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606 lines
18 KiB
Python
606 lines
18 KiB
Python
# Copyright 2026 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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"""Unit tests for canonical_xxx fields in LlmAgent."""
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import logging
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from typing import Any
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from typing import Optional
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from unittest import mock
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from google.adk.agents.callback_context import CallbackContext
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from google.adk.agents.invocation_context import InvocationContext
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from google.adk.agents.llm_agent import LlmAgent
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from google.adk.agents.readonly_context import ReadonlyContext
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from google.adk.models.anthropic_llm import Claude
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from google.adk.models.google_llm import Gemini
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from google.adk.models.lite_llm import LiteLlm
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from google.adk.models.llm_request import LlmRequest
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from google.adk.models.registry import LLMRegistry
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from google.adk.planners.built_in_planner import BuiltInPlanner
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from google.adk.sessions.in_memory_session_service import InMemorySessionService
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from google.adk.tools.google_search_tool import google_search
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from google.adk.tools.google_search_tool import GoogleSearchTool
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from google.adk.tools.vertex_ai_search_tool import VertexAiSearchTool
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from google.genai import types
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from pydantic import BaseModel
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import pytest
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async def _create_readonly_context(
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agent: LlmAgent, state: Optional[dict[str, Any]] = None
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) -> ReadonlyContext:
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session_service = InMemorySessionService()
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session = await session_service.create_session(
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app_name='test_app', user_id='test_user', state=state
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)
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invocation_context = InvocationContext(
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invocation_id='test_id',
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agent=agent,
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session=session,
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session_service=session_service,
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)
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return ReadonlyContext(invocation_context)
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@pytest.mark.parametrize(
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('default_model', 'expected_model_name', 'expected_model_type'),
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[
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(LlmAgent.DEFAULT_MODEL, LlmAgent.DEFAULT_MODEL, Gemini),
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('gemini-2.5-flash', 'gemini-2.5-flash', Gemini),
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],
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)
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def test_canonical_model_default_fallback(
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default_model, expected_model_name, expected_model_type
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):
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original_default = LlmAgent._default_model
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LlmAgent.set_default_model(default_model)
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try:
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agent = LlmAgent(name='test_agent')
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assert isinstance(agent.canonical_model, expected_model_type)
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assert agent.canonical_model.model == expected_model_name
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finally:
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LlmAgent.set_default_model(original_default)
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def test_canonical_model_str():
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agent = LlmAgent(name='test_agent', model='gemini-pro')
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assert agent.canonical_model.model == 'gemini-pro'
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def test_canonical_model_llm():
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llm = LLMRegistry.new_llm('gemini-pro')
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agent = LlmAgent(name='test_agent', model=llm)
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assert agent.canonical_model == llm
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def test_canonical_model_inherit():
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sub_agent = LlmAgent(name='sub_agent')
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parent_agent = LlmAgent(
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name='parent_agent', model='gemini-pro', sub_agents=[sub_agent]
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)
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assert sub_agent.canonical_model == parent_agent.canonical_model
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def test_canonical_live_model_default_fallback():
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original_default = LlmAgent._default_live_model
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LlmAgent.set_default_live_model('gemini-2.0-flash')
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try:
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agent = LlmAgent(name='test_agent')
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assert agent.canonical_live_model.model == 'gemini-2.0-flash'
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finally:
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LlmAgent.set_default_live_model(original_default)
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def test_canonical_live_model_str():
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agent = LlmAgent(name='test_agent', model='gemini-pro')
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assert agent.canonical_live_model.model == 'gemini-pro'
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def test_canonical_live_model_llm():
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llm = LLMRegistry.new_llm('gemini-pro')
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agent = LlmAgent(name='test_agent', model=llm)
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assert agent.canonical_live_model == llm
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def test_canonical_live_model_inherit():
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sub_agent = LlmAgent(name='sub_agent')
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parent_agent = LlmAgent(
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name='parent_agent', model='gemini-pro', sub_agents=[sub_agent]
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)
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assert sub_agent.canonical_live_model == parent_agent.canonical_live_model
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async def test_canonical_instruction_str():
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agent = LlmAgent(name='test_agent', instruction='instruction')
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ctx = await _create_readonly_context(agent)
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canonical_instruction, bypass_state_injection = (
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await agent.canonical_instruction(ctx)
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)
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assert canonical_instruction == 'instruction'
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assert not bypass_state_injection
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async def test_canonical_instruction():
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def _instruction_provider(ctx: ReadonlyContext) -> str:
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return f'instruction: {ctx.state["state_var"]}'
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agent = LlmAgent(name='test_agent', instruction=_instruction_provider)
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ctx = await _create_readonly_context(
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agent, state={'state_var': 'state_value'}
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)
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canonical_instruction, bypass_state_injection = (
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await agent.canonical_instruction(ctx)
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)
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assert canonical_instruction == 'instruction: state_value'
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assert bypass_state_injection
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async def test_async_canonical_instruction():
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async def _instruction_provider(ctx: ReadonlyContext) -> str:
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return f'instruction: {ctx.state["state_var"]}'
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agent = LlmAgent(name='test_agent', instruction=_instruction_provider)
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ctx = await _create_readonly_context(
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agent, state={'state_var': 'state_value'}
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)
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canonical_instruction, bypass_state_injection = (
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await agent.canonical_instruction(ctx)
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)
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assert canonical_instruction == 'instruction: state_value'
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assert bypass_state_injection
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async def test_canonical_global_instruction_str():
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agent = LlmAgent(name='test_agent', global_instruction='global instruction')
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ctx = await _create_readonly_context(agent)
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canonical_instruction, bypass_state_injection = (
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await agent.canonical_global_instruction(ctx)
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)
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assert canonical_instruction == 'global instruction'
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assert not bypass_state_injection
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async def test_canonical_global_instruction():
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def _global_instruction_provider(ctx: ReadonlyContext) -> str:
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return f'global instruction: {ctx.state["state_var"]}'
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agent = LlmAgent(
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name='test_agent', global_instruction=_global_instruction_provider
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)
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ctx = await _create_readonly_context(
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agent, state={'state_var': 'state_value'}
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)
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canonical_global_instruction, bypass_state_injection = (
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await agent.canonical_global_instruction(ctx)
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)
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assert canonical_global_instruction == 'global instruction: state_value'
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assert bypass_state_injection
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async def test_async_canonical_global_instruction():
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async def _global_instruction_provider(ctx: ReadonlyContext) -> str:
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return f'global instruction: {ctx.state["state_var"]}'
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agent = LlmAgent(
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name='test_agent', global_instruction=_global_instruction_provider
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)
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ctx = await _create_readonly_context(
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agent, state={'state_var': 'state_value'}
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)
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canonical_global_instruction, bypass_state_injection = (
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await agent.canonical_global_instruction(ctx)
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)
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assert canonical_global_instruction == 'global instruction: state_value'
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assert bypass_state_injection
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def test_output_schema_with_sub_agents_will_not_throw():
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class Schema(BaseModel):
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pass
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sub_agent = LlmAgent(
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name='sub_agent',
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)
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agent = LlmAgent(
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name='test_agent',
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output_schema=Schema,
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sub_agents=[sub_agent],
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)
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# Transfer is not disabled
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assert not agent.disallow_transfer_to_parent
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assert not agent.disallow_transfer_to_peers
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assert agent.output_schema == Schema
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assert agent.sub_agents == [sub_agent]
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def test_output_schema_with_tools_will_not_throw():
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class Schema(BaseModel):
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pass
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def _a_tool():
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pass
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LlmAgent(
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name='test_agent',
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output_schema=Schema,
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tools=[_a_tool],
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)
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def test_before_model_callback():
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def _before_model_callback(
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callback_context: CallbackContext,
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llm_request: LlmRequest,
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) -> None:
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return None
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agent = LlmAgent(
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name='test_agent', before_model_callback=_before_model_callback
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)
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# TODO: add more logic assertions later.
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assert agent.before_model_callback is not None
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def test_validate_generate_content_config_thinking_config_allow():
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"""Tests that thinking_config is now allowed directly in the agent init."""
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agent = LlmAgent(
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name='test_agent',
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generate_content_config=types.GenerateContentConfig(
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thinking_config=types.ThinkingConfig(include_thoughts=True)
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),
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)
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assert agent.generate_content_config.thinking_config.include_thoughts is True
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def test_thinking_config_precedence_warning():
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"""Tests that a UserWarning is issued when both manual config and planner exist."""
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config = types.GenerateContentConfig(
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thinking_config=types.ThinkingConfig(include_thoughts=True)
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)
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planner = BuiltInPlanner(
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thinking_config=types.ThinkingConfig(include_thoughts=True)
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)
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with pytest.warns(
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UserWarning, match="planner's configuration will take precedence"
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):
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LlmAgent(name='test_agent', generate_content_config=config, planner=planner)
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def test_validate_generate_content_config_tools_throw():
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"""Tests that tools cannot be set directly in config."""
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with pytest.raises(ValueError):
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_ = LlmAgent(
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name='test_agent',
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generate_content_config=types.GenerateContentConfig(
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tools=[types.Tool(function_declarations=[])]
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),
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)
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def test_validate_generate_content_config_system_instruction_throw():
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"""Tests that system instructions cannot be set directly in config."""
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with pytest.raises(ValueError):
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_ = LlmAgent(
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name='test_agent',
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generate_content_config=types.GenerateContentConfig(
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system_instruction='system instruction'
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),
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)
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def test_validate_generate_content_config_response_schema_throw():
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"""Tests that response schema cannot be set directly in config."""
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class Schema(BaseModel):
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pass
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with pytest.raises(ValueError):
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_ = LlmAgent(
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name='test_agent',
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generate_content_config=types.GenerateContentConfig(
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response_schema=Schema
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),
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)
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def test_allow_transfer_by_default():
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sub_agent = LlmAgent(name='sub_agent')
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agent = LlmAgent(name='test_agent', sub_agents=[sub_agent])
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assert not agent.disallow_transfer_to_parent
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assert not agent.disallow_transfer_to_peers
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# TODO(b/448114567): Remove TestCanonicalTools once the workaround
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# is no longer needed.
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class TestCanonicalTools:
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"""Unit tests for canonical_tools in LlmAgent."""
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@staticmethod
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def _my_tool(sides: int) -> int:
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return sides
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async def test_handle_google_search_with_other_tools(self):
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"""Test that google_search is wrapped into an agent."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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self._my_tool,
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GoogleSearchTool(bypass_multi_tools_limit=True),
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 2
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assert tools[0].name == '_my_tool'
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assert tools[0].__class__.__name__ == 'FunctionTool'
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assert tools[1].name == 'google_search_agent'
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assert tools[1].__class__.__name__ == 'GoogleSearchAgentTool'
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async def test_handle_google_search_with_other_tools_no_bypass(self):
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"""Test that google_search is not wrapped into an agent."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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self._my_tool,
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GoogleSearchTool(bypass_multi_tools_limit=False),
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 2
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assert tools[0].name == '_my_tool'
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assert tools[0].__class__.__name__ == 'FunctionTool'
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assert tools[1].name == 'google_search'
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assert tools[1].__class__.__name__ == 'GoogleSearchTool'
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async def test_handle_google_search_only(self):
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"""Test that google_search is not wrapped into an agent."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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google_search,
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 1
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assert tools[0].name == 'google_search'
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assert tools[0].__class__.__name__ == 'GoogleSearchTool'
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async def test_function_tool_only(self):
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"""Test that function tool is not affected."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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self._my_tool,
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 1
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assert tools[0].name == '_my_tool'
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assert tools[0].__class__.__name__ == 'FunctionTool'
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@mock.patch(
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'google.auth.default',
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mock.MagicMock(return_value=('credentials', 'project')),
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)
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async def test_handle_vais_with_other_tools(self):
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"""Test that VertexAiSearchTool is replaced with Discovery Engine Search."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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self._my_tool,
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VertexAiSearchTool(
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data_store_id='test_data_store_id',
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bypass_multi_tools_limit=True,
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),
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 2
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assert tools[0].name == '_my_tool'
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assert tools[0].__class__.__name__ == 'FunctionTool'
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assert tools[1].name == 'discovery_engine_search'
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assert tools[1].__class__.__name__ == 'DiscoveryEngineSearchTool'
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async def test_handle_vais_with_other_tools_no_bypass(self):
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"""Test that VertexAiSearchTool is not replaced."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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self._my_tool,
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VertexAiSearchTool(
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data_store_id='test_data_store_id',
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bypass_multi_tools_limit=False,
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),
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 2
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assert tools[0].name == '_my_tool'
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assert tools[0].__class__.__name__ == 'FunctionTool'
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assert tools[1].name == 'vertex_ai_search'
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assert tools[1].__class__.__name__ == 'VertexAiSearchTool'
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async def test_handle_vais_only(self):
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"""Test that VertexAiSearchTool is not wrapped into an agent."""
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[
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VertexAiSearchTool(data_store_id='test_data_store_id'),
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],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 1
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assert tools[0].name == 'vertex_ai_search'
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assert tools[0].__class__.__name__ == 'VertexAiSearchTool'
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async def test_multiple_tools_resolution(self):
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"""Test that multiple tools are resolved correctly."""
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def _tool_1():
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pass
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def _tool_2():
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pass
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[_tool_1, _tool_2],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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assert len(tools) == 2
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assert tools[0].name == '_tool_1'
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assert tools[1].name == '_tool_2'
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async def test_canonical_tools_graceful_degradation_on_toolset_error(self):
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"""Test that canonical_tools returns tools from working toolsets when one fails."""
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from google.adk.tools.base_tool import BaseTool
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from google.adk.tools.base_toolset import BaseToolset
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class FailingToolset(BaseToolset):
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async def get_tools(self, readonly_context=None):
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raise ConnectionError('MCP server unavailable')
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class WorkingToolset(BaseToolset):
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async def get_tools(self, readonly_context=None):
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tool = mock.MagicMock(spec=BaseTool)
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tool.name = 'working_tool'
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tool._get_declaration = mock.MagicMock(return_value=None)
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return [tool]
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def _regular_tool():
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pass
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agent = LlmAgent(
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name='test_agent',
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model='gemini-pro',
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tools=[_regular_tool, FailingToolset(), WorkingToolset()],
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)
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ctx = await _create_readonly_context(agent)
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tools = await agent.canonical_tools(ctx)
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# Should have the regular tool + working toolset tool, but not crash
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assert len(tools) == 2
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assert tools[0].name == '_regular_tool'
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assert tools[1].name == 'working_tool'
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# Tests for multi-provider model support via string model names
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@pytest.mark.parametrize(
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'model_name',
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[
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'gemini-2.5-flash',
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'gemini-2.5-pro',
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],
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)
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def test_agent_with_gemini_string_model(model_name):
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"""Test that Agent accepts Gemini model strings and resolves to Gemini."""
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agent = LlmAgent(name='test_agent', model=model_name)
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assert isinstance(agent.canonical_model, Gemini)
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assert agent.canonical_model.model == model_name
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@pytest.mark.parametrize(
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'model_name',
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[
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'claude-3-5-sonnet-v2@20241022',
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'claude-sonnet-4@20250514',
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],
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)
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def test_agent_with_claude_string_model(model_name):
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"""Test that Agent accepts Claude model strings and resolves to Claude."""
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agent = LlmAgent(name='test_agent', model=model_name)
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assert isinstance(agent.canonical_model, Claude)
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assert agent.canonical_model.model == model_name
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@pytest.mark.parametrize(
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'model_name',
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[
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'openai/gpt-4o',
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'groq/llama3-70b-8192',
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'anthropic/claude-3-opus-20240229',
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],
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)
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def test_agent_with_litellm_string_model(model_name):
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"""Test that Agent accepts LiteLLM provider strings."""
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agent = LlmAgent(name='test_agent', model=model_name)
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assert isinstance(agent.canonical_model, LiteLlm)
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assert agent.canonical_model.model == model_name
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def test_builtin_planner_overwrite_logging(caplog):
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"""Tests that the planner logs an DEBUG message when overwriting a config."""
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planner = BuiltInPlanner(
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thinking_config=types.ThinkingConfig(include_thoughts=True)
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)
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# Create a request that already has a thinking_config
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req = LlmRequest(
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contents=[],
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config=types.GenerateContentConfig(
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thinking_config=types.ThinkingConfig(include_thoughts=True)
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),
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)
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with caplog.at_level(
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logging.DEBUG, logger='google_adk.google.adk.planners.built_in_planner'
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):
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planner.apply_thinking_config(req)
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assert (
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'Overwriting `thinking_config` from `generate_content_config`'
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in caplog.text
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)
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