mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Python: Upgrade hosting server dependency and add more type support (#5459)
* Upgrade hosting server dependency and add more type support * Comments
This commit is contained in:
Tao Chen
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GitHub
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GitHub
parent
932ceddf95
commit
4adfd244ac
@@ -28,13 +28,35 @@ from azure.ai.agentserver.responses import (
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)
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from azure.ai.agentserver.responses.hosting import ResponsesAgentServerHost
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from azure.ai.agentserver.responses.models import (
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ApplyPatchToolCallItemParam,
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ApplyPatchToolCallOutputItemParam,
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ComputerCallOutputItemParam,
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ComputerScreenshotContent,
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CreateResponse,
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FunctionCallOutputItemParam,
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FunctionShellAction,
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FunctionShellCallItemParam,
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FunctionShellCallOutputContent,
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FunctionShellCallOutputExitOutcome,
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FunctionShellCallOutputItemParam,
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Item,
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ItemCodeInterpreterToolCall,
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ItemComputerToolCall,
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ItemCustomToolCall,
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ItemCustomToolCallOutput,
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ItemFileSearchToolCall,
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ItemFunctionToolCall,
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ItemImageGenToolCall,
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ItemLocalShellToolCall,
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ItemLocalShellToolCallOutput,
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ItemMcpApprovalRequest,
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ItemMcpToolCall,
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ItemMessage,
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ItemOutputMessage,
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ItemReasoningItem,
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ItemWebSearchToolCall,
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LocalEnvironmentResource,
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MCPApprovalResponse,
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MessageContent,
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MessageContentInputFileContent,
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MessageContentInputImageContent,
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@@ -174,9 +196,11 @@ class ResponsesHostServer(ResponsesAgentServerHost):
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context: ResponseContext,
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) -> AsyncIterable[ResponseStreamEvent | dict[str, Any]]:
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"""Handle the creation of a response for a regular (non-workflow) agent."""
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input_text = await context.get_input_text()
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input_items = await context.get_input_items()
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input_messages = _items_to_messages(input_items)
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history = await context.get_history()
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messages: list[str | Content | Message] = [*_to_messages(history), input_text]
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messages: list[str | Content | Message] = [*_output_items_to_messages(history), *input_messages]
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chat_options, are_options_set = _to_chat_options(request)
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@@ -243,7 +267,9 @@ class ResponsesHostServer(ResponsesAgentServerHost):
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The sandbox may be deactivated after some period of inactivity, and only data managed
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by the hosting infrastructure or files will be preserved upon deactivation.
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"""
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input_text = await context.get_input_text()
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input_items = await context.get_input_items()
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input_messages = _items_to_messages(input_items)
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is_streaming_request = self._is_streaming_request(request)
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_, are_options_set = _to_chat_options(request)
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@@ -296,7 +322,7 @@ class ResponsesHostServer(ResponsesAgentServerHost):
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if not is_streaming_request:
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# Run the agent in non-streaming mode
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response = await self._agent.run(input_text, stream=False, checkpoint_storage=checkpoint_storage)
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response = await self._agent.run(input_messages, stream=False, checkpoint_storage=checkpoint_storage)
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for message in response.messages:
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for content in message.contents:
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@@ -308,7 +334,7 @@ class ResponsesHostServer(ResponsesAgentServerHost):
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return
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# Run the agent in streaming mode
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response_stream = self._agent.run(input_text, stream=True, checkpoint_storage=checkpoint_storage)
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response_stream = self._agent.run(input_messages, stream=True, checkpoint_storage=checkpoint_storage)
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# Track the current active output item builder for streaming;
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# lazily created on matching content, closed when a different type arrives.
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@@ -532,7 +558,260 @@ def _to_chat_options(request: CreateResponse) -> tuple[ChatOptions, bool]:
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# region Input Message Conversion
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def _to_messages(history: Sequence[OutputItem]) -> list[Message]:
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def _items_to_messages(input_items: Sequence[Item]) -> list[Message]:
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"""Converts a sequence of input items to a list of Messages, one per item.
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Args:
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input_items: The input items to convert.
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Returns:
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A list of Messages, one per supported input item.
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"""
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messages: list[Message] = []
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for item in input_items:
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messages.append(_item_to_message(item))
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return messages
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def _item_to_message(item: Item) -> Message:
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"""Converts an Item to a Message.
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Args:
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item: The Item to convert.
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Returns:
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The converted Message.
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Raises:
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ValueError: If the Item type is not supported.
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"""
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if item.type == "message":
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msg = cast(ItemMessage, item)
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if isinstance(msg.content, str):
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return Message(role=msg.role, contents=[Content.from_text(msg.content)])
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return Message(role=msg.role, contents=[_convert_message_content(part) for part in msg.content])
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if item.type == "output_message":
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output_msg = cast(ItemOutputMessage, item)
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return Message(
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role=output_msg.role, contents=[_convert_output_message_content(part) for part in output_msg.content]
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)
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if item.type == "function_call":
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fc = cast(ItemFunctionToolCall, item)
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return Message(
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role="assistant",
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contents=[Content.from_function_call(fc.call_id, fc.name, arguments=fc.arguments)],
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)
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if item.type == "function_call_output":
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fco = cast(FunctionCallOutputItemParam, item)
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output = fco.output if isinstance(fco.output, str) else str(fco.output)
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return Message(
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role="tool",
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contents=[Content.from_function_result(fco.call_id, result=output)],
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)
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if item.type == "reasoning":
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reasoning = cast(ItemReasoningItem, item)
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reason_contents: list[Content] = []
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if reasoning.summary:
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for summary in reasoning.summary:
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reason_contents.append(Content.from_text(summary.text))
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return Message(role="assistant", contents=reason_contents)
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if item.type == "mcp_call":
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mcp = cast(ItemMcpToolCall, item)
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return Message(
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role="assistant",
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contents=[
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Content.from_mcp_server_tool_call(
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mcp.id,
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mcp.name,
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server_name=mcp.server_label,
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arguments=mcp.arguments,
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)
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],
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)
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if item.type == "mcp_approval_request":
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mcp_req = cast(ItemMcpApprovalRequest, item)
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mcp_call_content = Content.from_mcp_server_tool_call(
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mcp_req.id,
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mcp_req.name,
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server_name=mcp_req.server_label,
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arguments=mcp_req.arguments,
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)
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return Message(
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role="assistant",
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contents=[Content.from_function_approval_request(mcp_req.id, mcp_call_content)],
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)
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if item.type == "mcp_approval_response":
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mcp_resp = cast(MCPApprovalResponse, item)
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placeholder_content = Content.from_function_call(mcp_resp.approval_request_id, "mcp_approval")
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return Message(
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role="user",
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contents=[
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Content.from_function_approval_response(
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mcp_resp.approve, mcp_resp.approval_request_id, placeholder_content
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)
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],
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)
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if item.type == "code_interpreter_call":
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ci = cast(ItemCodeInterpreterToolCall, item)
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return Message(
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role="assistant",
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contents=[Content.from_code_interpreter_tool_call(call_id=ci.id)],
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)
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if item.type == "image_generation_call":
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ig = cast(ItemImageGenToolCall, item)
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return Message(
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role="assistant",
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contents=[Content.from_image_generation_tool_call(image_id=ig.id)],
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)
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if item.type == "shell_call":
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sc = cast(FunctionShellCallItemParam, item)
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return Message(
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role="assistant",
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contents=[
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Content.from_shell_tool_call(
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call_id=sc.call_id,
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commands=sc.action.commands,
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status=str(sc.status),
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)
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],
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)
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if item.type == "shell_call_output":
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sco = cast(FunctionShellCallOutputItemParam, item)
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outputs = [
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Content.from_shell_command_output(
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stdout=out.stdout or "",
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stderr=out.stderr or "",
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exit_code=getattr(out.outcome, "exit_code", None) if hasattr(out, "outcome") else None,
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)
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for out in (sco.output or [])
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]
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return Message(
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role="tool",
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contents=[
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Content.from_shell_tool_result(
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call_id=sco.call_id,
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outputs=outputs,
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max_output_length=sco.max_output_length,
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)
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],
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)
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if item.type == "local_shell_call":
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lsc = cast(ItemLocalShellToolCall, item)
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commands = lsc.action.command if hasattr(lsc.action, "command") and lsc.action.command else []
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return Message(
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role="assistant",
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contents=[
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Content.from_shell_tool_call(
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call_id=lsc.call_id,
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commands=commands,
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status=str(lsc.status),
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)
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],
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)
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if item.type == "local_shell_call_output":
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lsco = cast(ItemLocalShellToolCallOutput, item)
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return Message(
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role="tool",
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contents=[
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Content.from_shell_tool_result(
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call_id=lsco.id,
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outputs=[Content.from_shell_command_output(stdout=lsco.output)],
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)
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],
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)
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if item.type == "file_search_call":
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fs = cast(ItemFileSearchToolCall, item)
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return Message(
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role="assistant",
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contents=[
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Content.from_function_call(
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fs.id,
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"file_search",
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arguments=json.dumps({"queries": fs.queries}),
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)
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],
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)
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if item.type == "web_search_call":
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ws = cast(ItemWebSearchToolCall, item)
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return Message(
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role="assistant",
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contents=[Content.from_function_call(ws.id, "web_search")],
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)
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if item.type == "computer_call":
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cc = cast(ItemComputerToolCall, item)
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return Message(
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role="assistant",
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contents=[
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Content.from_function_call(
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cc.call_id,
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"computer_use",
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arguments=str(cc.action),
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)
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],
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)
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if item.type == "computer_call_output":
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cco = cast(ComputerCallOutputItemParam, item)
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return Message(
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role="tool",
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contents=[Content.from_function_result(cco.call_id, result=str(cco.output))],
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)
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if item.type == "custom_tool_call":
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ct = cast(ItemCustomToolCall, item)
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return Message(
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role="assistant",
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contents=[Content.from_function_call(ct.call_id, ct.name, arguments=ct.input)],
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)
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if item.type == "custom_tool_call_output":
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cto = cast(ItemCustomToolCallOutput, item)
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output = cto.output if isinstance(cto.output, str) else str(cto.output)
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return Message(
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role="tool",
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contents=[Content.from_function_result(cto.call_id, result=output)],
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)
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if item.type == "apply_patch_call":
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ap = cast(ApplyPatchToolCallItemParam, item)
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return Message(
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role="assistant",
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contents=[
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Content.from_function_call(
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ap.call_id,
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"apply_patch",
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arguments=str(ap.operation),
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)
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],
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)
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if item.type == "apply_patch_call_output":
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apo = cast(ApplyPatchToolCallOutputItemParam, item)
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return Message(
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role="tool",
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contents=[Content.from_function_result(apo.call_id, result=apo.output or "")],
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)
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raise ValueError(f"Unsupported Item type: {item.type}")
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def _output_items_to_messages(history: Sequence[OutputItem]) -> list[Message]:
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"""Converts a sequence of OutputItem objects to a list of Message objects.
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Args:
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@@ -543,11 +822,11 @@ def _to_messages(history: Sequence[OutputItem]) -> list[Message]:
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"""
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messages: list[Message] = []
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for item in history:
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messages.append(_to_message(item))
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messages.append(_output_item_to_message(item))
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return messages
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def _to_message(item: OutputItem) -> Message:
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def _output_item_to_message(item: OutputItem) -> Message:
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"""Converts an OutputItem to a Message.
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Args:
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@@ -4,7 +4,7 @@ description = "Foundry Hosting integration for Microsoft Agent Framework."
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authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
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readme = "README.md"
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requires-python = ">=3.10"
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version = "1.0.0a260423"
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version = "1.0.0a260424"
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license-files = ["LICENSE"]
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urls.homepage = "https://aka.ms/agent-framework"
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urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"
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@@ -24,9 +24,9 @@ classifiers = [
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]
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dependencies = [
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"agent-framework-core>=1.1.1,<2",
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"azure-ai-agentserver-core==2.0.0b2",
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"azure-ai-agentserver-responses==1.0.0b4",
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"azure-ai-agentserver-invocations==1.0.0b2",
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"azure-ai-agentserver-core==2.0.0b3",
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"azure-ai-agentserver-responses==1.0.0b5",
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"azure-ai-agentserver-invocations==1.0.0b3",
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]
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[tool.uv]
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File diff suppressed because it is too large
Load Diff
Generated
+13
-13
@@ -504,7 +504,7 @@ requires-dist = [
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[[package]]
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name = "agent-framework-foundry-hosting"
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version = "1.0.0a260423"
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version = "1.0.0a260424"
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source = { editable = "packages/foundry_hosting" }
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dependencies = [
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{ name = "agent-framework-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
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@@ -516,9 +516,9 @@ dependencies = [
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[package.metadata]
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requires-dist = [
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{ name = "agent-framework-core", editable = "packages/core" },
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{ name = "azure-ai-agentserver-core", specifier = "==2.0.0b2" },
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{ name = "azure-ai-agentserver-invocations", specifier = "==1.0.0b2" },
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{ name = "azure-ai-agentserver-responses", specifier = "==1.0.0b4" },
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{ name = "azure-ai-agentserver-core", specifier = "==2.0.0b3" },
|
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{ name = "azure-ai-agentserver-invocations", specifier = "==1.0.0b3" },
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{ name = "azure-ai-agentserver-responses", specifier = "==1.0.0b5" },
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]
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[[package]]
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@@ -1068,7 +1068,7 @@ wheels = [
|
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|
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[[package]]
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name = "azure-ai-agentserver-core"
|
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version = "2.0.0b2"
|
||||
version = "2.0.0b3"
|
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source = { registry = "https://pypi.org/simple" }
|
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dependencies = [
|
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{ name = "azure-monitor-opentelemetry-exporter", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
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@@ -1078,26 +1078,26 @@ dependencies = [
|
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{ name = "opentelemetry-sdk", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
||||
{ name = "starlette", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a0/25/25865cfa76cbc20c18c4e9ed337456fd7374c01e930dd151463b4c183ac0/azure_ai_agentserver_core-2.0.0b2.tar.gz", hash = "sha256:cc6c90fdc4c2b2ce594f0e85288fda84910c04939d1427a64a485b2d48d6d684", size = 41605, upload-time = "2026-04-19T08:58:09.27Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/84/29/1a9606d5252b02d77070a1b633dd0c26fe65a0f4a0fb0cfdaa751e2ed458/azure_ai_agentserver_core-2.0.0b3.tar.gz", hash = "sha256:e295b19a65d53c513929f52f0862bbb815cc9e9fc29d2a2825452f3136260123", size = 42573, upload-time = "2026-04-23T04:13:16.717Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/69/35/cf8a034f86d653fa902edb5ffa0a86005ea941f2840d2fa27302484856c1/azure_ai_agentserver_core-2.0.0b2-py3-none-any.whl", hash = "sha256:931e7a2d82275a01d7eb5ef08a70dba230938e3646be64c03d82749dd7be8afc", size = 27494, upload-time = "2026-04-19T08:58:10.588Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/9b/1fc87c05b55821f33c46c5e8a3b97a573aa2fc4bff387e75cca1a87800b4/azure_ai_agentserver_core-2.0.0b3-py3-none-any.whl", hash = "sha256:5ef921eb9fd9c0f15682fe930320fae50dccfa915d7518f9a16d99014bbcb3cb", size = 29127, upload-time = "2026-04-23T04:13:17.976Z" },
|
||||
]
|
||||
|
||||
[[package]]
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||||
name = "azure-ai-agentserver-invocations"
|
||||
version = "1.0.0b2"
|
||||
version = "1.0.0b3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "azure-ai-agentserver-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
||||
]
|
||||
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/0c/f4/057206e0fca266b30ea68a531fa425078fd883500e779d5552858fe33d5b/azure_ai_agentserver_invocations-1.0.0b2-py3-none-any.whl", hash = "sha256:e799a9e6e54a10499296ee4f61720377fb31f540204832b654bac6f20e801597", size = 11432, upload-time = "2026-04-19T09:43:32.744Z" },
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{ url = "https://files.pythonhosted.org/packages/5e/43/a421671296ae33b62af3a034869fa82ff1979e5f455a29924d30ae1b8307/azure_ai_agentserver_invocations-1.0.0b3-py3-none-any.whl", hash = "sha256:771a15a3509e049b56f71c43c87a3fdeecd12addddcae0f80339990adc41e678", size = 11433, upload-time = "2026-04-23T04:30:30.412Z" },
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]
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||||
|
||||
[[package]]
|
||||
name = "azure-ai-agentserver-responses"
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||||
version = "1.0.0b4"
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||||
version = "1.0.0b5"
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||||
source = { registry = "https://pypi.org/simple" }
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||||
dependencies = [
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||||
{ name = "aiohttp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
||||
@@ -1105,9 +1105,9 @@ dependencies = [
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||||
{ name = "azure-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
|
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{ name = "isodate", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/e6/27/3ecb7fe704ff8764199bfbe4cc1e584a520a9affe042470d9d50b6e1e73a/azure_ai_agentserver_responses-1.0.0b5.tar.gz", hash = "sha256:0b627b810359c792ea7b6fa6782abaf6df32d9bc9e5a569ad722afcffd0ce8d9", size = 410908, upload-time = "2026-04-23T04:31:15.414Z" }
|
||||
wheels = [
|
||||
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|
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{ url = "https://files.pythonhosted.org/packages/44/91/1e5c0d7ce95ca8b022e69e4ca6b23e413fc2d57f0191429c4633e02213d2/azure_ai_agentserver_responses-1.0.0b5-py3-none-any.whl", hash = "sha256:4c2a6ab56e71eeb330aa52b7cb2cc71b8ec6b5bbe0e7dc84310f2c7fbda393a3", size = 268362, upload-time = "2026-04-23T04:31:17.014Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user