Azure · nick863 · Aug 14, 2025 · Aug 14, 2025 · Aug 14, 2025
@@ -8,11 +8,14 @@
 ### Features Added
 
 - Add `RunStepDetailsActivity`, describing MCP function parameters.
+- Add `RunStepDeltaCustomBingGroundingToolCall`, describing `BingCustomSearchTool` updates in streaming scenario.
 
 ### Bugs Fixed
 
 ### Sample updates
 
+- Bing Grounding and Bing Custom Search samples were fixed to correctly present references.
+
 ## 1.2.0b2 (2025-08-12)
 
 ### Features Added

@@ -369,7 +369,7 @@ Here is an example:
 <!-- SNIPPET:sample_agents_bing_grounding.create_agent_with_bing_grounding_tool -->
 
 ```python
-conn_id = os.environ["AZURE_BING_CONNECTION_ID"]
+conn_id = project_client.connections.get(os.environ["BING_CONNECTION_NAME"]).id
 
 # Initialize agent bing tool and add the connection id
 bing = BingGroundingTool(connection_id=conn_id)
@@ -474,9 +474,7 @@ The tool approval flow looks like this:
 # Create and process agent run in thread with MCP tools
 mcp_tool.update_headers("SuperSecret", "123456")
 # mcp_tool.set_approval_mode("never")  # Uncomment to disable approval requirement
-run = agents_client.runs.create(
-    thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources
-)
+run = agents_client.runs.create(thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources)
 print(f"Created run, ID: {run.id}")
 
 while run.status in ["queued", "in_progress", "requires_action"]:

@@ -136,6 +136,7 @@
         "azure.ai.agents.models.RunStepDeltaCodeInterpreterLogOutput": "Azure.AI.Agents.RunStepDeltaCodeInterpreterLogOutput",
         "azure.ai.agents.models.RunStepDeltaCodeInterpreterToolCall": "Azure.AI.Agents.RunStepDeltaCodeInterpreterToolCall",
         "azure.ai.agents.models.RunStepDeltaConnectedAgentToolCall": "Azure.AI.Agents.RunStepDeltaConnectedAgentToolCall",
+        "azure.ai.agents.models.RunStepDeltaCustomBingGroundingToolCall": "Azure.AI.Agents.RunStepDeltaCustomBingGroundingToolCall",
         "azure.ai.agents.models.RunStepDeltaDeepResearchToolCall": "Azure.AI.Agents.RunStepDeltaDeepResearchToolCall",
         "azure.ai.agents.models.RunStepDeltaDetail": "Azure.AI.Agents.RunStepDeltaDetail",
         "azure.ai.agents.models.RunStepDeltaFileSearchToolCall": "Azure.AI.Agents.RunStepDeltaFileSearchToolCall",

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/ai/azure-ai-agents",
-  "Tag": "python/ai/azure-ai-agents_ad0998d8d9"
+  "Tag": "python/ai/azure-ai-agents_6e57db9f8e"
 }
@@ -145,6 +145,7 @@
     RunStepDeltaCodeInterpreterOutput,
     RunStepDeltaCodeInterpreterToolCall,
     RunStepDeltaConnectedAgentToolCall,
+    RunStepDeltaCustomBingGroundingToolCall,
     RunStepDeltaDeepResearchToolCall,
     RunStepDeltaDetail,
     RunStepDeltaFileSearchToolCall,
@@ -382,6 +383,7 @@
     "RunStepDeltaCodeInterpreterOutput",
     "RunStepDeltaCodeInterpreterToolCall",
     "RunStepDeltaConnectedAgentToolCall",
+    "RunStepDeltaCustomBingGroundingToolCall",
     "RunStepDeltaDeepResearchToolCall",
     "RunStepDeltaDetail",
     "RunStepDeltaFileSearchToolCall",

@@ -4425,15 +4425,16 @@ class RunStepBingCustomSearchToolCall(RunStepToolCall, discriminator="bing_custo
     :ivar type: The object type, which is always 'bing_custom_search'. Required. Default value is
      "bing_custom_search".
     :vartype type: str
-    :ivar bing_custom_search: Reserved for future use. Required.
+    :ivar bing_custom_search: The dictionary with request and response from Custom Bing Grounding
+     search tool. Required.
     :vartype bing_custom_search: dict[str, str]
     """
 
     type: Literal["bing_custom_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"])  # type: ignore
     """The object type, which is always 'bing_custom_search'. Required. Default value is
      \"bing_custom_search\"."""
     bing_custom_search: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"])
-    """Reserved for future use. Required."""
+    """The dictionary with request and response from Custom Bing Grounding search tool. Required."""
 
     @overload
     def __init__(
@@ -4465,15 +4466,16 @@ class RunStepBingGroundingToolCall(RunStepToolCall, discriminator="bing_groundin
     :ivar type: The object type, which is always 'bing_grounding'. Required. Default value is
      "bing_grounding".
     :vartype type: str
-    :ivar bing_grounding: Reserved for future use. Required.
+    :ivar bing_grounding: The dictionary with request and response from Bing Grounding search tool.
+     Required.
     :vartype bing_grounding: dict[str, str]
     """
 
     type: Literal["bing_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"])  # type: ignore
     """The object type, which is always 'bing_grounding'. Required. Default value is
      \"bing_grounding\"."""
     bing_grounding: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"])
-    """Reserved for future use. Required."""
+    """The dictionary with request and response from Bing Grounding search tool. Required."""
 
     @overload
     def __init__(
@@ -4985,10 +4987,11 @@ class RunStepDeltaToolCall(_Model):
     call details.
 
     You probably want to use the sub-classes and not this class directly. Known sub-classes are:
-    RunStepDeltaAzureAISearchToolCall, RunStepDeltaBingGroundingToolCall,
-    RunStepDeltaCodeInterpreterToolCall, RunStepDeltaConnectedAgentToolCall,
-    RunStepDeltaDeepResearchToolCall, RunStepDeltaFileSearchToolCall, RunStepDeltaFunctionToolCall,
-    RunStepDeltaMcpToolCall, RunStepDeltaOpenAPIToolCall
+    RunStepDeltaAzureAISearchToolCall, RunStepDeltaCustomBingGroundingToolCall,
+    RunStepDeltaBingGroundingToolCall, RunStepDeltaCodeInterpreterToolCall,
+    RunStepDeltaConnectedAgentToolCall, RunStepDeltaDeepResearchToolCall,
+    RunStepDeltaFileSearchToolCall, RunStepDeltaFunctionToolCall, RunStepDeltaMcpToolCall,
+    RunStepDeltaOpenAPIToolCall
 
     :ivar index: The index of the tool call detail in the run step's tool_calls array. Required.
     :vartype index: int
@@ -5078,15 +5081,16 @@ class RunStepDeltaBingGroundingToolCall(RunStepDeltaToolCall, discriminator="bin
     :ivar type: The object type, which is always "bing_grounding". Required. Default value is
      "bing_grounding".
     :vartype type: str
-    :ivar bing_grounding: Reserved for future use. Required.
+    :ivar bing_grounding: The dictionary with request and response from Bing Grounding search tool.
+     Required.
     :vartype bing_grounding: dict[str, str]
     """
 
     type: Literal["bing_grounding"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"])  # type: ignore
     """The object type, which is always \"bing_grounding\". Required. Default value is
      \"bing_grounding\"."""
     bing_grounding: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"])
-    """Reserved for future use. Required."""
+    """The dictionary with request and response from Bing Grounding search tool. Required."""
 
     @overload
     def __init__(
@@ -5418,6 +5422,47 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, type="connected_agent", **kwargs)
 
 
+class RunStepDeltaCustomBingGroundingToolCall(RunStepDeltaToolCall, discriminator="bing_custom_search"):
+    """Represents the Custom Bing Grounding tool call in a streaming run step.
+
+    :ivar index: The index of the tool call detail in the run step's tool_calls array. Required.
+    :vartype index: int
+    :ivar id: The ID of the tool call, used when submitting outputs to the run. Required.
+    :vartype id: str
+    :ivar type: The object type, which is always 'bing_custom_search'. Required. Default value is
+     "bing_custom_search".
+    :vartype type: str
+    :ivar bing_custom_search: The dictionary with request and response from Custom Bing Grounding
+     search tool. Required.
+    :vartype bing_custom_search: dict[str, str]
+    """
+
+    type: Literal["bing_custom_search"] = rest_discriminator(name="type", visibility=["read", "create", "update", "delete", "query"])  # type: ignore
+    """The object type, which is always 'bing_custom_search'. Required. Default value is
+     \"bing_custom_search\"."""
+    bing_custom_search: Dict[str, str] = rest_field(visibility=["read", "create", "update", "delete", "query"])
+    """The dictionary with request and response from Custom Bing Grounding search tool. Required."""
+
+    @overload
+    def __init__(
+        self,
+        *,
+        index: int,
+        id: str,  # pylint: disable=redefined-builtin
+        bing_custom_search: Dict[str, str],
+    ) -> None: ...
+
+    @overload
+    def __init__(self, mapping: Mapping[str, Any]) -> None:
+        """
+        :param mapping: raw JSON to initialize the model.
+        :type mapping: Mapping[str, Any]
+        """
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, type="bing_custom_search", **kwargs)
+
+
 class RunStepDeltaDeepResearchToolCall(RunStepDeltaToolCall, discriminator="deep_research"):
     """Represents the Deep research in a streaming run step.
 

@@ -7,7 +7,7 @@
 
 ########################################################################################################################
 # Agents tests
-# 
+#
 AZURE_AI_AGENTS_TESTS_PROJECT_CONNECTION_STRING=
 AZURE_AI_AGENTS_TESTS_PROJECT_ENDPOINT=
 AZURE_AI_AGENTS_TESTS_DATA_PATH=
@@ -17,4 +17,6 @@ AZURE_AI_AGENTS_TESTS_SEARCH_CONNECTION_ID=
 AZURE_AI_AGENTS_TESTS_IS_TEST_RUN=True
 AZURE_AI_AGENTS_TESTS_BING_CONNECTION_ID=
 AZURE_AI_AGENTS_TESTS_PLAYWRIGHT_CONNECTION_ID=
-AZURE_AI_AGENTS_TESTS_DEEP_RESEARCH_MODEL=
+AZURE_AI_AGENTS_TESTS_DEEP_RESEARCH_MODEL=
+AZURE_AI_AGENTS_TESTS_BING_CUSTOM_CONNECTION_ID=
+AZURE_AI_AGENTS_TESTS_BING_CONFIGURATION_NAME=
@@ -21,11 +21,12 @@
                           page of your Azure AI Foundry portal.
     2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
-    3) AZURE_BING_CONNECTION_ID - The connection id of the Bing connection, as found in the "Connected resources" tab
-       in your Azure AI Foundry project.
+    3) BING_CONNECTION_NAME - The name of a connection to the Bing search resource as it is
+       listed in Azure AI Foundry connected resources.
 """
 
 import os
+import re
 from typing import Any
 from azure.identity import DefaultAzureCredential
 from azure.ai.projects import AIProjectClient
@@ -45,7 +46,9 @@
 class MyEventHandler(AgentEventHandler):
 
     def on_message_delta(self, delta: "MessageDeltaChunk") -> None:
-        print(f"Text delta received: {delta.text}")
+        # Do not print reference text as we will show actual citation instead.
+        if re.match(r"\u3010(.+)\u3011", delta.text) is None:
+            print(f"Text delta received: {delta.text}")
         if delta.delta.content and isinstance(delta.delta.content[0], MessageDeltaTextContent):
             delta_text_content = delta.delta.content[0]
             if delta_text_content.text and delta_text_content.text.annotations:
@@ -85,7 +88,7 @@ def on_unhandled_event(self, event_type: str, event_data: Any) -> None:
 with project_client:
     agents_client = project_client.agents
 
-    bing_connection_id = os.environ["AZURE_BING_CONNECTION_ID"]
+    bing_connection_id = project_client.connections.get(os.environ["BING_CONNECTION_NAME"]).id
     print(f"Bing Connection ID: {bing_connection_id}")
 
     # Initialize agent bing tool and add the connection id
@@ -117,7 +120,12 @@ def on_unhandled_event(self, event_type: str, event_data: Any) -> None:
 
     response_message = agents_client.messages.get_last_message_by_role(thread_id=thread.id, role=MessageRole.AGENT)
     if response_message:
+        responses = []
         for text_message in response_message.text_messages:
-            print(f"Agent response: {text_message.text.value}")
+            responses.append(text_message.text.value)
+        message = " ".join(responses)
         for annotation in response_message.url_citation_annotations:
-            print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
+            message = message.replace(
+                annotation.text, f" [{annotation.url_citation.title}]({annotation.url_citation.url})"
+            )
+        print(f"Agent response: {message}")
@@ -21,11 +21,12 @@
                           page of your Azure AI Foundry portal.
     2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
-    3) AZURE_BING_CONNECTION_ID - The ID of the Bing connection, as found in the "Connected resources" tab
-       in your Azure AI Foundry project.
+    3) BING_CONNECTION_NAME - The name of a connection to the Bing search resource as it is
+       listed in Azure AI Foundry connected resources.
 """
 
 import os
+import re
 from azure.ai.projects import AIProjectClient
 from azure.ai.agents.models import AgentStreamEvent, RunStepDeltaChunk
 from azure.ai.agents.models import (
@@ -48,7 +49,7 @@
 with project_client:
     agents_client = project_client.agents
 
-    bing_connection_id = os.environ["AZURE_BING_CONNECTION_ID"]
+    bing_connection_id = project_client.connections.get(os.environ["BING_CONNECTION_NAME"]).id
     bing = BingGroundingTool(connection_id=bing_connection_id)
     print(f"Bing Connection ID: {bing_connection_id}")
 
@@ -69,12 +70,15 @@
     print(f"Created message, message ID {message.id}")
 
     # Process Agent run and stream events back to the client. It may take a few minutes for the agent to complete the run.
+    reference_text = re.compile(r"\u3010(.+)\u3011")
     with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id) as stream:
 
         for event_type, event_data, _ in stream:
 
             if isinstance(event_data, MessageDeltaChunk):
-                print(f"Text delta received: {event_data.text}")
+                # Do not print reference text as we will show actual citation instead.
+                if reference_text.match(event_data.text) is None:
+                    print(f"Text delta received: {event_data.text}")
                 if event_data.delta.content and isinstance(event_data.delta.content[0], MessageDeltaTextContent):
                     delta_text_content = event_data.delta.content[0]
                     if delta_text_content.text and delta_text_content.text.annotations:
@@ -113,7 +117,12 @@
 
     response_message = agents_client.messages.get_last_message_by_role(thread_id=thread.id, role=MessageRole.AGENT)
     if response_message:
+        responses = []
         for text_message in response_message.text_messages:
-            print(f"Agent response: {text_message.text.value}")
+            responses.append(text_message.text.value)
+        message = " ".join(responses)
         for annotation in response_message.url_citation_annotations:
-            print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
+            message = message.replace(
+                annotation.text, f" [{annotation.url_citation.title}]({annotation.url_citation.url})"
+            )
+        print(f"Agent response: {message}")
@@ -93,9 +93,7 @@
     # Process Agent run and stream events back to the client. It may take a few minutes for the agent to complete the run.
     mcp_tool.update_headers("SuperSecret", "123456")
     # mcp_tool.set_approval_mode("never")  # Uncomment to disable approval requirement
-    with agents_client.runs.stream(
-        thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources
-    ) as stream:
+    with agents_client.runs.stream(thread_id=thread.id, agent_id=agent.id, tool_resources=mcp_tool.resources) as stream:
 
         for event_type, event_data, _ in stream:
 
@@ -201,9 +199,7 @@
     agents_client.delete_agent(agent.id)
     print("Deleted agent")
 
-    response_message = agents_client.messages.get_last_message_by_role(
-        thread_id=thread.id, role=MessageRole.AGENT
-    )
+    response_message = agents_client.messages.get_last_message_by_role(thread_id=thread.id, role=MessageRole.AGENT)
     if response_message:
         for text_message in response_message.text_messages:
             print(f"Agent response: {text_message.text.value}")

@@ -23,24 +23,26 @@
                           page of your Azure AI Foundry portal.
     2) MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
        the "Models + endpoints" tab in your Azure AI Foundry project.
-    3) BING_CUSTOM_CONNECTION_ID  - The ID of the Bing Custom Search connection, in the format of:
-       /subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/providers/Microsoft.MachineLearningServices/workspaces/{workspace-name}/connections/{connection-name}
+    3) BING_CUSTOM_CONNECTION_NAME - The name of a connection to the custom search Bing resource as it is
+       listed in Azure AI Foundry connected resources.
+    4) BING_CONFIGURATION_NAME - the name of a search configuration in Grounding with Bing Custom Search
+       resource.
 """
 
 import os
 from azure.ai.projects import AIProjectClient
 from azure.identity import DefaultAzureCredential
-from azure.ai.agents.models import BingCustomSearchTool
+from azure.ai.agents.models import BingCustomSearchTool, ListSortOrder
 
 project_client = AIProjectClient(
     endpoint=os.environ["PROJECT_ENDPOINT"],
     credential=DefaultAzureCredential(),
 )
 
-conn_id = os.environ["BING_CUSTOM_CONNECTION_ID"]
+conn_id = project_client.connections.get(os.environ["BING_CUSTOM_CONNECTION_NAME"]).id
 
 # Initialize Bing Custom Search tool with connection id and instance name
-bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name="<config_instance_name>")
+bing_custom_tool = BingCustomSearchTool(connection_id=conn_id, instance_name=os.environ["BING_CONFIGURATION_NAME"])
 
 # Create Agent with the Bing Custom Search tool and process Agent run
 with project_client:
@@ -78,10 +80,15 @@
     print("Deleted agent")
 
     # Fetch and log all messages
-    messages = agents_client.messages.list(thread_id=thread.id)
+    messages = agents_client.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
     for msg in messages:
         if msg.text_messages:
+            responses = []
             for text_message in msg.text_messages:
-                print(f"Agent response: {text_message.text.value}")
+                responses.append(text_message.text.value)
+            message = " ".join(responses)
             for annotation in msg.url_citation_annotations:
-                print(f"URL Citation: [{annotation.url_citation.title}]({annotation.url_citation.url})")
+                message = message.replace(
+                    annotation.text, f" [{annotation.url_citation.title}]({annotation.url_citation.url})"
+                )
+            print(f"{msg.role}: {message}")