Python: [BREAKING] update to v1.0.0 (#5062)

* updates to final deprecated pieces and versions

* fix mypy

* fix readme links

python/samples/03-workflows/agents/azure_chat_agents_tool_calls_with_feedback.py

@@ -149,7 +149,10 @@ class Coordinator(Executor):
             # Human approved the draft as-is; forward it unchanged.
             await ctx.send_message(
                 AgentExecutorRequest(
-                    messages=[*original_request.conversation, *[Message("user", text="The draft is approved as-is.")]],
+                    messages=[
+                        *original_request.conversation,
+                        *[Message("user", contents=["The draft is approved as-is."])],
+                    ],
                     should_respond=True,
                 ),
                 target_id=self.final_editor_id,
@@ -164,7 +167,7 @@ class Coordinator(Executor):
             "Keep the response under 120 words and reflect any requested tone adjustments."
         )
         await ctx.send_message(
-            AgentExecutorRequest(messages=[Message("user", text=instruction)], should_respond=True),
+            AgentExecutorRequest(messages=[Message("user", contents=[instruction])], should_respond=True),
             target_id=self.writer_id,
         )
 

python/samples/03-workflows/agents/workflow_as_agent_reflection_pattern.py

@@ -84,15 +84,17 @@ class Reviewer(Executor):
         messages = [
             Message(
                 role="system",
-                text=(
-                    "You are a reviewer for an AI agent. Provide feedback on the "
-                    "exchange between a user and the agent. Indicate approval only if:\n"
-                    "- Relevance: response addresses the query\n"
-                    "- Accuracy: information is correct\n"
-                    "- Clarity: response is easy to understand\n"
-                    "- Completeness: response covers all aspects\n"
-                    "Do not approve until all criteria are satisfied."
-                ),
+                contents=[
+                    (
+                        "You are a reviewer for an AI agent. Provide feedback on the "
+                        "exchange between a user and the agent. Indicate approval only if:\n"
+                        "- Relevance: response addresses the query\n"
+                        "- Accuracy: information is correct\n"
+                        "- Clarity: response is easy to understand\n"
+                        "- Completeness: response covers all aspects\n"
+                        "Do not approve until all criteria are satisfied."
+                    )
+                ],
             )
         ]
         # Add conversation history.

python/samples/03-workflows/checkpoint/checkpoint_with_human_in_the_loop.py

@@ -97,7 +97,7 @@ class BriefPreparer(Executor):
         # Hand the prompt to the writer agent. We always route through the
         # workflow context so the runtime can capture messages for checkpointing.
         await ctx.send_message(
-            AgentExecutorRequest(messages=[Message("user", text=prompt)], should_respond=True),
+            AgentExecutorRequest(messages=[Message("user", contents=[prompt])], should_respond=True),
             target_id=self._agent_id,
         )
 
@@ -159,7 +159,7 @@ class ReviewGateway(Executor):
             f"Human guidance: {reply}"
         )
         await ctx.send_message(
-            AgentExecutorRequest(messages=[Message("user", text=prompt)], should_respond=True),
+            AgentExecutorRequest(messages=[Message("user", contents=[prompt])], should_respond=True),
             target_id=self._writer_id,
         )
 

python/samples/03-workflows/control-flow/edge_condition.py

@@ -129,7 +129,7 @@ async def to_email_assistant_request(
     """
     # Bridge executor. Converts a structured DetectionResult into a Message and forwards it as a new request.
     detection = DetectionResult.model_validate_json(response.agent_response.text)
-    user_msg = Message("user", text=detection.email_content)
+    user_msg = Message("user", contents=[detection.email_content])
     await ctx.send_message(AgentExecutorRequest(messages=[user_msg], should_respond=True))
 
 
@@ -200,7 +200,7 @@ async def main() -> None:
 
     # Execute the workflow. Since the start is an AgentExecutor, pass an AgentExecutorRequest.
     # The workflow completes when it becomes idle (no more work to do).
-    request = AgentExecutorRequest(messages=[Message("user", text=email)], should_respond=True)
+    request = AgentExecutorRequest(messages=[Message("user", contents=[email])], should_respond=True)
     events = await workflow.run(request)
     outputs = events.get_outputs()
     if outputs:

python/samples/03-workflows/control-flow/multi_selection_edge_group.py

@@ -97,7 +97,7 @@ async def store_email(email_text: str, ctx: WorkflowContext[AgentExecutorRequest
     ctx.set_state(CURRENT_EMAIL_ID_KEY, new_email.email_id)
 
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=new_email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[new_email.email_content])], should_respond=True)
     )
 
 
@@ -124,7 +124,7 @@ async def submit_to_email_assistant(analysis: AnalysisResult, ctx: WorkflowConte
 
     email: Email = ctx.get_state(f"{EMAIL_STATE_PREFIX}{analysis.email_id}")
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[email.email_content])], should_respond=True)
     )
 
 
@@ -139,7 +139,7 @@ async def summarize_email(analysis: AnalysisResult, ctx: WorkflowContext[AgentEx
     # Only called for long NotSpam emails by selection_func
     email: Email = ctx.get_state(f"{EMAIL_STATE_PREFIX}{analysis.email_id}")
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[email.email_content])], should_respond=True)
     )
 
 

python/samples/03-workflows/control-flow/simple_loop.py

@@ -102,7 +102,7 @@ class SubmitToJudgeAgent(Executor):
             f"Target: {self._target}\nGuess: {guess}\nResponse:"
         )
         await ctx.send_message(
-            AgentExecutorRequest(messages=[Message("user", text=prompt)], should_respond=True),
+            AgentExecutorRequest(messages=[Message("user", contents=[prompt])], should_respond=True),
             target_id=self._judge_agent_id,
         )
 

python/samples/03-workflows/control-flow/switch_case_edge_group.py

@@ -104,7 +104,7 @@ async def store_email(email_text: str, ctx: WorkflowContext[AgentExecutorRequest
 
     # Kick off the detector by forwarding the email as a user message to the spam_detection_agent.
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=new_email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[new_email.email_content])], should_respond=True)
     )
 
 
@@ -125,7 +125,7 @@ async def submit_to_email_assistant(detection: DetectionResult, ctx: WorkflowCon
     # Load the original content from workflow state using the id carried in DetectionResult.
     email: Email = ctx.get_state(f"{EMAIL_STATE_PREFIX}{detection.email_id}")
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[email.email_content])], should_respond=True)
     )
 
 

python/samples/03-workflows/human-in-the-loop/agents_with_HITL.py

@@ -103,7 +103,8 @@ class Coordinator(Executor):
             # Human approved the draft as-is; forward it unchanged.
             await ctx.send_message(
                 AgentExecutorRequest(
-                    messages=original_request.conversation + [Message("user", text="The draft is approved as-is.")],
+                    messages=original_request.conversation
+                    + [Message("user", contents=["The draft is approved as-is."])],
                     should_respond=True,
                 ),
                 target_id=self.final_editor_name,
@@ -118,7 +119,7 @@ class Coordinator(Executor):
             "Rewrite the draft from the previous assistant message into a polished final version. "
             "Keep the response under 120 words and reflect any requested tone adjustments."
         )
-        conversation.append(Message("user", text=instruction))
+        conversation.append(Message("user", contents=[instruction]))
         await ctx.send_message(
             AgentExecutorRequest(messages=conversation, should_respond=True),
             target_id=self.writer_name,

python/samples/03-workflows/human-in-the-loop/concurrent_request_info.py

@@ -85,13 +85,15 @@ async def aggregate_with_synthesis(results: list[AgentExecutorResponse]) -> Any:
 
     system_msg = Message(
         "system",
-        text=(
-            "You are a synthesis expert. Consolidate the following analyst perspectives "
-            "into one cohesive, balanced summary (3-4 sentences). If human guidance is provided, "
-            "prioritize aspects as directed."
-        ),
+        contents=[
+            (
+                "You are a synthesis expert. Consolidate the following analyst perspectives "
+                "into one cohesive, balanced summary (3-4 sentences). If human guidance is provided, "
+                "prioritize aspects as directed."
+            )
+        ],
     )
-    user_msg = Message("user", text="\n\n".join(expert_sections) + guidance_text)
+    user_msg = Message("user", contents=["\n\n".join(expert_sections) + guidance_text])
 
     response = await _chat_client.get_response([system_msg, user_msg])
     return response.messages[-1].text if response.messages else ""

python/samples/03-workflows/human-in-the-loop/guessing_game_with_human_input.py

@@ -91,7 +91,7 @@ class TurnManager(Executor):
         - Input is a simple starter token (ignored here).
         - Output is an AgentExecutorRequest that triggers the agent to produce a guess.
         """
-        user = Message("user", text="Start by making your first guess.")
+        user = Message("user", contents=["Start by making your first guess."])
         await ctx.send_message(AgentExecutorRequest(messages=[user], should_respond=True))
 
     @handler
@@ -150,7 +150,7 @@ class TurnManager(Executor):
             f"Feedback: {reply}. Your last guess was {last_guess}. "
             f"Use this feedback to adjust and make your next guess (1-10)."
         )
-        user_msg = Message("user", text=feedback_text)
+        user_msg = Message("user", contents=[feedback_text])
         await ctx.send_message(AgentExecutorRequest(messages=[user_msg], should_respond=True))
 
 

python/samples/03-workflows/orchestrations/concurrent_custom_aggregator.py

@@ -82,12 +82,14 @@ async def main() -> None:
         # Ask the model to synthesize a concise summary of the experts' outputs
         system_msg = Message(
             "system",
-            text=(
-                "You are a helpful assistant that consolidates multiple domain expert outputs "
-                "into one cohesive, concise summary with clear takeaways. Keep it under 200 words."
-            ),
+            contents=[
+                (
+                    "You are a helpful assistant that consolidates multiple domain expert outputs "
+                    "into one cohesive, concise summary with clear takeaways. Keep it under 200 words."
+                )
+            ],
         )
-        user_msg = Message("user", text="\n\n".join(expert_sections))
+        user_msg = Message("user", contents=["\n\n".join(expert_sections)])
 
         response = await client.get_response([system_msg, user_msg])
         # Return the model's final assistant text as the completion result

python/samples/03-workflows/parallelism/fan_out_fan_in_edges.py

@@ -49,7 +49,7 @@ class DispatchToExperts(Executor):
     @handler
     async def dispatch(self, prompt: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
         # Wrap the incoming prompt as a user message for each expert and request a response.
-        initial_message = Message("user", text=prompt)
+        initial_message = Message("user", contents=[prompt])
         await ctx.send_message(AgentExecutorRequest(messages=[initial_message], should_respond=True))
 
 

python/samples/03-workflows/state-management/state_with_agents.py

@@ -109,7 +109,7 @@ async def store_email(email_text: str, ctx: WorkflowContext[AgentExecutorRequest
     ctx.set_state(CURRENT_EMAIL_ID_KEY, new_email.email_id)
 
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=new_email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[new_email.email_content])], should_respond=True)
     )
 
 
@@ -140,7 +140,7 @@ async def submit_to_email_assistant(detection: DetectionResult, ctx: WorkflowCon
     # Load the original content by id from workflow state and forward it to the assistant.
     email: Email = ctx.get_state(f"{EMAIL_STATE_PREFIX}{detection.email_id}")
     await ctx.send_message(
-        AgentExecutorRequest(messages=[Message("user", text=email.email_content)], should_respond=True)
+        AgentExecutorRequest(messages=[Message("user", contents=[email.email_content])], should_respond=True)
     )
 
 

python/samples/03-workflows/visualization/concurrent_with_visualization.py

@@ -46,7 +46,7 @@ class DispatchToExperts(Executor):
     @handler
     async def dispatch(self, prompt: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
         # Wrap the incoming prompt as a user message for each expert and request a response.
-        initial_message = Message("user", text=prompt)
+        initial_message = Message("user", contents=[prompt])
         await ctx.send_message(AgentExecutorRequest(messages=[initial_message], should_respond=True))