Merge branch 'main' into feat/function-tools-enum-support

Author: SOORAJTS2001

contributing/samples/langchain_structured_tool_agent/agent.py

@@ -17,17 +17,19 @@
 """
 from google.adk.agents.llm_agent import Agent
 from google.adk.tools.langchain_tool import LangchainTool
-from langchain.tools import tool
+from langchain_core.tools import tool
 from langchain_core.tools.structured import StructuredTool
 from pydantic import BaseModel
 
 
 async def add(x, y) -> int:
+  """Adds two numbers."""
   return x + y
 
 
 @tool
 def minus(x, y) -> int:
+  """Subtracts two numbers."""
   return x - y
 
 

contributing/samples/live_bidi_streaming_multi_agent/agent.py

@@ -16,6 +16,7 @@
 
 from google.adk.agents.llm_agent import Agent
 from google.adk.examples.example import Example
+from google.adk.models.google_llm import Gemini
 from google.adk.tools.example_tool import ExampleTool
 from google.genai import types
 
@@ -28,6 +29,17 @@ def roll_die(sides: int) -> int:
 
 roll_agent = Agent(
     name="roll_agent",
+    model=Gemini(
+        # model="gemini-2.0-flash-live-preview-04-09",  # for Vertex project
+        model="gemini-live-2.5-flash-preview",  # for AI studio key
+        speech_config=types.SpeechConfig(
+            voice_config=types.VoiceConfig(
+                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                    voice_name="Kore",
+                )
+            )
+        ),
+    ),
     description="Handles rolling dice of different sizes.",
     instruction="""
       You are responsible for rolling dice based on the user's request.
@@ -69,6 +81,17 @@ def check_prime(nums: list[int]) -> str:
 
 prime_agent = Agent(
     name="prime_agent",
+    model=Gemini(
+        # model="gemini-2.0-flash-live-preview-04-09",  # for Vertex project
+        model="gemini-live-2.5-flash-preview",  # for AI studio key
+        speech_config=types.SpeechConfig(
+            voice_config=types.VoiceConfig(
+                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                    voice_name="Puck",
+                )
+            )
+        ),
+    ),
     description="Handles checking if numbers are prime.",
     instruction="""
       You are responsible for checking whether numbers are prime.
@@ -100,8 +123,17 @@ def get_current_weather(location: str):
 
 root_agent = Agent(
     # find supported models here: https://google.github.io/adk-docs/get-started/streaming/quickstart-streaming/
-    model="gemini-2.0-flash-live-preview-04-09",  # for Vertex project
-    # model="gemini-live-2.5-flash-preview",  # for AI studio key
+    model=Gemini(
+        # model="gemini-2.0-flash-live-preview-04-09",  # for Vertex project
+        model="gemini-live-2.5-flash-preview",  # for AI studio key
+        speech_config=types.SpeechConfig(
+            voice_config=types.VoiceConfig(
+                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                    voice_name="Zephyr",
+                )
+            )
+        ),
+    ),
     name="root_agent",
     instruction="""
       You are a helpful assistant that can check time, roll dice and check if numbers are prime.

contributing/samples/oauth_calendar_agent/README.md

@@ -4,37 +4,45 @@
 
 This sample tests and demos the OAuth support in ADK via two tools:
 
-* 1. list_calendar_events
+*   1. list_calendar_events
 
-  This is a customized tool that calls Google Calendar API to list calendar events.
-  It pass in the client id and client secrete to ADK and then get back the access token from ADK.
-  And then it uses the access token to call calendar api.
+    This is a customized tool that calls Google Calendar API to list calendar
+    events. It pass in the client id and client secrete to ADK and then get back
+    the access token from ADK. And then it uses the access token to call
+    calendar api.
 
-* 2. get_calendar_events
+*   2. get_calendar_events
 
-  This is an google calendar tool that calls Google Calendar API to get the details of a specific calendar.
-  This tool is from the ADK built-in Google Calendar ToolSet.
-  Everything is wrapped and the tool user just needs to pass in the client id and client secret.
+    This is an google calendar tool that calls Google Calendar API to get the
+    details of a specific calendar. This tool is from the ADK built-in Google
+    Calendar ToolSet. Everything is wrapped and the tool user just needs to pass
+    in the client id and client secret.
 
 ## How to use
 
-* 1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret.
-  Be sure to choose "web" as your client type.
+*   1. Follow
+    https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name.
+    to get your client id and client secret. Be sure to choose "web" as your
+    client type.
 
-* 2. Configure your `.env` file to add two variables:
+*   2. Configure your `.env` file to add two variables:
 
-  * OAUTH_CLIENT_ID={your client id}
-  * OAUTH_CLIENT_SECRET={your client secret}
+    *   OAUTH_CLIENT_ID={your client id}
+    *   OAUTH_CLIENT_SECRET={your client secret}
 
-  Note: don't create a separate `.env` file , instead put it to the same `.env` file that stores your Vertex AI or Dev ML credentials
+    Note: don't create a separate `.env` file , instead put it to the same
+        `.env` file that stores your Vertex AI or Dev ML credentials
 
-* 3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui/ to "Authorized redirect URIs".
+*   3. Follow
+    https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred
+    to add http://localhost/dev-ui/ to "Authorized redirect URIs".
 
-  Note: localhost here is just a hostname that you use to access the dev ui, replace it with the actual hostname you use to access the dev ui.
+    Note: localhost here is just a hostname that you use to access the dev ui,
+    replace it with the actual hostname you use to access the dev ui.
 
-* 4. For 1st run, allow popup for localhost in Chrome.
+*   4. For 1st run, allow popup for localhost in Chrome.
 
 ## Sample prompt
 
-* `List all my today's meeting from 7am to 7pm.`
-* `Get the details of the first event.`
+*   `List all my today's meeting from 7am to 7pm.`
+*   `Get the details of the first event.`

contributing/samples/output_schema_with_tools/README.md

@@ -1,22 +1,32 @@
 # Output Schema with Tools Sample Agent
 
-This sample demonstrates how to use structured output (`output_schema`) alongside other tools in an ADK agent. Previously, this combination was not allowed, but now it's supported through a special processor that handles the interaction.
+This sample demonstrates how to use structured output (`output_schema`)
+alongside other tools in an ADK agent. Previously, this combination was not
+allowed, but now it's supported through a special processor that handles the
+interaction.
 
 ## How it Works
 
 The agent combines:
-- **Tools**: `search_wikipedia` and `get_current_year` for gathering information
-- **Structured Output**: `PersonInfo` schema to ensure consistent response format
+
+-   **Tools**: `search_wikipedia` and `get_current_year` for gathering
+    information
+-   **Structured Output**: `PersonInfo` schema to ensure consistent response
+    format
 
 When both `output_schema` and `tools` are specified:
-1. ADK automatically adds a special `set_model_response` tool
-2. The model can use the regular tools for information gathering
-3. For the final response, the model uses `set_model_response` with structured data
-4. ADK extracts and validates the structured response
+
+1.  ADK automatically adds a special `set_model_response` tool
+2.  The model can use the regular tools for information gathering
+3.  For the final response, the model uses `set_model_response` with structured
+    data
+4.  ADK extracts and validates the structured response
 
 ## Expected Response Format
 
-The agent will return information in this structured format for user query "Tell me about Albert Einstein":
+The agent will return information in this structured format for user query
+
+> Tell me about Albert Einstein.
 
 ```json
 {
@@ -30,7 +40,7 @@ The agent will return information in this structured format for user query "Tell
 
 ## Key Features Demonstrated
 
-1. **Tool Usage**: Agent can search Wikipedia and get current year
-2. **Structured Output**: Response follows strict PersonInfo schema
-3. **Validation**: ADK validates the response matches the schema
-4. **Flexibility**: Works with any combination of tools and output schemas
+1.  **Tool Usage**: Agent can search Wikipedia and get current year
+2.  **Structured Output**: Response follows strict PersonInfo schema
+3.  **Validation**: ADK validates the response matches the schema
+4.  **Flexibility**: Works with any combination of tools and output schemas

pyproject.toml

@@ -114,7 +114,7 @@ test = [
   "anthropic>=0.43.0",                      # For anthropic model tests
   "kubernetes>=29.0.0",                     # For GkeCodeExecutor
   "langchain-community>=0.3.17",
-  "langgraph>=0.2.60, <= 0.4.10",            # For LangGraphAgent
+  "langgraph>=0.2.60, <0.4.8",              # For LangGraphAgent
   "litellm>=1.75.5, <2.0.0",                # For LiteLLM tests
   "llama-index-readers-file>=0.4.0",        # For retrieval tests
   "openai>=1.100.2",                        # For LiteLLM
@@ -144,7 +144,7 @@ extensions = [
   "crewai[tools];python_version>='3.10'",      # For CrewaiTool
   "docker>=7.0.0",                             # For ContainerCodeExecutor
   "kubernetes>=29.0.0",                        # For GkeCodeExecutor
-  "langgraph>=0.2.60",                         # For LangGraphAgent
+  "langgraph>=0.2.60, <0.4.8",                 # For LangGraphAgent
   "litellm>=1.75.5",                           # For LiteLlm class. Currently has OpenAI limitations. TODO: once LiteLlm fix it
   "llama-index-readers-file>=0.4.0",           # For retrieval using LlamaIndex.
   "llama-index-embeddings-google-genai>=0.3.0",# For files retrieval using LlamaIndex.

src/google/adk/agents/base_agent.py

@@ -282,27 +282,22 @@ async def run_async(
       Event: the events generated by the agent.
     """
 
-    async def _run_with_trace() -> AsyncGenerator[Event, None]:
-      with tracer.start_as_current_span(f'invoke_agent {self.name}') as span:
-        ctx = self._create_invocation_context(parent_context)
-        tracing.trace_agent_invocation(span, self, ctx)
-        if event := await self._handle_before_agent_callback(ctx):
-          yield event
-        if ctx.end_invocation:
-          return
-
-        async with Aclosing(self._run_async_impl(ctx)) as agen:
-          async for event in agen:
-            yield event
-
-        if ctx.end_invocation:
-          return
+    with tracer.start_as_current_span(f'invoke_agent {self.name}') as span:
+      ctx = self._create_invocation_context(parent_context)
+      tracing.trace_agent_invocation(span, self, ctx)
+      if event := await self._handle_before_agent_callback(ctx):
+        yield event
+      if ctx.end_invocation:
+        return
 
-        if event := await self._handle_after_agent_callback(ctx):
+      async with Aclosing(self._run_async_impl(ctx)) as agen:
+        async for event in agen:
           yield event
 
-    async with Aclosing(_run_with_trace()) as agen:
-      async for event in agen:
+      if ctx.end_invocation:
+        return
+
+      if event := await self._handle_after_agent_callback(ctx):
         yield event
 
   @final
@@ -320,24 +315,19 @@ async def run_live(
       Event: the events generated by the agent.
     """
 
-    async def _run_with_trace() -> AsyncGenerator[Event, None]:
-      with tracer.start_as_current_span(f'invoke_agent {self.name}') as span:
-        ctx = self._create_invocation_context(parent_context)
-        tracing.trace_agent_invocation(span, self, ctx)
-        if event := await self._handle_before_agent_callback(ctx):
-          yield event
-        if ctx.end_invocation:
-          return
-
-        async with Aclosing(self._run_live_impl(ctx)) as agen:
-          async for event in agen:
-            yield event
+    with tracer.start_as_current_span(f'invoke_agent {self.name}') as span:
+      ctx = self._create_invocation_context(parent_context)
+      tracing.trace_agent_invocation(span, self, ctx)
+      if event := await self._handle_before_agent_callback(ctx):
+        yield event
+      if ctx.end_invocation:
+        return
 
-        if event := await self._handle_after_agent_callback(ctx):
+      async with Aclosing(self._run_live_impl(ctx)) as agen:
+        async for event in agen:
           yield event
 
-    async with Aclosing(_run_with_trace()) as agen:
-      async for event in agen:
+      if event := await self._handle_after_agent_callback(ctx):
         yield event
 
   async def _run_async_impl(

src/google/adk/agents/llm_agent_config.py

@@ -143,7 +143,7 @@ class LlmAgentConfig(BaseAgentConfig):
 
     ```
     # tools.py
-    my_mcp_toolset = MCPToolset(
+    my_mcp_toolset = McpToolset(
         connection_params=StdioServerParameters(
             command="npx",
             args=["-y", "@notionhq/notion-mcp-server"],

src/google/adk/agents/run_config.py

@@ -35,7 +35,10 @@ class StreamingMode(Enum):
 
 
 class RunConfig(BaseModel):
-  """Configs for runtime behavior of agents."""
+  """Configs for runtime behavior of agents.
+
+  The configs here will be overriden by agent-specific configurations.
+  """
 
   model_config = ConfigDict(
       extra='forbid',