[Harineko0]

Link to Issue or Description of Change

Problem

When a RemoteA2aAgent is used as a static node in a Workflow graph that feeds into a JoinNode, the joined output contains None for every RemoteA2aAgent predecessor.

Reproducer (simplified from a real coordinator graph):

parallel_investigation_join = JoinNode(name="parallel_investigation_join")
Workflow(
    edges=[
        ("START", account_context_agent, parallel_investigation_join),  # RemoteA2aAgent
        ("START", ticket_history_agent, parallel_investigation_join),   # RemoteA2aAgent
        ("START", diagnostics_agent, parallel_investigation_join),      # RemoteA2aAgent
        ...
    ]
)

Observed JoinNode input:

parallel_investigation_join:
  account_context_agent: null
  ticket_history_agent: null
  diagnostics_agent: null

Root cause: RemoteA2aAgent inherits the default BaseAgent._run_impl, which iterates run_async and yields events without ever setting event.output or event.node_info.message_as_output. As a result, NodeRunner._track_event_in_context leaves ctx.output as None, and Workflow._handle_completion never records an entry in loop_state.node_outputs for that predecessor. JoinNode then sees None for it.

LlmAgent already solves the equivalent problem by overriding _run_impl and promoting the model's text reply to event.output (via process_llm_agent_output in _llm_agent_wrapper.py). RemoteA2aAgent had no equivalent hook.

Solution

Add a workflow-only override of _run_impl on RemoteA2aAgent that mirrors LlmAgent's behavior. For each event yielded by BaseAgent._run_impl, a new _promote_response_to_output helper joins the text of all parts that are not thoughts, function calls, or function responses, assigns it to event.output, and sets event.node_info.message_as_output = True (consistent with LlmAgent, prevents NodeRunner._flush_output_and_deltas from emitting a duplicate trailing output event).

The helper skips:

• partial events (streaming chunks)
• events not authored by this agent
• events whose event.output is already set
• events whose content carries only thoughts (streaming working / submitted task statuses that the legacy _handle_a2a_response marks thought=True)
• events whose content carries only function calls (the input_required / auth_required mock function call inserted by _create_mock_function_call_for_required_user_input — those should remain interrupts, not outputs)
• events whose A2A task state is non-final (submitted, working, input-required, auth-required, unknown). The v2 integration path (_handle_a2a_response_v2) delegates to converters that do not mark streaming working text as thought=True, so the thought filter alone is not enough. Without this guard, a working text event and the subsequent completed text event would each try to set event.output, causing NodeRunner to raise ValueError: Output already set on the second event and aborting the run before the real final answer ever surfaced. The state is read from event.custom_metadata['a2a:response']['status']['state'], which _run_async_impl already stamps before yield. Plain A2AMessage responses (no status field) and terminal task states (completed, failed, canceled, rejected) still promote.

In addition, _run_impl short-circuits after the first successful promotion. This protects against the case where a server emits multiple terminal-state events for one run (e.g. a completed status update followed by trailing artifact updates on the same already-completed task) — only the first terminal event becomes the node's output, subsequent ones pass through untouched.

Scope is intentionally narrow: only the agent boundary is touched. to_adk_event.py and the workflow scheduler are unchanged, since the same workaround (promoting content → output at the agent layer) is what LlmAgent does and what keeps the fix local.

Testing Plan

Unit Tests

• I have added or updated unit tests for my change.
• All unit tests pass locally.

Added TestRemoteA2aAgentWorkflowOutput in tests/unittests/agents/test_remote_a2a_agent.py (20 cases).

Manual End-to-End (E2E) Tests

I ran the failing workflow described in the Problem section against a real ADK app: a Workflow graph whose START fans out into multiple RemoteA2aAgent nodes that all feed into a single JoinNode. Each remote specialist runs as its own A2A server; the coordinator runs the workflow and forwards the joined dict to a downstream synthesis step.

Before the fix:

After the fix:

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

The fix mirrors the pattern LlmAgent already uses (process_llm_agent_output in src/google/adk/workflow/_llm_agent_wrapper.py), keeping output-promotion at the agent boundary rather than touching the workflow scheduler or the A2A converters. This minimizes blast radius and avoids regressions in non-workflow usages of RemoteA2aAgent, where the _run_impl path is not exercised.

[Harineko0]

Hi @rohityan thank you for the look. I noticed the request clarification label. I would like to know in more detail which parts require an clarification.

RemoteA2aAgent does not pass its output to JoinNode since it emits its response only as event.content, never as event.output, and the workflow scheduler reads ctx.output from event.output, so the predecessor's value stays None in the joined dict.

[rohityan]

Hi @Harineko0 ,Thank you for your contribution! We appreciate you taking the time to submit this pull request. Please fix formatting errors by running autoformat.sh

[Harineko0]

@rohityan Thank you for taking your time. I've rebased the PR to the latest main branch and confirmed that theuvx pre-commit run --all-files ran successfully. I would like you to run the CI again.

[rohityan]

Hi @wyf7107 , can you please review this.

[adk-bot]

Thank you @Harineko0 for your contribution! 🎉

Your changes have been successfully imported and merged via Copybara in commit 5a12ee0.

Closing this PR as the changes are now in the main branch.