Python: Fix sample bugs: incorrect API params, wrong client types, and invalid options (#4983)

* Fix sample bugs: incorrect API params, wrong client types, and invalid options

- typed_options.py: Fix AnthropicClient model->model_id, wrap raw strings in Message objects for get_response(), fix reasoning_effort->reasoning dict, fix budget_tokens minimum (1024), use OpenAIChatClient not FoundryChatClient, remove unused import

- client_reasoning.py: Fix deprecated model_id to model param

- client_with_hosted_mcp.py: Remove invalid store=True kwarg from Agent.run()

- code_defined_skill.py: Fix precision kwarg to use function_invocation_kwargs

- Various other samples: Fix deprecated API usage and incorrect params

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR review comments

- client_with_hosted_mcp.py: Fix remaining store=True kwarg on line 68 to use options dict

- client_with_session.py: Change store=True to store=False to match in-memory persistence demo intent

- typed_options.py: Remove non-existent import and model key from docstring example

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* new sample fixes

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

python/samples/02-agents/tools/agent_as_tool_with_session_propagation.py

@@ -4,7 +4,7 @@ import asyncio
 from collections.abc import Awaitable, Callable
 
 from agent_framework import Agent, AgentContext, AgentSession, FunctionInvocationContext, tool
-from agent_framework.openai import OpenAIResponsesClient
+from agent_framework.openai import OpenAIChatClient
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -63,7 +63,7 @@ def recall_findings(ctx: FunctionInvocationContext) -> str:
 async def main() -> None:
     print("=== Agent-as-Tool: Session Propagation ===\n")
 
-    client = OpenAIResponsesClient()
+    client = OpenAIChatClient()
 
     research_agent = Agent(
         client=client,

python/samples/02-agents/tools/control_total_tool_executions.py

@@ -4,7 +4,7 @@ import asyncio
 from typing import Annotated
 
 from agent_framework import Agent, tool
-from agent_framework.openai import OpenAIResponsesClient
+from agent_framework.openai import OpenAIChatClient
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
@@ -81,7 +81,7 @@ async def scenario_max_iterations():
     print("Scenario 1: max_iterations — limit LLM roundtrips")
     print("=" * 60)
 
-    client = OpenAIResponsesClient()
+    client = OpenAIChatClient()
 
     # 1. Set max_iterations to 3 — the tool loop will run at most 3 roundtrips
     #    to the model before forcing a text response.
@@ -116,7 +116,7 @@ async def scenario_max_function_calls():
     print("Scenario 2: max_function_calls — limit total tool executions")
     print("=" * 60)
 
-    client = OpenAIResponsesClient()
+    client = OpenAIChatClient()
 
     # 1. Allow many iterations but cap total function calls to 4.
     #    If the model requests 3 parallel searches per iteration, after 2
@@ -158,7 +158,7 @@ async def scenario_max_invocations():
     print("=" * 60)
 
     agent = Agent(
-        client=OpenAIResponsesClient(),
+        client=OpenAIChatClient(),
         name="APIAgent",
         instructions="Use call_expensive_api when asked to analyze something.",
         tools=[call_expensive_api],
@@ -214,7 +214,7 @@ async def scenario_per_agent_tool_limits():
     agent_a_lookup = tool(name="lookup", approval_mode="never_require", max_invocations=2)(_do_lookup)
     agent_b_lookup = tool(name="lookup", approval_mode="never_require", max_invocations=5)(_do_lookup)
 
-    client = OpenAIResponsesClient()
+    client = OpenAIChatClient()
     agent_a = Agent(
         client=client,
         name="AgentA",
@@ -259,7 +259,7 @@ async def scenario_combined():
     print("Scenario 5: Combined — all mechanisms together")
     print("=" * 60)
 
-    client = OpenAIResponsesClient()
+    client = OpenAIChatClient()
 
     # 1. Configure the client with both iteration and function call limits.
     client.function_invocation_configuration["max_iterations"] = 5  # max 5 LLM roundtrips

python/samples/02-agents/tools/function_tool_recover_from_failures.py

@@ -4,7 +4,7 @@ import asyncio
 from typing import Annotated
 
 from agent_framework import Agent, tool
-from agent_framework.openai import OpenAIResponsesClient
+from agent_framework.openai import OpenAIChatClient
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
@@ -46,7 +46,7 @@ def safe_divide(
 async def main():
     # tools = Tools()
     agent = Agent(
-        client=OpenAIResponsesClient(),
+        client=OpenAIChatClient(),
         name="ToolAgent",
         instructions="Use the provided tools.",
         tools=[greet, safe_divide],

python/samples/02-agents/tools/function_tool_with_approval.py

@@ -5,7 +5,7 @@ from random import randrange
 from typing import TYPE_CHECKING, Annotated, Any
 
 from agent_framework import Agent, AgentResponse, Message, tool
-from agent_framework.openai import OpenAIResponsesClient
+from agent_framework.openai import OpenAIChatClient
 from dotenv import load_dotenv
 
 if TYPE_CHECKING:
@@ -134,7 +134,7 @@ async def run_weather_agent_with_approval(stream: bool) -> None:
     print(f"\n=== Weather Agent with Approval Required ({'Streaming' if stream else 'Non-Streaming'}) ===\n")
 
     async with Agent(
-        client=OpenAIResponsesClient(),
+        client=OpenAIChatClient(),
         name="WeatherAgent",
         instructions=("You are a helpful weather assistant. Use the get_weather tool to provide weather information."),
         tools=[get_weather, get_weather_detail],