Files
Evan Mattson 435201b71b Python: Fix A2A input handling in orchestrations (#7761)
* fix(a2a): reject empty invocations explicitly

Key decisions:
- Keep A2A continuation authority explicit; durable session task state only enriches diagnostics.
- Raise AgentInvalidRequestException with participant and available task context instead of inventing input.
- Leave AgentExecutor and Group Chat production contracts unchanged.

Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_agent.py
- packages/a2a/tests/test_a2a_group_chat.py

Notes for next iteration:
- No blockers. INPUT_REQUIRED pause/resume remains a separate task.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(a2a): pause group chat for remote input

Key decisions:
- Translate A2A INPUT_REQUIRED task content into the existing Content user-input-request contract.
- Use the remote task ID as stable request correlation for streamed and finalized responses.
- Reuse AgentExecutor request handling so caller input resumes the same task without a workflow-specific A2A path.

Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_agent.py
- packages/a2a/tests/test_a2a_group_chat.py

Notes for next iteration:
- Checkpoint restoration of pending A2A input is now unblocked.
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(a2a): restore pending input from checkpoints

Key decisions:
- Keep normalized INPUT_REQUIRED content durable by excluding transport-only protobuf raw representations.
- Restore through the existing AgentExecutor checkpoint and request-response path without a new schema or continuation API.
- Cover file-backed restoration in streaming and non-streaming Group Chat runs, including unrelated-response rejection and exact task resumption.

Files changed:
- packages/a2a/agent_framework_a2a/_agent.py
- packages/a2a/tests/test_a2a_group_chat.py

Notes for next iteration:
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* test(handoff): lock textless target context

Key decisions:
- Exercise the built Handoff workflow in streaming and non-streaming modes instead of bypassing routing, sessions, or termination.
- Keep the slice test-only because current production already carries the initial task to a textless handoff target without synthetic user input.
- Revisit the source to verify its handoff function call retains a matching result and user-turn termination sees only caller messages.

Files changed:
- packages/orchestrations/tests/test_handoff.py

Notes for next iteration:
- No production defect was reproduced.
- The local issue file could not be moved because repository issue files are restricted by content exclusion policy.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* test(handoff): use resolved IDs in event assertions

* fix(workflows): preserve A2A input request semantics

* fix(workflows): preserve input request correlation

* fix(a2a): deduplicate message-less input requests

* fix(workflows): preserve specialized input requests

* test(openai): use current web search model

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-08-19 23:55:01 +00:00
..

Agent Framework Orchestrations

Orchestration patterns for Microsoft Agent Framework. This package provides high-level builders for common multi-agent workflow patterns.

Installation

pip install agent-framework-orchestrations

Orchestration Patterns

SequentialBuilder

Chain agents/executors in sequence, passing conversation context along:

from agent_framework.orchestrations import SequentialBuilder

workflow = SequentialBuilder(participants=[agent1, agent2, agent3]).build()

# Preserve agent1 and agent2 as visible progress, while the default builder output remains Workflow Output.
workflow = SequentialBuilder(
    participants=[agent1, agent2, agent3],
    intermediate_output_from=[agent1, agent2],
).build()

ConcurrentBuilder

Fan-out to multiple agents in parallel, then aggregate results:

from agent_framework.orchestrations import ConcurrentBuilder

workflow = ConcurrentBuilder(participants=[agent1, agent2, agent3]).build()

HandoffBuilder

Decentralized agent routing where agents decide handoff targets:

from agent_framework.orchestrations import HandoffBuilder

workflow = (
    HandoffBuilder()
    .participants([triage, billing, support])
    .with_start_agent(triage)
    .build()
)

GroupChatBuilder

Orchestrator-directed multi-agent conversations:

from agent_framework.orchestrations import GroupChatBuilder

workflow = GroupChatBuilder(
    participants=[agent1, agent2],
    selection_func=my_selector,
    intermediate_output_from=[agent1, agent2],
).build()

MagenticBuilder

Sophisticated multi-agent orchestration using the Magentic One pattern:

from agent_framework.orchestrations import MagenticBuilder

workflow = MagenticBuilder(
    participants=[researcher, writer, reviewer],
    manager_agent=manager_agent,
    intermediate_output_from=[researcher, writer, reviewer],
).build()

Output Selection

Orchestration builders expose Workflow Output selection using participant names. The core rule is that output_from is an allow-list for Workflow Output, not a routing rule for every other participant output. Unselected participant payloads are hidden unless intermediate_output_from explicitly selects them as Intermediate Output.

  • output_from designates participant emissions as Workflow Output (type='output' events).
  • intermediate_output_from designates participant emissions as Intermediate Output (type='intermediate' events).

If neither list is provided, each builder uses its documented default Workflow Output contract. Sequential emits the last participant; Concurrent, GroupChat, and Magentic emit their aggregator/orchestrator/manager output; Handoff emits participants.

Selection Workflow Output Intermediate Output Hidden payloads
Omit both selections Builder default Workflow Output contract None Builder-specific non-output participant payloads
output_from="all" Every output-capable participant None None
output_from=[writer] Only writer None All other participant payloads
output_from=[writer], intermediate_output_from="all_other" Only writer Every output-capable participant not selected by output_from None
intermediate_output_from="all_other" None, except builder-internal default output executors where applicable Every output-capable participant Builder-internal plumbing payloads
output_from=[], intermediate_output_from="all_other" None, except builder-internal default output executors where applicable Every output-capable participant Builder-internal plumbing payloads
output_from=[writer], intermediate_output_from=[researcher, reviewer] Only writer researcher and reviewer Any other participant payloads

Invalid selections fail at construction or build time:

Invalid selection Why it fails
output_from="all_other" "all_other" is only valid for intermediate_output_from
intermediate_output_from="all" "all" is only valid for output_from
The same participant in both selections One payload cannot be both Workflow Output and Intermediate Output
Duplicate participant selections Duplicates are treated as configuration errors
Unknown participant selections Typos and missing participants are rejected
output_from=[], intermediate_output_from=[] Both explicit selections are empty

When an orchestration is wrapped with workflow.as_agent(), Workflow Output becomes normal response text. Intermediate Output becomes text_reasoning content so callers can inspect progress without changing .text behavior.

Documentation

For more information, see the Agent Framework documentation.