Merge branch 'main' into copilot/move-workflow-and-agent-samples

python/samples/02-agents/declarative/README.md

@@ -68,7 +68,7 @@ Illustrates a basic agent using Azure OpenAI with structured responses.
 
 **Key concepts**: Azure OpenAI integration, credential management, structured outputs
 
-### 5. **OpenAI Responses Agent** ([`openai_responses_agent.py`](./openai_responses_agent.py))
+### 5. **OpenAI Responses Agent** ([`openai_agent.py`](./openai_agent.py))
 
 Demonstrates the simplest possible agent using OpenAI directly.
 
@@ -159,7 +159,7 @@ agent_factory = AgentFactory(
         "MyProvider": {
             "package": "my_custom_module",
             "name": "MyCustomChatClient",
-            "model_id_field": "model_id",
+            "model_field": "model",
         }
     }
 )
@@ -176,7 +176,7 @@ agent = agent_factory.create_agent_from_yaml_path(Path("custom_provider.yaml"))
 This allows you to extend the declarative framework with custom chat client implementations. The mapping requires:
 - **package**: The Python package/module to import from
 - **name**: The class name of your SupportsChatGetResponse implementation
-- **model_id_field**: The constructor parameter name that accepts the value of the `model.id` field from the YAML
+- **model_field**: The constructor parameter name that accepts the value of the `model.id` field from the YAML
 
 You can reference your custom provider using either `Provider.ApiType` format or just `Provider` in your YAML configuration, as long as it matches the registered mapping.
 

python/samples/02-agents/declarative/inline_yaml.py

@@ -18,7 +18,7 @@ Prerequisites:
 - `pip install agent-framework-foundry agent-framework-declarative --pre`
 - Set the following environment variables in a .env file or your environment:
     - FOUNDRY_PROJECT_ENDPOINT
-    - AZURE_OPENAI_MODEL
+    - FOUNDRY_MODEL
 """
 
 
@@ -31,7 +31,7 @@ instructions: Specialized diagnostic and issue detection agent for systems with
 description: A agent that performs diagnostics on systems and can escalate issues when critical errors are detected.
 
 model:
-  id: =Env.AZURE_OPENAI_MODEL
+  id: =Env.FOUNDRY_MODEL
 """
     # create the agent from the yaml
     async with (

python/samples/02-agents/declarative/microsoft_learn_agent.py

@@ -9,6 +9,20 @@ from dotenv import load_dotenv
 # Load environment variables from .env file
 load_dotenv()
 
+"""
+This sample demonstrates creating an agent from a declarative YAML file specification.
+
+It uses a MCP server to connect to the Microsoft Learn content and a FoundryChatClient.
+
+The yaml also has some chat options set, such as temperature and topP.
+These options do not work with newer OpenAI models, so ensure to use a compatible model such as gpt-4o-mini.
+
+Environment variables:
+- FOUNDRY_PROJECT_ENDPOINT: The endpoint URL for the Foundry project.
+- FOUNDRY_MODEL: The model ID to use for the agent, make sure it is compatible with the chat options specified in
+    the yaml, or remove the options.
+"""
+
 
 async def main():
     """Create an agent from a declarative yaml specification and run it."""

python/samples/02-agents/declarative/openai_agent.py

@@ -14,11 +14,8 @@ async def main():
     # get the path
     current_path = Path(__file__).parent
     yaml_path = current_path.parent.parent.parent.parent / "declarative-agents" / "agent-samples" / "openai" / "OpenAIResponses.yaml"
-    # load the yaml from the path
-    with yaml_path.open("r") as f:
-        yaml_str = f.read()
     # create the agent from the yaml
-    agent = AgentFactory(safe_mode=False).create_agent_from_yaml(yaml_str)
+    agent = AgentFactory(safe_mode=False).create_agent_from_yaml_path(yaml_path)
     # use the agent
     response = await agent.run("Why is the sky blue, answer in Dutch?")
     # Use response.value with try/except for safe parsing