Rename fields in agent metadata (#336)

* Rename fields in agent metadata

* team one fixes

* another fix

Author: jackgerrits

python/benchmarks/GAIA/Templates/TeamOne/scenario.py

@@ -29,7 +29,7 @@
 
 from agnext.components.models import AssistantMessage
 
-encoding = None 
+encoding = None
 def count_token(value: str) -> int:
     # TODO:: Migrate to model_client.count_tokens
     global encoding
@@ -40,7 +40,7 @@ def count_token(value: str) -> int:
 async def response_preparer(task: str, source: str, client: ChatCompletionClient, transcript: List[LLMMessage]) -> str:
     messages: List[LLMMessage] = []
 
-    # copy them to this context 
+    # copy them to this context
     for message in transcript:
         messages.append(
             UserMessage(
@@ -168,7 +168,7 @@ async def main() -> None:
 
     run_context = runtime.start()
 
-    actual_surfer = await runtime.try_get_underlying_agent_instance(web_surfer.id, type=MultimodalWebSurfer)  
+    actual_surfer = await runtime.try_get_underlying_agent_instance(web_surfer.id, type=MultimodalWebSurfer)
     await actual_surfer.init(model_client=client, downloads_folder=os.getcwd(), browser_channel="chromium")
 
     #await runtime.send_message(RequestReplyMessage(), user_proxy.id)
@@ -206,7 +206,7 @@ async def main() -> None:
     # Output the final answer
     actual_orchestrator = await runtime.try_get_underlying_agent_instance(orchestrator.id, type=LedgerOrchestrator)
     transcript: List[LLMMessage] = actual_orchestrator._chat_history # type: ignore
-    print(await response_preparer(task=task, source=(await orchestrator.metadata)["name"], client=client, transcript=transcript))
+    print(await response_preparer(task=task, source=(await orchestrator.metadata)["type"], client=client, transcript=transcript))
 
 
 

python/benchmarks/HumanEval/Templates/TwoAgents/scenario.py

@@ -100,7 +100,7 @@ async def handle_user_message(
         )
         assert isinstance(response.content, str)
         self._session_memory[session_id].append(
-            AssistantMessage(content=response.content, source=self.metadata["name"])
+            AssistantMessage(content=response.content, source=self.metadata["type"])
         )
 
         await self.publish_message(
@@ -138,7 +138,7 @@ async def handle_code_execution_result(
         )
         assert isinstance(response.content, str)
         self._session_memory[message.session_id].append(
-            AssistantMessage(content=response.content, source=self.metadata["name"])
+            AssistantMessage(content=response.content, source=self.metadata["type"])
         )
 
         if "TERMINATE" in response.content:

python/docs/src/cookbook/type-routed-agent.md

@@ -56,7 +56,7 @@ class MyAgent(TypeRoutedAgent):
         await self.publish_message(
             TextMessage(
                 content=f"I received a message from {message.source}. Message received #{self._received_count}",
-                source=self.metadata["name"],
+                source=self.metadata["type"],
             )
         )
 

python/samples/common/agents/_chat_completion_agent.py

@@ -175,7 +175,7 @@ async def _generate_response(
         # Get a response from the model.
         hisorical_messages = await self._memory.get_messages()
         response = await self._client.create(
-            self._system_messages + convert_messages_to_llm_messages(hisorical_messages, self.metadata["name"]),
+            self._system_messages + convert_messages_to_llm_messages(hisorical_messages, self.metadata["type"]),
             tools=self._tools,
             json_output=response_format == ResponseFormat.json_object,
         )
@@ -190,25 +190,25 @@ async def _generate_response(
         ):
             # Send a function call message to itself.
             response = await self.send_message(
-                message=FunctionCallMessage(content=response.content, source=self.metadata["name"]),
+                message=FunctionCallMessage(content=response.content, source=self.metadata["type"]),
                 recipient=self.id,
                 cancellation_token=cancellation_token,
             )
             # Make an assistant message from the response.
             hisorical_messages = await self._memory.get_messages()
             response = await self._client.create(
-                self._system_messages + convert_messages_to_llm_messages(hisorical_messages, self.metadata["name"]),
+                self._system_messages + convert_messages_to_llm_messages(hisorical_messages, self.metadata["type"]),
                 tools=self._tools,
                 json_output=response_format == ResponseFormat.json_object,
             )
 
         final_response: Message
         if isinstance(response.content, str):
             # If the response is a string, return a text message.
-            final_response = TextMessage(content=response.content, source=self.metadata["name"])
+            final_response = TextMessage(content=response.content, source=self.metadata["type"])
         elif isinstance(response.content, list) and all(isinstance(x, FunctionCall) for x in response.content):
             # If the response is a list of function calls, return a function call message.
-            final_response = FunctionCallMessage(content=response.content, source=self.metadata["name"])
+            final_response = FunctionCallMessage(content=response.content, source=self.metadata["type"])
         else:
             raise ValueError(f"Unexpected response: {response.content}")
 

python/samples/common/agents/_image_generation_agent.py

@@ -63,7 +63,7 @@ async def _generate_response(self, cancellation_token: CancellationToken) -> Mul
         messages = await self._memory.get_messages()
         if len(messages) == 0:
             return MultiModalMessage(
-                content=["I need more information to generate an image."], source=self.metadata["name"]
+                content=["I need more information to generate an image."], source=self.metadata["type"]
             )
         prompt = ""
         for m in messages:
@@ -74,5 +74,5 @@ async def _generate_response(self, cancellation_token: CancellationToken) -> Mul
         assert len(response.data) > 0 and response.data[0].b64_json is not None
         # Create a MultiModalMessage with the image.
         image = Image.from_base64(response.data[0].b64_json)
-        multi_modal_message = MultiModalMessage(content=[image], source=self.metadata["name"])
+        multi_modal_message = MultiModalMessage(content=[image], source=self.metadata["type"])
         return multi_modal_message

python/samples/common/agents/_oai_assistant.py

@@ -121,7 +121,7 @@ async def _generate_response(
             raise ValueError(f"Expected text content in the last message: {last_message_content}")
 
         # TODO: handle multiple text content.
-        return TextMessage(content=text_content[0].text.value, source=self.metadata["name"])
+        return TextMessage(content=text_content[0].text.value, source=self.metadata["type"])
 
     def save_state(self) -> Mapping[str, Any]:
         return {

python/samples/common/agents/_user_proxy.py

@@ -23,7 +23,7 @@ def __init__(self, description: str, user_input_prompt: str) -> None:
     async def on_publish_now(self, message: PublishNow, cancellation_token: CancellationToken) -> None:
         """Handle a publish now message. This method prompts the user for input, then publishes it."""
         user_input = await self.get_user_input(self._user_input_prompt)
-        await self.publish_message(TextMessage(content=user_input, source=self.metadata["name"]))
+        await self.publish_message(TextMessage(content=user_input, source=self.metadata["type"]))
 
     async def get_user_input(self, prompt: str) -> str:
         """Get user input from the console. Override this method to customize how user input is retrieved."""

python/samples/common/patterns/_group_chat_utils.py

@@ -23,11 +23,11 @@ async def select_speaker(memory: ChatMemory[Message], client: ChatCompletionClie
 
     # Construct agent roles.
     roles = "\n".join(
-        [f"{(await agent.metadata)['name']}: {(await agent.metadata)['description']}".strip() for agent in agents]
+        [f"{(await agent.metadata)['type']}: {(await agent.metadata)['description']}".strip() for agent in agents]
     )
 
     # Construct agent list.
-    participants = str([(await agent.metadata)["name"] for agent in agents])
+    participants = str([(await agent.metadata)["type"] for agent in agents])
 
     # Select the next speaker.
     select_speaker_prompt = f"""You are in a role play game. The following roles are available:
@@ -48,7 +48,7 @@ async def select_speaker(memory: ChatMemory[Message], client: ChatCompletionClie
     # Get the index of the selected agent by name
     agent_index = 0
     for i, agent in enumerate(agents):
-        if (await agent.metadata)["name"] == agent_name:
+        if (await agent.metadata)["type"] == agent_name:
             agent_index = i
             break
 
@@ -74,7 +74,7 @@ async def mentioned_agents(message_content: str, agents: List[AgentProxy]) -> Di
     for agent in agents:
         # Finds agent mentions, taking word boundaries into account,
         # accommodates escaping underscores and underscores as spaces
-        name = (await agent.metadata)["name"]
+        name = (await agent.metadata)["type"]
         regex = (
             r"(?<=\W)("
             + re.escape(name)

python/samples/common/patterns/_orchestrator_chat.py

@@ -73,7 +73,7 @@ async def on_text_message(
 
             # Send the task specs to the orchestrator and specialists.
             for agent in [*self._specialists, self._orchestrator]:
-                await (await self.send_message(TextMessage(content=task_specs, source=self.metadata["name"]), agent))
+                await (await self.send_message(TextMessage(content=task_specs, source=self.metadata["type"]), agent))
 
             # Inner loop.
             stalled_turns = 0
@@ -85,7 +85,7 @@ async def on_text_message(
                 if data["is_request_satisfied"]["answer"]:
                     return TextMessage(
                         content=f"The task has been successfully addressed. {data['is_request_satisfied']['reason']}",
-                        source=self.metadata["name"],
+                        source=self.metadata["type"],
                     )
 
                 # Update stalled turns.
@@ -111,7 +111,7 @@ async def on_text_message(
                         if educated_guess["has_educated_guesses"]["answer"]:
                             return TextMessage(
                                 content=f"The task is addressed with an educated guess. {educated_guess['has_educated_guesses']['reason']}",
-                                source=self.metadata["name"],
+                                source=self.metadata["type"],
                             )
 
                     # Come up with a new plan.
@@ -129,7 +129,7 @@ async def on_text_message(
                 for agent in [*self._specialists, self._orchestrator]:
                     _ = await (
                         await self.send_message(
-                            TextMessage(content=subtask, source=self.metadata["name"]),
+                            TextMessage(content=subtask, source=self.metadata["type"]),
                             agent,
                         )
                     )
@@ -161,7 +161,7 @@ async def on_text_message(
 
         return TextMessage(
             content="The task was not addressed. The maximum number of turns was reached.",
-            source=self.metadata["name"],
+            source=self.metadata["type"],
         )
 
     async def _prepare_task(self, task: str, sender: str) -> Tuple[str, str, str, str]:

python/samples/core/inner_outer_direct.py

@@ -27,7 +27,7 @@ def __init__(self) -> None:
 
     @message_handler()
     async def on_new_message(self, message: MessageType, cancellation_token: CancellationToken) -> MessageType:
-        return MessageType(body=f"Inner: {message.body}", sender=self.metadata["name"])
+        return MessageType(body=f"Inner: {message.body}", sender=self.metadata["type"])
 
 
 class Outer(TypeRoutedAgent):
@@ -40,7 +40,7 @@ async def on_new_message(self, message: MessageType, cancellation_token: Cancell
         inner_response = self.send_message(message, self._inner)
         inner_message = await inner_response
         assert isinstance(inner_message, MessageType)
-        return MessageType(body=f"Outer: {inner_message.body}", sender=self.metadata["name"])
+        return MessageType(body=f"Outer: {inner_message.body}", sender=self.metadata["type"])
 
 
 async def main() -> None:

python/samples/core/two_agents_pub_sub.py

@@ -63,13 +63,13 @@ async def handle_message(self, message: Message, cancellation_token: Cancellatio
             return
         llm_messages: List[LLMMessage] = []
         for m in self._memory[-10:]:
-            if m.source == self.metadata["name"]:
-                llm_messages.append(AssistantMessage(content=m.content, source=self.metadata["name"]))
+            if m.source == self.metadata["type"]:
+                llm_messages.append(AssistantMessage(content=m.content, source=self.metadata["type"]))
             else:
                 llm_messages.append(UserMessage(content=m.content, source=m.source))
         response = await self._model_client.create(self._system_messages + llm_messages)
         assert isinstance(response.content, str)
-        await self.publish_message(Message(content=response.content, source=self.metadata["name"]))
+        await self.publish_message(Message(content=response.content, source=self.metadata["type"]))
 
 
 async def main() -> None:

python/samples/demos/assistant.py

@@ -108,7 +108,7 @@ async def on_publish_now(self, message: PublishNow, cancellation_token: Cancella
                 return
             else:
                 # Publish user input and exit handler.
-                await self.publish_message(TextMessage(content=user_input, source=self.metadata["name"]))
+                await self.publish_message(TextMessage(content=user_input, source=self.metadata["type"]))
                 return
 
 

python/samples/demos/chat_room.py

@@ -60,13 +60,13 @@ async def on_chat_room_message(self, message: TextMessage, cancellation_token: C
         # Get a response from the model.
         raw_response = await self._client.create(
             self._system_messages
-            + convert_messages_to_llm_messages(await self._memory.get_messages(), self_name=self.metadata["name"]),
+            + convert_messages_to_llm_messages(await self._memory.get_messages(), self_name=self.metadata["type"]),
             json_output=True,
         )
         assert isinstance(raw_response.content, str)
 
         # Save the response to memory.
-        await self._memory.add_message(TextMessage(source=self.metadata["name"], content=raw_response.content))
+        await self._memory.add_message(TextMessage(source=self.metadata["type"], content=raw_response.content))
 
         # Parse the response.
         data = json.loads(raw_response.content)
@@ -75,7 +75,7 @@ async def on_chat_room_message(self, message: TextMessage, cancellation_token: C
 
         # Publish the response if needed.
         if respond is True or str(respond).lower().strip() == "true":
-            await self.publish_message(TextMessage(source=self.metadata["name"], content=str(response)))
+            await self.publish_message(TextMessage(source=self.metadata["type"], content=str(response)))
 
 
 class ChatRoomUserAgent(TextualUserAgent):

python/samples/demos/illustrator_critics.py

@@ -86,9 +86,9 @@ async def illustrator_critics(runtime: AgentRuntime, app: TextualChatApp) -> Non
 
     app.welcoming_notice = f"""You are now in a group chat with the following agents:
 
-1. 🤖 {(await descriptor.metadata)['name']}: {(await descriptor.metadata).get('description')}
-2. 🤖 {(await illustrator.metadata)['name']}: {(await illustrator.metadata).get('description')}
-3. 🤖 {(await critic.metadata)['name']}: {(await critic.metadata).get('description')}
+1. 🤖 {(await descriptor.metadata)['type']}: {(await descriptor.metadata).get('description')}
+2. 🤖 {(await illustrator.metadata)['type']}: {(await illustrator.metadata).get('description')}
+3. 🤖 {(await critic.metadata)['type']}: {(await critic.metadata).get('description')}
 
 Provide a prompt for the illustrator to generate an image.
 """

python/samples/demos/utils.py

@@ -171,7 +171,7 @@ async def on_multi_modal_message(self, message: MultiModalMessage, cancellation_
         # Generate a ramdom file name.
         for content in message.content:
             if isinstance(content, Image):
-                filename = f"{self.metadata['name']}_{message.source}_{random.randbytes(16).hex()}.png"
+                filename = f"{self.metadata['type']}_{message.source}_{random.randbytes(16).hex()}.png"
                 content.image.save(filename)
         await self._app.post_runtime_message(message)
 

python/samples/patterns/coder_executor.py

@@ -97,7 +97,7 @@ async def handle_task(self, message: TaskMessage, cancellation_token: Cancellati
         response = await self._model_client.create(self._system_messages + self._session_memory[session_id])
         assert isinstance(response.content, str)
         self._session_memory[session_id].append(
-            AssistantMessage(content=response.content, source=self.metadata["name"])
+            AssistantMessage(content=response.content, source=self.metadata["type"])
         )
 
         # Publish the code execution task.
@@ -116,7 +116,7 @@ async def handle_code_execution_result(
         response = await self._model_client.create(self._system_messages + self._session_memory[message.session_id])
         assert isinstance(response.content, str)
         self._session_memory[message.session_id].append(
-            AssistantMessage(content=response.content, source=self.metadata["name"])
+            AssistantMessage(content=response.content, source=self.metadata["type"])
         )
 
         if "TERMINATE" in response.content:

python/samples/patterns/coder_reviewer.py

@@ -100,7 +100,7 @@ async def handle_code_review_task(self, message: CodeReviewTask, cancellation_to
 """
         # Generate a response using the chat completion API.
         response = await self._model_client.create(
-            self._system_messages + [UserMessage(content=prompt, source=self.metadata["name"])]
+            self._system_messages + [UserMessage(content=prompt, source=self.metadata["type"])]
         )
         assert isinstance(response.content, str)
         # TODO: use structured generation library e.g. guidance to ensure the response is in the expected format.
@@ -162,7 +162,7 @@ async def handle_code_writing_task(
         self._session_memory.setdefault(session_id, []).append(message)
         # Generate a response using the chat completion API.
         response = await self._model_client.create(
-            self._system_messages + [UserMessage(content=message.task, source=self.metadata["name"])]
+            self._system_messages + [UserMessage(content=message.task, source=self.metadata["type"])]
         )
         assert isinstance(response.content, str)
         # Extract the code block from the response.

python/samples/patterns/group_chat.py

@@ -97,13 +97,13 @@ async def handle_request_to_speak(self, message: RequestToSpeak, cancellation_to
             return
         llm_messages: List[LLMMessage] = []
         for m in self._memory[-10:]:
-            if m.source == self.metadata["name"]:
-                llm_messages.append(AssistantMessage(content=m.content, source=self.metadata["name"]))
+            if m.source == self.metadata["type"]:
+                llm_messages.append(AssistantMessage(content=m.content, source=self.metadata["type"]))
             else:
                 llm_messages.append(UserMessage(content=m.content, source=m.source))
         response = await self._model_client.create(self._system_messages + llm_messages)
         assert isinstance(response.content, str)
-        speach = Message(content=response.content, source=self.metadata["name"])
+        speach = Message(content=response.content, source=self.metadata["type"])
         self._memory.append(speach)
         await self.publish_message(speach)