Python: [BREAKING] Fix #3613 chat/agent message typing alignment (#3920)

* Fix #3613 message typing across chat and agents

* Address #3613 review feedback and sample input style

* refactor: use shared AgentRunMessages aliases (#3613)

* refactor: rename agent run input aliases for #3613

* samples: inline image content in run calls

* core: export AgentRunInputs from package init

* core: use explicit init re-exports without __all__

* updated logging and inits

* Fix core mypy export and samples XML note

* Remove AgentRunInputsOrNone and dedupe loggers

* Remove prepare_messages helper

* fix integration tests

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

@@ -178,12 +178,15 @@ The `configure_otel_providers()` function automatically reads **standard OpenTel
 > **Note**: These are standard OpenTelemetry environment variables. See the [OpenTelemetry spec](https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/) for more details.
 
 #### Logging
-Agent Framework has a built-in logging configuration that works well with telemetry. It sets the format to a standard format that includes timestamp, pathname, line number, and log level. You can use that by calling the `setup_logging()` function from the `agent_framework` module.
+Use standard Python logging configuration to align logs with telemetry output.
 
 ```python
-from agent_framework import setup_logging
+import logging
 
-setup_logging()
+logging.basicConfig(
+    format="[%(asctime)s - %(pathname)s:%(lineno)d - %(levelname)s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
 ```
 You can control at what level logging happens and thus what logs get exported, you can do this, by adding this:
 

python/samples/02-agents/observability/configure_otel_providers_with_parameters.py

@@ -2,11 +2,12 @@
 
 import argparse
 import asyncio
+import logging
 from contextlib import suppress
 from random import randint
 from typing import TYPE_CHECKING, Annotated, Literal
 
-from agent_framework import setup_logging, tool
+from agent_framework import tool
 from agent_framework.observability import configure_otel_providers, get_tracer
 from agent_framework.openai import OpenAIResponsesClient
 from opentelemetry import trace
@@ -31,7 +32,9 @@ Use this approach when you need custom exporter configuration beyond what enviro
 SCENARIOS = ["client", "client_stream", "tool", "all"]
 
 
-# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
+# NOTE: approval_mode="never_require" is for sample brevity.
+# Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py
+# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
 @tool(approval_mode="never_require")
 async def get_weather(
     location: Annotated[str, Field(description="The location to get the weather for.")],
@@ -101,7 +104,10 @@ async def main(scenario: Literal["client", "client_stream", "tool", "all"] = "al
     """Run the selected scenario(s)."""
 
     # Setup the logging with the more complete format
-    setup_logging()
+    logging.basicConfig(
+        format="[%(asctime)s - %(pathname)s:%(lineno)d - %(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
 
     # Create custom OTLP exporters with specific configuration
     # Note: You need to install opentelemetry-exporter-otlp-proto-grpc or -http separately

python/samples/02-agents/providers/azure_openai/azure_responses_client_image_analysis.py

@@ -2,7 +2,7 @@
 
 import asyncio
 
-from agent_framework import Content, Message
+from agent_framework import Content
 from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 
@@ -20,24 +20,17 @@ async def main():
     # 1. Create an Azure Responses agent with vision capabilities
     agent = AzureOpenAIResponsesClient(credential=AzureCliCredential()).as_agent(
         name="VisionAgent",
-        instructions="You are a helpful agent that can analyze images.",
+        instructions="You are a image analysist, you get a image and need to respond with what you see in the picture.",
     )
 
-    # 2. Create a simple message with both text and image content
-    user_message = Message(
-        role="user",
-        contents=[
-            Content.from_text("What do you see in this image?"),
-            Content.from_uri(
-                uri="https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=800",
-                media_type="image/jpeg",
-            ),
-        ],
-    )
-
-    # 3. Get the agent's response
+    # 2. Get the agent's response
     print("User: What do you see in this image? [Image provided]")
-    result = await agent.run(user_message)
+    result = await agent.run(
+        Content.from_uri(
+            uri="https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=800",
+            media_type="image/jpeg",
+        )
+    )
     print(f"Agent: {result.text}")
     print()
 

python/samples/02-agents/providers/openai/openai_responses_client_image_analysis.py

@@ -2,7 +2,7 @@
 
 import asyncio
 
-from agent_framework import Content, Message
+from agent_framework import Content
 from agent_framework.openai import OpenAIResponsesClient
 
 """
@@ -19,24 +19,17 @@ async def main():
     # 1. Create an OpenAI Responses agent with vision capabilities
     agent = OpenAIResponsesClient().as_agent(
         name="VisionAgent",
-        instructions="You are a helpful agent that can analyze images.",
+        instructions="You are a image analysist, you get a image and need to respond with what you see in the picture.",
     )
 
-    # 2. Create a simple message with both text and image content
-    user_message = Message(
-        role="user",
-        contents=[
-            Content.from_text(text="What do you see in this image?"),
-            Content.from_uri(
-                uri="https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=800",
-                media_type="image/jpeg",
-            ),
-        ],
-    )
-
-    # 3. Get the agent's response
+    # 2. Get the agent's response
     print("User: What do you see in this image? [Image provided]")
-    result = await agent.run(user_message)
+    result = await agent.run(
+        Content.from_uri(
+            uri="https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=800",
+            media_type="image/jpeg",
+        )
+    )
     print(f"Agent: {result.text}")
     print()
 

python/samples/README.md

@@ -38,6 +38,10 @@ export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
 
 For Azure authentication, run `az login` before running samples.
 
+## Note on XML tags
+
+Some sample files include XML-style snippet tags (for example `<snippet_name>` and `</snippet_name>`). These are used by our documentation tooling and can be ignored or removed when you use the samples outside this repository.
+
 ## Additional Resources
 
 - [Agent Framework Documentation](https://learn.microsoft.com/agent-framework/)