fix: update model id to gemini-2.0-flash-exp

README.md

@@ -42,7 +42,7 @@ from google.adk.tools import google_search
 
 root_agent = Agent(
     name="search_assistant",
-    model="gemini-2.0-flash",
+    model="gemini-2.0-flash-exp",
     instruction="You are a helpful assistant. Answer user questions using Google Search when needed.",
     description="An assistant that can search the web.",
     tools=[google_search]

docs/agents/custom-agents.md

@@ -220,7 +220,7 @@ These are standard `LlmAgent` definitions, responsible for specific tasks. Their
 ```python
 # agent.py (LLM Agent Definitions part)
 
-GEMINI_FLASH = "gemini-1.5-flash" # Define model constant
+GEMINI_FLASH = "gemini-2.0-flash-exp" # Define model constant
 
 story_generator = LlmAgent(
     name="StoryGenerator", model=GEMINI_FLASH,

docs/agents/llm-agents.md

@@ -14,12 +14,12 @@ First, you need to establish what the agent *is* and what it's *for*.
 
 * **`description` (Optional, Recommended for Multi-Agent):** Provide a concise summary of the agent's capabilities. This description is primarily used by *other* LLM agents to determine if they should route a task to this agent. Make it specific enough to differentiate it from peers (e.g., "Handles inquiries about current billing statements," not just "Billing agent").
 
-* **`model` (Required):** Specify the underlying LLM that will power this agent's reasoning. This is a string identifier like `"gemini-2.0-flash-001"`. The choice of model impacts the agent's capabilities, cost, and performance. See the [Models](models.md) page for available options and considerations.
+* **`model` (Required):** Specify the underlying LLM that will power this agent's reasoning. This is a string identifier like `"gemini-2.0-flash-exp"`. The choice of model impacts the agent's capabilities, cost, and performance. See the [Models](models.md) page for available options and considerations.
 
 ```python
 # Example: Defining the basic identity
 capital_agent = LlmAgent(
-    model="gemini-2.0-flash-001",
+    model="gemini-2.0-flash-exp",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country."
     # instruction and tools will be added next
@@ -46,7 +46,7 @@ The `instruction` parameter is arguably the most critical for shaping an `LlmAge
 ```python
 # Example: Adding instructions
 capital_agent = LlmAgent(
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country.",
     instruction="""You are an agent that provides the capital city of a country.
@@ -84,7 +84,7 @@ def get_capital_city(country: str) -> str:
 
 # Add the tool to the agent
 capital_agent = LlmAgent(
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     name="capital_agent",
     description="Answers user questions about the capital city of a given country.",
     instruction="""You are an agent that provides the capital city of a country... (previous instruction text)""",
@@ -171,7 +171,7 @@ Here's the complete basic `capital_agent`:
 
 ```python
 # Full example code for the basic capital agent
---8<-- "examples/python/snippets/agents/llm-agent/capital-agent.py"
+--8<-- "examples/python/snippets/agents/llm-agent/capital_agent.py"
 ```
 
 _(This example demonstrates the core concepts. More complex agents might incorporate schemas, context control, planning, etc.)_

docs/agents/models.md

@@ -56,7 +56,7 @@ from google.adk.agents import LlmAgent
 # --- Example using a stable Gemini Flash model ---
 agent_gemini_flash = LlmAgent(
     # Use the latest stable Flash model identifier
-    model="gemini-2.0-flash-001",
+    model="gemini-2.0-flash-exp",
     name="gemini_flash_agent",
     instruction="You are a fast and helpful Gemini assistant.",
     # ... other agent parameters

docs/agents/multi-agents.md

@@ -29,13 +29,13 @@ The foundation for structuring multi-agent systems is the parent-child relations
 from google.adk.agents import LlmAgent, BaseAgent
 
 # Define individual agents
-greeter = LlmAgent(name="Greeter", model="gemini-2.0-flash-001")
+greeter = LlmAgent(name="Greeter", model="gemini-2.0-flash-exp")
 task_doer = BaseAgent(name="TaskExecutor") # Custom non-LLM agent
 
 # Create parent agent and assign children via sub_agents
 coordinator = LlmAgent(
     name="Coordinator",
-    model="gemini-2.0-flash-001",
+    model="gemini-2.0-flash-exp",
     description="I coordinate greetings and tasks.",
     sub_agents=[ # Assign sub_agents here
         greeter,
@@ -196,7 +196,7 @@ image_tool = AgentTool(agent=image_agent) # Wrap the agent
 # Parent agent uses the AgentTool
 artist_agent = LlmAgent(
     name="Artist",
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     instruction="Create a prompt and use the ImageGen tool to generate the image.",
     tools=[image_tool] # Include the AgentTool
 )
@@ -229,7 +229,7 @@ support_agent = LlmAgent(name="Support", description="Handles technical support
 
 coordinator = LlmAgent(
     name="HelpDeskCoordinator",
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     instruction="Route user requests: Use Billing agent for payment issues, Support agent for technical problems.",
     description="Main help desk router.",
     # allow_transfer=True is often implicit with sub_agents in AutoFlow
@@ -317,15 +317,15 @@ summarizer = LlmAgent(name="Summarizer", description="Summarizes text.")
 # Mid-level agent combining tools
 research_assistant = LlmAgent(
     name="ResearchAssistant",
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     description="Finds and summarizes information on a topic.",
     tools=[AgentTool(agent=web_searcher), AgentTool(agent=summarizer)]
 )
 
 # High-level agent delegating research
 report_writer = LlmAgent(
     name="ReportWriter",
-    model="gemini-1.5-flash",
+    model="gemini-2.0-flash-exp",
     instruction="Write a report on topic X. Use the ResearchAssistant to gather information.",
     tools=[AgentTool(agent=research_assistant)]
     # Alternatively, could use LLM Transfer if research_assistant is a sub_agent

docs/agents/workflow-agents/loop-agents.md

@@ -42,5 +42,5 @@ In this setup, the `LoopAgent` would manage the iterative process.  The `CriticA
 #### Full code
 
 ```py
---8<-- "examples/python/snippets/agents/workflow-agents/loop-agent-doc-improv-agent.py"
+--8<-- "examples/python/snippets/agents/workflow-agents/loop_agent_doc_improv_agent.py"
 ```

docs/callbacks/index.md

@@ -36,7 +36,7 @@ def my_before_model_logic(
 # --- Register it during Agent creation ---
 my_agent = LlmAgent(
     name="MyCallbackAgent",
-    model="gemini-2.0-flash", # Or your desired model
+    model="gemini-2.0-flash-exp", # Or your desired model
     instruction="Be helpful.",
     # Other agent parameters...
     before_model_callback=my_before_model_logic # Pass the function here
@@ -132,7 +132,7 @@ def block_forbidden_input(
 # Agent definition using the callback
 guardrail_agent = LlmAgent(
     name="GuardrailAgent",
-    model="gemini-2.0-flash",
+    model="gemini-2.0-flash-exp",
     instruction="Answer user questions.",
     before_model_callback=block_forbidden_input
 )

docs/get-started/running-the-agent.md

@@ -125,7 +125,7 @@ from agents.sessions import InMemorySessionService
 
 # Step 1: Define your agent:
 root_agent = Agent(name="my_agent",
-                   model="gemini-2.0-flash",
+                   model="gemini-2.0-flash-exp",
  		      instruction="Answer questions.")
 
 # Step 2: Initiate Session

docs/guides/responsible-agents.md

@@ -156,7 +156,7 @@ def validate_tool_params(
 
 # Hypothetical Agent setup
 root_agent = LlmAgent( # Use specific agent type
-    model='gemini-1.5-flash-001',
+    model='gemini-2.0-flash-exp',
     name='root_agent',
     instruction="...",
     before_tool_callback=validate_tool_params, # Assign the callback

docs/runtime/artifacts.md

@@ -106,7 +106,7 @@ from google.adk.agents import LlmAgent # Any agent
 from google.adk.sessions import InMemorySessionService
 
 # Example: Configuring the Runner with an Artifact Service
-my_agent = LlmAgent(name="artifact_user_agent", model="gemini-2.0-flash")
+my_agent = LlmAgent(name="artifact_user_agent", model="gemini-2.0-flash-exp")
 artifact_service = InMemoryArtifactService() # Choose an implementation
 session_service = InMemorySessionService()
 
@@ -206,7 +206,7 @@ from google.adk.agents import LlmAgent
 from google.adk.sessions import InMemorySessionService
 
 # Your agent definition
-agent = LlmAgent(name="my_agent", model="gemini-2.0-flash")
+agent = LlmAgent(name="my_agent", model="gemini-2.0-flash-exp")
 
 # Instantiate the desired artifact service
 artifact_service = InMemoryArtifactService()

docs/sessions/memory.md

@@ -83,7 +83,7 @@ from google.genai.types import Content, Part
 # --- Constants ---
 APP_NAME = "memory_example_app"
 USER_ID = "mem_user"
-MODEL = "gemini-1.5-flash" # Use a valid model
+MODEL = "gemini-2.0-flash-exp" # Use a valid model
 
 # --- Agent Definitions ---
 # Agent 1: Simple agent to capture information

docs/sessions/state.md

@@ -81,7 +81,7 @@ from google.genai.types import Content, Part
 # Define agent with output_key
 greeting_agent = LlmAgent(
     name="Greeter",
-    model="gemini-1.5-flash", # Use a valid model
+    model="gemini-2.0-flash-exp", # Use a valid model
     instruction="Generate a short, friendly greeting.",
     output_key="last_greeting" # Save response to state['last_greeting']
 )

docs/tools/google-cloud-tools.md

@@ -83,7 +83,7 @@ Note: this tutorial includes an agent creation. If you already have an agent, yo
     from .tools import sample_toolset
 
     root_agent = LlmAgent(
-        model='gemini-2.0-flash',
+        model='gemini-2.0-flash-exp',
         name='enterprise_assistant',
         instruction='Help user, leverage the tools you have access to',
         tools=sample_toolset.get_tools(),)

docs/tools/mcp-tools.md

@@ -234,7 +234,7 @@ async def get_agent_async():
   tools, exit_stack = await get_tools_async()
   print(f"Fetched {len(tools)} tools from MCP server.")
   root_agent = LlmAgent(
-      model='gemini-1.5-flash', # Adjust if needed
+      model='gemini-2.0-flash-exp', # Adjust if needed
       name='maps_assistant',
       instruction='Help user with mapping and directions using available tools.',
       tools=tools,

docs/tools/openapi-tools.md

@@ -70,7 +70,7 @@ Follow these steps to integrate an OpenAPI spec into your agent:
 
     my_agent = LlmAgent(
         name="api_interacting_agent",
-        model="gemini-2.0-flash-001", # Or your preferred model
+        model="gemini-2.0-flash-exp", # Or your preferred model
         tools=api_tools, # Pass the list of generated tools
         # ... other agent config ...
     )

docs/tools/third-party-tools.md

@@ -55,7 +55,7 @@ ADK provides the `LangchainTool` wrapper to integrate tools from the LangChain e
     # Define the ADK agent, including the wrapped tool
     my_agent = Agent(
         name="langchain_tool_agent",
-        model="gemini-2.0-flash",
+        model="gemini-2.0-flash-exp",
         description="Agent to answer questions using TavilySearch.",
         instruction="I can answer your questions by searching the internet. Just ask me anything!",
         tools=[adk_tavily_tool] # Add the wrapped tool here
@@ -125,7 +125,7 @@ ADK provides the `CrewaiTool` wrapper to integrate tools from the CrewAI library
     # Define the ADK agent
     my_agent = Agent(
         name="crewai_search_agent",
-        model="gemini-2.0-flash",
+        model="gemini-2.0-flash-exp",
         description="Agent to find recent news using the Serper search tool.",
         instruction="I can find the latest news for you. What topic are you interested in?",
         tools=[adk_serper_tool] # Add the wrapped tool here

examples/python/snippets/agents/custom-agent/storyflow-agent.py

@@ -2,7 +2,7 @@
 from typing import AsyncGenerator
 from typing_extensions import override
 
-from google.adk.agents import Agent, LlmAgent, BaseAgent, LoopAgent, SequentialAgent
+from google.adk.agents import LlmAgent, BaseAgent, LoopAgent, SequentialAgent
 from google.adk.agents.invocation_context import InvocationContext
 from google.genai import types
 from google.adk.sessions import InMemorySessionService
@@ -14,7 +14,7 @@
 APP_NAME = "story_app"
 USER_ID = "12345"
 SESSION_ID = "123344"
-GEMINI_2_FLASH = "gemini-2.0-flash-001"
+GEMINI_2_FLASH = "gemini-2.0-flash-exp"
 
 # --- Configure Logging ---
 logging.basicConfig(level=logging.INFO)
@@ -240,7 +240,9 @@ def call_agent(user_input_topic: str):
     Sends a new topic to the agent (overwriting the initial one if needed)
     and runs the workflow.
     """
-    current_session = session_service.get_session(APP_NAME, USER_ID, SESSION_ID)
+    current_session = session_service.get_session(app_name=APP_NAME, 
+                                                  user_id=USER_ID, 
+                                                  session_id=SESSION_ID)
     if not current_session:
         logger.error("Session not found!")
         return
@@ -260,7 +262,9 @@ def call_agent(user_input_topic: str):
     print("\n--- Agent Interaction Result ---")
     print("Agent Final Response: ", final_response)
 
-    final_session = session_service.get_session(APP_NAME, USER_ID, SESSION_ID)
+    final_session = session_service.get_session(app_name=APP_NAME, 
+                                                user_id=USER_ID, 
+                                                session_id=SESSION_ID)
     print("Final Session State:")
     import json
     print(json.dumps(final_session.state, indent=2))

examples/python/snippets/agents/llm-agent/capital-agent.py → examples/python/snippets/agents/llm-agent/capital_agent.py

@@ -1,5 +1,4 @@
 # --- Full example code demonstrating LlmAgent with Tools vs. Output Schema ---
-import asyncio
 import json # Needed for pretty printing dicts
 
 from google.adk.agents import LlmAgent
@@ -13,7 +12,7 @@
 USER_ID = "test_user_456"
 SESSION_ID_TOOL_AGENT = "session_tool_agent_xyz"
 SESSION_ID_SCHEMA_AGENT = "session_schema_agent_xyz"
-MODEL_NAME = "gemini-2.0-flash-001"
+MODEL_NAME = "gemini-2.0-flash-exp"
 
 # --- 2. Define Schemas ---
 
@@ -117,7 +116,9 @@ async def call_agent_and_print(
 
     print(f"<<< Agent '{agent_instance.name}' Response: {final_response_content}")
 
-    current_session = session_service.get_session(APP_NAME, USER_ID, session_id)
+    current_session = session_service.get_session(app_name=APP_NAME,
+                                                  user_id=USER_ID,
+                                                  session_id=session_id)
     stored_output = current_session.state.get(agent_instance.output_key)
 
     # Pretty print if the stored output looks like JSON (likely from output_schema)

examples/python/snippets/agents/workflow-agents/loop-agent-doc-improv-agent.py → examples/python/snippets/agents/workflow-agents/loop_agent_doc_improv_agent.py

@@ -8,7 +8,7 @@
 APP_NAME = "doc_writing_app"
 USER_ID = "dev_user_01"
 SESSION_ID = "session_01"
-GEMINI_MODEL = "gemini-2.0-flash-001"
+GEMINI_MODEL = "gemini-2.0-flash-exp"
 
 # --- State Keys ---
 STATE_INITIAL_TOPIC = "quantum physics"
@@ -55,12 +55,12 @@
 
 # Agent Interaction
 def call_agent(query):
-  content = types.Content(role='user', parts=[types.Part(text=query)])
-  events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
+    content = types.Content(role='user', parts=[types.Part(text=query)])
+    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
 
-  for event in events:
-      if event.is_final_response():
-          final_response = event.content.parts[0].text
-          print("Agent Response: ", final_response)
+    for event in events:
+        if event.is_final_response():
+            final_response = event.content.parts[0].text
+            print("Agent Response: ", final_response)
 
-call_agent("execute")
+call_agent("execute")

examples/python/snippets/agents/workflow-agents/parallel_agent_web_research.py

@@ -8,7 +8,7 @@
 APP_NAME = "parallel_research_app"
 USER_ID = "research_user_01"
 SESSION_ID = "parallel_research_session"
-GEMINI_MODEL = "gemini-2.0-flash-001"
+GEMINI_MODEL = "gemini-2.0-flash-exp"
 
 # --- Define Researcher Sub-Agents ---
 

examples/python/snippets/agents/workflow-agents/sequential-agent-code-development-agent.py → examples/python/snippets/agents/workflow-agents/sequential_agent_code_development_agent.py

@@ -8,7 +8,7 @@
 APP_NAME = "code_pipeline_app"
 USER_ID = "dev_user_01"
 SESSION_ID = "pipeline_session_01"
-GEMINI_MODEL = "gemini-2.0-flash-001"
+GEMINI_MODEL = "gemini-2.0-flash-exp"
 
 # --- 1. Define Sub-Agents for Each Pipeline Stage ---
 
@@ -77,12 +77,12 @@
 
 # Agent Interaction
 def call_agent(query):
-  content = types.Content(role='user', parts=[types.Part(text=query)])
-  events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
+    content = types.Content(role='user', parts=[types.Part(text=query)])
+    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
 
-  for event in events:
-      if event.is_final_response():
-          final_response = event.content.parts[0].text
-          print("Agent Response: ", final_response)
+    for event in events:
+        if event.is_final_response():
+            final_response = event.content.parts[0].text
+            print("Agent Response: ", final_response)
 
-call_agent("perform math addition")
+call_agent("perform math addition")

examples/python/snippets/get-started/google_search_agent/agent.py

@@ -5,7 +5,7 @@
    # A unique name for the agent.
    name="basic_search_agent",
    # The Large Language Model (LLM) that agent will use.
-   model="gemini-2.0-flash",
+   model="gemini-2.0-flash-exp",
    # A short description of the agent's purpose.
    description="Agent to answer questions using Google Search.",
    # Instructions to set the agent's behavior.

examples/python/snippets/get-started/multi_tool_agent/agent.py

@@ -57,7 +57,7 @@ def get_current_time(city: str) -> dict:
 
 root_agent = Agent(
     name="weather_time_agent",
-    model="gemini-2.0-flash",
+    model="gemini-2.0-flash-exp",
     description=(
         "Agent to answer questions about the time and weather in a city."
     ),