Merge c8bdd31936 into 71a4eaedf9

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>

.github/ISSUE_TEMPLATE/1-bug_report.yml

@@ -90,6 +90,7 @@ body:
       multiple: false
       options:
         - "Python dev (main branch)"
+        - "Python 0.5.2"
         - "Python 0.5.1"
         - "Python 0.4.9"
         - "Python 0.4.8"

.github/workflows/docs.yml

@@ -33,7 +33,7 @@ jobs:
           [
             # For main use the workflow target
             { ref: "${{github.ref}}", dest-dir: dev, uv-version: "0.5.13", sphinx-release-override: "dev" },
-            { ref: "python-v0.5.1", dest-dir: stable, uv-version: "0.5.13", sphinx-release-override: "stable" },
+            { ref: "python-v0.5.2", dest-dir: stable, uv-version: "0.5.13", sphinx-release-override: "stable" },
             { ref: "v0.4.0.post1", dest-dir: "0.4.0", uv-version: "0.5.13", sphinx-release-override: "" },
             { ref: "v0.4.1", dest-dir: "0.4.1", uv-version: "0.5.13", sphinx-release-override: "" },
             { ref: "v0.4.2", dest-dir: "0.4.2", uv-version: "0.5.13", sphinx-release-override: "" },
@@ -45,6 +45,7 @@ jobs:
             { ref: "python-v0.4.8", dest-dir: "0.4.8", uv-version: "0.5.13", sphinx-release-override: "" },
             { ref: "python-v0.4.9-website", dest-dir: "0.4.9", uv-version: "0.5.13", sphinx-release-override: "" },
             { ref: "python-v0.5.1", dest-dir: "0.5.1", uv-version: "0.5.13", sphinx-release-override: "" },
+            { ref: "python-v0.5.2", dest-dir: "0.5.2", uv-version: "0.5.13", sphinx-release-override: "" },
           ]
     steps:
       - name: Checkout

docs/switcher.json

@@ -5,11 +5,16 @@
         "url": "/autogen/dev/"
     },
     {
-        "name": "0.5.1 (stable)",
+        "name": "0.5.2 (stable)",
         "version": "stable",
         "url": "/autogen/stable/",
         "preferred": true
     },
+    {
+        "name": "0.5.1",
+        "version": "0.5.1",
+        "url": "/autogen/0.5.1/"
+    },
     {
         "name": "0.4.9",
         "version": "0.4.9",

python/packages/autogen-agentchat/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "autogen-agentchat"
-version = "0.5.1"
+version = "0.5.2"
 license = {file = "LICENSE-CODE"}
 description = "AutoGen agents and teams library"
 readme = "README.md"
@@ -15,7 +15,7 @@ classifiers = [
     "Operating System :: OS Independent",
 ]
 dependencies = [
-    "autogen-core==0.5.1",
+    "autogen-core==0.5.2",
 ]
 
 [tool.ruff]

python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py

@@ -46,6 +46,7 @@ from ..messages import (
     MemoryQueryEvent,
     ModelClientStreamingChunkEvent,
     StructuredMessage,
+    StructuredMessageFactory,
     TextMessage,
     ThoughtEvent,
     ToolCallExecutionEvent,
@@ -74,6 +75,7 @@ class AssistantAgentConfig(BaseModel):
     reflect_on_tool_use: bool
     tool_call_summary_format: str
     metadata: Dict[str, str] | None = None
+    structured_message_factory: ComponentModel | None = None
 
 
 class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
@@ -183,6 +185,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
             This will be used with the model client to generate structured output.
             If this is set, the agent will respond with a :class:`~autogen_agentchat.messages.StructuredMessage` instead of a :class:`~autogen_agentchat.messages.TextMessage`
             in the final response, unless `reflect_on_tool_use` is `False` and a tool call is made.
+        format_string (str | None, optional): The format string used to create the content for a :class:`~autogen_agentchat.messages.StructuredMessage` response.
         tool_call_summary_format (str, optional): The format string used to create the content for a :class:`~autogen_agentchat.messages.ToolCallSummaryMessage` response.
             The format string is used to format the tool call summary for every tool call result.
             Defaults to "{result}".
@@ -635,6 +638,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
         reflect_on_tool_use: bool | None = None,
         tool_call_summary_format: str = "{result}",
         output_content_type: type[BaseModel] | None = None,
+        format_string: str | None = None,
         memory: Sequence[Memory] | None = None,
         metadata: Dict[str, str] | None = None,
     ):
@@ -643,6 +647,13 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
         self._model_client = model_client
         self._model_client_stream = model_client_stream
         self._output_content_type: type[BaseModel] | None = output_content_type
+        self._format_string = format_string
+        self._structured_message_factory: StructuredMessageFactory | None = None
+        if output_content_type is not None:
+            self._structured_message_factory = StructuredMessageFactory(
+                input_model=output_content_type, format_string=format_string
+            )
+
         self._memory = None
         if memory is not None:
             if isinstance(memory, list):
@@ -771,6 +782,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
         reflect_on_tool_use = self._reflect_on_tool_use
         tool_call_summary_format = self._tool_call_summary_format
         output_content_type = self._output_content_type
+        format_string = self._format_string
 
         # STEP 1: Add new user/handoff messages to the model context
         await self._add_messages_to_context(
@@ -840,6 +852,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
             reflect_on_tool_use=reflect_on_tool_use,
             tool_call_summary_format=tool_call_summary_format,
             output_content_type=output_content_type,
+            format_string=format_string,
         ):
             yield output_event
 
@@ -942,6 +955,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
         reflect_on_tool_use: bool,
         tool_call_summary_format: str,
         output_content_type: type[BaseModel] | None,
+        format_string: str | None = None,
     ) -> AsyncGenerator[BaseAgentEvent | BaseChatMessage | Response, None]:
         """
         Handle final or partial responses from model_result, including tool calls, handoffs,
@@ -957,6 +971,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
                         content=content,
                         source=agent_name,
                         models_usage=model_result.usage,
+                        format_string=format_string,
                     ),
                     inner_messages=inner_messages,
                 )
@@ -1277,9 +1292,6 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
     def _to_config(self) -> AssistantAgentConfig:
         """Convert the assistant agent to a declarative config."""
 
-        if self._output_content_type:
-            raise ValueError("AssistantAgent with output_content_type does not support declarative config.")
-
         return AssistantAgentConfig(
             name=self.name,
             model_client=self._model_client.dump_component(),
@@ -1294,12 +1306,23 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
             model_client_stream=self._model_client_stream,
             reflect_on_tool_use=self._reflect_on_tool_use,
             tool_call_summary_format=self._tool_call_summary_format,
+            structured_message_factory=self._structured_message_factory.dump_component()
+            if self._structured_message_factory
+            else None,
             metadata=self._metadata,
         )
 
     @classmethod
     def _from_config(cls, config: AssistantAgentConfig) -> Self:
         """Create an assistant agent from a declarative config."""
+        if config.structured_message_factory:
+            structured_message_factory = StructuredMessageFactory.load_component(config.structured_message_factory)
+            format_string = structured_message_factory.format_string
+            output_content_type = structured_message_factory.ContentModel
+
+        else:
+            format_string = None
+            output_content_type = None
 
         return cls(
             name=config.name,
@@ -1313,5 +1336,7 @@ class AssistantAgent(BaseChatAgent, Component[AssistantAgentConfig]):
             model_client_stream=config.model_client_stream,
             reflect_on_tool_use=config.reflect_on_tool_use,
             tool_call_summary_format=config.tool_call_summary_format,
+            output_content_type=output_content_type,
+            format_string=format_string,
             metadata=config.metadata,
         )

python/packages/autogen-agentchat/src/autogen_agentchat/messages.py

@@ -5,15 +5,15 @@ class and includes specific fields relevant to the type of message being sent.
 """
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Generic, List, Literal, Mapping, TypeVar, Optional, Type
+from typing import Any, Dict, Generic, List, Literal, Mapping, Optional, Type, TypeVar
 
-from autogen_core import FunctionCall, Image
+from autogen_core import Component, ComponentBase, FunctionCall, Image
 from autogen_core.memory import MemoryContent
 from autogen_core.models import FunctionExecutionResult, LLMMessage, RequestUsage, UserMessage
+from autogen_core.utils import schema_to_pydantic_model
 from pydantic import BaseModel, Field, computed_field
 from typing_extensions import Annotated, Self
-from autogen_core import Component, ComponentBase
-from autogen_core.utils import schema_to_pydantic_model
+
 
 class BaseMessage(BaseModel, ABC):
     """Abstract base class for all message types in AgentChat.
@@ -180,6 +180,7 @@ class StructuredMessage(BaseChatMessage, Generic[StructuredContentType]):
         from pydantic import BaseModel
         from autogen_agentchat.messages import StructuredMessage
 
+
         class MyMessageContent(BaseModel):
             text: str
             number: int
@@ -199,6 +200,13 @@ class StructuredMessage(BaseChatMessage, Generic[StructuredContentType]):
     `Pydantic BaseModel <https://docs.pydantic.dev/latest/concepts/models/>`_."""
 
     format_string: Optional[str] = None
+    """
+    An optional format string to render the content into a human-readable format.
+    The format string can use the fields of the content model as placeholders.
+    For example, if the content model has a field `name`, you can use
+    `{name}` in the format string to include the value of that field.
+    The format string is used in the :meth:`to_text` method to create a
+    human-readable representation of the message."""
 
     @computed_field
     def type(self) -> str:
@@ -222,13 +230,16 @@ class StructuredMessage(BaseChatMessage, Generic[StructuredContentType]):
             source=self.source,
         )
 
+
 class StructureMessageConfig(BaseModel):
     """The declarative configuration for the structured input."""
-    json_schema: dict
-    format_string: Optional[str] = None
-    content_model_name: str 
 
-class StructuredMessageComponent(ComponentBase[StructureMessageConfig], Component[StructureMessageConfig]):
+    json_schema: Dict[str, Any]
+    format_string: Optional[str] = None
+    content_model_name: str
+
+
+class StructuredMessageFactory(ComponentBase[StructureMessageConfig], Component[StructureMessageConfig]):
     """
     A component that creates structured chat messages from Pydantic models or JSON schemas.
 
@@ -243,28 +254,33 @@ class StructuredMessageComponent(ComponentBase[StructureMessageConfig], Componen
     .. code-block:: python
 
         from pydantic import BaseModel
-        from autogen_agentchat.messages import StructuredMessageComponent
+        from autogen_agentchat.messages import StructuredMessageFactory
+
 
         class TestContent(BaseModel):
             field1: str
             field2: int
 
+
         format_string = "This is a string {field1} and this is an int {field2}"
-        sm_component = StructuredMessageComponent(input_model=TestContent, format_string=format_string)
+        sm_component = StructuredMessageFactory(input_model=TestContent, format_string=format_string)
 
         message = sm_component.StructuredMessage(
-            source="test_agent",
-            content=TestContent(field1="Hello", field2=42),
-            format_string=format_string
+            source="test_agent", content=TestContent(field1="Hello", field2=42), format_string=format_string
         )
 
-        print(message.to_model_text()) # Output: This is a string Hello and this is an int 42
+        print(message.to_model_text())  # Output: This is a string Hello and this is an int 42
 
-        config = sm_component._to_config()
-        s_m_dyn = StructuredMessageComponent._from_config(config)
-        message = s_m_dyn.StructuredMessage(source="test_agent", content=s_m_dyn.ContentModel(field1="dyn agent", field2=43), format_string=s_m_dyn.format_string)
+        config = sm_component.dump_component()
+
+        s_m_dyn = StructuredMessageFactory.load_component(config)
+        message = s_m_dyn.StructuredMessage(
+            source="test_agent",
+            content=s_m_dyn.ContentModel(field1="dyn agent", field2=43),
+            format_string=s_m_dyn.format_string,
+        )
         print(type(message))  # StructuredMessage[GeneratedModel]
-        print(message.to_model_text()) # Output: This is a string dyn agent and this is an int 43
+        print(message.to_model_text())  # Output: This is a string dyn agent and this is an int 43
 
     Attributes:
         component_config_schema (StructureMessageConfig): Defines the configuration structure for this component.
@@ -282,35 +298,42 @@ class StructuredMessageComponent(ComponentBase[StructureMessageConfig], Componen
     """
 
     component_config_schema = StructureMessageConfig
-    component_provider_override = "autogen_agentchat.messages.StructuredMessageComponent"
+    component_provider_override = "autogen_agentchat.messages.StructuredMessageFactory"
     component_type = "structured_message"
 
-    def __init__(self, json_schema: Optional[str]=None, input_model: Optional[Type[BaseModel]] = None, format_string: Optional[str] = None, content_model_name: Optional[str] = None) -> None:
+    def __init__(
+        self,
+        json_schema: Optional[Dict[str, Any]] = None,
+        input_model: Optional[Type[BaseModel]] = None,
+        format_string: Optional[str] = None,
+        content_model_name: Optional[str] = None,
+    ) -> None:
         self.format_string = format_string
-        
-        if not json_schema and not input_model:
-            raise ValueError("Either `input_json_schema` or `input_model` must be provided.")
-        
-        if input_model:
+
+        if json_schema:
+            self.ContentModel = schema_to_pydantic_model(
+                json_schema, model_name=content_model_name or "GeneratedContentModel"
+            )
+        elif input_model:
             self.ContentModel = input_model
         else:
-            self.ContentModel = schema_to_pydantic_model(json_schema, model_name=content_model_name or "GeneratedContentModel")
-        
-        self.StructuredMessage = StructuredMessage[self.ContentModel]
-            
+            raise ValueError("Either `json_schema` or `input_model` must be provided.")
+
+        self.StructuredMessage = StructuredMessage[self.ContentModel]  # type: ignore[name-defined]
+
     def _to_config(self) -> StructureMessageConfig:
         return StructureMessageConfig(
             json_schema=self.ContentModel.model_json_schema(),
             format_string=self.format_string,
-            content_model_name=self.ContentModel.__name__
+            content_model_name=self.ContentModel.__name__,
         )
 
     @classmethod
-    def _from_config(cls, config: StructureMessageConfig) -> "StructuredMessageComponent":
+    def _from_config(cls, config: StructureMessageConfig) -> "StructuredMessageFactory":
         return cls(
             json_schema=config.json_schema,
             format_string=config.format_string,
-            content_model_name=config.content_model_name
+            content_model_name=config.content_model_name,
         )
 
 

python/packages/autogen-agentchat/src/autogen_agentchat/teams/_group_chat/_base_group_chat.py

@@ -21,6 +21,7 @@ from ...messages import (
     MessageFactory,
     ModelClientStreamingChunkEvent,
     StopMessage,
+    StructuredMessage,
     TextMessage,
 )
 from ...state import TeamState
@@ -68,6 +69,15 @@ class BaseGroupChat(Team, ABC, ComponentBase[BaseModel]):
             for message_type in custom_message_types:
                 self._message_factory.register(message_type)
 
+        for agent in participants:
+            for message_type in agent.produced_message_types:
+                try:
+                    if issubclass(message_type, StructuredMessage):
+                        self._message_factory.register(message_type)  # type: ignore[reportUnknownArgumentType]
+                except TypeError:
+                    # Not a class or not a valid subclassable type (skip)
+                    pass
+
         # The team ID is a UUID that is used to identify the team and its participants
         # in the agent runtime. It is used to create unique topic types for each participant.
         # Currently, team ID is binded to an object instance of the group chat class.

python/packages/autogen-agentchat/tests/test_assistant_agent.py

@@ -36,7 +36,7 @@ from autogen_core.models._model_client import ModelFamily
 from autogen_core.tools import BaseTool, FunctionTool
 from autogen_ext.models.openai import OpenAIChatCompletionClient
 from autogen_ext.models.replay import ReplayChatCompletionClient
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 from utils import FileLogHandler
 
 logger = logging.getLogger(EVENT_LOGGER_NAME)
@@ -1104,3 +1104,103 @@ async def test_model_client_stream_with_tool_calls() -> None:
         elif isinstance(message, ModelClientStreamingChunkEvent):
             chunks.append(message.content)
     assert "".join(chunks) == "Example response 2 to task"
+
+
+@pytest.mark.asyncio
+async def test_invalid_structured_output_format() -> None:
+    class AgentResponse(BaseModel):
+        response: str
+        status: str
+
+    model_client = ReplayChatCompletionClient(
+        [
+            CreateResult(
+                finish_reason="stop",
+                content='{"response": "Hello"}',
+                usage=RequestUsage(prompt_tokens=10, completion_tokens=5),
+                cached=False,
+            ),
+        ]
+    )
+
+    agent = AssistantAgent(
+        name="assistant",
+        model_client=model_client,
+        output_content_type=AgentResponse,
+    )
+
+    with pytest.raises(ValidationError):
+        await agent.run()
+
+
+@pytest.mark.asyncio
+async def test_structured_message_factory_serialization() -> None:
+    class AgentResponse(BaseModel):
+        result: str
+        status: str
+
+    model_client = ReplayChatCompletionClient(
+        [
+            CreateResult(
+                finish_reason="stop",
+                content=AgentResponse(result="All good", status="ok").model_dump_json(),
+                usage=RequestUsage(prompt_tokens=10, completion_tokens=5),
+                cached=False,
+            )
+        ]
+    )
+
+    agent = AssistantAgent(
+        name="structured_agent",
+        model_client=model_client,
+        output_content_type=AgentResponse,
+        format_string="{result} - {status}",
+    )
+
+    dumped = agent.dump_component()
+    restored_agent = AssistantAgent.load_component(dumped)
+    result = await restored_agent.run()
+
+    assert isinstance(result.messages[0], StructuredMessage)
+    assert result.messages[0].content.result == "All good"  # type: ignore[reportUnknownMemberType]
+    assert result.messages[0].content.status == "ok"  # type: ignore[reportUnknownMemberType]
+
+
+@pytest.mark.asyncio
+async def test_structured_message_format_string() -> None:
+    class AgentResponse(BaseModel):
+        field1: str
+        field2: str
+
+    expected = AgentResponse(field1="foo", field2="bar")
+
+    model_client = ReplayChatCompletionClient(
+        [
+            CreateResult(
+                finish_reason="stop",
+                content=expected.model_dump_json(),
+                usage=RequestUsage(prompt_tokens=10, completion_tokens=5),
+                cached=False,
+            )
+        ]
+    )
+
+    agent = AssistantAgent(
+        name="formatted_agent",
+        model_client=model_client,
+        output_content_type=AgentResponse,
+        format_string="{field1} - {field2}",
+    )
+
+    result = await agent.run()
+
+    assert len(result.messages) == 1
+    message = result.messages[0]
+
+    # Check that it's a StructuredMessage with the correct content model
+    assert isinstance(message, StructuredMessage)
+    assert isinstance(message.content, AgentResponse)  # type: ignore[reportUnknownMemberType]
+    assert message.content == expected
+
+    # Check that the format_string was applied correctly
+    assert message.to_model_text() == "foo - bar"

python/packages/autogen-agentchat/tests/test_group_chat.py

@@ -1441,3 +1441,87 @@ async def test_declarative_groupchats_with_config(runtime: AgentRuntime | None)
     assert selector.dump_component().provider == "autogen_agentchat.teams.SelectorGroupChat"
     assert swarm.dump_component().provider == "autogen_agentchat.teams.Swarm"
     assert magentic.dump_component().provider == "autogen_agentchat.teams.MagenticOneGroupChat"
+
+
+class _StructuredContent(BaseModel):
+    message: str
+
+
+class _StructuredAgent(BaseChatAgent):
+    def __init__(self, name: str, description: str) -> None:
+        super().__init__(name, description)
+        self._message = _StructuredContent(message="Structured hello")
+
+    @property
+    def produced_message_types(self) -> Sequence[type[BaseChatMessage]]:
+        return (StructuredMessage[_StructuredContent],)
+
+    async def on_messages(self, messages: Sequence[BaseChatMessage], cancellation_token: CancellationToken) -> Response:
+        return Response(
+            chat_message=StructuredMessage[_StructuredContent](
+                source=self.name,
+                content=self._message,
+                format_string="Structured says: {message}",
+            )
+        )
+
+    async def on_reset(self, cancellation_token: CancellationToken) -> None:
+        pass
+
+
+@pytest.mark.asyncio
+async def test_message_type_auto_registration(runtime: AgentRuntime | None) -> None:
+    agent1 = _StructuredAgent("structured", description="emits structured messages")
+    agent2 = _EchoAgent("echo", description="echoes input")
+
+    team = RoundRobinGroupChat(participants=[agent1, agent2], max_turns=2, runtime=runtime)
+
+    result = await team.run(task="Say something structured")
+
+    assert len(result.messages) == 3
+    assert isinstance(result.messages[0], TextMessage)
+    assert isinstance(result.messages[1], StructuredMessage)
+    assert isinstance(result.messages[2], TextMessage)
+    assert result.messages[1].to_text() == "Structured says: Structured hello"
+
+
+@pytest.mark.asyncio
+async def test_structured_message_state_roundtrip(runtime: AgentRuntime | None) -> None:
+    agent1 = _StructuredAgent("structured", description="sends structured")
+    agent2 = _EchoAgent("echo", description="echoes")
+
+    team1 = RoundRobinGroupChat(
+        participants=[agent1, agent2],
+        termination_condition=MaxMessageTermination(2),
+        runtime=runtime,
+    )
+
+    await team1.run(task="Say something structured")
+    state1 = await team1.save_state()
+
+    # Recreate team without needing custom_message_types
+    agent3 = _StructuredAgent("structured", description="sends structured")
+    agent4 = _EchoAgent("echo", description="echoes")
+    team2 = RoundRobinGroupChat(
+        participants=[agent3, agent4],
+        termination_condition=MaxMessageTermination(2),
+        runtime=runtime,
+    )
+
+    await team2.load_state(state1)
+    state2 = await team2.save_state()
+
+    # Assert full state equality
+    assert state1 == state2
+
+    # Assert message thread content match
+    manager1 = await team1._runtime.try_get_underlying_agent_instance(  # pyright: ignore
+        AgentId(f"{team1._group_chat_manager_name}_{team1._team_id}", team1._team_id),  # pyright: ignore
+        RoundRobinGroupChatManager,
+    )
+    manager2 = await team2._runtime.try_get_underlying_agent_instance(  # pyright: ignore
+        AgentId(f"{team2._group_chat_manager_name}_{team2._team_id}", team2._team_id),  # pyright: ignore
+        RoundRobinGroupChatManager,
+    )
+
+    assert manager1._message_thread == manager2._message_thread  # pyright: ignore

python/packages/autogen-agentchat/tests/test_messages.py

@@ -11,7 +11,7 @@ from autogen_agentchat.messages import (
     MultiModalMessage,
     StopMessage,
     StructuredMessage,
-    StructuredMessageComponent,
+    StructuredMessageFactory,
     TextMessage,
     ToolCallExecutionEvent,
     ToolCallRequestEvent,
@@ -52,18 +52,21 @@ def test_structured_message() -> None:
     assert dumped_message["content"]["field2"] == 42
     assert dumped_message["type"] == "StructuredMessage[TestContent]"
 
+
 def test_structured_message_component() -> None:
     # Create a structured message with the test contentformat_string="this is a string {field1} and this is an int {field2}"
-    format_string="this is a string {field1} and this is an int {field2}"
-    s_m = StructuredMessageComponent(input_model=TestContent, format_string=format_string)
-    config = s_m._to_config()
-    s_m_dyn = StructuredMessageComponent._from_config(config)
-    message = s_m_dyn.StructuredMessage(source="test_agent", content=s_m_dyn.ContentModel(field1="test", field2=42), format_string=s_m_dyn.format_string)
+    format_string = "this is a string {field1} and this is an int {field2}"
+    s_m = StructuredMessageFactory(input_model=TestContent, format_string=format_string)
+    config = s_m.dump_component()
+    s_m_dyn = StructuredMessageFactory.load_component(config)
+    message = s_m_dyn.StructuredMessage(
+        source="test_agent", content=s_m_dyn.ContentModel(field1="test", field2=42), format_string=s_m_dyn.format_string
+    )
 
     assert isinstance(message.content, s_m_dyn.ContentModel)
     assert not isinstance(message.content, TestContent)
-    assert message.content.field1 == "test"
-    assert message.content.field2 == 42
+    assert message.content.field1 == "test"  # type: ignore[attr-defined]
+    assert message.content.field2 == 42  # type: ignore[attr-defined]
 
     dumped_message = message.model_dump()
     assert dumped_message["source"] == "test_agent"
@@ -71,6 +74,7 @@ def test_structured_message_component() -> None:
     assert dumped_message["content"]["field2"] == 42
     assert message.to_model_text() == format_string.format(field1="test", field2=42)
 
+
 def test_message_factory() -> None:
     factory = MessageFactory()
 
@@ -128,6 +132,22 @@ def test_message_factory() -> None:
     assert structured_message.content.field2 == 42
     assert structured_message.type == "StructuredMessage[TestContent]"  # type: ignore[comparison-overlap]
 
+    sm_factory = StructuredMessageFactory(input_model=TestContent, format_string=None, content_model_name="TestContent")
+    config = sm_factory.dump_component()
+    config.config["content_model_name"] = "DynamicTestContent"
+    sm_factory_dynamic = StructuredMessageFactory.load_component(config)
+
+    factory.register(sm_factory_dynamic.StructuredMessage)
+    msg = sm_factory_dynamic.StructuredMessage(
+        content=sm_factory_dynamic.ContentModel(field1="static", field2=123), source="static_agent"
+    )
+    restored = factory.create(msg.dump())
+    assert isinstance(restored, StructuredMessage)
+    assert isinstance(restored.content, sm_factory_dynamic.ContentModel)  # type: ignore[reportUnkownMemberType]
+    assert restored.source == "static_agent"
+    assert restored.content.field1 == "static"  # type: ignore[attr-defined]
+    assert restored.content.field2 == 123  # type: ignore[attr-defined]
+
 
 class TestContainer(BaseModel):
     chat_messages: List[ChatMessage]

python/packages/autogen-core/docs/src/user-guide/core-user-guide/cookbook/llamaindex-agent.ipynb

@@ -38,7 +38,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "from pydantic import BaseModel\n",
     "from typing import List, Optional\n",
     "\n",
     "from autogen_core import AgentId, MessageContext, RoutedAgent, SingleThreadedAgentRuntime, message_handler\n",
@@ -57,7 +56,8 @@
     "from llama_index.embeddings.openai import OpenAIEmbedding\n",
     "from llama_index.llms.azure_openai import AzureOpenAI\n",
     "from llama_index.llms.openai import OpenAI\n",
-    "from llama_index.tools.wikipedia import WikipediaToolSpec"
+    "from llama_index.tools.wikipedia import WikipediaToolSpec\n",
+    "from pydantic import BaseModel"
    ]
   },
   {

python/packages/autogen-core/docs/src/user-guide/core-user-guide/design-patterns/multi-agent-debate.ipynb

@@ -291,7 +291,7 @@
                 "A --- B\n",
                 "|     |\n",
                 "|     |\n",
-                "C --- D\n",
+                "D --- C\n",
                 "```\n",
                 "\n",
                 "Each solver agent is connected to two other solver agents. \n",

python/packages/autogen-core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "autogen-core"
-version = "0.5.1"
+version = "0.5.2"
 license = {file = "LICENSE-CODE"}
 description = "Foundational interfaces and agent runtime implementation for AutoGen"
 readme = "README.md"
@@ -69,7 +69,7 @@ dev = [
     "pygments",
     "sphinxext-rediraffe",
 
-    "autogen_ext==0.5.1",
+    "autogen_ext==0.5.2",
 
     # Documentation tooling
     "diskcache",

python/packages/autogen-core/src/autogen_core/utils/_json_to_pydantic.py

@@ -1,5 +1,6 @@
 import datetime
-from typing import Annotated, Any, Dict, ForwardRef, List, Literal, Optional, Type, Union
+from ipaddress import IPv4Address, IPv6Address
+from typing import Annotated, Any, Dict, ForwardRef, List, Literal, Optional, Type, Union, cast
 
 from pydantic import (
     UUID1,
@@ -10,7 +11,6 @@ from pydantic import (
     BaseModel,
     EmailStr,
     Field,
-    IPvAnyAddress,
     conbytes,
     confloat,
     conint,
@@ -18,6 +18,7 @@ from pydantic import (
     constr,
     create_model,
 )
+from pydantic.fields import FieldInfo
 
 
 class SchemaConversionError(Exception):
@@ -44,14 +45,14 @@ class UnsupportedKeywordError(SchemaConversionError):
     pass
 
 
-TYPE_MAPPING: Dict[str, Any] = {
+TYPE_MAPPING: Dict[str, Type[Any]] = {
     "string": str,
     "integer": int,
     "boolean": bool,
     "number": float,
     "array": List,
     "object": dict,
-    "null": None,
+    "null": type(None),
 }
 
 FORMAT_MAPPING: Dict[str, Any] = {
@@ -64,10 +65,10 @@ FORMAT_MAPPING: Dict[str, Any] = {
     "email": EmailStr,
     "uri": AnyUrl,
     "hostname": constr(strict=True),
-    "ipv4": IPvAnyAddress,
-    "ipv6": IPvAnyAddress,
-    "ipv4-network": IPvAnyAddress,
-    "ipv6-network": IPvAnyAddress,
+    "ipv4": IPv4Address,
+    "ipv6": IPv6Address,
+    "ipv4-network": IPv4Address,
+    "ipv6-network": IPv6Address,
     "date-time": datetime.datetime,
     "date": datetime.date,
     "time": datetime.time,
@@ -84,13 +85,28 @@ FORMAT_MAPPING: Dict[str, Any] = {
 }
 
 
+def _make_field(
+    default: Any,
+    *,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+) -> Any:
+    """Construct a Pydantic Field with proper typing."""
+    field_kwargs: Dict[str, Any] = {}
+    if title is not None:
+        field_kwargs["title"] = title
+    if description is not None:
+        field_kwargs["description"] = description
+    return Field(default, **field_kwargs)
+
+
 class _JSONSchemaToPydantic:
-    def __init__(self):
-        self._model_cache = {}
+    def __init__(self) -> None:
+        self._model_cache: Dict[str, Optional[Union[Type[BaseModel], ForwardRef]]] = {}
 
     def _resolve_ref(self, ref: str, schema: Dict[str, Any]) -> Dict[str, Any]:
         ref_key = ref.split("/")[-1]
-        definitions = schema.get("$defs", {})
+        definitions = cast(dict[str, dict[str, Any]], schema.get("$defs", {}))
 
         if ref_key not in definitions:
             raise ReferenceNotFoundError(
@@ -110,7 +126,7 @@ class _JSONSchemaToPydantic:
 
         return self._model_cache[ref_name]
 
-    def _process_definitions(self, root_schema: Dict[str, Any]):
+    def _process_definitions(self, root_schema: Dict[str, Any]) -> None:
         if "$defs" in root_schema:
             for model_name in root_schema["$defs"]:
                 if model_name not in self._model_cache:
@@ -132,7 +148,7 @@ class _JSONSchemaToPydantic:
             schema = {**resolved, **{k: v for k, v in schema.items() if k != "$ref"}}
 
         if "allOf" in schema:
-            merged = {"type": "object", "properties": {}, "required": []}
+            merged: Dict[str, Any] = {"type": "object", "properties": {}, "required": []}
             for s in schema["allOf"]:
                 part = self._resolve_ref(s["$ref"], root_schema) if "$ref" in s else s
                 merged["properties"].update(part.get("properties", {}))
@@ -146,7 +162,7 @@ class _JSONSchemaToPydantic:
         return self._json_schema_to_model(schema, model_name, root_schema)
 
     def _resolve_union_types(self, schemas: List[Dict[str, Any]]) -> List[Any]:
-        types = []
+        types: List[Any] = []
         for s in schemas:
             if "$ref" in s:
                 types.append(self.get_ref(s["$ref"].split("/")[-1]))
@@ -167,7 +183,7 @@ class _JSONSchemaToPydantic:
             )
 
         base_type = TYPE_MAPPING[json_type]
-        constraints = {}
+        constraints: Dict[str, Any] = {}
 
         if json_type == "string":
             if "minLength" in value:
@@ -219,7 +235,7 @@ class _JSONSchemaToPydantic:
                     )
                 item_type = TYPE_MAPPING[item_type_name]
 
-            base_type = conlist(item_type, **constraints) if constraints else List[item_type]
+            base_type = conlist(item_type, **constraints) if constraints else List[item_type]  # type: ignore[valid-type]
 
         if "format" in value:
             format_type = FORMAT_MAPPING.get(value["format"])
@@ -237,7 +253,7 @@ class _JSONSchemaToPydantic:
         self, schema: Dict[str, Any], model_name: str, root_schema: Dict[str, Any]
     ) -> Type[BaseModel]:
         if "allOf" in schema:
-            merged = {"type": "object", "properties": {}, "required": []}
+            merged: Dict[str, Any] = {"type": "object", "properties": {}, "required": []}
             for s in schema["allOf"]:
                 part = self._resolve_ref(s["$ref"], root_schema) if "$ref" in s else s
                 merged["properties"].update(part.get("properties", {}))
@@ -248,7 +264,7 @@ class _JSONSchemaToPydantic:
             merged["required"] = list(set(merged["required"]))
             schema = merged
 
-        fields = {}
+        fields: Dict[str, tuple[Any, FieldInfo]] = {}
         required_fields = set(schema.get("required", []))
 
         for key, value in schema.get("properties", {}).items():
@@ -299,9 +315,16 @@ class _JSONSchemaToPydantic:
             if "description" in value:
                 field_args["description"] = value["description"]
 
-            fields[key] = (field_type, Field(**field_args))
+            fields[key] = (
+                field_type,
+                _make_field(
+                    default_value if not is_required else ...,
+                    title=value.get("title"),
+                    description=value.get("description"),
+                ),
+            )
 
-        model = create_model(model_name, **fields)
+        model: Type[BaseModel] = create_model(model_name, **cast(dict[str, Any], fields))
         model.model_rebuild()
         return model
 

python/packages/autogen-core/tests/test_json_to_pydantic.py

@@ -1,12 +1,15 @@
-from typing import Any, Dict, List, Literal, Optional
+import types
+from typing import Any, Dict, List, Literal, Optional, Type, get_args, get_origin
 from uuid import UUID, uuid4
 
 import pytest
 from autogen_core.utils._json_to_pydantic import (
+    FORMAT_MAPPING,
+    TYPE_MAPPING,
     FormatNotSupportedError,
     ReferenceNotFoundError,
     UnsupportedKeywordError,
-    _JSONSchemaToPydantic,
+    _JSONSchemaToPydantic,  # pyright: ignore[reportPrivateUsage]
 )
 from pydantic import BaseModel, EmailStr, Field, ValidationError
 
@@ -44,31 +47,31 @@ class ComplexModel(BaseModel):
 
 
 @pytest.fixture
-def converter():
+def converter() -> _JSONSchemaToPydantic:
     """Fixture to create a fresh instance of JSONSchemaToPydantic for every test."""
     return _JSONSchemaToPydantic()
 
 
 @pytest.fixture
-def sample_json_schema():
+def sample_json_schema() -> Dict[str, Any]:
     """Fixture that returns a JSON schema dynamically using model_json_schema()."""
     return User.model_json_schema()
 
 
 @pytest.fixture
-def sample_json_schema_recursive():
+def sample_json_schema_recursive() -> Dict[str, Any]:
     """Fixture that returns a self-referencing JSON schema."""
     return Employee.model_json_schema()
 
 
 @pytest.fixture
-def sample_json_schema_nested():
+def sample_json_schema_nested() -> Dict[str, Any]:
     """Fixture that returns a nested schema with arrays of objects."""
     return Department.model_json_schema()
 
 
 @pytest.fixture
-def sample_json_schema_complex():
+def sample_json_schema_complex() -> Dict[str, Any]:
     """Fixture that returns a complex schema with multiple structures."""
     return ComplexModel.model_json_schema()
 
@@ -82,7 +85,13 @@ def sample_json_schema_complex():
         (sample_json_schema_complex, "ComplexModel", ["user", "extra_info", "sub_items"]),
     ],
 )
-def test_json_schema_to_pydantic(converter, schema_fixture, model_name, expected_fields, request):
+def test_json_schema_to_pydantic(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    expected_fields: List[str],
+    request: Any,
+) -> None:
     """Test conversion of JSON Schema to Pydantic model using the class instance."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -155,7 +164,13 @@ def test_json_schema_to_pydantic(converter, schema_fixture, model_name, expected
         ),
     ],
 )
-def test_valid_data_model(converter, schema_fixture, model_name, valid_data, request):
+def test_valid_data_model(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    valid_data: Dict[str, Any],
+    request: Any,
+) -> None:
     """Test that valid data is accepted by the generated model."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -230,7 +245,13 @@ def test_valid_data_model(converter, schema_fixture, model_name, valid_data, req
         ),
     ],
 )
-def test_invalid_data_model(converter, schema_fixture, model_name, invalid_data, request):
+def test_invalid_data_model(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    invalid_data: Dict[str, Any],
+    request: Any,
+) -> None:
     """Test that invalid data raises ValidationError."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -258,19 +279,19 @@ class NestedListModel(BaseModel):
 
 
 @pytest.fixture
-def sample_json_schema_list_dict():
+def sample_json_schema_list_dict() -> Dict[str, Any]:
     """Fixture for `List[Dict[str, Any]]`"""
     return ListDictModel.model_json_schema()
 
 
 @pytest.fixture
-def sample_json_schema_dict_list():
+def sample_json_schema_dict_list() -> Dict[str, Any]:
     """Fixture for `Dict[str, List[Any]]`"""
     return DictListModel.model_json_schema()
 
 
 @pytest.fixture
-def sample_json_schema_nested_list():
+def sample_json_schema_nested_list() -> Dict[str, Any]:
     """Fixture for `List[List[str]]`"""
     return NestedListModel.model_json_schema()
 
@@ -283,7 +304,13 @@ def sample_json_schema_nested_list():
         (sample_json_schema_nested_list, "NestedListModel", ["matrix"]),
     ],
 )
-def test_json_schema_to_pydantic_nested(converter, schema_fixture, model_name, expected_fields, request):
+def test_json_schema_to_pydantic_nested(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    expected_fields: list[str],
+    request: Any,
+) -> None:
     """Test conversion of JSON Schema to Pydantic model using the class instance."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -324,7 +351,13 @@ def test_json_schema_to_pydantic_nested(converter, schema_fixture, model_name, e
         ),
     ],
 )
-def test_valid_data_model_nested(converter, schema_fixture, model_name, valid_data, request):
+def test_valid_data_model_nested(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    valid_data: Dict[str, Any],
+    request: Any,
+) -> None:
     """Test that valid data is accepted by the generated model."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -364,7 +397,13 @@ def test_valid_data_model_nested(converter, schema_fixture, model_name, valid_da
         ),
     ],
 )
-def test_invalid_data_model_nested(converter, schema_fixture, model_name, invalid_data, request):
+def test_invalid_data_model_nested(
+    converter: _JSONSchemaToPydantic,
+    schema_fixture: Any,
+    model_name: str,
+    invalid_data: Dict[str, Any],
+    request: Any,
+) -> None:
     """Test that invalid data raises ValidationError."""
     schema = request.getfixturevalue(schema_fixture.__name__)
     Model = converter.json_schema_to_pydantic(schema, model_name)
@@ -373,25 +412,25 @@ def test_invalid_data_model_nested(converter, schema_fixture, model_name, invali
         Model(**invalid_data)
 
 
-def test_reference_not_found(converter):
+def test_reference_not_found(converter: _JSONSchemaToPydantic) -> None:
     schema = {"type": "object", "properties": {"manager": {"$ref": "#/$defs/MissingRef"}}}
     with pytest.raises(ReferenceNotFoundError):
         converter.json_schema_to_pydantic(schema, "MissingRefModel")
 
 
-def test_format_not_supported(converter):
+def test_format_not_supported(converter: _JSONSchemaToPydantic) -> None:
     schema = {"type": "object", "properties": {"custom_field": {"type": "string", "format": "unsupported-format"}}}
     with pytest.raises(FormatNotSupportedError):
         converter.json_schema_to_pydantic(schema, "UnsupportedFormatModel")
 
 
-def test_unsupported_keyword(converter):
+def test_unsupported_keyword(converter: _JSONSchemaToPydantic) -> None:
     schema = {"type": "object", "properties": {"broken_field": {"title": "Missing type"}}}
     with pytest.raises(UnsupportedKeywordError):
         converter.json_schema_to_pydantic(schema, "MissingTypeModel")
 
 
-def test_enum_field_schema():
+def test_enum_field_schema() -> None:
     schema = {
         "type": "object",
         "properties": {
@@ -401,18 +440,24 @@ def test_enum_field_schema():
         "required": ["status"],
     }
 
-    converter = _JSONSchemaToPydantic()
+    converter: _JSONSchemaToPydantic = _JSONSchemaToPydantic()
     Model = converter.json_schema_to_pydantic(schema, "Task")
 
-    assert Model.model_fields["status"].annotation == Literal["pending", "approved", "rejected"]
-    assert Model.model_fields["priority"].annotation == Optional[Literal[1, 2, 3]]
+    status_ann = Model.model_fields["status"].annotation
+    assert get_origin(status_ann) is Literal
+    assert set(get_args(status_ann)) == {"pending", "approved", "rejected"}
+
+    priority_ann = Model.model_fields["priority"].annotation
+    args = get_args(priority_ann)
+    assert type(None) in args
+    assert Literal[1, 2, 3] in args
 
     instance = Model(status="approved", priority=2)
-    assert instance.status == "approved"
-    assert instance.priority == 2
+    assert instance.status == "approved"  # type: ignore[attr-defined]
+    assert instance.priority == 2  # type: ignore[attr-defined]
 
 
-def test_metadata_title_description(converter):
+def test_metadata_title_description(converter: _JSONSchemaToPydantic) -> None:
     schema = {
         "title": "CustomerProfile",
         "description": "A profile containing personal and contact info",
@@ -437,7 +482,7 @@ def test_metadata_title_description(converter):
         "required": ["first_name"],
     }
 
-    Model = converter.json_schema_to_pydantic(schema, "CustomerProfile")
+    Model: Type[BaseModel] = converter.json_schema_to_pydantic(schema, "CustomerProfile")
     generated_schema = Model.model_json_schema()
 
     assert generated_schema["title"] == "CustomerProfile"
@@ -460,7 +505,7 @@ def test_metadata_title_description(converter):
     assert email["description"] == "Primary email"
 
 
-def test_oneof_with_discriminator(converter):
+def test_oneof_with_discriminator(converter: _JSONSchemaToPydantic) -> None:
     schema = {
         "title": "PetWrapper",
         "type": "object",
@@ -491,11 +536,11 @@ def test_oneof_with_discriminator(converter):
 
     # Instantiate with a Cat
     cat = Model(pet={"pet_type": "cat", "hunting_skill": "expert"})
-    assert cat.pet.pet_type == "cat"
+    assert cat.pet.pet_type == "cat"  # type: ignore[attr-defined]
 
     # Instantiate with a Dog
     dog = Model(pet={"pet_type": "dog", "pack_size": 4})
-    assert dog.pet.pet_type == "dog"
+    assert dog.pet.pet_type == "dog"  # type: ignore[attr-defined]
 
     # Check round-trip schema includes discriminator
     model_schema = Model.model_json_schema()
@@ -503,7 +548,7 @@ def test_oneof_with_discriminator(converter):
     assert model_schema["properties"]["pet"]["discriminator"]["propertyName"] == "pet_type"
 
 
-def test_allof_merging_with_refs(converter):
+def test_allof_merging_with_refs(converter: _JSONSchemaToPydantic) -> None:
     schema = {
         "title": "EmployeeWithDepartment",
         "allOf": [{"$ref": "#/$defs/Employee"}, {"$ref": "#/$defs/Department"}],
@@ -525,15 +570,15 @@ def test_allof_merging_with_refs(converter):
 
     Model = converter.json_schema_to_pydantic(schema, "EmployeeWithDepartment")
     instance = Model(id="123", name="Alice", department="Engineering")
-    assert instance.id == "123"
-    assert instance.name == "Alice"
-    assert instance.department == "Engineering"
+    assert instance.id == "123"  # type: ignore[attr-defined]
+    assert instance.name == "Alice"  # type: ignore[attr-defined]
+    assert instance.department == "Engineering"  # type: ignore[attr-defined]
 
     dumped = instance.model_dump()
     assert dumped == {"id": "123", "name": "Alice", "department": "Engineering"}
 
 
-def test_nested_allof_merging(converter):
+def test_nested_allof_merging(converter: _JSONSchemaToPydantic) -> None:
     schema = {
         "title": "ContainerModel",
         "type": "object",
@@ -565,8 +610,8 @@ def test_nested_allof_merging(converter):
     Model = converter.json_schema_to_pydantic(schema, "ContainerModel")
     instance = Model(nested={"data": {"base_field": "abc", "extra": "xyz"}})
 
-    assert instance.nested.data.base_field == "abc"
-    assert instance.nested.data.extra == "xyz"
+    assert instance.nested.data.base_field == "abc"  # type: ignore[attr-defined]
+    assert instance.nested.data.extra == "xyz"  # type: ignore[attr-defined]
 
 
 @pytest.mark.parametrize(
@@ -620,12 +665,15 @@ def test_nested_allof_merging(converter):
         ),
     ],
 )
-def test_field_constraints(schema, field_name, valid_values, invalid_values):
+def test_field_constraints(
+    schema: Dict[str, Any],
+    field_name: str,
+    valid_values: List[Any],
+    invalid_values: List[Any],
+) -> None:
     converter = _JSONSchemaToPydantic()
     Model = converter.json_schema_to_pydantic(schema, "ConstraintModel")
 
-    import json
-
     for value in valid_values:
         instance = Model(**{field_name: value})
         assert getattr(instance, field_name) == value
@@ -650,7 +698,60 @@ def test_field_constraints(schema, field_name, valid_values, invalid_values):
         },
     ],
 )
-def test_unknown_type_raises(schema):
+def test_unknown_type_raises(schema: Dict[str, Any]) -> None:
     converter = _JSONSchemaToPydantic()
     with pytest.raises(UnsupportedKeywordError):
         converter.json_schema_to_pydantic(schema, "UnknownTypeModel")
+
+
+@pytest.mark.parametrize("json_type, expected_type", list(TYPE_MAPPING.items()))
+def test_basic_type_mapping(json_type: str, expected_type: type) -> None:
+    schema = {
+        "type": "object",
+        "properties": {"field": {"type": json_type}},
+        "required": ["field"],
+    }
+    converter = _JSONSchemaToPydantic()
+    Model = converter.json_schema_to_pydantic(schema, f"{json_type.capitalize()}Model")
+
+    assert "field" in Model.__annotations__
+    field_type = Model.__annotations__["field"]
+
+    # For array/object/null we check the outer type only
+    if json_type == "null":
+        assert field_type is type(None)
+    elif json_type == "array":
+        assert getattr(field_type, "__origin__", None) is list
+    elif json_type == "object":
+        assert field_type in (dict, Dict) or getattr(field_type, "__origin__", None) in (dict, Dict)
+
+    else:
+        assert field_type == expected_type
+
+
+@pytest.mark.parametrize("format_name, expected_type", list(FORMAT_MAPPING.items()))
+def test_format_mapping(format_name: str, expected_type: Any) -> None:
+    schema = {
+        "type": "object",
+        "properties": {"field": {"type": "string", "format": format_name}},
+        "required": ["field"],
+    }
+    converter = _JSONSchemaToPydantic()
+    Model = converter.json_schema_to_pydantic(schema, f"{format_name.capitalize()}Model")
+
+    assert "field" in Model.__annotations__
+    field_type = Model.__annotations__["field"]
+    if isinstance(expected_type, types.FunctionType):  # if it's a constrained constructor (e.g., conint)
+        assert callable(field_type)
+    else:
+        assert field_type == expected_type
+
+
+def test_unknown_format_raises() -> None:
+    schema = {
+        "type": "object",
+        "properties": {"bad_field": {"type": "string", "format": "definitely-not-a-format"}},
+    }
+    converter = _JSONSchemaToPydantic()
+    with pytest.raises(FormatNotSupportedError):
+        converter.json_schema_to_pydantic(schema, "UnknownFormatModel")

python/packages/autogen-ext/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "autogen-ext"
-version = "0.5.1"
+version = "0.5.2"
 license = {file = "LICENSE-CODE"}
 description = "AutoGen extensions library"
 readme = "README.md"
@@ -15,7 +15,7 @@ classifiers = [
     "Operating System :: OS Independent",
 ]
 dependencies = [
-    "autogen-core==0.5.1",
+    "autogen-core==0.5.2",
 ]
 
 [project.optional-dependencies]
@@ -31,7 +31,7 @@ docker = ["docker~=7.0", "asyncio_atexit>=1.0.1"]
 ollama = ["ollama>=0.4.7", "tiktoken>=0.8.0"]
 openai = ["openai>=1.66.5", "tiktoken>=0.8.0", "aiofiles"]
 file-surfer = [
-    "autogen-agentchat==0.5.1",
+    "autogen-agentchat==0.5.2",
     "magika>=0.6.1rc2",
     "markitdown[all]~=0.1.0a3",
 ]
@@ -43,21 +43,21 @@ llama-cpp = [
 graphrag = ["graphrag>=1.0.1"]
 chromadb = ["chromadb>=1.0.0"]
 web-surfer = [
-    "autogen-agentchat==0.5.1",
+    "autogen-agentchat==0.5.2",
     "playwright>=1.48.0",
     "pillow>=11.0.0",
     "magika>=0.6.1rc2",
     "markitdown[all]~=0.1.0a3",
 ]
 magentic-one = [
-    "autogen-agentchat==0.5.1",
+    "autogen-agentchat==0.5.2",
     "magika>=0.6.1rc2",
     "markitdown[all]~=0.1.0a3",
     "playwright>=1.48.0",
     "pillow>=11.0.0",
 ]
 video-surfer = [
-    "autogen-agentchat==0.5.1",
+    "autogen-agentchat==0.5.2",
     "opencv-python>=4.5",
     "ffmpeg-python",
     "openai-whisper",
@@ -137,7 +137,7 @@ rich = ["rich>=13.9.4"]
 
 mcp = [
     "mcp>=1.6.0",
-    "json-schema-to-pydantic>=0.2.3"
+    "json-schema-to-pydantic>=0.2.4"
 ]
 
 [tool.hatch.build.targets.wheel]

python/packages/autogen-ext/src/autogen_ext/experimental/task_centric_memory/__init__.py

@@ -1,4 +1,4 @@
-from .memory_controller import MemoryController, MemoryControllerConfig
 from ._memory_bank import MemoryBankConfig
+from .memory_controller import MemoryController, MemoryControllerConfig
 
 __all__ = ["MemoryController", "MemoryControllerConfig", "MemoryBankConfig"]

python/packages/autogen-studio/pyproject.toml

@@ -32,9 +32,9 @@ dependencies = [
     "loguru",
     "pyyaml",
     "html2text",
-    "autogen-core>=0.4.9.2,<0.5",
-    "autogen-agentchat>=0.4.9.2,<0.5",
-    "autogen-ext[magentic-one, openai, azure]>=0.4.2,<0.5",
+    "autogen-core>=0.4.9.2,<0.6",
+    "autogen-agentchat>=0.4.9.2,<0.6",
+    "autogen-ext[magentic-one, openai, azure]>=0.4.2,<0.6",
     "anthropic",
 ]
 optional-dependencies = {web = ["fastapi", "uvicorn"], database = ["psycopg"]}

python/uv.lock

@@ -1,4 +1,5 @@
 version = 1
+revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
@@ -90,7 +91,6 @@ wheels = [
 
 [[package]]
 name = "agbench"
-version = "0.0.1a1"
 source = { editable = "packages/agbench" }
 dependencies = [
     { name = "azure-identity" },
@@ -452,7 +452,7 @@ wheels = [
 
 [[package]]
 name = "autogen-agentchat"
-version = "0.5.1"
+version = "0.5.2"
 source = { editable = "packages/autogen-agentchat" }
 dependencies = [
     { name = "autogen-core" },
@@ -463,7 +463,7 @@ requires-dist = [{ name = "autogen-core", editable = "packages/autogen-core" }]
 
 [[package]]
 name = "autogen-core"
-version = "0.5.1"
+version = "0.5.2"
 source = { editable = "packages/autogen-core" }
 dependencies = [
     { name = "jsonref" },
@@ -582,7 +582,7 @@ dev = [
 
 [[package]]
 name = "autogen-ext"
-version = "0.5.1"
+version = "0.5.2"
 source = { editable = "packages/autogen-ext" }
 dependencies = [
     { name = "autogen-core" },
@@ -745,7 +745,7 @@ requires-dist = [
     { name = "httpx", marker = "extra == 'http-tool'", specifier = ">=0.27.0" },
     { name = "ipykernel", marker = "extra == 'jupyter-executor'", specifier = ">=6.29.5" },
     { name = "json-schema-to-pydantic", marker = "extra == 'http-tool'", specifier = ">=0.2.0" },
-    { name = "json-schema-to-pydantic", marker = "extra == 'mcp'", specifier = ">=0.2.3" },
+    { name = "json-schema-to-pydantic", marker = "extra == 'mcp'", specifier = ">=0.2.4" },
     { name = "langchain-core", marker = "extra == 'langchain'", specifier = "~=0.3.3" },
     { name = "llama-cpp-python", marker = "extra == 'llama-cpp'", specifier = ">=0.3.8" },
     { name = "magika", marker = "extra == 'file-surfer'", specifier = ">=0.6.1rc2" },
@@ -780,6 +780,7 @@ requires-dist = [
     { name = "tiktoken", marker = "extra == 'ollama'", specifier = ">=0.8.0" },
     { name = "tiktoken", marker = "extra == 'openai'", specifier = ">=0.8.0" },
 ]
+provides-extras = ["anthropic", "langchain", "azure", "docker", "ollama", "openai", "file-surfer", "llama-cpp", "graphrag", "chromadb", "web-surfer", "magentic-one", "video-surfer", "diskcache", "redis", "grpc", "jupyter-executor", "task-centric-memory", "semantic-kernel-core", "gemini", "semantic-kernel-google", "semantic-kernel-hugging-face", "semantic-kernel-mistralai", "semantic-kernel-ollama", "semantic-kernel-onnx", "semantic-kernel-anthropic", "semantic-kernel-pandas", "semantic-kernel-aws", "semantic-kernel-dapr", "http-tool", "semantic-kernel-all", "rich", "mcp"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -808,7 +809,6 @@ requires-dist = [
 
 [[package]]
 name = "autogenstudio"
-version = "0.4.2"
 source = { editable = "packages/autogen-studio" }
 dependencies = [
     { name = "aiofiles" },
@@ -862,6 +862,7 @@ requires-dist = [
     { name = "uvicorn", marker = "extra == 'web'" },
     { name = "websockets" },
 ]
+provides-extras = ["web", "database"]
 
 [[package]]
 name = "autograd"
@@ -3044,14 +3045,14 @@ wheels = [
 
 [[package]]
 name = "json-schema-to-pydantic"
-version = "0.2.3"
+version = "0.2.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pydantic" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f2/8d/da0e791baf63a957ff67e0706d59386b72ab87858e616b6fcfc9b58cd910/json_schema_to_pydantic-0.2.3.tar.gz", hash = "sha256:c76db1f6001996895328e7aa174aae201d85d1f5e79d592c272ea03c8586e453", size = 35305 }
+sdist = { url = "https://files.pythonhosted.org/packages/0e/5a/82ce52917b4b021e739dc02384bb3257b5ddd04e40211eacdc32c88bdda5/json_schema_to_pydantic-0.2.4.tar.gz", hash = "sha256:c24060aa7694ae7be0465ce11339a6d1cc8a72cd8f4378c889d19722fa7da1ee", size = 37816 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/4a/55/81bbfbc806aab8dc4a21ad1c9c7fd61f94f2b4076ea64f1730a0368831a2/json_schema_to_pydantic-0.2.3-py3-none-any.whl", hash = "sha256:fe0c04357aa8d27ad5a46e54c2d6a8f35ca6c10b36e76a95c39827e38397f427", size = 11699 },
+    { url = "https://files.pythonhosted.org/packages/2e/86/35135e8e4b1da50e6e8ed2afcacce589e576f3460c892d5e616390a4eb71/json_schema_to_pydantic-0.2.4-py3-none-any.whl", hash = "sha256:5c46675df0ab2685d92ed805da38348a34488654cb95ceb1a564dda23dcc3a89", size = 11940 },
 ]
 
 [[package]]
@@ -4690,7 +4691,6 @@ name = "nvidia-cublas-cu12"
 version = "12.4.5.8"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/7f/7fbae15a3982dc9595e49ce0f19332423b260045d0a6afe93cdbe2f1f624/nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0f8aa1706812e00b9f19dfe0cdb3999b092ccb8ca168c0db5b8ea712456fd9b3", size = 363333771 },
     { url = "https://files.pythonhosted.org/packages/ae/71/1c91302526c45ab494c23f61c7a84aa568b8c1f9d196efa5993957faf906/nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl", hash = "sha256:2fc8da60df463fdefa81e323eef2e36489e1c94335b5358bcb38360adf75ac9b", size = 363438805 },
 ]
 
@@ -4699,7 +4699,6 @@ name = "nvidia-cuda-cupti-cu12"
 version = "12.4.127"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/93/b5/9fb3d00386d3361b03874246190dfec7b206fd74e6e287b26a8fcb359d95/nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a", size = 12354556 },
     { url = "https://files.pythonhosted.org/packages/67/42/f4f60238e8194a3106d06a058d494b18e006c10bb2b915655bd9f6ea4cb1/nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9dec60f5ac126f7bb551c055072b69d85392b13311fcc1bcda2202d172df30fb", size = 13813957 },
 ]
 
@@ -4708,7 +4707,6 @@ name = "nvidia-cuda-nvrtc-cu12"
 version = "12.4.127"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/77/aa/083b01c427e963ad0b314040565ea396f914349914c298556484f799e61b/nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198", size = 24133372 },
     { url = "https://files.pythonhosted.org/packages/2c/14/91ae57cd4db3f9ef7aa99f4019cfa8d54cb4caa7e00975df6467e9725a9f/nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338", size = 24640306 },
 ]
 
@@ -4717,7 +4715,6 @@ name = "nvidia-cuda-runtime-cu12"
 version = "12.4.127"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a1/aa/b656d755f474e2084971e9a297def515938d56b466ab39624012070cb773/nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3", size = 894177 },
     { url = "https://files.pythonhosted.org/packages/ea/27/1795d86fe88ef397885f2e580ac37628ed058a92ed2c39dc8eac3adf0619/nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5", size = 883737 },
 ]
 
@@ -4740,7 +4737,6 @@ dependencies = [
     { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7a/8a/0e728f749baca3fbeffad762738276e5df60851958be7783af121a7221e7/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399", size = 211422548 },
     { url = "https://files.pythonhosted.org/packages/27/94/3266821f65b92b3138631e9c8e7fe1fb513804ac934485a8d05776e1dd43/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9", size = 211459117 },
 ]
 
@@ -4749,7 +4745,6 @@ name = "nvidia-curand-cu12"
 version = "10.3.5.147"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/80/9c/a79180e4d70995fdf030c6946991d0171555c6edf95c265c6b2bf7011112/nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1f173f09e3e3c76ab084aba0de819c49e56614feae5c12f69883f4ae9bb5fad9", size = 56314811 },
     { url = "https://files.pythonhosted.org/packages/8a/6d/44ad094874c6f1b9c654f8ed939590bdc408349f137f9b98a3a23ccec411/nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a88f583d4e0bb643c49743469964103aa59f7f708d862c3ddb0fc07f851e3b8b", size = 56305206 },
 ]
 
@@ -4763,7 +4758,6 @@ dependencies = [
     { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/46/6b/a5c33cf16af09166845345275c34ad2190944bcc6026797a39f8e0a282e0/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e", size = 127634111 },
     { url = "https://files.pythonhosted.org/packages/3a/e1/5b9089a4b2a4790dfdea8b3a006052cfecff58139d5a4e34cb1a51df8d6f/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260", size = 127936057 },
 ]
 
@@ -4775,7 +4769,6 @@ dependencies = [
     { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/a9/c0d2f83a53d40a4a41be14cea6a0bf9e668ffcf8b004bd65633f433050c0/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_aarch64.whl", hash = "sha256:9d32f62896231ebe0480efd8a7f702e143c98cfaa0e8a76df3386c1ba2b54df3", size = 207381987 },
     { url = "https://files.pythonhosted.org/packages/db/f7/97a9ea26ed4bbbfc2d470994b8b4f338ef663be97b8f677519ac195e113d/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1", size = 207454763 },
 ]
 
@@ -4792,7 +4785,6 @@ name = "nvidia-nvjitlink-cu12"
 version = "12.4.127"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/02/45/239d52c05074898a80a900f49b1615d81c07fceadd5ad6c4f86a987c0bc4/nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4abe7fef64914ccfa909bc2ba39739670ecc9e820c83ccc7a6ed414122599b83", size = 20552510 },
     { url = "https://files.pythonhosted.org/packages/ff/ff/847841bacfbefc97a00036e0fce5a0f086b640756dc38caea5e1bb002655/nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57", size = 21066810 },
 ]
 
@@ -4801,7 +4793,6 @@ name = "nvidia-nvtx-cu12"
 version = "12.4.127"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/06/39/471f581edbb7804b39e8063d92fc8305bdc7a80ae5c07dbe6ea5c50d14a5/nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7959ad635db13edf4fc65c06a6e9f9e55fc2f92596db928d169c0bb031e88ef3", size = 100417 },
     { url = "https://files.pythonhosted.org/packages/87/20/199b8713428322a2f22b722c62b8cc278cc53dffa9705d744484b5035ee9/nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a", size = 99144 },
 ]