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feat(workflows): support combined message + checkpoint_id for multi-turn continuation
Allow Workflow.run(message=..., checkpoint_id=...) so callers can restore prior workflow state from a checkpoint AND deliver a new message to the start executor in a single call. The existing reset_context logic already preserves shared state when checkpoint_id is set, so this gives us 'fresh start executor invocation with prior state intact' - exactly what hosted multi-turn declarative workflows need. - _workflow.py: drop the message+checkpoint_id mutual exclusion and update _execute_with_message_or_checkpoint to do both (restore then execute) when both are provided. - _agent.py: in _run_core's checkpoint branch, also forward input_messages so WorkflowAgent.run(messages, checkpoint_id=...) works end-to-end. Falls back to the legacy 'restore only' behavior when messages are absent. - _declarative_base.py: detect continuation in _ensure_state_initialized by checking whether DECLARATIVE_STATE_KEY already exists in shared state; if so, refresh inputs/LastMessage* and append non-user trigger messages instead of calling state.initialize() (which would wipe Conversation/Local/System). - foundry_hosting/_responses.py: collapse the host's two-call pattern (restore-only, then fresh run) into a single combined call now that the underlying APIs support it. - tests: drop the assertion that combined message+checkpoint_id raises. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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@@ -914,20 +914,26 @@ class DeclarativeActionExecutor(Executor):
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state.initialize(trigger) # type: ignore
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elif isinstance(trigger, list) and all(isinstance(m, Message) for m in trigger):
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# list[Message] (e.g. from WorkflowAgent / as_agent()).
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# Populate the full conversation rather than collapsing to a
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# single string, so workflows that operate on the message list
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# (InvokeAzureAgent with =Conversation.messages, history-aware
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# agents, multi-modal content, etc.) see the complete input.
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messages_list = cast(list[Message], trigger)
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# Locate the trailing user message: WorkflowAgent merges session
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# history with the caller's new input and forwards the combined
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# list, so the most recent user message represents "this turn"
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# (everything before it is prior history). InvokeAzureAgent's
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# contract is that Conversation.messages holds PRIOR turns only -
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# the executor appends the new user input itself before invoking
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# the agent. To avoid duplicating the latest user turn we split
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# the trigger at that boundary.
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# Detect continuation: if the workflow's shared state already
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# carries declarative data from a prior turn (because the host
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# restored a checkpoint and dispatched this run with
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# reset_context=False), we MUST NOT call state.initialize() -
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# that would wipe Conversation.messages, Local.*, System.* etc.
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# Instead, treat the trigger as the new turn's user input only:
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# update Inputs.input, append the new user message to existing
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# Conversation history, and refresh System.LastMessage*.
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existing_state = state._state.get(DECLARATIVE_STATE_KEY)
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# Continuation = declarative state already exists in the workflow's
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# shared state (either left over in-memory from a prior turn on
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# the same instance, or restored from a checkpoint just before
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# this run). In that case state.initialize() would wipe Local.*,
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# System.*, Conversation.* etc., destroying the cross-turn
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# context we're trying to preserve.
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is_continuation = existing_state is not None and isinstance(existing_state, dict)
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# Locate the trailing user message in the trigger.
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last_user_index = -1
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for idx in range(len(messages_list) - 1, -1, -1):
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if str(messages_list[idx].role).lower() == "user":
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@@ -938,51 +944,59 @@ class DeclarativeActionExecutor(Executor):
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last_user_msg = messages_list[last_user_index]
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last_user_text = last_user_msg.text or ""
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last_user_id = getattr(last_user_msg, "message_id", "") or ""
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# Prior history excludes the latest user turn; trailing
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# non-user messages (e.g. tool results) are preserved so
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# later actions still see them in Conversation.messages.
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history_messages = (
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messages_list[:last_user_index] + messages_list[last_user_index + 1:]
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)
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else:
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# No user message in the list - rare path (e.g. resume after
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# an assistant-only sequence). Treat the whole list as prior
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# history and surface the last message's text for backwards
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# compatibility with =System.LastMessageText.
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history_messages = list(messages_list)
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tail = messages_list[-1] if messages_list else None
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last_user_text = (tail.text or "") if tail is not None else ""
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last_user_id = (
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getattr(tail, "message_id", "") or "" if tail is not None else ""
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)
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last_user_msg = tail
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# Initialize state. Using the last user text as Inputs.input
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# keeps simple yamls (=inputs.input / =System.LastMessageText)
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# working, and matches what InvokeAzureAgent expects to find via
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# its input_text fallback chain.
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state.initialize({"input": last_user_text})
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# Populate Conversation.messages/.history with PRIOR turns only
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# (matching the executor contract above). Raw Message objects
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# are stored - matching what agent executors append at runtime.
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for msg in history_messages:
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state.append("Conversation.messages", msg)
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state.append("Conversation.history", msg)
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# Mirror to System.conversations.{ConversationId}.messages so
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# actions resolving conversation-scoped paths see the same
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# history.
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conversation_id = state.get("System.ConversationId")
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if conversation_id:
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conv_path = f"System.conversations.{conversation_id}.messages"
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if is_continuation:
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# Continuation turn: keep prior Conversation.messages intact.
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# Refresh inputs and surface the new user message via the
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# System.LastMessage* fields. We deliberately do NOT append
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# the new user message to Conversation.messages here: agent
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# executors append the live user input themselves before
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# invoking the inner agent (matching the first-turn
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# contract where Conversation.messages holds prior turns
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# only).
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state.set("Inputs.input", last_user_text)
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# Trailing non-user messages (e.g. tool results) sandwiched
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# before the new user message in the trigger are still
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# appended so later actions see them.
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for msg in history_messages:
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state.append(conv_path, msg)
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state.append("Conversation.messages", msg)
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state.append("Conversation.history", msg)
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conversation_id = state.get("System.ConversationId")
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if conversation_id:
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conv_path = f"System.conversations.{conversation_id}.messages"
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for msg in history_messages:
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state.append(conv_path, msg)
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state.set("System.LastMessage", {"Text": last_user_text, "Id": last_user_id})
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state.set("System.LastMessageText", last_user_text)
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state.set("System.LastMessageId", last_user_id)
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else:
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# First turn: full initialization.
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state.initialize({"input": last_user_text})
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# System.LastMessage* mirrors the most recent USER message
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# (matching .NET DefaultTransform semantics for agent input).
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state.set("System.LastMessage", {"Text": last_user_text, "Id": last_user_id})
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state.set("System.LastMessageText", last_user_text)
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state.set("System.LastMessageId", last_user_id)
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for msg in history_messages:
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state.append("Conversation.messages", msg)
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state.append("Conversation.history", msg)
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conversation_id = state.get("System.ConversationId")
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if conversation_id:
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conv_path = f"System.conversations.{conversation_id}.messages"
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for msg in history_messages:
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state.append(conv_path, msg)
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state.set("System.LastMessage", {"Text": last_user_text, "Id": last_user_id})
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state.set("System.LastMessageText", last_user_text)
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state.set("System.LastMessageId", last_user_id)
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elif isinstance(trigger, str):
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# String input - wrap in dict and populate System.LastMessage.Text
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# so YAML expressions like =System.LastMessage.Text see the user input
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