent_framework.ollama import OllamaChatClient
 
 """
 Ollama Agent Basic Example
@@ -18,6 +18,7 @@ https://ollama.com/
 
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_time(location: str) -> str:

python/samples/getting_started/agents/ollama/ollama_chat_client.py

@@ -3,8 +3,8 @@
 import asyncio
 from datetime import datetime
 
-from agent_framework.ollama import OllamaChatClient
 from agent_framework import tool
+from agent_framework.ollama import OllamaChatClient
 
 """
 Ollama Chat Client Example
@@ -18,6 +18,7 @@ https://ollama.com/
 
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_time():

python/samples/getting_started/agents/ollama/ollama_chat_multimodal.py

@@ -2,7 +2,7 @@
 
 import asyncio
 
-from agent_framework import ChatMessage, Content, Role
+from agent_framework import ChatMessage, Content
 from agent_framework.ollama import OllamaChatClient
 
 """
@@ -33,7 +33,7 @@ async def test_image() -> None:
     image_uri = create_sample_image()
 
     message = ChatMessage(
-        role=Role.USER,
+        role="user",
         contents=[
             Content.from_text(text="What's in this image?"),
             Content.from_uri(uri=image_uri, media_type="image/png"),

python/samples/getting_started/agents/ollama/ollama_with_openai_chat_client.py

@@ -5,8 +5,8 @@ import os
 from random import randint
 from typing import Annotated
 
-from agent_framework.openai import OpenAIChatClient
 from agent_framework import tool
+from agent_framework.openai import OpenAIChatClient
 
 """
 Ollama with OpenAI Chat Client Example
@@ -20,6 +20,7 @@ Environment Variables:
 - OLLAMA_MODEL: The model name to use (e.g., "mistral", "llama3.2", "phi3")
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_assistants_basic.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistants Basic Example
@@ -17,6 +17,7 @@ This sample demonstrates basic usage of OpenAIAssistantProvider with automatic
 assistant lifecycle management, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_assistants_provider_methods.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistant Provider Methods Example
@@ -19,6 +19,7 @@ This sample demonstrates the methods available on the OpenAIAssistantProvider cl
 - as_agent(): Wrap an SDK Assistant object without making HTTP calls
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_assistants_with_existing_assistant.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistants with Existing Assistant Example
@@ -17,6 +17,7 @@ This sample demonstrates working with pre-existing OpenAI Assistants
 using the provider's get_agent() and as_agent() methods.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_assistants_with_explicit_settings.py

@@ -5,10 +5,10 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistants with Explicit Settings Example
@@ -17,6 +17,7 @@ This sample demonstrates creating OpenAI Assistants with explicit configuration
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_assistants_with_function_tools.py

@@ -6,10 +6,10 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistants with Function Tools Example
@@ -18,6 +18,7 @@ This sample demonstrates function tool integration with OpenAI Assistants,
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -27,6 +28,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/openai/openai_assistants_with_response_format.py

@@ -59,13 +59,14 @@ async def main() -> None:
 
             result1 = await agent.run(query1)
 
-            if weather := result1.try_parse_value(WeatherInfo):
+            try:
+                weather = result1.value
                 print("Agent:")
                 print(f"  Location: {weather.location}")
                 print(f"  Temperature: {weather.temperature}")
                 print(f"  Conditions: {weather.conditions}")
                 print(f"  Recommendation: {weather.recommendation}")
-            else:
+            except Exception:
                 print(f"Failed to parse response: {result1.text}")
 
             # Request 2: Override response_format at runtime with CityInfo
@@ -75,12 +76,13 @@ async def main() -> None:
 
             result2 = await agent.run(query2, options={"response_format": CityInfo})
 
-            if city := result2.try_parse_value(CityInfo):
+            try:
+                city = result2.value
                 print("Agent:")
                 print(f"  City: {city.city_name}")
                 print(f"  Population: {city.population}")
                 print(f"  Country: {city.country}")
-            else:
+            except Exception:
                 print(f"Failed to parse response: {result2.text}")
         finally:
             await client.beta.assistants.delete(agent.id)

python/samples/getting_started/agents/openai/openai_assistants_with_thread.py

@@ -5,8 +5,7 @@ import os
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread
-from agent_framework import tool
+from agent_framework import AgentThread, tool
 from agent_framework.openai import OpenAIAssistantProvider
 from openai import AsyncOpenAI
 from pydantic import Field
@@ -18,6 +17,7 @@ This sample demonstrates thread management with OpenAI Assistants, showing
 persistent conversation threads and context preservation across interactions.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_chat_client_basic.py

@@ -4,8 +4,8 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework.openai import OpenAIChatClient
 from agent_framework import tool
+from agent_framework.openai import OpenAIChatClient
 
 """
 OpenAI Chat Client Basic Example
@@ -14,6 +14,7 @@ This sample demonstrates basic usage of OpenAIChatClient for direct chat-based
 interactions, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_chat_client_with_explicit_settings.py

@@ -5,9 +5,9 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Chat Client with Explicit Settings Example
@@ -16,6 +16,7 @@ This sample demonstrates creating OpenAI Chat Client with explicit configuration
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_chat_client_with_function_tools.py

@@ -5,8 +5,7 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field
 
@@ -17,6 +16,7 @@ This sample demonstrates function tool integration with OpenAI Chat Client,
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -26,6 +26,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/openai/openai_chat_client_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread, ChatAgent, ChatMessageStore
-from agent_framework import tool
+from agent_framework import AgentThread, ChatAgent, ChatMessageStore, tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field
 
@@ -16,6 +15,7 @@ This sample demonstrates thread management with OpenAI Chat Client, showing
 conversation threads and message history preservation across interactions.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_responses_client_basic.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field
 
@@ -16,6 +15,7 @@ This sample demonstrates basic usage of OpenAIResponsesClient for structured
 response generation, showing both streaming and non-streaming responses.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_responses_client_with_code_interpreter.py

@@ -8,7 +8,6 @@ from agent_framework import (
     CodeInterpreterToolResultContent,
     Content,
     HostedCodeInterpreterTool,
-    tool,
 )
 from agent_framework.openai import OpenAIResponsesClient
 

python/samples/getting_started/agents/openai/openai_responses_client_with_explicit_settings.py

@@ -5,9 +5,9 @@ import os
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Responses Client with Explicit Settings Example
@@ -16,6 +16,7 @@ This sample demonstrates creating OpenAI Responses Client with explicit configur
 settings rather than relying on environment variable defaults.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/agents/openai/openai_responses_client_with_function_tools.py

@@ -5,8 +5,7 @@ from datetime import datetime, timezone
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field
 
@@ -17,6 +16,7 @@ This sample demonstrates function tool integration with OpenAI Responses Client,
 showing both agent-level and query-level tool configuration patterns.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -26,6 +26,7 @@ def get_weather(
     conditions = ["sunny", "cloudy", "rainy", "stormy"]
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time() -> str:
     """Get the current UTC time."""

python/samples/getting_started/agents/openai/openai_responses_client_with_hosted_mcp.py

@@ -29,10 +29,10 @@ async def handle_approvals_without_thread(query: str, agent: "AgentProtocol"):
                 f"User Input Request for function from {agent.name}: {user_input_needed.function_call.name}"
                 f" with arguments: {user_input_needed.function_call.arguments}"
             )
-            new_inputs.append(ChatMessage(role="assistant", contents=[user_input_needed]))
+            new_inputs.append(ChatMessage("assistant", [user_input_needed]))
             user_approval = input("Approve function call? (y/n): ")
             new_inputs.append(
-                ChatMessage(role="user", contents=[user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
+                ChatMessage("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
             )
 
         result = await agent.run(new_inputs)
@@ -70,7 +70,7 @@ async def handle_approvals_with_thread_streaming(query: str, agent: "AgentProtoc
     new_input_added = True
     while new_input_added:
         new_input_added = False
-        new_input.append(ChatMessage(role="user", text=query))
+        new_input.append(ChatMessage("user", [query]))
         async for update in agent.run_stream(new_input, thread=thread, store=True):
             if update.user_input_requests:
                 for user_input_needed in update.user_input_requests:

python/samples/getting_started/agents/openai/openai_responses_client_with_thread.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import AgentThread, ChatAgent
-from agent_framework import tool
+from agent_framework import AgentThread, ChatAgent, tool
 from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field
 
@@ -16,6 +15,7 @@ This sample demonstrates thread management with OpenAI Responses Client, showing
 persistent conversation context and simplified response handling.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/azure_functions/02_multi_agent/function_app.py

@@ -11,12 +11,13 @@ Prerequisites: set `AZURE_OPENAI_ENDPOINT` and `AZURE_OPENAI_CHAT_DEPLOYMENT_NAM
 import logging
 from typing import Any
 
+from agent_framework import tool
 from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
-from agent_framework import tool
 
 logger = logging.getLogger(__name__)
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(location: str) -> dict[str, Any]:
@@ -32,6 +33,7 @@ def get_weather(location: str) -> dict[str, Any]:
     logger.info(f"✓ [TOOL RESULT] {result}")
     return result
 
+
 @tool(approval_mode="never_require")
 def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> dict[str, Any]:
     """Calculate tip amount and total bill."""

python/samples/getting_started/azure_functions/03_reliable_streaming/redis_stream_response_handler.py

@@ -8,9 +8,9 @@ as a message broker. It enables clients to disconnect and reconnect without losi
 
 import asyncio
 import time
+from collections.abc import AsyncIterator
 from dataclasses import dataclass
 from datetime import timedelta
-from collections.abc import AsyncIterator
 
 import redis.asyncio as aioredis
 

python/samples/getting_started/azure_functions/03_reliable_streaming/tools.py

@@ -153,13 +153,12 @@ def _get_weather_recommendation(condition: str) -> str:
 
     if "rain" in condition_lower or "drizzle" in condition_lower:
         return "Bring an umbrella and waterproof jacket. Consider indoor activities for backup."
-    elif "fog" in condition_lower:
+    if "fog" in condition_lower:
         return "Morning visibility may be limited. Plan outdoor sightseeing for afternoon."
-    elif "cold" in condition_lower:
+    if "cold" in condition_lower:
         return "Layer up with warm clothing. Hot drinks and cozy cafés recommended."
-    elif "hot" in condition_lower or "warm" in condition_lower:
+    if "hot" in condition_lower or "warm" in condition_lower:
         return "Stay hydrated and use sunscreen. Plan strenuous activities for cooler morning hours."
-    elif "thunder" in condition_lower or "storm" in condition_lower:
+    if "thunder" in condition_lower or "storm" in condition_lower:
         return "Keep an eye on weather updates. Have indoor alternatives ready."
-    else:
-        return "Pleasant conditions expected. Great day for outdoor exploration!"
+    return "Pleasant conditions expected. Great day for outdoor exploration!"

python/samples/getting_started/azure_functions/06_multi_agent_orchestration_conditionals/function_app.py

@@ -102,9 +102,10 @@ def spam_detection_orchestration(context: DurableOrchestrationContext) -> Genera
         options={"response_format": SpamDetectionResult},
     )
 
-    spam_result = spam_result_raw.try_parse_value(SpamDetectionResult)
-    if spam_result is None:
-        raise ValueError("Failed to parse spam detection result")
+    try:
+        spam_result = spam_result_raw.value
+    except Exception as ex:
+        raise ValueError("Failed to parse spam detection result") from ex
 
     if spam_result.is_spam:
         result = yield context.call_activity("handle_spam_email", spam_result.reason)  # type: ignore[misc]
@@ -125,9 +126,10 @@ def spam_detection_orchestration(context: DurableOrchestrationContext) -> Genera
         options={"response_format": EmailResponse},
     )
 
-    email_result = email_result_raw.try_parse_value(EmailResponse)
-    if email_result is None:
-        raise ValueError("Failed to parse email response")
+    try:
+        email_result = email_result_raw.value
+    except Exception as ex:
+        raise ValueError("Failed to parse email response") from ex
 
     result = yield context.call_activity("send_email", email_result.response)  # type: ignore[misc]
     return result

python/samples/getting_started/azure_functions/07_single_agent_orchestration_hitl/function_app.py

@@ -137,11 +137,11 @@ def content_generation_hitl_orchestration(context: DurableOrchestrationContext)
             context.set_custom_status(
                 "Content rejected by human reviewer. Incorporating feedback and regenerating..."
             )
-            
+
             # Check if we've exhausted attempts
             if attempt >= payload.max_review_attempts:
                 break
-            
+
             rewrite_prompt = (
                 "The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.\n\n"
                 f"Human Feedback: {approval_payload.feedback or 'No feedback provided.'}"
@@ -152,9 +152,10 @@ def content_generation_hitl_orchestration(context: DurableOrchestrationContext)
                 options={"response_format": GeneratedContent},
             )
 
-            content = rewritten_raw.try_parse_value(GeneratedContent)
-            if content is None:
-                raise ValueError("Agent returned no content after rewrite.")
+            try:
+                content = rewritten_raw.value
+            except Exception as ex:
+                raise ValueError("Agent returned no content after rewrite.") from ex
         else:
             context.set_custom_status(
                 f"Human approval timed out after {payload.approval_timeout_hours} hour(s). Treating as rejection."
@@ -162,7 +163,7 @@ def content_generation_hitl_orchestration(context: DurableOrchestrationContext)
             raise TimeoutError(
                 f"Human approval timed out after {payload.approval_timeout_hours} hour(s)."
             )
-    
+
     # If we exit the loop without returning, max attempts were exhausted
     context.set_custom_status("Max review attempts exhausted.")
     raise RuntimeError(

python/samples/getting_started/chat_client/azure_ai_chat_client.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure AI Chat Client Direct Usage Example
@@ -16,6 +16,7 @@ Demonstrates direct AzureAIChatClient usage for chat interactions with Azure AI
 Shows function calling capabilities with custom business logic.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/chat_client/azure_assistants_client.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure Assistants Client Direct Usage Example
@@ -16,6 +16,7 @@ Demonstrates direct AzureAssistantsClient usage for chat interactions with Azure
 Shows function calling capabilities and automatic assistant creation.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/chat_client/azure_chat_client.py

@@ -4,10 +4,10 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field
-from agent_framework import tool
 
 """
 Azure Chat Client Direct Usage Example
@@ -16,6 +16,7 @@ Demonstrates direct AzureChatClient usage for chat interactions with Azure OpenA
 Shows function calling capabilities with custom business logic.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/chat_client/azure_responses_client.py

@@ -4,8 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatResponse
-from agent_framework import tool
+from agent_framework import ChatResponse, tool
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel, Field
@@ -17,6 +16,7 @@ Demonstrates direct AzureResponsesClient usage for structured response generatio
 Shows function calling capabilities with custom business logic.
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -42,19 +42,21 @@ async def main() -> None:
     stream = True
     print(f"User: {message}")
     if stream:
-        response = await ChatResponse.from_chat_response_generator(
+        response = await ChatResponse.from_update_generator(
             client.get_streaming_response(message, tools=get_weather, options={"response_format": OutputStruct}),
             output_format_type=OutputStruct,
         )
-        if result := response.try_parse_value(OutputStruct):
+        try:
+            result = response.value
             print(f"Assistant: {result}")
-        else:
+        except Exception:
             print(f"Assistant: {response.text}")
     else:
         response = await client.get_response(message, tools=get_weather, options={"response_format": OutputStruct})
-        if result := response.try_parse_value(OutputStruct):
+        try:
+            result = response.value
             print(f"Assistant: {result}")
-        else:
+        except Exception:
             print(f"Assistant: {response.text}")
 
 

python/samples/getting_started/chat_client/openai_assistants_client.py

@@ -4,9 +4,9 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIAssistantsClient
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Assistants Client Direct Usage Example
@@ -16,6 +16,7 @@ Shows function calling capabilities and automatic assistant creation.
 
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/chat_client/openai_chat_client.py

@@ -4,9 +4,9 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIChatClient
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Chat Client Direct Usage Example
@@ -16,6 +16,7 @@ Shows function calling capabilities with custom business logic.
 
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/chat_client/openai_responses_client.py

@@ -4,9 +4,9 @@ import asyncio
 from random import randint
 from typing import Annotated
 
+from agent_framework import tool
 from agent_framework.openai import OpenAIResponsesClient
 from pydantic import Field
-from agent_framework import tool
 
 """
 OpenAI Responses Client Direct Usage Example
@@ -16,6 +16,7 @@ Shows function calling capabilities with custom business logic.
 
 """
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(

python/samples/getting_started/context_providers/mem0/mem0_basic.py

@@ -3,10 +3,11 @@
 import asyncio
 import uuid
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentClient
 from agent_framework.mem0 import Mem0Provider
 from azure.identity.aio import AzureCliCredential
-from agent_framework import tool
+
 
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")

python/samples/getting_started/context_providers/mem0/mem0_oss.py

@@ -3,11 +3,12 @@
 import asyncio
 import uuid
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentClient
 from agent_framework.mem0 import Mem0Provider
 from azure.identity.aio import AzureCliCredential
 from mem0 import AsyncMemory
-from agent_framework import tool
+
 
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")

python/samples/getting_started/context_providers/mem0/mem0_threads.py

@@ -3,10 +3,11 @@
 import asyncio
 import uuid
 
+from agent_framework import tool
 from agent_framework.azure import AzureAIAgentClient
 from agent_framework.mem0 import Mem0Provider
 from azure.identity.aio import AzureCliCredential
-from agent_framework import tool
+
 
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")

python/samples/getting_started/context_providers/redis/redis_basics.py

@@ -30,13 +30,13 @@ Run:
 import asyncio
 import os
 
-from agent_framework import ChatMessage, Role
-from agent_framework import tool
+from agent_framework import ChatMessage, tool
 from agent_framework.openai import OpenAIChatClient
 from agent_framework_redis._provider import RedisProvider
 from redisvl.extensions.cache.embeddings import EmbeddingsCache
 from redisvl.utils.vectorize import OpenAITextVectorizer
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def search_flights(origin_airport_code: str, destination_airport_code: str, detailed: bool = False) -> str:
@@ -128,9 +128,9 @@ async def main() -> None:
 
     # Build sample chat messages to persist to Redis
     messages = [
-        ChatMessage(role=Role.USER, text="runA CONVO: User Message"),
-        ChatMessage(role=Role.ASSISTANT, text="runA CONVO: Assistant Message"),
-        ChatMessage(role=Role.SYSTEM, text="runA CONVO: System Message"),
+        ChatMessage("user", ["runA CONVO: User Message"]),
+        ChatMessage("assistant", ["runA CONVO: Assistant Message"]),
+        ChatMessage("system", ["runA CONVO: System Message"]),
     ]
 
     # Declare/start a conversation/thread and write messages under 'runA'.
@@ -142,7 +142,7 @@ async def main() -> None:
     # Retrieve relevant memories for a hypothetical model call. The provider uses
     # the current request messages as the retrieval query and returns context to
     # be injected into the model's instructions.
-    ctx = await provider.invoking([ChatMessage(role=Role.SYSTEM, text="B: Assistant Message")])
+    ctx = await provider.invoking([ChatMessage("system", ["B: Assistant Message"])])
 
     # Inspect retrieved memories that would be injected into instructions
     # (Debug-only output so you can verify retrieval works as expected.)

python/samples/getting_started/context_providers/simple_context_provider.py

@@ -39,7 +39,7 @@ class UserInfoMemory(ContextProvider):
     ) -> None:
         """Extract user information from messages after each agent call."""
         # Check if we need to extract user info from user messages
-        user_messages = [msg for msg in request_messages if hasattr(msg, "role") and msg.role.value == "user"]  # type: ignore
+        user_messages = [msg for msg in request_messages if hasattr(msg, "role") and msg.role == "user"]  # type: ignore
 
         if (self.user_info.name is None or self.user_info.age is None) and user_messages:
             try:
@@ -52,11 +52,14 @@ class UserInfoMemory(ContextProvider):
                 )
 
                 # Update user info with extracted data
-                if extracted := result.try_parse_value(UserInfo):
+                try:
+                    extracted = result.value
                     if self.user_info.name is None and extracted.name:
                         self.user_info.name = extracted.name
                     if self.user_info.age is None and extracted.age:
                         self.user_info.age = extracted.age
+                except Exception:
+                    pass  # Failed to extract, continue without updating
 
             except Exception:
                 pass  # Failed to extract, continue without updating

python/samples/getting_started/declarative/azure_openai_responses_agent.py

@@ -20,10 +20,11 @@ async def main():
     agent = AgentFactory(client_kwargs={"credential": AzureCliCredential()}).create_agent_from_yaml(yaml_str)
     # use the agent
     response = await agent.run("Why is the sky blue, answer in Dutch?")
-    # Use try_parse_value() for safe parsing - returns None if no response_format or parsing fails
-    if parsed := response.try_parse_value():
+    # Use response.value with try/except for safe parsing
+    try:
+        parsed = response.value
         print("Agent response:", parsed.model_dump_json(indent=2))
-    else:
+    except Exception:
         print("Agent response:", response.text)
 
 

python/samples/getting_started/declarative/openai_responses_agent.py

@@ -19,10 +19,11 @@ async def main():
     agent = AgentFactory().create_agent_from_yaml(yaml_str)
     # use the agent
     response = await agent.run("Why is the sky blue, answer in Dutch?")
-    # Use try_parse_value() for safe parsing - returns None if no response_format or parsing fails
-    if parsed := response.try_parse_value():
+    # Use response.value with try/except for safe parsing
+    try:
+        parsed = response.value
         print("Agent response:", parsed)
-    else:
+    except Exception:
         print("Agent response:", response.text)
 
 

python/samples/getting_started/devui/fanout_workflow/workflow.py

@@ -25,7 +25,6 @@ from agent_framework import (
     WorkflowBuilder,
     WorkflowContext,
     handler,
-    tool,
 )
 from pydantic import BaseModel, Field
 from typing_extensions import Never

python/samples/getting_started/devui/foundry_agent/agent.py

@@ -8,12 +8,12 @@ Make sure to run 'az login' before starting devui.
 import os
 from typing import Annotated
 
-from agent_framework import ChatAgent
-from agent_framework import tool
+from agent_framework import ChatAgent, tool
 from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
 
+
 # NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
 @tool(approval_mode="never_require")
 def get_weather(
@@ -24,6 +24,7 @@ def get_weather(
     temperature = 22
     return f"The weather in {location} is {conditions[0]} with a high of {temperature}°C."
 
+
 @tool(approval_mode="never_require")
 def get_forecast(
     location: Annotated[str, Field(description="The location to get the forecast for.")],

python/samples/getting_started/devui/in_memory_mode.py

@@ -10,8 +10,7 @@ import logging
 import os
 from typing import Annotated
 
-from agent_framework import ChatAgent, Executor, WorkflowBuilder, WorkflowContext, handler
-from agent_framework import tool
+from agent_framework import ChatAgent, Executor, WorkflowBuilder, WorkflowContext, handler, tool
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework.devui import serve
 from typing_extensions import Never
@@ -29,6 +28,7 @@ def get_weather(
     temperature = 53
     return f"The weather in {location} is {conditions[0]} with a high of {temperature}°C."
 
+
 @tool(approval_mode="never_require")
 def get_time(
     timezone: Annotated[str, "The timezone to get time for."] = "UTC",

python/samples/getting_started/devui/spam_workflow/workflow.py

@@ -27,7 +27,6 @@ from agent_framework import (
     WorkflowContext,
     handler,
     response_handler,
-    tool,
 )
 from pydantic import BaseModel, Field
 from typing_extensions import Never

python/samples/getting_started/devui/weather_agent_azure/agent.py

@@ -14,10 +14,9 @@ from agent_framework import (
     ChatResponseUpdate,
     Content,
     FunctionInvocationContext,
-    Role,
-    tool,
     chat_middleware,
     function_middleware,
+    tool,
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from agent_framework_devui import register_cleanup
@@ -43,7 +42,7 @@ async def security_filter_middleware(
 
     # Check only the last message (most recent user input)
     last_message = context.messages[-1] if context.messages else None
-    if last_message and last_message.role == Role.USER and last_message.text:
+    if last_message and last_message.role == "user" and last_message.text:
         message_lower = last_message.text.lower()
         for term in blocked_terms:
             if term in message_lower:
@@ -58,7 +57,7 @@ async def security_filter_middleware(
                     async def blocked_stream() -> AsyncIterable[ChatResponseUpdate]:
                         yield ChatResponseUpdate(
                             contents=[Content.from_text(text=error_message)],
-                            role=Role.ASSISTANT,
+                            role="assistant",
                         )
 
                     context.result = blocked_stream()
@@ -67,7 +66,7 @@ async def security_filter_middleware(
                     context.result = ChatResponse(
                         messages=[
                             ChatMessage(
-                                role=Role.ASSISTANT,
+                                role="assistant",
                                 text=error_message,
                             )
                         ]

python/samples/getting_started/durabletask/01_single_agent/client.py

@@ -40,12 +40,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     dts_client = DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -53,7 +53,7 @@ def get_client(
         token_credential=credential,
         log_handler=log_handler
     )
-    
+
     return DurableAIAgentClient(dts_client)
 
 
@@ -66,12 +66,12 @@ def run_client(agent_client: DurableAIAgentClient) -> None:
     # Get a reference to the Joker agent
     logger.debug("Getting reference to Joker agent...")
     joker = agent_client.get_agent("Joker")
-    
+
     # Create a new thread for the conversation
     thread = joker.get_new_thread()
     logger.debug(f"Thread ID: {thread.session_id}")
     logger.info("Start chatting with the Joker agent! (Type 'exit' to quit)")
-    
+
     # Interactive conversation loop
     while True:
         # Get user input
@@ -80,33 +80,33 @@ def run_client(agent_client: DurableAIAgentClient) -> None:
         except (EOFError, KeyboardInterrupt):
             logger.info("\nExiting...")
             break
-        
+
         # Check for exit command
         if user_message.lower() == "exit":
             logger.info("Goodbye!")
             break
-        
+
         # Skip empty messages
         if not user_message:
             continue
-        
+
         # Send message to agent and get response
         try:
             response = joker.run(user_message, thread=thread)
             logger.info(f"Joker: {response.text} \n")
         except Exception as e:
             logger.error(f"Error getting response: {e}")
-    
+
     logger.info("Conversation completed.")
 
 
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task Agent Client...")
-    
+
     # Create client using helper function
     agent_client = get_client()
-    
+
     try:
         run_client(agent_client)
     except Exception as e:

python/samples/getting_started/durabletask/01_single_agent/sample.py

@@ -15,40 +15,40 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 # Configure logging (must be after imports to override their basicConfig)
 logging.basicConfig(level=logging.INFO, force=True)
 logger = logging.getLogger(__name__)
 
+
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Durable Task Agent Sample (Combined Worker + Client)...")
 
     silent_handler = logging.NullHandler()
-    
+
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         agent_client = get_client(log_handler=silent_handler)
-        
+
         try:
             # Run client interactions using helper function
             run_client(agent_client)
         except Exception as e:
             logger.exception(f"Error during agent interaction: {e}")
-        
+
         logger.debug("Sample completed. Worker shutting down...")
 
 

python/samples/getting_started/durabletask/01_single_agent/worker.py

@@ -52,12 +52,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -78,42 +78,42 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker)
-    
+
     # Create and register the Joker agent
     logger.debug("Creating and registering Joker agent...")
     joker_agent = create_joker_agent()
     agent_worker.add_agent(joker_agent)
-    
+
     logger.debug(f"✓ Registered agent: {joker_agent.name}")
     logger.debug(f"  Entity name: dafx-{joker_agent.name}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Agent Worker...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agents
     setup_worker(worker)
-    
+
     logger.info("Worker is ready and listening for requests...")
     logger.info("Press Ctrl+C to stop.")
     logger.info("")
-    
+
     try:
         # Start the worker (this blocks until stopped)
         worker.start()
-        
+
         # Keep the worker running
         while True:
             await asyncio.sleep(1)
     except KeyboardInterrupt:
         logger.debug("Worker shutdown initiated")
-    
+
     logger.debug("Worker stopped")
 
 

python/samples/getting_started/durabletask/02_multi_agent/client.py

@@ -41,12 +41,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     dts_client = DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -54,7 +54,7 @@ def get_client(
         token_credential=credential,
         log_handler=log_handler
     )
-    
+
     return DurableAIAgentClient(dts_client)
 
 
@@ -65,45 +65,45 @@ def run_client(agent_client: DurableAIAgentClient) -> None:
         agent_client: The DurableAIAgentClient instance
     """
     logger.debug("Testing WeatherAgent")
-    
+
     # Get reference to WeatherAgent
     weather_agent = agent_client.get_agent("WeatherAgent")
     weather_thread = weather_agent.get_new_thread()
-    
+
     logger.debug(f"Created weather conversation thread: {weather_thread.session_id}")
-    
+
     # Test WeatherAgent
     weather_message = "What is the weather in Seattle?"
     logger.info(f"User: {weather_message}")
-    
+
     weather_response = weather_agent.run(weather_message, thread=weather_thread)
     logger.info(f"WeatherAgent: {weather_response.text} \n")
-    
+
     logger.debug("Testing MathAgent")
-    
+
     # Get reference to MathAgent
     math_agent = agent_client.get_agent("MathAgent")
     math_thread = math_agent.get_new_thread()
-    
+
     logger.debug(f"Created math conversation thread: {math_thread.session_id}")
-    
+
     # Test MathAgent
     math_message = "Calculate a 20% tip on a $50 bill"
     logger.info(f"User: {math_message}")
-    
+
     math_response = math_agent.run(math_message, thread=math_thread)
     logger.info(f"MathAgent: {math_response.text} \n")
-    
+
     logger.debug("Both agents completed successfully!")
 
 
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task Multi-Agent Client...")
-    
+
     # Create client using helper function
     agent_client = get_client()
-    
+
     try:
         run_client(agent_client)
     except Exception as e:

python/samples/getting_started/durabletask/02_multi_agent/sample.py

@@ -15,10 +15,9 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 # Configure logging
@@ -29,26 +28,26 @@ logger = logging.getLogger(__name__)
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Durable Task Multi-Agent Sample (Combined Worker + Client)...")
-    
+
     silent_handler = logging.NullHandler()
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         agent_client = get_client(log_handler=silent_handler)
-        
+
         try:
             # Run client interactions using helper function
             run_client(agent_client)
         except Exception as e:
             logger.exception(f"Error during agent interaction: {e}")
-        
+
         logger.debug("Sample completed. Worker shutting down...")
 
 

python/samples/getting_started/durabletask/02_multi_agent/worker.py

@@ -101,12 +101,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -127,43 +127,43 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker)
-    
+
     # Create and register both agents
     logger.debug("Creating and registering agents...")
     weather_agent = create_weather_agent()
     math_agent = create_math_agent()
-    
+
     agent_worker.add_agent(weather_agent)
     agent_worker.add_agent(math_agent)
-    
+
     logger.debug(f"✓ Registered agents: {weather_agent.name}, {math_agent.name}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Multi-Agent Worker...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agents
     setup_worker(worker)
-    
+
     logger.info("Worker is ready and listening for requests...")
     logger.info("Press Ctrl+C to stop. \n")
-    
+
     try:
         # Start the worker (this blocks until stopped)
         worker.start()
-        
+
         # Keep the worker running
         while True:
             await asyncio.sleep(1)
     except KeyboardInterrupt:
         logger.debug("Worker shutdown initiated")
-    
+
     logger.info("Worker stopped")
 
 

python/samples/getting_started/durabletask/03_single_agent_streaming/client.py

@@ -23,7 +23,6 @@ import redis.asyncio as aioredis
 from agent_framework.azure import DurableAIAgentClient
 from azure.identity import DefaultAzureCredential
 from durabletask.azuremanaged.client import DurableTaskSchedulerClient
-
 from redis_stream_response_handler import RedisStreamResponseHandler
 
 # Configure logging
@@ -71,12 +70,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     dts_client = DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -84,7 +83,7 @@ def get_client(
         token_credential=credential,
         log_handler=log_handler
     )
-    
+
     return DurableAIAgentClient(dts_client)
 
 
@@ -100,33 +99,33 @@ async def stream_from_redis(thread_id: str, cursor: str | None = None) -> None:
     logger.debug(f"To manually check Redis, run: redis-cli XLEN {stream_key}")
     if cursor:
         logger.info(f"Resuming from cursor: {cursor}")
-    
+
     async with await get_stream_handler() as stream_handler:
-        logger.info(f"Stream handler created, starting to read...")
+        logger.info("Stream handler created, starting to read...")
         try:
             chunk_count = 0
             async for chunk in stream_handler.read_stream(thread_id, cursor):
                 chunk_count += 1
                 logger.debug(f"Received chunk #{chunk_count}: error={chunk.error}, is_done={chunk.is_done}, text_len={len(chunk.text) if chunk.text else 0}")
-                
+
                 if chunk.error:
                     logger.error(f"Stream error: {chunk.error}")
                     break
-                
+
                 if chunk.is_done:
                     print("\n✓ Response complete!", flush=True)
                     logger.info(f"Stream completed after {chunk_count} chunks")
                     break
-                
+
                 if chunk.text:
                     # Print directly to console with flush for immediate display
-                    print(chunk.text, end='', flush=True)
-            
+                    print(chunk.text, end="", flush=True)
+
             if chunk_count == 0:
                 logger.warning("No chunks received from Redis stream!")
                 logger.warning(f"Check Redis manually: redis-cli XLEN {stream_key}")
                 logger.warning(f"View stream contents: redis-cli XREAD STREAMS {stream_key} 0")
-                
+
         except Exception as ex:
             logger.error(f"Error reading from Redis: {ex}", exc_info=True)
 
@@ -140,47 +139,47 @@ def run_client(agent_client: DurableAIAgentClient) -> None:
     # Get a reference to the TravelPlanner agent
     logger.debug("Getting reference to TravelPlanner agent...")
     travel_planner = agent_client.get_agent("TravelPlanner")
-    
+
     # Create a new thread for the conversation
     thread = travel_planner.get_new_thread()
     if not thread.session_id:
         logger.error("Failed to create a new thread with session ID!")
         return
-    
+
     key = thread.session_id.key
     logger.info(f"Thread ID: {key}")
-    
+
     # Get user input
     print("\nEnter your travel planning request:")
     user_message = input("> ").strip()
-    
+
     if not user_message:
         logger.warning("No input provided. Using default message.")
         user_message = "Plan a 3-day trip to Tokyo with emphasis on culture and food"
-    
+
     logger.info(f"\nYou: {user_message}\n")
     logger.info("TravelPlanner (streaming from Redis):")
     logger.info("-" * 80)
-    
+
     # Start the agent run with wait_for_response=False for non-blocking execution
     # This signals the agent to start processing without waiting for completion
     # The agent will execute in the background and write chunks to Redis
     travel_planner.run(user_message, thread=thread, options={"wait_for_response": False})
-    
+
     # Stream the response from Redis
     # This demonstrates that the client can stream from Redis while
     # the agent is still processing (or after it completes)
     asyncio.run(stream_from_redis(str(key)))
-    
+
     logger.info("\nDemo completed!")
 
 
 if __name__ == "__main__":
     from dotenv import load_dotenv
     load_dotenv()
-    
+
     # Create the client
     client = get_client()
-    
+
     # Run the demo
     run_client(client)

python/samples/getting_started/durabletask/03_single_agent_streaming/redis_stream_response_handler.py

@@ -8,9 +8,9 @@ as a message broker. It enables clients to disconnect and reconnect without losi
 
 import asyncio
 import time
+from collections.abc import AsyncIterator
 from dataclasses import dataclass
 from datetime import timedelta
-from collections.abc import AsyncIterator
 
 import redis.asyncio as aioredis
 

python/samples/getting_started/durabletask/03_single_agent_streaming/sample.py

@@ -20,40 +20,40 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 # Configure logging (must be after imports to override their basicConfig)
 logging.basicConfig(level=logging.INFO, force=True)
 logger = logging.getLogger(__name__)
 
+
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Durable Task Agent Sample with Redis Streaming...")
 
     silent_handler = logging.NullHandler()
-    
+
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents and callbacks using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         agent_client = get_client(log_handler=silent_handler)
-        
+
         try:
             # Run client interactions using helper function
             run_client(agent_client)
         except Exception as e:
             logger.exception(f"Error during agent interaction: {e}")
-        
+
         logger.debug("Sample completed. Worker shutting down...")
 
 

python/samples/getting_started/durabletask/03_single_agent_streaming/tools.py

@@ -153,13 +153,12 @@ def _get_weather_recommendation(condition: str) -> str:
 
     if "rain" in condition_lower or "drizzle" in condition_lower:
         return "Bring an umbrella and waterproof jacket. Consider indoor activities for backup."
-    elif "fog" in condition_lower:
+    if "fog" in condition_lower:
         return "Morning visibility may be limited. Plan outdoor sightseeing for afternoon."
-    elif "cold" in condition_lower:
+    if "cold" in condition_lower:
         return "Layer up with warm clothing. Hot drinks and cozy cafés recommended."
-    elif "hot" in condition_lower or "warm" in condition_lower:
+    if "hot" in condition_lower or "warm" in condition_lower:
         return "Stay hydrated and use sunscreen. Plan strenuous activities for cooler morning hours."
-    elif "thunder" in condition_lower or "storm" in condition_lower:
+    if "thunder" in condition_lower or "storm" in condition_lower:
         return "Keep an eye on weather updates. Have indoor alternatives ready."
-    else:
-        return "Pleasant conditions expected. Great day for outdoor exploration!"
+    return "Pleasant conditions expected. Great day for outdoor exploration!"

python/samples/getting_started/durabletask/03_single_agent_streaming/worker.py

@@ -27,7 +27,6 @@ from agent_framework.azure import (
 )
 from azure.identity import AzureCliCredential, DefaultAzureCredential
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
-
 from redis_stream_response_handler import RedisStreamResponseHandler
 from tools import get_local_events, get_weather_forecast
 
@@ -186,12 +185,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -212,34 +211,34 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Create the Redis streaming callback
     redis_callback = RedisStreamCallback()
-    
+
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker, callback=redis_callback)
-    
+
     # Create and register the TravelPlanner agent
     logger.debug("Creating and registering TravelPlanner agent...")
     travel_agent = create_travel_agent()
     agent_worker.add_agent(travel_agent)
-    
+
     logger.debug(f"✓ Registered agent: {travel_agent.name}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Agent Worker with Redis Streaming...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agent and callback
     setup_worker(worker)
-    
+
     # Start the worker
     logger.debug("Worker started and listening for requests...")
     worker.start()
-    
+
     try:
         # Keep the worker running
         while True:

python/samples/getting_started/durabletask/04_single_agent_orchestration_chaining/client.py

@@ -41,12 +41,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -63,32 +63,32 @@ def run_client(client: DurableTaskSchedulerClient) -> None:
         client: The DurableTaskSchedulerClient instance
     """
     logger.debug("Starting single agent chaining orchestration...")
-    
+
     # Start the orchestration
     instance_id = client.schedule_new_orchestration(    # type: ignore
         orchestrator="single_agent_chaining_orchestration",
         input="",
     )
-    
+
     logger.info(f"Orchestration started with instance ID: {instance_id}")
     logger.debug("Waiting for orchestration to complete...")
-    
+
     # Retrieve the final state
     metadata = client.wait_for_orchestration_completion(
         instance_id=instance_id,
         timeout=300
     )
-    
+
     if metadata and metadata.runtime_status.name == "COMPLETED":
         result = metadata.serialized_output
-        
+
         logger.debug("Orchestration completed successfully!")
-        
+
         # Parse and display the result
         if result:
             final_text = json.loads(result)
             logger.info("Final refined sentence: %s \n", final_text)
-        
+
     elif metadata:
         logger.error(f"Orchestration ended with status: {metadata.runtime_status.name}")
         if metadata.serialized_output:
@@ -100,10 +100,10 @@ def run_client(client: DurableTaskSchedulerClient) -> None:
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task Single Agent Chaining Orchestration Client...")
-    
+
     # Create client using helper function
     client = get_client()
-    
+
     try:
         run_client(client)
     except Exception as e:

python/samples/getting_started/durabletask/04_single_agent_orchestration_chaining/sample.py

@@ -21,10 +21,9 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 # Configure logging
@@ -35,22 +34,22 @@ logger = logging.getLogger(__name__)
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Single Agent Orchestration Chaining Sample...")
-    
+
     silent_handler = logging.NullHandler()
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents and orchestrations using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         client = get_client(log_handler=silent_handler)
-        
+
         logger.debug("CLIENT: Starting orchestration...")
-        
+
         # Run the client in the same process
         try:
             run_client(client)
@@ -60,7 +59,7 @@ def main():
             logger.exception(f"Error during orchestration: {e}")
         finally:
             logger.debug("Worker stopping...")
-    
+
     logger.debug("")
     logger.debug("Sample completed")
 

python/samples/getting_started/durabletask/04_single_agent_orchestration_chaining/worker.py

@@ -11,15 +11,15 @@ Prerequisites:
 """
 
 import asyncio
-from collections.abc import Generator
 import logging
 import os
+from collections.abc import Generator
 
 from agent_framework import AgentResponse, ChatAgent
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
-from durabletask.task import OrchestrationContext, Task
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
+from durabletask.task import OrchestrationContext, Task
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -42,7 +42,7 @@ def create_writer_agent() -> "ChatAgent":
         "You refine short pieces of text. When given an initial sentence you enhance it;\n"
         "when given an improved sentence you polish it further."
     )
-    
+
     return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
         name=WRITER_AGENT_NAME,
         instructions=instructions,
@@ -78,18 +78,18 @@ def single_agent_chaining_orchestration(
         str: The final refined text from the second agent run
     """
     logger.debug("[Orchestration] Starting single agent chaining...")
-    
+
     # Wrap the orchestration context to access agents
     agent_context = DurableAIAgentOrchestrationContext(context)
-    
+
     # Get the writer agent using the agent context
     writer = agent_context.get_agent(WRITER_AGENT_NAME)
-    
+
     # Create a new thread for the conversation - this will be shared across both runs
     writer_thread = writer.get_new_thread()
-    
+
     logger.debug(f"[Orchestration] Created thread: {writer_thread.session_id}")
-    
+
     prompt = "Write a concise inspirational sentence about learning."
     # First run: Generate an initial inspirational sentence
     logger.info("[Orchestration] First agent run: Generating initial sentence about: %s", prompt)
@@ -98,21 +98,21 @@ def single_agent_chaining_orchestration(
         thread=writer_thread,
     )
     logger.info(f"[Orchestration] Initial response: {initial_response.text}")
-    
+
     # Second run: Refine the initial response on the same thread
     improved_prompt = (
         f"Improve this further while keeping it under 25 words: "
         f"{initial_response.text}"
     )
-    
+
     logger.info("[Orchestration] Second agent run: Refining the sentence: %s", improved_prompt)
     refined_response = yield writer.run(
         messages=improved_prompt,
         thread=writer_thread,
     )
-    
+
     logger.info(f"[Orchestration] Refined response: {refined_response.text}")
-    
+
     logger.debug("[Orchestration] Chaining complete")
     return refined_response.text
 
@@ -134,12 +134,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -160,45 +160,45 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker)
-    
+
     # Create and register the Writer agent
     logger.debug("Creating and registering Writer agent...")
     writer_agent = create_writer_agent()
     agent_worker.add_agent(writer_agent)
-    
+
     logger.debug(f"✓ Registered agent: {writer_agent.name}")
-    
+
     # Register the orchestration function
     logger.debug("Registering orchestration function...")
     worker.add_orchestrator(single_agent_chaining_orchestration)    # type: ignore
     logger.debug(f"✓ Registered orchestration: {single_agent_chaining_orchestration.__name__}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Single Agent Chaining Worker with Orchestration...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agents and orchestrations
     setup_worker(worker)
-    
+
     logger.debug("Worker is ready and listening for requests...")
     logger.debug("Press Ctrl+C to stop.")
-    
+
     try:
         # Start the worker (this blocks until stopped)
         worker.start()
-        
+
         # Keep the worker running
         while True:
             await asyncio.sleep(1)
     except KeyboardInterrupt:
         logger.debug("Worker shutdown initiated")
-    
+
     logger.debug("Worker stopped")
 
 

python/samples/getting_started/durabletask/05_multi_agent_orchestration_concurrency/client.py

@@ -41,12 +41,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -68,25 +68,25 @@ def run_client(client: DurableTaskSchedulerClient, prompt: str = "What is temper
         orchestrator="multi_agent_concurrent_orchestration",
         input=prompt,
     )
-    
+
     logger.info(f"Orchestration started with instance ID: {instance_id}")
     logger.debug("Waiting for orchestration to complete...")
-    
+
     # Retrieve the final state
     metadata = client.wait_for_orchestration_completion(
         instance_id=instance_id,
     )
-    
+
     if metadata and metadata.runtime_status.name == "COMPLETED":
         result = metadata.serialized_output
-        
+
         logger.debug("Orchestration completed successfully!")
-                
+
         # Parse and display the result
         if result:
             result_json = json.loads(result) if isinstance(result, str) else result
             logger.info("Orchestration Results:\n%s", json.dumps(result_json, indent=2))
-        
+
     elif metadata:
         logger.error(f"Orchestration ended with status: {metadata.runtime_status.name}")
         if metadata.serialized_output:
@@ -98,10 +98,10 @@ def run_client(client: DurableTaskSchedulerClient, prompt: str = "What is temper
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task Multi-Agent Orchestration Client...")
-    
+
     # Create client using helper function
     client = get_client()
-    
+
     try:
         run_client(client)
     except Exception as e:

python/samples/getting_started/durabletask/05_multi_agent_orchestration_concurrency/sample.py

@@ -18,10 +18,9 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 # Configure logging
@@ -32,20 +31,20 @@ logger = logging.getLogger(__name__)
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Durable Task Multi-Agent Orchestration Sample (Combined Worker + Client)...")
-    
+
     silent_handler = logging.NullHandler()
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents and orchestrations using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         client = get_client(log_handler=silent_handler)
-        
+
         # Define the prompt
         prompt = "What is temperature?"
         logger.debug("CLIENT: Starting orchestration...")
@@ -55,7 +54,7 @@ def main():
             run_client(client, prompt)
         except Exception as e:
             logger.exception(f"Error during sample execution: {e}")
-        
+
         logger.debug("Sample completed. Worker shutting down...")
 
 

python/samples/getting_started/durabletask/05_multi_agent_orchestration_concurrency/worker.py

@@ -11,16 +11,16 @@ Prerequisites:
 """
 
 import asyncio
-from collections.abc import Generator
 import logging
 import os
+from collections.abc import Generator
 from typing import Any
 
 from agent_framework import AgentResponse, ChatAgent
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
-from durabletask.task import OrchestrationContext, when_all, Task
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
+from durabletask.task import OrchestrationContext, Task, when_all
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -68,44 +68,44 @@ def multi_agent_concurrent_orchestration(context: OrchestrationContext, prompt:
     Returns:
         dict: Dictionary with 'physicist' and 'chemist' response texts
     """
-    
+
     logger.info(f"[Orchestration] Starting concurrent execution for prompt: {prompt}")
-    
+
     # Wrap the orchestration context to access agents
     agent_context = DurableAIAgentOrchestrationContext(context)
-    
+
     # Get agents using the agent context (returns DurableAIAgent proxies)
     physicist = agent_context.get_agent(PHYSICIST_AGENT_NAME)
     chemist = agent_context.get_agent(CHEMIST_AGENT_NAME)
-    
+
     # Create separate threads for each agent
     physicist_thread = physicist.get_new_thread()
     chemist_thread = chemist.get_new_thread()
-    
+
     logger.debug(f"[Orchestration] Created threads - Physicist: {physicist_thread.session_id}, Chemist: {chemist_thread.session_id}")
-    
+
     # Create tasks from agent.run() calls - these return DurableAgentTask instances
     physicist_task = physicist.run(messages=str(prompt), thread=physicist_thread)
     chemist_task = chemist.run(messages=str(prompt), thread=chemist_thread)
-    
+
     logger.debug("[Orchestration] Created agent tasks, executing concurrently...")
-    
+
     # Execute both tasks concurrently using when_all
     # The DurableAgentTask instances wrap the underlying entity calls
     task_results = yield when_all([physicist_task, chemist_task])
-    
+
     logger.debug("[Orchestration] Both agents completed")
-    
+
     # Extract results from the tasks - DurableAgentTask yields AgentResponse
     physicist_result: AgentResponse = task_results[0]
     chemist_result: AgentResponse = task_results[1]
-    
+
     result = {
         "physicist": physicist_result.text,
         "chemist": chemist_result.text,
     }
-    
-    logger.debug(f"[Orchestration] Aggregated results ready")
+
+    logger.debug("[Orchestration] Aggregated results ready")
     return result
 
 
@@ -126,12 +126,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -152,48 +152,48 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker)
-    
+
     # Create and register both agents
     logger.debug("Creating and registering agents...")
     physicist_agent = create_physicist_agent()
     chemist_agent = create_chemist_agent()
-    
+
     agent_worker.add_agent(physicist_agent)
     agent_worker.add_agent(chemist_agent)
-    
+
     logger.debug(f"✓ Registered agents: {physicist_agent.name}, {chemist_agent.name}")
-    
+
     # Register the orchestration function
     logger.debug("Registering orchestration function...")
     worker.add_orchestrator(multi_agent_concurrent_orchestration)   # type: ignore
     logger.debug(f"✓ Registered orchestration: {multi_agent_concurrent_orchestration.__name__}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Multi-Agent Worker with Orchestration...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agents and orchestrations
     setup_worker(worker)
-    
+
     logger.debug("Worker is ready and listening for requests...")
     logger.debug("Press Ctrl+C to stop.")
-    
+
     try:
         # Start the worker (this blocks until stopped)
         worker.start()
-        
+
         # Keep the worker running
         while True:
             await asyncio.sleep(1)
     except KeyboardInterrupt:
         logger.debug("Worker shutdown initiated")
-    
+
     logger.debug("Worker stopped")
 
 

python/samples/getting_started/durabletask/06_multi_agent_orchestration_conditionals/client.py

@@ -39,12 +39,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -70,36 +70,36 @@ def run_client(
         "email_id": email_id,
         "email_content": email_content,
     }
-    
+
     logger.debug("Starting spam detection orchestration...")
-    
+
     # Start the orchestration with the email payload
     instance_id = client.schedule_new_orchestration(    # type: ignore
         orchestrator="spam_detection_orchestration",
         input=payload,
     )
-    
+
     logger.debug(f"Orchestration started with instance ID: {instance_id}")
     logger.debug("Waiting for orchestration to complete...")
-    
+
     # Retrieve the final state
     metadata = client.wait_for_orchestration_completion(
         instance_id=instance_id,
         timeout=300
     )
-    
+
     if metadata and metadata.runtime_status.name == "COMPLETED":
         result = metadata.serialized_output
-        
+
         logger.debug("Orchestration completed successfully!")
-        
+
         # Parse and display the result
         if result:
             # Remove quotes if present
             if result.startswith('"') and result.endswith('"'):
                 result = result[1:-1]
             logger.info(f"Result: {result}")
-        
+
     elif metadata:
         logger.error(f"Orchestration ended with status: {metadata.runtime_status.name}")
         if metadata.serialized_output:
@@ -111,29 +111,29 @@ def run_client(
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task Spam Detection Orchestration Client...")
-    
+
     # Create client using helper function
     client = get_client()
-    
+
     try:
         # Test with a legitimate email
         logger.info("TEST 1: Legitimate Email")
-        
+
         run_client(
             client,
             email_id="email-001",
             email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week."
         )
-        
+
         # Test with a spam email
         logger.info("TEST 2: Spam Email")
-        
+
         run_client(
             client,
             email_id="email-002",
             email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
         )
-        
+
     except Exception as e:
         logger.exception(f"Error during orchestration: {e}")
     finally:

python/samples/getting_started/durabletask/06_multi_agent_orchestration_conditionals/sample.py

@@ -18,10 +18,9 @@ To run this sample:
 
 import logging
 
-from dotenv import load_dotenv
-
 # Import helper functions from worker and client modules
 from client import get_client, run_client
+from dotenv import load_dotenv
 from worker import get_worker, setup_worker
 
 logging.basicConfig(
@@ -34,43 +33,43 @@ logger = logging.getLogger()
 def main():
     """Main entry point - runs both worker and client in single process."""
     logger.debug("Starting Durable Task Spam Detection Orchestration Sample (Combined Worker + Client)...")
-    
+
     silent_handler = logging.NullHandler()
     # Create and start the worker using helper function and context manager
     with get_worker(log_handler=silent_handler) as dts_worker:
         # Register agents, orchestrations, and activities using helper function
         setup_worker(dts_worker)
-        
+
         # Start the worker
         dts_worker.start()
         logger.debug("Worker started and listening for requests...")
-        
+
         # Create the client using helper function
         client = get_client(log_handler=silent_handler)
         logger.debug("CLIENT: Starting orchestration tests...")
-        
+
         try:
             # Test 1: Legitimate email
             # logger.info("TEST 1: Legitimate Email")
-            
+
             run_client(
                 client,
                 email_id="email-001",
                 email_content="Hello! I wanted to reach out about our upcoming project meeting scheduled for next week."
             )
-                
+
             # Test 2: Spam email
             logger.info("TEST 2: Spam Email")
-            
+
             run_client(
                 client,
                 email_id="email-002",
                 email_content="URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
             )
-            
+
         except Exception as e:
             logger.exception(f"Error during sample execution: {e}")
-        
+
         logger.debug("Sample completed. Worker shutting down...")
 
 

python/samples/getting_started/durabletask/06_multi_agent_orchestration_conditionals/worker.py

@@ -11,16 +11,16 @@ Prerequisites:
 """
 
 import asyncio
-from collections.abc import Generator
 import logging
 import os
+from collections.abc import Generator
 from typing import Any, cast
 
 from agent_framework import AgentResponse, ChatAgent
 from agent_framework.azure import AzureOpenAIChatClient, DurableAIAgentOrchestrationContext, DurableAIAgentWorker
 from azure.identity import AzureCliCredential, DefaultAzureCredential
-from durabletask.task import ActivityContext, OrchestrationContext, Task
 from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker
+from durabletask.task import ActivityContext, OrchestrationContext, Task
 from pydantic import BaseModel, ValidationError
 
 # Configure logging
@@ -118,24 +118,24 @@ def spam_detection_orchestration(context: OrchestrationContext, payload_raw: Any
         str: Result message from activity functions
     """
     logger.debug("[Orchestration] Starting spam detection orchestration")
-    
+
     # Validate input
     if not isinstance(payload_raw, dict):
         raise ValueError("Email data is required")
-    
+
     try:
         payload = EmailPayload.model_validate(payload_raw)
     except ValidationError as exc:
         raise ValueError(f"Invalid email payload: {exc}") from exc
-    
+
     logger.debug(f"[Orchestration] Processing email ID: {payload.email_id}")
-    
+
     # Wrap the orchestration context to access agents
     agent_context = DurableAIAgentOrchestrationContext(context)
-    
+
     # Get spam detection agent
     spam_agent = agent_context.get_agent(SPAM_AGENT_NAME)
-    
+
     # Run spam detection
     spam_prompt = (
         "Analyze this email for spam content and return a JSON response with 'is_spam' (boolean) "
@@ -143,52 +143,52 @@ def spam_detection_orchestration(context: OrchestrationContext, payload_raw: Any
         f"Email ID: {payload.email_id}\n"
         f"Content: {payload.email_content}"
     )
-    
+
     logger.info("[Orchestration] Running spam detection agent: %s", spam_prompt)
     spam_result_task = spam_agent.run(
         messages=spam_prompt,
         options={"response_format": SpamDetectionResult},
     )
-    
+
     spam_result_raw: AgentResponse = yield spam_result_task
     spam_result = cast(SpamDetectionResult, spam_result_raw.value)
-    
+
     logger.info("[Orchestration] Spam detection result: is_spam=%s", spam_result.is_spam)
-    
+
     # Branch based on spam detection result
     if spam_result.is_spam:
         logger.debug("[Orchestration] Email is spam, handling...")
         result_task: Task[str] = context.call_activity("handle_spam_email", input=spam_result.reason)
         result: str = yield result_task
         return result
-    
+
     # Email is legitimate - draft a response
     logger.debug("[Orchestration] Email is legitimate, drafting response...")
-    
+
     email_agent = agent_context.get_agent(EMAIL_AGENT_NAME)
-    
+
     email_prompt = (
         "Draft a professional response to this email. Return a JSON response with a 'response' field "
         "containing the reply:\n\n"
         f"Email ID: {payload.email_id}\n"
         f"Content: {payload.email_content}"
     )
-    
+
     logger.info("[Orchestration] Running email assistant agent: %s", email_prompt)
     email_result_task = email_agent.run(
         messages=email_prompt,
         options={"response_format": EmailResponse},
     )
-    
+
     email_result_raw: AgentResponse = yield email_result_task
     email_result = cast(EmailResponse, email_result_raw.value)
-    
+
     logger.debug("[Orchestration] Email response drafted, sending...")
     result_task: Task[str] = context.call_activity("send_email", input=email_result.response)
     result: str = yield result_task
 
     logger.info("Sent Email: %s", result)
-    
+
     return result
 
 
@@ -209,12 +209,12 @@ def get_worker(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerWorker(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -235,55 +235,55 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker:
     """
     # Wrap it with the agent worker
     agent_worker = DurableAIAgentWorker(worker)
-    
+
     # Create and register both agents
     logger.debug("Creating and registering agents...")
     spam_agent = create_spam_agent()
     email_agent = create_email_agent()
-    
+
     agent_worker.add_agent(spam_agent)
     agent_worker.add_agent(email_agent)
-    
+
     logger.debug(f"✓ Registered agents: {spam_agent.name}, {email_agent.name}")
-    
+
     # Register activity functions
     logger.debug("Registering activity functions...")
     worker.add_activity(handle_spam_email)  # type: ignore[arg-type]
     worker.add_activity(send_email)  # type: ignore[arg-type]
-    logger.debug(f"✓ Registered activity: handle_spam_email")
-    logger.debug(f"✓ Registered activity: send_email")
-    
+    logger.debug("✓ Registered activity: handle_spam_email")
+    logger.debug("✓ Registered activity: send_email")
+
     # Register the orchestration function
     logger.debug("Registering orchestration function...")
     worker.add_orchestrator(spam_detection_orchestration)   # type: ignore[arg-type]
     logger.debug(f"✓ Registered orchestration: {spam_detection_orchestration.__name__}")
-    
+
     return agent_worker
 
 
 async def main():
     """Main entry point for the worker process."""
     logger.debug("Starting Durable Task Spam Detection Worker with Orchestration...")
-    
+
     # Create a worker using the helper function
     worker = get_worker()
-    
+
     # Setup worker with agents, orchestrations, and activities
     setup_worker(worker)
-    
+
     logger.debug("Worker is ready and listening for requests...")
     logger.debug("Press Ctrl+C to stop.")
-    
+
     try:
         # Start the worker (this blocks until stopped)
         worker.start()
-        
+
         # Keep the worker running
         while True:
             await asyncio.sleep(1)
     except KeyboardInterrupt:
         logger.debug("Worker shutdown initiated")
-    
+
     logger.debug("Worker stopped")
 
 

python/samples/getting_started/durabletask/07_single_agent_orchestration_hitl/client.py

@@ -45,12 +45,12 @@ def get_client(
     """
     taskhub_name = taskhub or os.getenv("TASKHUB", "default")
     endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
-    
+
     logger.debug(f"Using taskhub: {taskhub_name}")
     logger.debug(f"Using endpoint: {endpoint_url}")
-    
+
     credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
-    
+
     return DurableTaskSchedulerClient(
         host_address=endpoint_url,
         secure_channel=endpoint_url != "http://localhost:8080",
@@ -70,16 +70,16 @@ def _log_completion_result(
     """
     if metadata and metadata.runtime_status.name == "COMPLETED":
         result = metadata.serialized_output
-        
-        logger.debug(f"Orchestration completed successfully!")
-        
+
+        logger.debug("Orchestration completed successfully!")
+
         if result:
             try:
                 result_dict = json.loads(result)
                 logger.info("Final Result: %s", json.dumps(result_dict, indent=2))
             except json.JSONDecodeError:
                 logger.debug(f"Result: {result}")
-        
+
     elif metadata:
         logger.error(f"Orchestration ended with status: {metadata.runtime_status.name}")
         if metadata.serialized_output:
@@ -105,7 +105,7 @@ def _wait_and_log_completion(
         instance_id=instance_id,
         timeout=timeout
     )
-    
+
     _log_completion_result(metadata)
 
 
@@ -127,18 +127,18 @@ def send_approval(
         "approved": approved,
         "feedback": feedback
     }
-    
+
     logger.debug(f"Sending {'APPROVAL' if approved else 'REJECTION'} to instance {instance_id}")
     if feedback:
         logger.debug(f"Feedback: {feedback}")
-    
+
     # Raise the external event
     client.raise_orchestration_event(
         instance_id=instance_id,
         event_name=HUMAN_APPROVAL_EVENT,
         data=approval_data
     )
-    
+
     logger.debug("Event sent successfully")
 
 
@@ -160,14 +160,14 @@ def wait_for_notification(
         True if notification detected, False if timeout
     """
     logger.debug("Waiting for orchestration to reach notification point...")
-    
+
     start_time = time.time()
     while time.time() - start_time < timeout_seconds:
         try:
             metadata = client.get_orchestration_state(
                 instance_id=instance_id,
             )
-            
+
             if metadata:
                 # Check if we're waiting for approval by examining custom status
                 if metadata.serialized_custom_status:
@@ -183,19 +183,19 @@ def wait_for_notification(
                         if metadata.serialized_custom_status.lower().startswith("requesting human feedback"):
                             logger.debug("Orchestration is requesting human feedback")
                             return True
-                
+
                 # Check for terminal states
                 if metadata.runtime_status.name == "COMPLETED":
                     logger.debug("Orchestration already completed")
                     return False
-                elif metadata.runtime_status.name == "FAILED":
+                if metadata.runtime_status.name == "FAILED":
                     logger.error("Orchestration failed")
                     return False
         except Exception as e:
             logger.debug(f"Status check: {e}")
-        
+
         time.sleep(1)
-    
+
     logger.warning("Timeout waiting for notification")
     return False
 
@@ -208,94 +208,93 @@ def run_interactive_client(client: DurableTaskSchedulerClient) -> None:
     """
     # Get user inputs
     logger.debug("Content Generation - Human-in-the-Loop")
-    
+
     topic = input("Enter the topic for content generation: ").strip()
     if not topic:
         topic = "The benefits of cloud computing"
         logger.info(f"Using default topic: {topic}")
-    
+
     max_attempts_str = input("Enter max review attempts (default: 3): ").strip()
     max_review_attempts = int(max_attempts_str) if max_attempts_str else 3
-    
+
     timeout_hours_str = input("Enter approval timeout in hours (default: 5): ").strip()
     timeout_hours = float(timeout_hours_str) if timeout_hours_str else 5.0
     approval_timeout_seconds = int(timeout_hours * 3600)
-    
+
     payload = {
         "topic": topic,
         "max_review_attempts": max_review_attempts,
         "approval_timeout_seconds": approval_timeout_seconds
     }
-    
+
     logger.debug(f"Configuration: Topic={topic}, Max attempts={max_review_attempts}, Timeout={timeout_hours}h")
-    
+
     # Start the orchestration
     logger.debug("Starting content generation orchestration...")
     instance_id = client.schedule_new_orchestration(    # type: ignore
         orchestrator="content_generation_hitl_orchestration",
         input=payload,
     )
-    
+
     logger.info(f"Orchestration started with instance ID: {instance_id}")
-    
+
     # Review loop
     attempt = 1
     while attempt <= max_review_attempts:
         logger.info(f"Review Attempt {attempt}/{max_review_attempts}")
-        
+
         # Wait for orchestration to reach notification point
         logger.debug("Waiting for content generation...")
         if not wait_for_notification(client, instance_id, timeout_seconds=120):
             logger.error("Failed to receive notification. Orchestration may have completed or failed.")
             break
-        
+
         logger.info("Content is ready for review! Please review the content in the worker logs.")
-        
+
         # Get user decision
         while True:
             decision = input("Do you approve this content? (yes/no): ").strip().lower()
-            if decision in ['yes', 'y', 'no', 'n']:
+            if decision in ["yes", "y", "no", "n"]:
                 break
             logger.info("Please enter 'yes' or 'no'")
-        
-        approved = decision in ['yes', 'y']
-        
+
+        approved = decision in ["yes", "y"]
+
         if approved:
             logger.debug("Sending approval...")
             send_approval(client, instance_id, approved=True)
             logger.info("Approval sent. Waiting for orchestration to complete...")
             _wait_and_log_completion(client, instance_id, timeout=60)
             break
-        else:
-            feedback = input("Enter feedback for improvement: ").strip()
-            if not feedback:
-                feedback = "Please revise the content."
-            
-            logger.debug("Sending rejection with feedback...")
-            send_approval(client, instance_id, approved=False, feedback=feedback)
-            logger.info("Rejection sent. Content will be regenerated...")
-            
-            attempt += 1
-            
-            if attempt > max_review_attempts:
-                logger.info(f"Maximum review attempts ({max_review_attempts}) reached.")
-                _wait_and_log_completion(client, instance_id, timeout=30)
-                break
-            
-            # Small pause before next iteration
-            time.sleep(2)
+        feedback = input("Enter feedback for improvement: ").strip()
+        if not feedback:
+            feedback = "Please revise the content."
+
+        logger.debug("Sending rejection with feedback...")
+        send_approval(client, instance_id, approved=False, feedback=feedback)
+        logger.info("Rejection sent. Content will be regenerated...")
+
+        attempt += 1
+
+        if attempt > max_review_attempts:
+            logger.info(f"Maximum review attempts ({max_review_attempts}) reached.")
+            _wait_and_log_completion(client, instance_id, timeout=30)
+            break
+
+        # Small pause before next iteration
+        time.sleep(2)
 
 
 async def main() -> None:
     """Main entry point for the client application."""
     logger.debug("Starting Durable Task HITL Content Generation Client")
-    
+
     # Create client using helper function
     client = get_client()
-    
+
     try:
         run_interactive_client(client)
-        
+
     except KeyboardInterrupt:
         logger.info("Interrupted by user")
     except Exception as e: