Fix usage metadata write and empty response
Pull Request: Improve metadata persistence and sub-agent traceability Please ensure you have read the contribution guide before creating a pull request.
Link to Issue or Description of Change https://github.com/google/adk-python/issues/3686 https://github.com/google/adk-python/issues/3095 https://github.com/google/adk-python/issues/3467
Problem:
There were four main issues in ADK:
Metadata loss in DatabaseSessionService: The usage_metadata field was not being persisted correctly in the database due to how SQLAlchemy handles mutable fields (MutableDict/DynamicJSON). This resulted in loss of important information about token usage and metrics.
Lack of traceability in sub-agents: When an agent called another agent as a tool (via AgentTool), the sub-agent's events were not copied to the main session, making it impossible to audit or debug the complete execution of multi-agent workflows.
Content loss in streaming: The AgentTool only collected the last content from streaming, losing intermediate chunks that could contain important information.
Empty responses handling: The AgentTool was not properly handling cases where the sub-agent returned empty responses, which could cause issues in multi-agent workflows.
Solution:
I implemented four coordinated improvements:
- DatabaseSessionService (src/google/adk/sessions/database_session_service.py):
Added flag_modified() to force SQLAlchemy to detect changes in mutable JSON fields Improved usage_metadata handling with hasattr() checks and exception handling Used exclude_none=False to preserve all metric fields (including zeros) Improved citation_metadata handling with existence checks 2. AgentTool (src/google/adk/tools/agent_tool.py):
Implemented collection of all text chunks during streaming (not just the last one) Added automatic copying of sub-agent events to the main session Implemented branch hierarchy (parent_agent.sub_agent) for traceability Improved handling of unstructured arguments Fixed empty response handling: Now properly returns empty string when no content is generated, preventing downstream errors Preservation of all metadata (usage, citation, grounding, custom) Why this solution:
flag_modified() is the recommended way by SQLAlchemy for mutable fields Event copying enables complete auditing without modifying existing architecture Chunk collection ensures no content is lost Empty response handling prevents crashes in multi-agent workflows Exception handling ensures robustness without breaking existing flows Testing Plan Unit Tests:
I have added or updated unit tests for my change. All unit tests pass locally. Tests Created:
Run the new tests
pytest tests/unittests/tools/test_agent_tool_new_features.py -v
Specific tests
pytest tests/unittests/tools/test_agent_tool_new_features.py::test_agent_tool_handles_dict_args -v pytest tests/unittests/tools/test_agent_tool_new_features.py::test_database_session_service_persists_usage_metadata -v pytest tests/unittests/tools/test_agent_tool_new_features.py::test_database_session_service_persists_citation_metadata -v
Run all related tests
pytest tests/unittests/sessions/test_session_service.py tests/unittests/tools/test_agent_tool.py tests/unittests/tools/test_agent_tool_new_features.py -v
bash
Test Coverage:
test_agent_tool_handles_dict_args: Validates that AgentTool now accepts dictionary arguments with custom keys (not just 'request'), testing the changes in lines 136-142 of agent_tool.py
test_database_session_service_persists_usage_metadata: Validates that usage_metadata is correctly persisted in the database using flag_modified, testing the changes in lines 339-345 and 736-744 of database_session_service.py
test_database_session_service_persists_citation_metadata: Validates that citation_metadata is correctly persisted with improved handling using hasattr(), testing the changes in lines 347-349 of database_session_service.py
Test Results:
$ pytest tests/unittests/tools/test_agent_tool_new_features.py -v
============================= test session starts ============================== collected 3 items
test_agent_tool_handles_dict_args PASSED [ 33%] test_database_session_service_persists_usage_metadata PASSED [ 66%] test_database_session_service_persists_citation_metadata PASSED [100%]
========================= 3 passed, 1 warning in 0.89s =========================
bash
✅ 100% of tests passing!
Manual End-to-End (E2E) Tests:
Test 1: Persistence of usage_metadata
Setup
from google.adk.sessions.database_session_service import DatabaseSessionService from google.adk.events.event import Event from google.genai import types
Create session
service = DatabaseSessionService("sqlite+aiosqlite:///test.db") session = await service.create_session( app_name="test_app", user_id="user123" )
Create event with usage_metadata
event = Event( id="evt1", invocation_id="inv1", author="model", usage_metadata=types.GenerateContentResponseUsageMetadata( prompt_token_count=100, candidates_token_count=50, total_token_count=150 ) )
Persist
await service.append_event(session, event)
Verify
retrieved_session = await service.get_session( app_name="test_app", user_id="user123", session_id=session.id )
Expected result: usage_metadata is present and correct
assert retrieved_session.events[0].usage_metadata is not None assert retrieved_session.events[0].usage_metadata.total_token_count == 150
python
Result: ✅ usage_metadata persisted correctly
Test 2: Empty response handling
Setup - Agent that might return empty response
from google.adk.agents import Agent from google.adk.tools.agent_tool import AgentTool
agent = Agent( name="empty_agent", model="gemini-2.0-flash", instruction="Return nothing" )
tool = AgentTool(agent)
Execute with empty response
result = await tool.run_async( args={"request": "test"}, tool_context=context )
Verify empty response is handled gracefully
assert result == '' # Returns empty string instead of crashing print("Empty response handled correctly")
python
Result: ✅ Empty responses return empty string without errors
Screenshot/Log:
Empty response handled correctly No crashes or exceptions raised
txt
Test 3: Sub-agent traceability
Setup
from google.adk.agents import Agent from google.adk.tools.agent_tool import AgentTool from google.adk.runners import Runner
Create agents
sub_agent = Agent( name="calculator", model="gemini-2.0-flash", instruction="You are a calculator" )
main_agent = Agent( name="assistant", model="gemini-2.0-flash", instruction="You are a helpful assistant", tools=[AgentTool(sub_agent)] )
Execute
runner = Runner(agent=main_agent) events = [] async for event in runner.run_async( user_id="user123", new_message="Calculate 2+2" ): events.append((event.author, event.branch)) print(f"Event: {event.author} - Branch: {event.branch}")
Verify sub-agent events are present
sub_agent_events = [e for e in events if e[1] and "calculator" in e[1]] assert len(sub_agent_events) > 0
python
Result: ✅ Sub-agent events appear with correct branch assistant.calculator
Test 4: Complete chunk collection
Setup - Agent that generates long streaming response
from google.adk.agents import Agent from google.adk.tools.agent_tool import AgentTool
agent = Agent( name="writer", model="gemini-2.0-flash", instruction="Write a long story with multiple paragraphs" )
tool = AgentTool(agent)
Execute and collect result
result = await tool.run_async( args={"request": "Write a story about AI"}, tool_context=context )
Verify all content was collected
assert len(result) > 100 # Complete story assert "Once upon a time" in result # Has beginning assert "The end" in result # Has ending print(f"Total characters collected: {len(result)}")
python
Result: ✅ All content is collected (not just last chunk)
Checklist I have read the CONTRIBUTING.md document. I have performed a self-review of my own code. I have commented my code, particularly in hard-to-understand areas. I have added tests that prove my fix is effective or that my feature works. New and existing unit tests pass locally with my changes. I have manually tested my changes end-to-end. Any dependent changes have been merged and published in downstream modules. Additional context Modified Files:
src/google/adk/sessions/database_session_service.py - Improvements in metadata persistence src/google/adk/tools/agent_tool.py - Sub-agent traceability, complete chunk collection, and empty response handling tests/unittests/tools/test_agent_tool_new_features.py - 3 new tests (NEW FILE) Key Changes in AgentTool:
Before (lines 189-191):
if not last_content: return '' merged_text = '\n'.join(p.text for p in last_content.parts if p.text)
python
After (lines 245-248):
Merge all collected chunks into final text
merged_text = "".join(chunks)
if not merged_text: return ''
python
Impact:
✅ Collects all chunks during streaming (not just last) ✅ Properly handles empty responses by returning empty string ✅ Prevents crashes when sub-agent generates no content Compatibility:
✅ Fully backward compatible ✅ Does not break existing APIs ✅ Features are opt-in (events are copied automatically if main session exists) ✅ Relative imports maintained according to project standards Benefits:
📊 Complete token usage metrics in multi-agent workflows 🔍 Facilitated auditing and debugging 🎯 End-to-end execution traceability 💾 Reliable metadata persistence 🛡️ Greater robustness with error handling (including empty responses) 📝 Complete interaction history preserved ✅ No crashes on empty sub-agent responses Impacted Use Cases:
Complex multi-agent workflows Systems that need to track token usage for billing Applications requiring complete decision auditing Debugging issues in sub-agents Cost calculation in production systems Agent performance analysis Workflows where sub-agents might return empty responses Related Tests that Pass:
tests/unittests/sessions/test_session_service.py - Tests session persistence tests/unittests/tools/test_agent_tool.py - Tests AgentTool functionality tests/unittests/tools/test_agent_tool_new_features.py - Tests new features (NEW)
Summary of Changes
Hello @marttinslucas, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly improves the reliability and observability of multi-agent systems by resolving critical data persistence issues, enhancing the traceability of sub-agent interactions, and making streaming and empty response handling more robust. These changes ensure complete token usage metrics, facilitate auditing and debugging, and prevent crashes in complex workflows.
Highlights
- Metadata Persistence Fixes: Addressed issues where usage_metadata and citation_metadata were not correctly persisted in DatabaseSessionService due to SQLAlchemy's handling of mutable JSON fields.
- Enhanced Sub-Agent Traceability: Implemented automatic copying of sub-agent events to the main session, including a branch hierarchy, to improve auditing and debugging of multi-agent workflows.
- Complete Streaming Content Collection: Modified AgentTool to collect all text chunks from streaming responses, preventing loss of intermediate content.
- Robust Empty Response Handling: Ensured AgentTool gracefully handles empty responses from sub-agents by returning an empty string instead of causing errors.
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Hi @marttinslucas, Thank you for your contribution through this pull request! This PR has merge conflicts that require changes from your end. Could you please rebase your branch with the latest main branch to address these? Once this is complete, please let us know so we can proceed with the review.