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Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990)
* [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces Also clean up follow-on docs, environment guidance, package metadata, and lab test stability. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix deleted semantic-kernel sample links Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * improve foundry language * Fix A2A Foundry sample regression Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
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3a49b1d6dd
@@ -47,58 +47,73 @@ class ProviderTypeMapping(TypedDict, total=True):
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package: str
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name: str
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model_id_field: str
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endpoint_field: str | None
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api_key_field: str | None
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PROVIDER_TYPE_OBJECT_MAPPING: dict[str, ProviderTypeMapping] = {
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"AzureOpenAI.Chat": {
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"package": "agent_framework.azure",
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"name": "AzureOpenAIChatClient",
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"model_id_field": "deployment_name",
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"AzureOpenAI": {
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"package": "agent_framework.openai",
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"name": "OpenAIChatClient",
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"model_id_field": "model",
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"endpoint_field": "azure_endpoint",
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"api_key_field": "api_key",
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},
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"AzureOpenAI.Assistants": {
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"package": "agent_framework.azure",
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"name": "AzureOpenAIAssistantsClient",
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"model_id_field": "deployment_name",
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"AzureOpenAI.Chat": {
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"package": "agent_framework.openai",
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"name": "OpenAIChatCompletionClient",
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"model_id_field": "model",
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"endpoint_field": "azure_endpoint",
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"api_key_field": "api_key",
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},
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"AzureOpenAI.Responses": {
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"package": "agent_framework.azure",
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"name": "AzureOpenAIResponsesClient",
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"model_id_field": "deployment_name",
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"package": "agent_framework.openai",
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"name": "OpenAIChatClient",
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"model_id_field": "model",
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"endpoint_field": "azure_endpoint",
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"api_key_field": "api_key",
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},
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"Foundry": {
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"package": "agent_framework.foundry",
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"name": "FoundryChatClient",
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"model_id_field": "model",
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"endpoint_field": "project_endpoint",
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"api_key_field": None,
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},
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"OpenAI.Chat": {
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"package": "agent_framework.openai",
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"name": "OpenAIChatClient",
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"model_id_field": "model_id",
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},
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"OpenAI.Assistants": {
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"package": "agent_framework.openai",
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"name": "OpenAIAssistantsClient",
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"model_id_field": "model_id",
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"model_id_field": "model",
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"endpoint_field": "base_url",
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"api_key_field": "api_key",
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},
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"OpenAI.Responses": {
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"package": "agent_framework.openai",
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"name": "OpenAIResponsesClient",
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"model_id_field": "model_id",
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},
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"AzureAIAgentClient": {
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"package": "agent_framework.azure",
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"name": "AzureAIAgentClient",
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"model_id_field": "model_deployment_name",
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},
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"AzureAIClient": {
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"package": "agent_framework.azure",
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"name": "AzureAIClient",
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"model_id_field": "model_deployment_name",
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},
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"AzureAI.ProjectProvider": {
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"package": "agent_framework.azure",
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"name": "AzureAIProjectAgentProvider",
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"name": "OpenAIChatClient",
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"model_id_field": "model",
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"endpoint_field": "base_url",
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"api_key_field": "api_key",
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},
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"OpenAI": {
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"package": "agent_framework.openai",
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"name": "OpenAIChatClient",
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"model_id_field": "model",
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"endpoint_field": "base_url",
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"api_key_field": "api_key",
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},
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"Foundry.Chat": {
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"package": "agent_framework.foundry",
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"name": "FoundryChatClient",
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"model_id_field": "model",
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"endpoint_field": "project_endpoint",
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"api_key_field": None,
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},
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"Anthropic.Chat": {
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"package": "agent_framework.anthropic",
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"name": "AnthropicChatClient",
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"model_id_field": "model_id",
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"endpoint_field": None,
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"api_key_field": "api_key",
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},
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}
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@@ -137,11 +152,11 @@ class AgentFactory:
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.. code-block:: python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework_declarative import AgentFactory
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# With pre-configured chat client
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client = AzureOpenAIChatClient()
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client = OpenAIChatClient()
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factory = AgentFactory(client=client)
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agent = factory.create_agent_from_yaml_path("agent.yaml")
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@@ -171,7 +186,7 @@ class AgentFactory:
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connections: Mapping[str, Any] | None = None,
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client_kwargs: Mapping[str, Any] | None = None,
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additional_mappings: Mapping[str, ProviderTypeMapping] | None = None,
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default_provider: str = "AzureAIClient",
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default_provider: str = "OpenAI",
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safe_mode: bool = True,
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env_file_path: str | None = None,
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env_file_encoding: str | None = None,
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@@ -192,13 +207,15 @@ class AgentFactory:
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..code-block:: python
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additional_mappings = {
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"Provider.ApiType": {
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"package": "package.name",
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"name": "ClassName",
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"model_id_field": "field_name_in_constructor",
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},
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...
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}
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"Provider.ApiType": {
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"package": "package.name",
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"name": "ClassName",
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"model_id_field": "field_name_in_constructor",
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"endpoint_field": "endpoint_kwarg_name_or_null",
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"api_key_field": "api_key_kwarg_name_or_null",
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},
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...
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}
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Here, "Provider.ApiType" is the lookup key used when both provider and apiType are specified in the
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model, "Provider" is also allowed.
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@@ -206,7 +223,7 @@ class AgentFactory:
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SupportsChatGetResponse implementation, and model_id_field is the name of the field in the
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constructor that accepts the model.id value.
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default_provider: The default provider used when model.provider is not specified,
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default is "AzureAIClient".
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default is "OpenAI".
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safe_mode: Whether to run in safe mode, default is True.
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When safe_mode is True, environment variables are not accessible in the powerfx expressions.
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You can still use environment variables, but through the constructors of the classes.
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@@ -227,11 +244,11 @@ class AgentFactory:
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.. code-block:: python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework_declarative import AgentFactory
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# With shared chat client
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client = AzureOpenAIChatClient()
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client = OpenAIChatClient()
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factory = AgentFactory(
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client=client,
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env_file_path=".env",
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@@ -457,8 +474,8 @@ class AgentFactory:
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async def create_agent_from_yaml_path_async(self, yaml_path: str | Path) -> Agent:
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"""Async version: Create a Agent from a YAML file path.
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Use this method when the provider requires async initialization, such as
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AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
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This is the async counterpart to ``create_agent_from_dict`` and is useful when
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the rest of your setup is already async.
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Args:
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yaml_path: Path to the YAML file representation of a PromptAgent.
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@@ -473,7 +490,7 @@ class AgentFactory:
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factory = AgentFactory(
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client_kwargs={"credential": credential},
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default_provider="AzureAI.ProjectProvider",
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default_provider="Foundry",
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)
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agent = await factory.create_agent_from_yaml_path_async("agent.yaml")
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"""
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@@ -487,8 +504,8 @@ class AgentFactory:
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async def create_agent_from_yaml_async(self, yaml_str: str) -> Agent:
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"""Async version: Create a Agent from a YAML string.
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Use this method when the provider requires async initialization, such as
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AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
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Use this method when the surrounding call site is already async and you
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want to build an agent directly from YAML text.
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Args:
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yaml_str: YAML string representation of a PromptAgent.
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@@ -507,7 +524,7 @@ class AgentFactory:
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instructions: You are a helpful assistant.
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model:
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id: gpt-4o
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provider: AzureAI.ProjectProvider
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provider: Foundry
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'''
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factory = AgentFactory(client_kwargs={"credential": credential})
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@@ -518,8 +535,8 @@ class AgentFactory:
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async def create_agent_from_dict_async(self, agent_def: dict[str, Any]) -> Agent:
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"""Async version: Create a Agent from a dictionary definition.
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Use this method when the provider requires async initialization, such as
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AzureAI.ProjectProvider which creates agents on the Azure AI Agent Service.
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This is the async counterpart to ``create_agent_from_dict`` and is useful when
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the rest of your setup is already async.
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Args:
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agent_def: Dictionary representation of a PromptAgent.
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@@ -538,7 +555,7 @@ class AgentFactory:
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"instructions": "You are a helpful assistant.",
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"model": {
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"id": "gpt-4o",
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"provider": "AzureAI.ProjectProvider",
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"provider": "Foundry",
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},
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}
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@@ -551,12 +568,6 @@ class AgentFactory:
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if not isinstance(prompt_agent, PromptAgent):
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raise DeclarativeLoaderError("Only definitions for a PromptAgent are supported for agent creation.")
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# Check if we're using a provider-based approach (like AzureAIProjectAgentProvider)
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mapping = self._retrieve_provider_configuration(prompt_agent.model) if prompt_agent.model else None
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if mapping and mapping["name"] == "AzureAIProjectAgentProvider":
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return await self._create_agent_with_provider(prompt_agent, mapping)
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# Fall back to standard ChatClient approach
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client = self._get_client(prompt_agent)
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chat_options = self._parse_chat_options(prompt_agent.model)
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if tools := self._parse_tools(prompt_agent.tools):
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@@ -572,48 +583,42 @@ class AgentFactory:
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)
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async def _create_agent_with_provider(self, prompt_agent: PromptAgent, mapping: ProviderTypeMapping) -> Agent:
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"""Create a Agent using AzureAIProjectAgentProvider.
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"""Create an Agent through a provider object that exposes ``create_agent``.
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This method handles the special case where we use a provider that creates
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agents on a remote service (like Azure AI Agent Service) and returns
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Agent instances directly.
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This remains available as an internal escape hatch for provider-style custom mappings
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that return a fully constructed ``Agent`` rather than a chat client.
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"""
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# Import the provider class
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module_name = mapping["package"]
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class_name = mapping["name"]
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module = __import__(module_name, fromlist=[class_name])
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provider_class = getattr(module, class_name)
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# Build provider kwargs from client_kwargs and connection info
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provider_kwargs: dict[str, Any] = {}
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provider_kwargs.update(self.client_kwargs)
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# Handle connection settings for the model
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endpoint_field = mapping.get("endpoint_field")
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api_key_field = mapping.get("api_key_field", "api_key")
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if prompt_agent.model and prompt_agent.model.connection:
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match prompt_agent.model.connection:
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case RemoteConnection() | AnonymousConnection():
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if prompt_agent.model.connection.endpoint:
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provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
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case ApiKeyConnection():
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if prompt_agent.model.connection.endpoint:
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provider_kwargs["project_endpoint"] = prompt_agent.model.connection.endpoint
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if api_key_field:
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provider_kwargs[api_key_field] = prompt_agent.model.connection.apiKey
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if prompt_agent.model.connection.endpoint and endpoint_field:
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provider_kwargs[endpoint_field] = prompt_agent.model.connection.endpoint
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case RemoteConnection() | AnonymousConnection():
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if prompt_agent.model.connection.endpoint and endpoint_field:
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provider_kwargs[endpoint_field] = prompt_agent.model.connection.endpoint
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case ReferenceConnection():
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# Reference connections are resolved by concrete providers when supported.
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pass
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# Create the provider and use it to create the agent
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provider = provider_class(**provider_kwargs)
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# Parse tools
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tools = self._parse_tools(prompt_agent.tools) if prompt_agent.tools else None
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# Parse response format into default_options
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default_options: dict[str, Any] | None = None
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if prompt_agent.outputSchema:
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default_options = {"response_format": prompt_agent.outputSchema.to_json_schema()}
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# Create the agent using the provider
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# The provider's create_agent returns a Agent directly
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return cast(
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Agent,
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await provider.create_agent(
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@@ -637,18 +642,35 @@ class AgentFactory:
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"alternatively define a model in the PromptAgent."
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)
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mapping = self._retrieve_provider_configuration(prompt_agent.model)
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setup_dict: dict[str, Any] = {}
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setup_dict.update(self.client_kwargs)
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endpoint_field = mapping.get("endpoint_field")
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api_key_field = mapping.get("api_key_field", "api_key")
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# parse connections
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if prompt_agent.model.connection:
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match prompt_agent.model.connection:
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case ApiKeyConnection():
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setup_dict["api_key"] = prompt_agent.model.connection.apiKey
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if api_key_field:
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setup_dict[api_key_field] = prompt_agent.model.connection.apiKey
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elif prompt_agent.model.connection.apiKey:
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raise DeclarativeLoaderError(
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f"{mapping['name']} does not support API key-based model connections."
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)
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if prompt_agent.model.connection.endpoint:
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setup_dict["endpoint"] = prompt_agent.model.connection.endpoint
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if not endpoint_field:
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raise DeclarativeLoaderError(
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f"{mapping['name']} does not support endpoint-based model connections."
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)
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setup_dict[endpoint_field] = prompt_agent.model.connection.endpoint
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case RemoteConnection() | AnonymousConnection():
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setup_dict["endpoint"] = prompt_agent.model.connection.endpoint
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if prompt_agent.model.connection.endpoint:
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if not endpoint_field:
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raise DeclarativeLoaderError(
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f"{mapping['name']} does not support endpoint-based model connections."
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)
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setup_dict[endpoint_field] = prompt_agent.model.connection.endpoint
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case ReferenceConnection():
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if not self.connections:
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raise ValueError("Connections must be provided to resolve ReferenceConnection")
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@@ -673,7 +695,6 @@ class AgentFactory:
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"ChatClient must be provided to create agent from PromptAgent, or define model.id in the PromptAgent."
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)
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# if provider is defined, use that, if possible with apiType, fallback to default_provider
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mapping = self._retrieve_provider_configuration(prompt_agent.model)
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module_name = mapping["package"]
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class_name = mapping["name"]
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module = __import__(module_name, fromlist=[class_name])
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@@ -70,11 +70,11 @@ class WorkflowFactory:
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.. code-block:: python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework.declarative import WorkflowFactory
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# Pre-register agents for InvokeAzureAgent actions
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client = AzureOpenAIChatClient()
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client = OpenAIChatClient()
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agent = client.as_agent(name="MyAgent", instructions="You are helpful.")
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factory = WorkflowFactory(agents={"MyAgent": agent})
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@@ -116,11 +116,11 @@ class WorkflowFactory:
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.. code-block:: python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework.declarative import WorkflowFactory
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# With pre-registered agents
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client = AzureOpenAIChatClient()
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client = OpenAIChatClient()
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agents = {
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"WriterAgent": client.as_agent(name="Writer", instructions="Write content."),
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"ReviewerAgent": client.as_agent(name="Reviewer", instructions="Review content."),
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@@ -535,10 +535,10 @@ class WorkflowFactory:
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Examples:
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.. code-block:: python
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework.declarative import WorkflowFactory
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client = AzureOpenAIChatClient()
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client = OpenAIChatClient()
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# Method chaining to register multiple agents
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factory = (
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