chore: Replace old model gemini-2.0 references by 2.5

Merge #4705

### Link to Issue or Description of Change

- Closes: #4666

**Problem:**
Gemini-2.0-flash model is still used in integration tests, but disabled for new users

**Solution:**
Replace all occurrences of Gemini-2.0-flash by Gemini-2.5-flash, in integration tests and docstrings.
Because gemini-2.0 is still enabled for some users, this PR doesn't clean the model from unit tests.

### Testing Plan
Run already existing unittests and integration tests

### Checklist

- [x] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document.
- [x] I have performed a self-review of my own code.
- [x] I have commented my code, particularly in hard-to-understand areas.
- [x] I have added tests that prove my fix is effective or that my feature works.
- [x] New and existing unit tests pass locally with my changes.
- [x] I have manually tested my changes end-to-end.
- [x] Any dependent changes have been merged and published in downstream modules.

COPYBARA_INTEGRATE_REVIEW=#4705 from morganroux:fix/issues/4666 3c390a2
PiperOrigin-RevId: 900973552

Author: morganroux

src/google/adk/agents/common_configs.py

@@ -118,7 +118,7 @@ class AgentRefConfig(BaseModel):
     my_custom_agent = LlmAgent(
         name="my_custom_agent",
         instruction="You are a helpful custom agent.",
-        model="gemini-2.0-flash",
+        model="gemini-2.5-flash",
     )
     ```
 

src/google/adk/agents/config_schemas/AgentConfig.json

@@ -2461,7 +2461,7 @@
             }
           ],
           "default": null,
-          "description": "Optional. LlmAgent.model. Provide a model name string (e.g. \"gemini-2.0-flash\"). If not set, the model will be inherited from the ancestor or fall back to the system default (gemini-2.5-flash unless overridden via LlmAgent.set_default_model). To construct a model instance from code, use model_code.",
+          "description": "Optional. LlmAgent.model. Provide a model name string (e.g. \"gemini-2.5-flash\"). If not set, the model will be inherited from the ancestor or fall back to the system default (gemini-2.5-flash unless overridden via LlmAgent.set_default_model). To construct a model instance from code, use model_code.",
           "title": "Model"
         },
         "instruction": {

src/google/adk/agents/llm_agent_config.py

@@ -55,7 +55,7 @@ class LlmAgentConfig(BaseAgentConfig):
       default=None,
       description=(
           'Optional. LlmAgent.model. Provide a model name string (e.g.'
-          ' "gemini-2.0-flash"). If not set, the model will be inherited from'
+          ' "gemini-2.5-flash"). If not set, the model will be inherited from'
           ' the ancestor or fall back to the system default (gemini-2.5-flash'
           ' unless overridden via LlmAgent.set_default_model). To construct a'
           ' model instance from code, use model_code.'

src/google/adk/tools/mcp_tool/mcp_toolset.py

@@ -82,7 +82,7 @@ class McpToolset(BaseToolset):
 
     # Use in an agent
     agent = LlmAgent(
-        model='gemini-2.0-flash',
+        model='gemini-2.5-flash',
         name='enterprise_assistant',
         instruction='Help user accessing their file systems',
         tools=[toolset],

src/google/adk/utils/instructions_utils.py

@@ -51,7 +51,7 @@ async def build_instruction(
     )
 
   agent = Agent(
-      model="gemini-2.0-flash",
+      model="gemini-2.5-flash",
       name="agent",
       instruction=build_instruction,
   )

src/google/adk/utils/model_name_utils.py

@@ -41,7 +41,7 @@ def extract_model_name(model_string: str) -> str:
 
   Args:
     model_string: Either a simple model name like "gemini-2.5-pro" or a
-      path-based model name like "projects/.../models/gemini-2.0-flash-001"
+      path-based model name like "projects/.../models/gemini-2.5-flash"
 
   Returns:
     The extracted model name (e.g., "gemini-2.5-pro")

tests/integration/fixture/ecommerce_customer_service_agent/agent.py

@@ -288,7 +288,7 @@ def get_user_id_from_cookie() -> str:
 
 
 root_agent = Agent(
-    model="gemini-2.0-flash-001",
+    model="gemini-2.5-flash",
     name="Ecommerce_Customer_Service",
     instruction="""
       You are an intelligent customer service assistant for an e-commerce platform. Your goal is to accurately understand user queries and use the appropriate tools to fulfill requests. Follow these guidelines:

tests/integration/fixture/hello_world_agent/agent.py

@@ -60,7 +60,7 @@ def check_prime(nums: list[int]) -> list[str]:
 
 
 root_agent = Agent(
-    model='gemini-2.0-flash-001',
+    model='gemini-2.5-flash',
     name='data_processing_agent',
     instruction="""
       You roll dice and answer questions about the outcome of the dice rolls.

tests/integration/fixture/hello_world_agent_async/agent.py

@@ -68,7 +68,7 @@ async def get_agent_async() -> (
 ):
   """Returns the root agent."""
   root_agent = Agent(
-      model='gemini-2.0-flash-001',
+      model='gemini-2.5-flash',
       name='data_processing_agent',
       instruction="""
       You roll dice and answer questions about the outcome of the dice rolls.

tests/integration/fixture/home_automation_agent/agent.py

@@ -284,7 +284,7 @@ def list_devices(status: str = "", location: str = "") -> list:
 
 
 root_agent = Agent(
-    model="gemini-2.0-flash-001",
+    model="gemini-2.5-flash",
     name="Home_automation_agent",
     instruction="""
     You are Home Automation Agent. You are responsible for controlling the devices in the home.