Python: [BREAKING] Renamed create_agent to as_agent

python/packages/azure-ai/agent_framework_azure_ai/_chat_client.py

@@ -4,25 +4,29 @@
 import json
 import re
 import sys
-from collections.abc import AsyncIterable, Mapping, MutableMapping, MutableSequence, Sequence
+from collections.abc import AsyncIterable, Callable, Mapping, MutableMapping, MutableSequence, Sequence
 from typing import Any, ClassVar, Generic, TypedDict
 
 from agent_framework import (
     AGENT_FRAMEWORK_USER_AGENT,
     BaseChatClient,
+    ChatAgent,
     ChatMessage,
+    ChatMessageStoreProtocol,
     ChatOptions,
     ChatResponse,
     ChatResponseUpdate,
     CitationAnnotation,
     Contents,
+    ContextProvider,
     DataContent,
     FunctionApprovalRequestContent,
     FunctionApprovalResponseContent,
     FunctionCallContent,
     FunctionResultContent,
     HostedFileContent,
     HostedMCPTool,
+    Middleware,
     Role,
     TextContent,
     TextSpanRegion,
@@ -1162,3 +1166,59 @@ def service_url(self) -> str:
             The service URL for the chat client, or None if not set.
         """
         return self.agents_client._config.endpoint  # type: ignore
+
+    @override
+    def as_agent(
+        self,
+        *,
+        id: str | None = None,
+        name: str | None = None,
+        description: str | None = None,
+        instructions: str | None = None,
+        tools: ToolProtocol
+        | Callable[..., Any]
+        | MutableMapping[str, Any]
+        | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]]
+        | None = None,
+        default_options: TAzureAIAgentOptions | None = None,
+        chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] | None = None,
+        context_provider: ContextProvider | None = None,
+        middleware: Sequence[Middleware] | None = None,
+        **kwargs: Any,
+    ) -> ChatAgent[TAzureAIAgentOptions]:
+        """Convert this chat client to a ChatAgent.
+
+        This method creates a ChatAgent instance with this client pre-configured.
+        It does NOT create an agent on the Azure AI service - the actual agent
+        will be created on the server during the first invocation (run).
+
+        For creating and managing persistent agents on the server, use
+        :class:`~agent_framework_azure_ai.AzureAIAgentsProvider` instead.
+
+        Keyword Args:
+            id: The unique identifier for the agent. Will be created automatically if not provided.
+            name: The name of the agent.
+            description: A brief description of the agent's purpose.
+            instructions: Optional instructions for the agent.
+            tools: The tools to use for the request.
+            default_options: A TypedDict containing chat options.
+            chat_message_store_factory: Factory function to create an instance of ChatMessageStoreProtocol.
+            context_provider: Context providers to include during agent invocation.
+            middleware: List of middleware to intercept agent and function invocations.
+            kwargs: Any additional keyword arguments.
+
+        Returns:
+            A ChatAgent instance configured with this chat client.
+        """
+        return super().as_agent(
+            id=id,
+            name=name,
+            description=description,
+            instructions=instructions,
+            tools=tools,
+            default_options=default_options,
+            chat_message_store_factory=chat_message_store_factory,
+            context_provider=context_provider,
+            middleware=middleware,
+            **kwargs,
+        )

python/packages/azure-ai/agent_framework_azure_ai/_client.py

@@ -1,14 +1,19 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import sys
-from collections.abc import Mapping, MutableSequence
+from collections.abc import Callable, Mapping, MutableMapping, MutableSequence, Sequence
 from typing import TYPE_CHECKING, Any, ClassVar, Generic, TypedDict, TypeVar, cast
 
 from agent_framework import (
     AGENT_FRAMEWORK_USER_AGENT,
+    ChatAgent,
     ChatMessage,
+    ChatMessageStoreProtocol,
+    ContextProvider,
     HostedMCPTool,
+    Middleware,
     TextContent,
+    ToolProtocol,
     get_logger,
     use_chat_middleware,
     use_function_invocation,
@@ -511,3 +516,59 @@ def _prepare_mcp_tool(tool: HostedMCPTool) -> MCPTool:  # type: ignore[override]
                         mcp["require_approval"] = {"never": {"tool_names": list(never_require_approvals)}}
 
         return mcp
+
+    @override
+    def as_agent(
+        self,
+        *,
+        id: str | None = None,
+        name: str | None = None,
+        description: str | None = None,
+        instructions: str | None = None,
+        tools: ToolProtocol
+        | Callable[..., Any]
+        | MutableMapping[str, Any]
+        | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]]
+        | None = None,
+        default_options: TAzureAIClientOptions | None = None,
+        chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] | None = None,
+        context_provider: ContextProvider | None = None,
+        middleware: Sequence[Middleware] | None = None,
+        **kwargs: Any,
+    ) -> ChatAgent[TAzureAIClientOptions]:
+        """Convert this chat client to a ChatAgent.
+
+        This method creates a ChatAgent instance with this client pre-configured.
+        It does NOT create an agent on the Azure AI service - the actual agent
+        will be created on the server during the first invocation (run).
+
+        For creating and managing persistent agents on the server, use
+        :class:`~agent_framework_azure_ai.AzureAIProjectAgentProvider` instead.
+
+        Keyword Args:
+            id: The unique identifier for the agent. Will be created automatically if not provided.
+            name: The name of the agent.
+            description: A brief description of the agent's purpose.
+            instructions: Optional instructions for the agent.
+            tools: The tools to use for the request.
+            default_options: A TypedDict containing chat options.
+            chat_message_store_factory: Factory function to create an instance of ChatMessageStoreProtocol.
+            context_provider: Context providers to include during agent invocation.
+            middleware: List of middleware to intercept agent and function invocations.
+            kwargs: Any additional keyword arguments.
+
+        Returns:
+            A ChatAgent instance configured with this chat client.
+        """
+        return super().as_agent(
+            id=id,
+            name=name,
+            description=description,
+            instructions=instructions,
+            tools=tools,
+            default_options=default_options,
+            chat_message_store_factory=chat_message_store_factory,
+            context_provider=context_provider,
+            middleware=middleware,
+            **kwargs,
+        )

python/packages/azurefunctions/agent_framework_azurefunctions/_app.py

@@ -104,13 +104,13 @@ class AgentFunctionApp(DFAppBase):
         from agent_framework.azure import AgentFunctionApp, AzureOpenAIChatClient
 
         # Create agents with unique names
-        weather_agent = AzureOpenAIChatClient(...).create_agent(
+        weather_agent = AzureOpenAIChatClient(...).as_agent(
             name="WeatherAgent",
             instructions="You are a helpful weather agent.",
             tools=[get_weather],
         )
 
-        math_agent = AzureOpenAIChatClient(...).create_agent(
+        math_agent = AzureOpenAIChatClient(...).as_agent(
             name="MathAgent",
             instructions="You are a helpful math assistant.",
             tools=[calculate],

python/packages/core/agent_framework/_clients.py

@@ -377,7 +377,7 @@ def service_url(self) -> str:
         """
         return "Unknown"
 
-    def create_agent(
+    def as_agent(
         self,
         *,
         id: str | None = None,
@@ -428,7 +428,7 @@ def create_agent(
                 client = OpenAIChatClient(model_id="gpt-4")
 
                 # Create an agent using the convenience method
-                agent = client.create_agent(
+                agent = client.as_agent(
                     name="assistant",
                     instructions="You are a helpful assistant.",
                     default_options={"temperature": 0.7, "max_tokens": 500},

python/packages/core/agent_framework/_workflows/_handoff.py

@@ -710,9 +710,9 @@ def participants(self, participants: Sequence[AgentProtocol]) -> "HandoffBuilder
             from agent_framework.openai import OpenAIChatClient
 
             client = OpenAIChatClient()
-            triage = client.create_agent(instructions="...", name="triage_agent")
-            refund = client.create_agent(instructions="...", name="refund_agent")
-            billing = client.create_agent(instructions="...", name="billing_agent")
+            triage = client.as_agent(instructions="...", name="triage_agent")
+            refund = client.as_agent(instructions="...", name="refund_agent")
+            billing = client.as_agent(instructions="...", name="billing_agent")
 
             builder = HandoffBuilder().participants([triage, refund, billing])
             builder.with_start_agent(triage)

python/packages/core/agent_framework/openai/_assistants_client.py

@@ -9,8 +9,12 @@
     Mapping,
     MutableMapping,
     MutableSequence,
+    Sequence,
 )
-from typing import Any, Generic, Literal, TypedDict, cast
+from typing import TYPE_CHECKING, Any, Generic, Literal, TypedDict, cast
+
+if TYPE_CHECKING:
+    from .._agents import ChatAgent
 
 from openai import AsyncOpenAI
 from openai.types.beta.threads import (
@@ -28,11 +32,14 @@
 from pydantic import ValidationError
 
 from .._clients import BaseChatClient
-from .._middleware import use_chat_middleware
+from .._memory import ContextProvider
+from .._middleware import Middleware, use_chat_middleware
+from .._threads import ChatMessageStoreProtocol
 from .._tools import (
     AIFunction,
     HostedCodeInterpreterTool,
     HostedFileSearchTool,
+    ToolProtocol,
     use_function_invocation,
 )
 from .._types import (
@@ -761,3 +768,59 @@ def _update_agent_name_and_description(self, agent_name: str | None, description
             self.assistant_name = agent_name
         if description and not self.assistant_description:
             self.assistant_description = description
+
+    @override
+    def as_agent(
+        self,
+        *,
+        id: str | None = None,
+        name: str | None = None,
+        description: str | None = None,
+        instructions: str | None = None,
+        tools: ToolProtocol
+        | Callable[..., Any]
+        | MutableMapping[str, Any]
+        | Sequence[ToolProtocol | Callable[..., Any] | MutableMapping[str, Any]]
+        | None = None,
+        default_options: TOpenAIAssistantsOptions | None = None,
+        chat_message_store_factory: Callable[[], ChatMessageStoreProtocol] | None = None,
+        context_provider: ContextProvider | None = None,
+        middleware: Sequence[Middleware] | None = None,
+        **kwargs: Any,
+    ) -> "ChatAgent[TOpenAIAssistantsOptions]":
+        """Convert this chat client to a ChatAgent.
+
+        This method creates a ChatAgent instance with this client pre-configured.
+        It does NOT create an assistant on the OpenAI service - the actual assistant
+        will be created on the server during the first invocation (run).
+
+        For creating and managing persistent assistants on the server, use
+        :class:`~agent_framework.openai.OpenAIAssistantProvider` instead.
+
+        Keyword Args:
+            id: The unique identifier for the agent. Will be created automatically if not provided.
+            name: The name of the agent.
+            description: A brief description of the agent's purpose.
+            instructions: Optional instructions for the agent.
+            tools: The tools to use for the request.
+            default_options: A TypedDict containing chat options.
+            chat_message_store_factory: Factory function to create an instance of ChatMessageStoreProtocol.
+            context_provider: Context providers to include during agent invocation.
+            middleware: List of middleware to intercept agent and function invocations.
+            kwargs: Any additional keyword arguments.
+
+        Returns:
+            A ChatAgent instance configured with this chat client.
+        """
+        return super().as_agent(
+            id=id,
+            name=name,
+            description=description,
+            instructions=instructions,
+            tools=tools,
+            default_options=default_options,
+            chat_message_store_factory=chat_message_store_factory,
+            context_provider=context_provider,
+            middleware=middleware,
+            **kwargs,
+        )

python/packages/declarative/agent_framework_declarative/_workflows/_factory.py

@@ -72,7 +72,7 @@ class WorkflowFactory:
 
             # Pre-register agents for InvokeAzureAgent actions
             chat_client = AzureOpenAIChatClient()
-            agent = chat_client.create_agent(name="MyAgent", instructions="You are helpful.")
+            agent = chat_client.as_agent(name="MyAgent", instructions="You are helpful.")
 
             factory = WorkflowFactory(agents={"MyAgent": agent})
             workflow = factory.create_workflow_from_yaml_path("workflow.yaml")
@@ -115,8 +115,8 @@ def __init__(
                 # With pre-registered agents
                 client = AzureOpenAIChatClient()
                 agents = {
-                    "WriterAgent": client.create_agent(name="Writer", instructions="Write content."),
-                    "ReviewerAgent": client.create_agent(name="Reviewer", instructions="Review content."),
+                    "WriterAgent": client.as_agent(name="Writer", instructions="Write content."),
+                    "ReviewerAgent": client.as_agent(name="Reviewer", instructions="Review content."),
                 }
                 factory = WorkflowFactory(agents=agents)
 
@@ -533,14 +533,14 @@ def register_agent(self, name: str, agent: AgentProtocol | AgentExecutor) -> "Wo
                     WorkflowFactory()
                     .register_agent(
                         "Writer",
-                        client.create_agent(
+                        client.as_agent(
                             name="Writer",
                             instructions="Write content.",
                         ),
                     )
                     .register_agent(
                         "Reviewer",
-                        client.create_agent(
+                        client.as_agent(
                             name="Reviewer",
                             instructions="Review content.",
                         ),

python/packages/foundry_local/agent_framework_foundry_local/_foundry_local_client.py

@@ -169,7 +169,7 @@ def __init__(
 
                 client = FoundryLocalClient(model_id="phi-4-mini")
 
-                agent = client.create_agent(
+                agent = client.as_agent(
                     name="LocalAgent",
                     instructions="You are a helpful agent.",
                     tools=get_weather,

python/packages/foundry_local/samples/foundry_local_agent.py

@@ -65,7 +65,7 @@ async def main() -> None:
             print(
                 f"- {model.alias} for {model.task} - id={model.id} - {(model.file_size_mb / 1000):.2f} GB - {model.license}"
             )
-    agent = client.create_agent(
+    agent = client.as_agent(
         name="LocalAgent",
         instructions="You are a helpful agent.",
         tools=get_weather,

python/packages/lab/gaia/samples/azure_ai_agent.py

@@ -49,7 +49,7 @@ async def create_gaia_agent() -> AsyncIterator[ChatAgent]:
     """
     async with (
         AzureCliCredential() as credential,
-        AzureAIAgentClient(credential=credential).create_agent(
+        AzureAIAgentClient(credential=credential).as_agent(
             name="GaiaAgent",
             instructions="Solve tasks to your best ability. Use Bing Search to find "
             "information and Code Interpreter to perform calculations and data analysis.",

python/packages/lab/gaia/samples/openai_agent.py

@@ -49,7 +49,7 @@ async def create_gaia_agent() -> AsyncIterator[ChatAgent]:
     """
     chat_client = OpenAIResponsesClient()
 
-    async with chat_client.create_agent(
+    async with chat_client.as_agent(
         name="GaiaAgent",
         instructions="Solve tasks to your best ability. Use Web Search to find "
         "information and Code Interpreter to perform calculations and data analysis.",