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feat: Add ADK examples for litellm with add_function_to_prompt
Add examples for for https://github.com/google/adk-python/issues/1273 PiperOrigin-RevId: 775352677
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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from . import agent
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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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from google.adk import Agent
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from google.adk.models.lite_llm import LiteLlm
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from langchain_core.utils.function_calling import convert_to_openai_function
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def roll_die(sides: int) -> int:
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"""Roll a die and return the rolled result.
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Args:
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sides: The integer number of sides the die has.
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Returns:
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An integer of the result of rolling the die.
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"""
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return random.randint(1, sides)
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def check_prime(number: int) -> str:
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"""Check if a given number is prime.
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Args:
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number: The input number to check.
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Returns:
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A str indicating the number is prime or not.
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"""
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if number <= 1:
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return f"{number} is not prime."
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is_prime = True
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for i in range(2, int(number**0.5) + 1):
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if number % i == 0:
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is_prime = False
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break
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if is_prime:
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return f"{number} is prime."
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else:
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return f"{number} is not prime."
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root_agent = Agent(
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model=LiteLlm(
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model="vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas",
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# If the model is not trained with functions and you would like to
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# enable function calling, you can add functions to the models, and the
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# functions will be added to the prompts during inferences.
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functions=[
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convert_to_openai_function(roll_die),
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convert_to_openai_function(check_prime),
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],
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),
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name="data_processing_agent",
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description="""You are a helpful assistant.""",
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instruction="""
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You are a helpful assistant, and call tools optionally.
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If call tools, the tool format should be in json, and the tool arguments should be parsed from users inputs.
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""",
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tools=[
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roll_die,
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check_prime,
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],
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)
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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import time
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import agent
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from dotenv import load_dotenv
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from google.adk import Runner
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from google.adk.artifacts import InMemoryArtifactService
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from google.adk.cli.utils import logs
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from google.adk.sessions import InMemorySessionService
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from google.adk.sessions import Session
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from google.genai import types
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load_dotenv(override=True)
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logs.log_to_tmp_folder()
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async def main():
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app_name = 'my_app'
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user_id_1 = 'user1'
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session_service = InMemorySessionService()
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artifact_service = InMemoryArtifactService()
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runner = Runner(
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app_name=app_name,
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agent=agent.root_agent,
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artifact_service=artifact_service,
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session_service=session_service,
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)
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session_11 = await session_service.create_session(
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app_name=app_name, user_id=user_id_1
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)
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async def run_prompt(session: Session, new_message: str):
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content = types.Content(
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role='user', parts=[types.Part.from_text(text=new_message)]
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)
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print('** User says:', content.model_dump(exclude_none=True))
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async for event in runner.run_async(
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user_id=user_id_1,
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session_id=session.id,
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new_message=content,
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):
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if event.content.parts:
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part = event.content.parts[0]
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if part.text:
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print(f'** {event.author}: {part.text}')
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if part.function_call:
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print(f'** {event.author} calls tool: {part.function_call}')
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if part.function_response:
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print(
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f'** {event.author} gets tool response: {part.function_response}'
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)
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start_time = time.time()
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print('Start time:', start_time)
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print('------------------------------------')
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await run_prompt(session_11, 'Hi, introduce yourself.')
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await run_prompt(session_11, 'Roll a die with 100 sides.')
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await run_prompt(session_11, 'Check if it is prime.')
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end_time = time.time()
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print('------------------------------------')
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print('End time:', end_time)
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print('Total time:', end_time - start_time)
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if __name__ == '__main__':
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asyncio.run(main())
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@@ -29,6 +29,7 @@ from typing import Tuple
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from typing import Union
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from google.genai import types
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import litellm
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from litellm import acompletion
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from litellm import ChatCompletionAssistantMessage
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from litellm import ChatCompletionAssistantToolCall
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@@ -53,6 +54,9 @@ from .base_llm import BaseLlm
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from .llm_request import LlmRequest
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from .llm_response import LlmResponse
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# This will add functions to prompts if functions are provided.
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litellm.add_function_to_prompt = True
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logger = logging.getLogger("google_adk." + __name__)
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_NEW_LINE = "\n"
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@@ -662,6 +666,10 @@ class LiteLlm(BaseLlm):
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messages, tools, response_format = _get_completion_inputs(llm_request)
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if "functions" in self._additional_args:
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# LiteLLM does not support both tools and functions together.
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tools = None
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completion_args = {
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"model": self.model,
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"messages": messages,
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@@ -17,11 +17,8 @@ from google.adk.models.lite_llm import LiteLlm
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from google.genai import types
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from google.genai.types import Content
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from google.genai.types import Part
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import litellm
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import pytest
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litellm.add_function_to_prompt = True
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_TEST_MODEL_NAME = "vertex_ai/meta/llama-3.1-405b-instruct-maas"
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_SYSTEM_PROMPT = """
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