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feat(ultravox): support cancel_on_interruption=False via placeholder + final-as-text

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Replaces the prior "log a warning and skip" approach with actual handling
of async-tool messages on Ultravox.

The catch with Ultravox is that its API freezes the conversation between
client_tool_invocation and the matching client_tool_result — there's no
"keep talking while the tool runs" channel like NON_BLOCKING on Gemini
or function_call_output-without-blocking on OpenAI Realtime. So:

- When the model invokes an async-registered function (cancel_on_inter
  ruption=False), the service immediately ships a placeholder
  client_tool_result that tells the model "the actual result isn't
  ready yet; a follow-up will arrive shortly; keep the conversation
  going". This unfreezes the conversation. The placeholder is sent
  from _handle_tool_invocation, since the started async-tool message
  doesn't reach the context-frame path until later.
- When the real tool finishes, the final async-tool message lands in
  the context. _handle_context now forward-iterates and routes
  async-tool messages: started is a no-op (placeholder already sent),
  intermediate is logged-as-error and dropped (matching the other
  realtime services), and final is injected as user-side text via
  user_text_message with bracketed framing — the only mechanism
  Ultravox offers for adding non-tool input mid-conversation.

Hoists the registry-lookup helper to LLMService as
_function_is_async(name) so future services can use the same pattern
without re-implementing it.

Adds an async-tool example file for Ultravox modeled on the existing
ones for the other realtime services.
This commit is contained in:
Paul Kompfner
2026-05-08 16:20:40 -04:00
parent 2c65713c99
commit 4864eddbc7
4 changed files with 296 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
src/pipecat/services/llm_service.py
View File

@@ -751,6 +751,19 @@ class LLMService(UserTurnCompletionLLMServiceMixin, AIService, Generic[TAdapter]
return True
return function_name in self._functions.keys()
def _function_is_async(self, function_name: str) -> bool:
"""Whether the named function was registered with cancel_on_interruption=False.
Mirrors the registry-lookup pattern in :meth:`run_function_calls`:
a name-specific entry takes precedence; if there isn't one, fall
back to the ``None``-keyed catch-all entry. Returns ``False`` if
no entry matches.
"""
item = self._functions.get(function_name)
if item is None:
item = self._functions.get(None)
return item is not None and not item.cancel_on_interruption
async def run_function_calls(self, function_calls: Sequence[FunctionCallFromLLM]):
"""Execute a sequence of function calls from the LLM.

130
src/pipecat/services/ultravox/llm.py
View File

@@ -60,6 +60,17 @@ except ModuleNotFoundError as e:
raise Exception(f"Missing module: {e}")
# Placeholder shipped as the client_tool_result for async-registered functions
# (cancel_on_interruption=False). Sending it immediately unfreezes the
# conversation so the model can keep talking while the real tool runs; the
# actual result is injected later as user-side text once the tool finishes.
_ASYNC_TOOL_PLACEHOLDER_RESULT = (
"The actual result for this tool call is not yet ready. A follow-up "
"message will arrive shortly with the actual result. In the meantime, "
"keep the conversation going naturally."
)
@dataclass
class UltravoxRealtimeLLMSettings(LLMSettings):
"""Settings for UltravoxRealtimeLLMService.
@@ -219,7 +230,11 @@ class UltravoxRealtimeLLMService(LLMService):
self._disconnecting = False
self._bot_responding: Literal[None, "text", "voice"] = None
self._last_user_id: str | None = None
self._async_tool_warning_logged: bool = False
self._completed_tool_calls: set[str] = set()
# Tracks tool_call_ids for which we've already shipped the
# async-tool placeholder client_tool_result that unfreezes the
# conversation while the real tool runs. See _handle_tool_invocation.
self._started_placeholder_sent: set[str] = set()
self._sample_rate = 48000
self._resampler = create_stream_resampler()
@@ -375,6 +390,8 @@ class UltravoxRealtimeLLMService(LLMService):
if self._receive_task:
await self.cancel_task(self._receive_task, timeout=1.0)
self._receive_task = None
self._completed_tool_calls = set()
self._started_placeholder_sent = set()
async def _update_settings(self, delta: Settings):
changed = await super()._update_settings(delta)
@@ -415,47 +432,79 @@ class UltravoxRealtimeLLMService(LLMService):
await self.push_frame(frame, direction)
async def _handle_context(self, context: LLMContext):
# If the user registered a function with cancel_on_interruption=False,
# the aggregator emits async-tool-style messages into the context. We
# don't (currently) honor those on Ultravox: the Ultravox API freezes
# the conversation during tool execution
# (https://docs.ultravox.ai/tools/async-tools#custom-tool-timeouts),
# so the "keep talking while the tool runs" intent of the flag is
# structurally not achievable here. Surface a one-time warning so
# users see they're not getting what they expect.
if not self._async_tool_warning_logged:
for message in context.get_messages():
if isinstance(message, LLMSpecificMessage):
# Ultravox handles all context server-side, so the only context we
# need to handle here is function-call results.
for message in context.get_messages():
# LLMSpecificMessages are opaque provider-specific payloads, not
# standard tool-result messages — skip them.
if isinstance(message, LLMSpecificMessage):
continue
# Async-tool messages live alongside regular tool messages in the
# context; detect and route them before the regular logic so we
# don't try to send the async-tool envelope JSON as a tool result.
async_payload = async_tool_messages.parse_message(message)
if async_payload is not None:
if async_payload.kind == "started":
# The placeholder client_tool_result that unfreezes the
# conversation was already shipped from
# _handle_tool_invocation when the model issued the
# call. Nothing more to do here.
continue
if async_tool_messages.parse_message(message) is not None:
if async_payload.kind == "intermediate":
logger.error(
f"{self}: cancel_on_interruption=False is not supported by "
f"Ultravox: the conversation freezes during tool execution, so "
f"the 'keep talking while the tool runs' intent of the flag "
f"would not be achievable anyway. Use "
f"cancel_on_interruption=True (the default) or a non-realtime "
f"LLM service if your tool needs the async semantics."
f"{self}: Ultravox does not support streamed async "
f"tool results; dropping intermediate result for "
f"tool_call_id={async_payload.tool_call_id}. Use a "
f"non-realtime LLM service if your tool needs to "
f"stream intermediate results."
)
await self.push_error(
error_msg="cancel_on_interruption=False is not supported by Ultravox.",
error_msg="Ultravox does not support streamed async tool results.",
)
self._async_tool_warning_logged = True
break
continue
if async_payload.kind == "final":
if async_payload.tool_call_id in self._completed_tool_calls:
continue
# The placeholder client_tool_result has already
# "completed" the tool call from Ultravox's perspective,
# so the actual result is delivered as user-side text.
# Bracketed framing helps the model treat this as a
# tool-result update rather than fresh user input.
await self._send_user_text(
f"[Async tool result for tool_call_id="
f"{async_payload.tool_call_id}] {async_payload.result}"
)
self._completed_tool_calls.add(async_payload.tool_call_id)
continue
# Defensive: any async-tool message must not fall through
# to the regular tool-result block below, even if it
# carries a kind we don't recognize.
continue
# Ultravox handles all context server-side, so the only context we may
# need to handle here is new function call results.
for message in reversed(context.messages):
if message.get("role") != "tool":
break
content = message.get("content")
socket_message = {
# Look for newly-completed "regular" (as opposed to async-tool) results
if message.get("role") == "tool" and message.get("content") != "IN_PROGRESS":
tool_call_id = message.get("tool_call_id")
if tool_call_id and tool_call_id not in self._completed_tool_calls:
content = message.get("content")
result = (
content
if isinstance(content, str)
else "".join(t.get("text") for t in content)
)
await self._send_tool_result(tool_call_id, result)
self._completed_tool_calls.add(tool_call_id)
async def _send_tool_result(self, tool_call_id: str, result: str):
"""Send a tool call result to Ultravox."""
logger.debug(f"Sending tool result to Ultravox for tool_call_id={tool_call_id}")
await self._send(
{
"type": "client_tool_result",
"invocationId": message.get("tool_call_id"),
"result": content
if isinstance(content, str)
else "".join(t.get("text") for t in content),
"invocationId": tool_call_id,
"result": result,
}
await self._send(socket_message)
)
async def _handle_vad_user_stopped_speaking(self, frame: VADUserStoppedSpeakingFrame):
"""Handle VAD user stopped speaking frame.
@@ -596,6 +645,19 @@ class UltravoxRealtimeLLMService(LLMService):
async def _handle_tool_invocation(
self, tool_name: str, invocation_id: str, parameters: dict[str, Any]
):
# Ultravox freezes the conversation between client_tool_invocation
# and the matching client_tool_result. For functions registered
# with cancel_on_interruption=False the actual result won't be
# available for some time, so ship a placeholder result now to
# unfreeze the conversation. The real result will be injected
# later as user-side text from _handle_context.
if (
self._function_is_async(tool_name)
and invocation_id not in self._started_placeholder_sent
):
await self._send_tool_result(invocation_id, _ASYNC_TOOL_PLACEHOLDER_RESULT)
self._started_placeholder_sent.add(invocation_id)
await self.run_function_calls(
[
FunctionCallFromLLM(