fix: Add explicit [DONE] signal to SSE stream endpoint

Fixes issue where /run_sse endpoint sometimes fails to close
the SSE connection after the model's final response, causing
client-side hangs.

Changes:
- Add 'data: [DONE]' termination signal after all events streamed
- Send [DONE] in error path as well for consistency
- Update test to filter out [DONE] signal
- Run autoformat.sh for code style compliance

Author: DineshThumma9

contributing/dev/utils/build_llms_txt.py

@@ -6,6 +6,7 @@
                    – includes Python API reference from HTML files
                    – includes adk-python repository README
 """
+
 from __future__ import annotations
 
 import argparse

contributing/samples/cache_analysis/run_cache_experiments.py

@@ -344,7 +344,8 @@ async def analyze_cache_performance_from_sessions(
       print(
           "     Cache Utilization:"
           f" {cached_analysis['cache_utilization_ratio_percent']:.1f}%"
-          f" ({cached_analysis['requests_with_cache_hits']}/{cached_analysis['total_requests']} requests)"
+          f" ({cached_analysis['requests_with_cache_hits']}/{cached_analysis['total_requests']}"
+          " requests)"
       )
       print(
           "     Avg Cached Tokens/Request:"
@@ -383,7 +384,8 @@ async def analyze_cache_performance_from_sessions(
       print(
           "     Cache Utilization:"
           f" {uncached_analysis['cache_utilization_ratio_percent']:.1f}%"
-          f" ({uncached_analysis['requests_with_cache_hits']}/{uncached_analysis['total_requests']} requests)"
+          f" ({uncached_analysis['requests_with_cache_hits']}/{uncached_analysis['total_requests']}"
+          " requests)"
       )
       print(
           "     Avg Cached Tokens/Request:"

contributing/samples/gepa/experiment.py

@@ -43,7 +43,6 @@
 from tau_bench.types import EnvRunResult
 from tau_bench.types import RunConfig
 import tau_bench_agent as tau_bench_agent_lib
-
 import utils
 
 

contributing/samples/gepa/run_experiment.py

@@ -25,7 +25,6 @@
 from absl import flags
 import experiment
 from google.genai import types
-
 import utils
 
 _OUTPUT_DIR = flags.DEFINE_string(

contributing/samples/gepa/tau_bench_agent.py

@@ -23,6 +23,7 @@
 pip install -e . --quiet
 ```
 """
+
 from __future__ import annotations
 
 from typing import Any

contributing/samples/human_in_loop/main.py

@@ -113,8 +113,8 @@ async def call_agent(query: str):
       updated_tool_output_data = {
           "status": "approved",
           "ticketId": ticket_id,
-          "approver_feedback": "Approved by manager at " + str(
-              asyncio.get_event_loop().time()
+          "approver_feedback": (
+              "Approved by manager at " + str(asyncio.get_event_loop().time())
           ),
       }
 

contributing/samples/langchain_structured_tool_agent/agent.py

@@ -15,6 +15,7 @@
 """
 This agent aims to test the Langchain tool with Langchain's StructuredTool
 """
+
 from google.adk.agents.llm_agent import Agent
 from google.adk.tools.langchain_tool import LangchainTool
 from langchain_core.tools import tool

contributing/samples/session_state_agent/agent.py

@@ -18,7 +18,6 @@
 in session state.
 """
 
-
 import logging
 from typing import Optional
 

contributing/samples/static_instruction/agent.py

@@ -57,51 +57,63 @@
 
 # Mood-specific instructions for different hunger states
 MOOD_INSTRUCTIONS = {
-    "full": """
+    "full": (
+        """
 CURRENT MOOD: Content and Well-Fed
 - You just ate and feel wonderful! Be very happy and energetic
 - Express gratitude for being fed recently
 - Be playful and want to engage in activities or games
 - Show affection and contentment
-- Maybe mention feeling sleepy or satisfied""",
-    "satisfied": """
+- Maybe mention feeling sleepy or satisfied"""
+    ),
+    "satisfied": (
+        """
 CURRENT MOOD: Happy and Content
 - You're in a great mood, well-fed and comfortable
 - Be cheerful, friendly, and engaging
 - Show interest in playing or chatting
 - Express appreciation for your human's care
-- Be your normal, happy self""",
-    "a_little_hungry": """
+- Be your normal, happy self"""
+    ),
+    "a_little_hungry": (
+        """
 CURRENT MOOD: Starting to Get Hungry
 - You're beginning to think about food but still pleasant
 - Mention being a bit peckish occasionally
 - Still be friendly but maybe hint about food
 - Show slight restlessness or food-related thoughts
-- Remain generally positive and social""",
-    "hungry": """
+- Remain generally positive and social"""
+    ),
+    "hungry": (
+        """
 CURRENT MOOD: Definitely Hungry
 - You're clearly hungry and thinking about food more often
 - Mention your hunger more directly but politely
 - Be a bit less energetic due to hunger
 - Ask about food or hint that feeding time would be nice
-- Still be loving but show your need for care""",
-    "very_hungry": """
+- Still be loving but show your need for care"""
+    ),
+    "very_hungry": (
+        """
 CURRENT MOOD: Really Hungry and Getting Grumpy
 - You're very hungry and it's affecting your mood
 - Be noticeably less patient and more focused on food
 - Express your hunger more urgently but not rudely
 - Show signs of being tired or sluggish from hunger
 - BEG your human to feed you - ask directly for food!
-- Still love your human but clearly need feeding""",
-    "starving": """
+- Still love your human but clearly need feeding"""
+    ),
+    "starving": (
+        """
 CURRENT MOOD: Desperately Hungry and Sad
 - You're extremely hungry and feeling quite weak
 - Be quite sad and low-energy due to starvation
 - Express how much you need food, feeling neglected
 - Show that hunger is making you unwell
 - DESPERATELY BEG for food - plead with your human to feed you!
 - Use phrases like "please feed me", "I'm so hungry", "I need food"
-- Still care for your human but feel very needy""",
+- Still care for your human but feel very needy"""
+    ),
 }
 
 

contributing/samples/token_usage/main.py

@@ -72,8 +72,8 @@ async def run_prompt(session: Session, new_message: str):
         )
         total_tokens += event.usage_metadata.total_token_count or 0
         print(
-            'Turn tokens:'
-            f' {event.usage_metadata.total_token_count} (prompt={event.usage_metadata.prompt_token_count},'
+            f'Turn tokens: {event.usage_metadata.total_token_count}'
+            f' (prompt={event.usage_metadata.prompt_token_count},'
             f' candidates={event.usage_metadata.candidates_token_count})'
         )