feat: Mark Vertex calls made from non-gemini models

PiperOrigin-RevId: 864253424

src/google/adk/models/anthropic_llm.py

@@ -36,6 +36,7 @@ from google.genai import types
 from pydantic import BaseModel
 from typing_extensions import override
 
+from ..utils._google_client_headers import get_tracking_headers
 from .base_llm import BaseLlm
 from .llm_response import LlmResponse
 
@@ -345,4 +346,5 @@ class Claude(AnthropicLlm):
     return AsyncAnthropicVertex(
         project_id=os.environ["GOOGLE_CLOUD_PROJECT"],
         region=os.environ["GOOGLE_CLOUD_LOCATION"],
+        default_headers=get_tracking_headers(),
     )

src/google/adk/models/google_llm.py

@@ -30,7 +30,8 @@ from google.genai import types
 from google.genai.errors import ClientError
 from typing_extensions import override
 
-from ..utils._client_labels_utils import get_client_labels
+from ..utils._google_client_headers import get_tracking_headers
+from ..utils._google_client_headers import merge_tracking_headers
 from ..utils.context_utils import Aclosing
 from ..utils.streaming_utils import StreamingResponseAggregator
 from ..utils.variant_utils import GoogleLLMVariant
@@ -316,13 +317,7 @@ class Gemini(BaseLlm):
     )
 
   def _tracking_headers(self) -> dict[str, str]:
-    labels = get_client_labels()
-    header_value = ' '.join(labels)
-    tracking_headers = {
-        'x-goog-api-client': header_value,
-        'user-agent': header_value,
-    }
-    return tracking_headers
+    return get_tracking_headers()
 
   @cached_property
   def _live_api_version(self) -> str:
@@ -362,8 +357,10 @@ class Gemini(BaseLlm):
     ):
       if not llm_request.live_connect_config.http_options.headers:
         llm_request.live_connect_config.http_options.headers = {}
-      llm_request.live_connect_config.http_options.headers.update(
-          self._tracking_headers()
+      llm_request.live_connect_config.http_options.headers = (
+          self._merge_tracking_headers(
+              llm_request.live_connect_config.http_options.headers
+          )
       )
       llm_request.live_connect_config.http_options.api_version = (
           self._live_api_version
@@ -456,20 +453,7 @@ class Gemini(BaseLlm):
 
   def _merge_tracking_headers(self, headers: dict[str, str]) -> dict[str, str]:
     """Merge tracking headers to the given headers."""
-    headers = headers or {}
-    for key, tracking_header_value in self._tracking_headers().items():
-      custom_value = headers.get(key, None)
-      if not custom_value:
-        headers[key] = tracking_header_value
-        continue
-
-      # Merge tracking headers with existing headers and avoid duplicates.
-      value_parts = tracking_header_value.split(' ')
-      for custom_value_part in custom_value.split(' '):
-        if custom_value_part not in value_parts:
-          value_parts.append(custom_value_part)
-      headers[key] = ' '.join(value_parts)
-    return headers
+    return merge_tracking_headers(headers)
 
 
 def _build_function_declaration_log(

src/google/adk/models/lite_llm.py

@@ -51,6 +51,7 @@ from pydantic import BaseModel
 from pydantic import Field
 from typing_extensions import override
 
+from ..utils._google_client_headers import merge_tracking_headers
 from .base_llm import BaseLlm
 from .llm_request import LlmRequest
 from .llm_response import LlmResponse
@@ -1699,6 +1700,18 @@ Functions:
 """
 
 
+def _is_litellm_vertex_model(model_string: str) -> bool:
+  """Check if the model is a Vertex AI model accessed via LiteLLM.
+
+  Args:
+    model_string: A LiteLLM model string (e.g., "vertex_ai/gemini-2.5-flash")
+
+  Returns:
+    True if it's a Vertex AI model accessed via LiteLLM, False otherwise
+  """
+  return model_string.startswith("vertex_ai/")
+
+
 def _is_litellm_gemini_model(model_string: str) -> bool:
   """Check if the model is a Gemini model accessed via LiteLLM.
 
@@ -1867,6 +1880,14 @@ class LiteLlm(BaseLlm):
     }
     completion_args.update(self._additional_args)
 
+    # merge headers
+    if _is_litellm_vertex_model(effective_model) or _is_litellm_gemini_model(
+        effective_model
+    ):
+      completion_args["headers"] = merge_tracking_headers(
+          completion_args.get("headers")
+      )
+
     if generation_params:
       completion_args.update(generation_params)
 

src/google/adk/utils/_google_client_headers.py

@@ -0,0 +1,56 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from ._client_labels_utils import get_client_labels
+
+
+def get_tracking_headers() -> dict[str, str]:
+  """Returns a dictionary of HTTP headers for tracking API requests.
+
+  These headers are used to identify HTTP calls made by ADK towards
+   Vertex AI LLM APIs.
+  """
+  labels = get_client_labels()
+  header_value = " ".join(labels)
+  return {
+      "x-goog-api-client": header_value,
+      "user-agent": header_value,
+  }
+
+
+def merge_tracking_headers(headers: dict[str, str] | None) -> dict[str, str]:
+  """Merge tracking headers to the given headers.
+
+  Args:
+    headers: headers to merge tracking headers into.
+
+  Returns:
+    A dictionary of HTTP headers with tracking headers merged.
+  """
+  new_headers = (headers or {}).copy()
+  for key, tracking_header_value in get_tracking_headers().items():
+    custom_value = new_headers.get(key, None)
+    if not custom_value:
+      new_headers[key] = tracking_header_value
+      continue
+
+    # Merge tracking headers with existing headers and avoid duplicates.
+    value_parts = tracking_header_value.split(" ")
+    for custom_value_part in custom_value.split(" "):
+      if custom_value_part not in value_parts:
+        value_parts.append(custom_value_part)
+    new_headers[key] = " ".join(value_parts)
+  return new_headers