Merge branch 'main' into feat/expose-mcps-streamable-http-custom-httpx-factory-parameter

Author: wukath

AGENTS.md

@@ -119,7 +119,8 @@ app = App(
 ### Requirements
 
 **Minimum requirements:**
-- Python 3.9+ (**Python 3.11+ strongly recommended** for best performance)
+
+- Python 3.10+ (**Python 3.11+ strongly recommended** for best performance)
 - `uv` package manager (**required** - faster than pip/venv)
 
 **Install uv if not already installed:**
@@ -326,11 +327,6 @@ pytest tests/unittests -n auto
 # Run a specific test file during development
 pytest tests/unittests/agents/test_llm_agent.py
 
-# Python 3.9 compatibility mode (excludes features requiring 3.10+)
-pytest tests/unittests \
-  --ignore=tests/unittests/a2a \
-  --ignore=tests/unittests/tools/mcp_tool \
-  --ignore=tests/unittests/artifacts/test_artifact_service.py
 ```
 
 ### Testing Philosophy

CONTRIBUTING.md

@@ -145,7 +145,7 @@ part before or alongside your code PR.
 
 3.  **Create and activate a virtual environment:**
 
-    **NOTE**: ADK supports Python 3.9+. Python 3.11 and above is strongly
+    **NOTE**: ADK supports Python 3.10+. Python 3.11 and above is strongly
     recommended.
 
     Create a workspace venv using uv.

pyproject.toml

@@ -5,7 +5,7 @@
 name = "google-adk"
 description = "Agent Development Kit"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = { file = "LICENSE" }
 authors = [{ name = "Google LLC", email = "googleapis-packages@google.com" }]
 classifiers = [ # List of https://pypi.org/classifiers/
@@ -27,7 +27,7 @@ dependencies = [
   # go/keep-sorted start
   "PyYAML>=6.0.2, <7.0.0",                                   # For APIHubToolset.
   "aiosqlite>=0.21.0",                                       # For SQLite database
-  "anyio>=4.9.0, <5.0.0;python_version>='3.10'",             # For MCP Session Manager
+  "anyio>=4.9.0, <5.0.0",                                    # For MCP Session Manager
   "authlib>=1.5.1, <2.0.0",                                  # For RestAPI Tool
   "click>=8.1.8, <9.0.0",                                    # For CLI tools
   "fastapi>=0.115.0, <0.119.0",                              # FastAPI framework
@@ -44,7 +44,7 @@ dependencies = [
   "google-genai>=1.45.0, <2.0.0",                            # Google GenAI SDK
   "graphviz>=0.20.2, <1.0.0",                                # Graphviz for graph rendering
   "jsonschema>=4.23.0, <5.0.0",                              # Agent Builder config validation
-  "mcp>=1.8.0, <2.0.0;python_version>='3.10'",               # For MCP Toolset
+  "mcp>=1.8.0, <2.0.0",                                      # For MCP Toolset
   "opentelemetry-api>=1.37.0, <=1.37.0",                     # OpenTelemetry - limit upper version for sdk and api to not risk breaking changes from unstable _logs package.
   "opentelemetry-exporter-gcp-logging>=1.9.0a0, <2.0.0",
   "opentelemetry-exporter-gcp-monitoring>=1.9.0a0, <2.0.0",
@@ -93,7 +93,7 @@ dev = [
 
 a2a = [
   # go/keep-sorted start
-  "a2a-sdk>=0.3.11,<0.4.0;python_version>='3.10'",
+  "a2a-sdk>=0.3.4,<0.4.0",
   # go/keep-sorted end
 ]
 
@@ -114,9 +114,9 @@ eval = [
 
 test = [
   # go/keep-sorted start
-  "a2a-sdk>=0.3.0,<0.4.0;python_version>='3.10'",
+  "a2a-sdk>=0.3.0,<0.4.0",
   "anthropic>=0.43.0",                            # For anthropic model tests
-  "crewai[tools];python_version>='3.10' and python_version<'3.14'",         # For CrewaiTool tests
+  "crewai[tools];python_version>='3.10' and python_version<'3.12'",         # For CrewaiTool tests; chromadb/pypika fail on 3.12+
   "kubernetes>=29.0.0",                           # For GkeCodeExecutor
   "langchain-community>=0.3.17",
   "langgraph>=0.2.60, <0.4.8",                    # For LangGraphAgent
@@ -126,7 +126,7 @@ test = [
   "pytest-asyncio>=0.25.0",
   "pytest-mock>=3.14.0",
   "pytest-xdist>=3.6.1",
-  "pytest>=8.3.4",
+  "pytest>=9.0.0,<10.0.0",
   "python-multipart>=0.0.9",
   "rouge-score>=0.1.2",
   "tabulate>=0.9.0",
@@ -146,7 +146,7 @@ docs = [
 extensions = [
   "anthropic>=0.43.0",                          # For anthropic model support
   "beautifulsoup4>=3.2.2",                      # For load_web_page tool.
-  "crewai[tools];python_version>='3.10' and python_version<'3.14'",       # For CrewaiTool
+  "crewai[tools];python_version>='3.10' and python_version<'3.12'",       # For CrewaiTool; chromadb/pypika fail on 3.12+
   "docker>=7.0.0",                              # For ContainerCodeExecutor
   "kubernetes>=29.0.0",                         # For GkeCodeExecutor
   "langgraph>=0.2.60, <0.4.8",                  # For LangGraphAgent
@@ -208,7 +208,7 @@ asyncio_mode = "auto"
 
 
 [tool.mypy]
-python_version = "3.9"
+python_version = "3.10"
 exclude = "tests/"
 plugins = ["pydantic.mypy"]
 # Start with non-strict mode, and swtich to strict mode later.

src/google/adk/features/_feature_registry.py

@@ -24,8 +24,9 @@
 class FeatureName(str, Enum):
   """Feature names."""
 
-  JSON_SCHEMA_FOR_FUNC_DECL = "JSON_SCHEMA_FOR_FUNC_DECL"
   COMPUTER_USE = "COMPUTER_USE"
+  JSON_SCHEMA_FOR_FUNC_DECL = "JSON_SCHEMA_FOR_FUNC_DECL"
+  PROGRESSIVE_SSE_STREAMING = "PROGRESSIVE_SSE_STREAMING"
 
 
 class FeatureStage(Enum):
@@ -58,11 +59,14 @@ class FeatureConfig:
 
 # Central registry: FeatureName -> FeatureConfig
 _FEATURE_REGISTRY: dict[FeatureName, FeatureConfig] = {
+    FeatureName.COMPUTER_USE: FeatureConfig(
+        FeatureStage.EXPERIMENTAL, default_on=True
+    ),
     FeatureName.JSON_SCHEMA_FOR_FUNC_DECL: FeatureConfig(
         FeatureStage.WIP, default_on=False
     ),
-    FeatureName.COMPUTER_USE: FeatureConfig(
-        FeatureStage.EXPERIMENTAL, default_on=True
+    FeatureName.PROGRESSIVE_SSE_STREAMING: FeatureConfig(
+        FeatureStage.WIP, default_on=False
     ),
 }
 

src/google/adk/flows/llm_flows/base_llm_flow.py

@@ -38,6 +38,8 @@
 from ...agents.run_config import StreamingMode
 from ...agents.transcription_entry import TranscriptionEntry
 from ...events.event import Event
+from ...features import FeatureName
+from ...features import is_feature_enabled
 from ...models.base_llm_connection import BaseLlmConnection
 from ...models.llm_request import LlmRequest
 from ...models.llm_response import LlmResponse
@@ -525,6 +527,16 @@ async def _postprocess_async(
 
     # Handles function calls.
     if model_response_event.get_function_calls():
+
+      if is_feature_enabled(FeatureName.PROGRESSIVE_SSE_STREAMING):
+        # In progressive SSE streaming mode stage 1, we skip partial FC events
+        # Only execute FCs in the final aggregated event (partial=False)
+        if (
+            invocation_context.run_config.streaming_mode == StreamingMode.SSE
+            and model_response_event.partial
+        ):
+          return
+
       async with Aclosing(
           self._postprocess_handle_function_calls_async(
               invocation_context, model_response_event, llm_request

src/google/adk/models/anthropic_llm.py

@@ -22,7 +22,6 @@
 import os
 from typing import Any
 from typing import AsyncGenerator
-from typing import Generator
 from typing import Iterable
 from typing import Literal
 from typing import Optional
@@ -98,14 +97,29 @@ def part_to_message_block(
     )
   elif part.function_response:
     content = ""
-    if (
-        "result" in part.function_response.response
-        and part.function_response.response["result"]
-    ):
+    response_data = part.function_response.response
+
+    # Handle response with content array
+    if "content" in response_data and response_data["content"]:
+      content_items = []
+      for item in response_data["content"]:
+        if isinstance(item, dict):
+          # Handle text content blocks
+          if item.get("type") == "text" and "text" in item:
+            content_items.append(item["text"])
+          else:
+            # Handle other structured content
+            content_items.append(str(item))
+        else:
+          content_items.append(str(item))
+      content = "\n".join(content_items) if content_items else ""
+    # Handle traditional result format
+    elif "result" in response_data and response_data["result"]:
       # Transformation is required because the content is a list of dict.
       # ToolResultBlockParam content doesn't support list of dict. Converting
       # to str to prevent anthropic.BadRequestError from being thrown.
-      content = str(part.function_response.response["result"])
+      content = str(response_data["result"])
+
     return anthropic_types.ToolResultBlockParam(
         tool_use_id=part.function_response.id or "",
         type="tool_result",
@@ -219,23 +233,27 @@ def function_declaration_to_tool_param(
   """Converts a function declaration to an Anthropic tool param."""
   assert function_declaration.name
 
-  properties = {}
-  required_params = []
-  if function_declaration.parameters:
-    if function_declaration.parameters.properties:
-      for key, value in function_declaration.parameters.properties.items():
-        value_dict = value.model_dump(exclude_none=True)
-        _update_type_string(value_dict)
-        properties[key] = value_dict
-    if function_declaration.parameters.required:
-      required_params = function_declaration.parameters.required
-
-  input_schema = {
-      "type": "object",
-      "properties": properties,
-  }
-  if required_params:
-    input_schema["required"] = required_params
+  # Use parameters_json_schema if available, otherwise convert from parameters
+  if function_declaration.parameters_json_schema:
+    input_schema = function_declaration.parameters_json_schema
+  else:
+    properties = {}
+    required_params = []
+    if function_declaration.parameters:
+      if function_declaration.parameters.properties:
+        for key, value in function_declaration.parameters.properties.items():
+          value_dict = value.model_dump(exclude_none=True)
+          _update_type_string(value_dict)
+          properties[key] = value_dict
+      if function_declaration.parameters.required:
+        required_params = function_declaration.parameters.required
+
+    input_schema = {
+        "type": "object",
+        "properties": properties,
+    }
+    if required_params:
+      input_schema["required"] = required_params
 
   return anthropic_types.ToolParam(
       name=function_declaration.name,