feat: Update for anthropic models

Enable parallel tools for anthropic models, and add agent examples, and also added functional test for anthropic models.

PiperOrigin-RevId: 766703018

contributing/samples/hello_world_anthropic/__init__.py

@@ -0,0 +1,16 @@
+# 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 . import agent

contributing/samples/hello_world_anthropic/agent.py

@@ -0,0 +1,90 @@
+# 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.
+
+
+import random
+
+from google.adk import Agent
+from google.adk.models.anthropic_llm import Claude
+
+
+def roll_die(sides: int) -> int:
+  """Roll a die and return the rolled result.
+
+  Args:
+    sides: The integer number of sides the die has.
+
+  Returns:
+    An integer of the result of rolling the die.
+  """
+  return random.randint(1, sides)
+
+
+async def check_prime(nums: list[int]) -> str:
+  """Check if a given list of numbers are prime.
+
+  Args:
+    nums: The list of numbers to check.
+
+  Returns:
+    A str indicating which number is prime.
+  """
+  primes = set()
+  for number in nums:
+    number = int(number)
+    if number <= 1:
+      continue
+    is_prime = True
+    for i in range(2, int(number**0.5) + 1):
+      if number % i == 0:
+        is_prime = False
+        break
+    if is_prime:
+      primes.add(number)
+  return (
+      "No prime numbers found."
+      if not primes
+      else f"{', '.join(str(num) for num in primes)} are prime numbers."
+  )
+
+
+root_agent = Agent(
+    model=Claude(model="claude-3-5-sonnet-v2@20241022"),
+    name="data_processing_agent",
+    description=(
+        "hello world agent that can roll a dice of 8 sides and check prime"
+        " numbers."
+    ),
+    instruction="""
+      You roll dice and answer questions about the outcome of the dice rolls.
+      You can roll dice of different sizes.
+      You can use multiple tools in parallel by calling functions in parallel(in one request and in one round).
+      It is ok to discuss previous dice roles, and comment on the dice rolls.
+      When you are asked to roll a die, you must call the roll_die tool with the number of sides. Be sure to pass in an integer. Do not pass in a string.
+      You should never roll a die on your own.
+      When checking prime numbers, call the check_prime tool with a list of integers. Be sure to pass in a list of integers. You should never pass in a string.
+      You should not check prime numbers before calling the tool.
+      When you are asked to roll a die and check prime numbers, you should always make the following two function calls:
+      1. You should first call the roll_die tool to get a roll. Wait for the function response before calling the check_prime tool.
+      2. After you get the function response from roll_die tool, you should call the check_prime tool with the roll_die result.
+        2.1 If user asks you to check primes based on previous rolls, make sure you include the previous rolls in the list.
+      3. When you respond, you must include the roll_die result from step 1.
+      You should always perform the previous 3 steps when asking for a roll and checking prime numbers.
+      You should not rely on the previous history on prime results.
+    """,
+    tools=[
+        roll_die,
+        check_prime,
+    ],
+)

contributing/samples/hello_world_anthropic/main.py

@@ -0,0 +1,76 @@
+# 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.
+
+
+import asyncio
+import time
+
+import agent
+from dotenv import load_dotenv
+from google.adk import Runner
+from google.adk.artifacts import InMemoryArtifactService
+from google.adk.cli.utils import logs
+from google.adk.sessions import InMemorySessionService
+from google.adk.sessions import Session
+from google.genai import types
+
+load_dotenv(override=True)
+logs.log_to_tmp_folder()
+
+
+async def main():
+  app_name = 'my_app'
+  user_id_1 = 'user1'
+  session_service = InMemorySessionService()
+  artifact_service = InMemoryArtifactService()
+  runner = Runner(
+      app_name=app_name,
+      agent=agent.root_agent,
+      artifact_service=artifact_service,
+      session_service=session_service,
+  )
+  session_11 = await session_service.create_session(
+      app_name=app_name, user_id=user_id_1
+  )
+
+  async def run_prompt(session: Session, new_message: str):
+    content = types.Content(
+        role='user', parts=[types.Part.from_text(text=new_message)]
+    )
+    print('** User says:', content.model_dump(exclude_none=True))
+    async for event in runner.run_async(
+        user_id=user_id_1,
+        session_id=session.id,
+        new_message=content,
+    ):
+      if event.content.parts and event.content.parts[0].text:
+        print(f'** {event.author}: {event.content.parts[0].text}')
+
+  start_time = time.time()
+  print('Start time:', start_time)
+  print('------------------------------------')
+  await run_prompt(session_11, 'Hi, introduce yourself.')
+  await run_prompt(
+      session_11,
+      'Run the following request 10 times: roll a die with 100 sides and check'
+      ' if it is prime',
+  )
+  end_time = time.time()
+  print('------------------------------------')
+  print('End time:', end_time)
+  print('Total time:', end_time - start_time)
+
+
+if __name__ == '__main__':
+  asyncio.run(main())

src/google/adk/models/anthropic_llm.py

@@ -135,6 +135,10 @@ def content_block_to_part(
 def message_to_generate_content_response(
     message: anthropic_types.Message,
 ) -> LlmResponse:
+  logger.info(
+      "Claude response: %s",
+      message.model_dump_json(indent=2, exclude_none=True),
+  )
 
   return LlmResponse(
       content=types.Content(
@@ -229,14 +233,11 @@ class Claude(BaseLlm):
           for tool in llm_request.config.tools[0].function_declarations
       ]
     tool_choice = (
-        anthropic_types.ToolChoiceAutoParam(
+        anthropic_types.ToolChoiceAutoParam(type="auto")
-            type="auto",
-            # TODO: allow parallel tool use.
-            disable_parallel_tool_use=True,
-        )
         if llm_request.tools_dict
         else NOT_GIVEN
     )
+    # TODO(b/421255973): Enable streaming for anthropic models.
     message = self._anthropic_client.messages.create(
         model=llm_request.model,
         system=llm_request.config.system_instruction,
@@ -245,10 +246,6 @@ class Claude(BaseLlm):
         tool_choice=tool_choice,
         max_tokens=MAX_TOKEN,
     )
-    logger.info(
-        "Claude response: %s",
-        message.model_dump_json(indent=2, exclude_none=True),
-    )
     yield message_to_generate_content_response(message)
 
   @cached_property

tests/unittests/models/test_anthropic_llm.py

@@ -0,0 +1,124 @@
+# 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.
+
+import os
+import sys
+from unittest import mock
+
+from anthropic import types as anthropic_types
+from google.adk import version as adk_version
+from google.adk.models import anthropic_llm
+from google.adk.models.anthropic_llm import Claude
+from google.adk.models.llm_request import LlmRequest
+from google.adk.models.llm_response import LlmResponse
+from google.genai import types
+from google.genai import version as genai_version
+from google.genai.types import Content
+from google.genai.types import Part
+import pytest
+
+
+@pytest.fixture
+def generate_content_response():
+  return anthropic_types.Message(
+      id="msg_vrtx_testid",
+      content=[
+          anthropic_types.TextBlock(
+              citations=None, text="Hi! How can I help you today?", type="text"
+          )
+      ],
+      model="claude-3-5-sonnet-v2-20241022",
+      role="assistant",
+      stop_reason="end_turn",
+      stop_sequence=None,
+      type="message",
+      usage=anthropic_types.Usage(
+          cache_creation_input_tokens=0,
+          cache_read_input_tokens=0,
+          input_tokens=13,
+          output_tokens=12,
+          server_tool_use=None,
+          service_tier=None,
+      ),
+  )
+
+
+@pytest.fixture
+def generate_llm_response():
+  return LlmResponse.create(
+      types.GenerateContentResponse(
+          candidates=[
+              types.Candidate(
+                  content=Content(
+                      role="model",
+                      parts=[Part.from_text(text="Hello, how can I help you?")],
+                  ),
+                  finish_reason=types.FinishReason.STOP,
+              )
+          ]
+      )
+  )
+
+
+@pytest.fixture
+def claude_llm():
+  return Claude(model="claude-3-5-sonnet-v2@20241022")
+
+
+@pytest.fixture
+def llm_request():
+  return LlmRequest(
+      model="claude-3-5-sonnet-v2@20241022",
+      contents=[Content(role="user", parts=[Part.from_text(text="Hello")])],
+      config=types.GenerateContentConfig(
+          temperature=0.1,
+          response_modalities=[types.Modality.TEXT],
+          system_instruction="You are a helpful assistant",
+      ),
+  )
+
+
+def test_supported_models():
+  models = Claude.supported_models()
+  assert len(models) == 2
+  assert models[0] == r"claude-3-.*"
+  assert models[1] == r"claude-.*-4.*"
+
+
+@pytest.mark.asyncio
+async def test_generate_content_async(
+    claude_llm, llm_request, generate_content_response, generate_llm_response
+):
+  with mock.patch.object(claude_llm, "_anthropic_client") as mock_client:
+    with mock.patch.object(
+        anthropic_llm,
+        "message_to_generate_content_response",
+        return_value=generate_llm_response,
+    ):
+      # Create a mock coroutine that returns the generate_content_response.
+      async def mock_coro():
+        return generate_content_response
+
+      # Assign the coroutine to the mocked method
+      mock_client.messages.create.return_value = mock_coro()
+
+      responses = [
+          resp
+          async for resp in claude_llm.generate_content_async(
+              llm_request, stream=False
+          )
+      ]
+      assert len(responses) == 1
+      assert isinstance(responses[0], LlmResponse)
+      assert responses[0].content.parts[0].text == "Hello, how can I help you?"