feat: support injecting custom LLM clients into Gemini and AnthropicLlm

src/google/adk/models/anthropic_llm.py

@@ -359,6 +359,9 @@ class AnthropicLlm(BaseLlm):
   model: str = "claude-sonnet-4-20250514"
   max_tokens: int = 8192
 
+  client: Optional[Union[AsyncAnthropic, AsyncAnthropicVertex]] = None
+  """An optional pre-configured Anthropic client."""
+
   @classmethod
   @override
   def supported_models(cls) -> list[str]:
@@ -495,6 +498,8 @@ async def _generate_content_streaming(
 
   @cached_property
   def _anthropic_client(self) -> AsyncAnthropic:
+    if self.client:
+      return self.client
     return AsyncAnthropic()
 
 

src/google/adk/models/apigee_llm.py

@@ -220,6 +220,9 @@ def api_client(self) -> Client:
     Returns:
       The api client.
     """
+    if self.client:
+      return self.client
+
     from google.genai import Client
 
     kwargs_for_http_options = {}

src/google/adk/models/google_llm.py

@@ -26,6 +26,7 @@
 from typing import TYPE_CHECKING
 from typing import Union
 
+from google.genai import Client
 from google.genai import types
 from google.genai.errors import ClientError
 from typing_extensions import override
@@ -91,6 +92,13 @@ class Gemini(BaseLlm):
 
   model: str = 'gemini-2.5-flash'
 
+  client: Optional[Client] = None
+  """An optional pre-configured google-genai Client.
+
+  When provided, this client will be used for all API calls instead of
+  constructing a new one from environment variables or other attributes.
+  """
+
   base_url: Optional[str] = None
   """The base URL for the AI platform service endpoint."""
 
@@ -302,6 +310,9 @@ def api_client(self) -> Client:
     Returns:
       The api client.
     """
+    if self.client:
+      return self.client
+
     from google.genai import Client
 
     return Client(

tests/unittests/models/test_custom_client.py

@@ -0,0 +1,110 @@
+# Copyright 2026 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.
+
+import pytest
+from unittest import mock
+from google.genai import Client
+from anthropic import AsyncAnthropic
+from google.adk.models.google_llm import Gemini
+from google.adk.models.anthropic_llm import AnthropicLlm
+from google.adk.models.llm_request import LlmRequest
+from google.genai import types
+from google.genai.types import Content, Part
+
+def test_gemini_custom_client():
+    """Verify that Gemini uses the provided custom client."""
+    mock_client = mock.MagicMock(spec=Client)
+    gemini = Gemini(model="gemini-1.5-flash", client=mock_client)
+
+    assert gemini.api_client is mock_client
+    # Verify it persists (cached_property)
+    assert gemini.api_client is mock_client
+
+def test_anthropic_custom_client():
+    """Verify that AnthropicLlm uses the provided custom client."""
+    mock_client = mock.MagicMock(spec=AsyncAnthropic)
+    anthropic_llm = AnthropicLlm(model="claude-3-5-sonnet-20241022", client=mock_client)
+
+    assert anthropic_llm._anthropic_client is mock_client
+
+@pytest.mark.asyncio
+async def test_gemini_uses_custom_client_in_call():
+    """Verify that Gemini calls use the provided custom client's methods."""
+    mock_client = mock.MagicMock(spec=Client)
+    # Mock the nested aio.models.generate_content
+    mock_aio_models = mock_client.aio.models
+
+    gemini = Gemini(model="gemini-1.5-flash", client=mock_client)
+
+    request = LlmRequest(
+        model="gemini-1.5-flash",
+        contents=[Content(role="user", parts=[Part.from_text(text="Hi")])]
+    )
+
+    # Mock the response
+    mock_response = types.GenerateContentResponse(
+        candidates=[
+            types.Candidate(
+                content=Content(role="model", parts=[Part.from_text(text="Hello")]),
+                finish_reason=types.FinishReason.STOP
+            )
+        ]
+    )
+
+    async def mock_coro(*args, **kwargs):
+        return mock_response
+
+    mock_aio_models.generate_content.return_value = mock_coro()
+
+    # We use stream=False to simplify the mock
+    responses = [r async for r in gemini.generate_content_async(request, stream=False)]
+
+    assert len(responses) == 1
+    assert responses[0].content.parts[0].text == "Hello"
+    mock_aio_models.generate_content.assert_called()
+
+@pytest.mark.asyncio
+async def test_anthropic_uses_custom_client_in_call():
+    """Verify that AnthropicLlm calls use the provided custom client's methods."""
+    mock_client = mock.MagicMock(spec=AsyncAnthropic)
+    mock_messages = mock_client.messages
+
+    anthropic_llm = AnthropicLlm(model="claude-3-5-sonnet-20241022", client=mock_client)
+
+    request = LlmRequest(
+        model="claude-3-5-sonnet-20241022",
+        contents=[Content(role="user", parts=[Part.from_text(text="Hi")])]
+    )
+
+    from anthropic import types as anthropic_types
+    mock_response = anthropic_types.Message(
+        id="msg_test",
+        content=[anthropic_types.TextBlock(text="Hello", type="text")],
+        model="claude-3-5-sonnet-20241022",
+        role="assistant",
+        stop_reason="end_turn",
+        type="message",
+        usage=anthropic_types.Usage(input_tokens=1, output_tokens=1)
+    )
+
+    async def mock_coro(*args, **kwargs):
+        return mock_response
+
+    mock_messages.create.return_value = mock_coro()
+
+    responses = [r async for r in anthropic_llm.generate_content_async(request, stream=False)]
+
+    assert len(responses) == 1
+    assert responses[0].content.parts[0].text == "Hello"
+    mock_messages.create.assert_called()