Files
strix/strix/core/execution.py
T
58df71d3db fix(agents): let an agent wait on what it already said (#1020)
* let an agent wait on what it already said

An agent that answers in plain text is nudged to call a tool, and the only tool
that hands control back takes a required message. So it says the same thing
twice: once as text the user has already read, once as the argument it had to
supply to stop. Seen on a run whose whole instruction was "hi" - a greeting, then
the same greeting again through respond_to_user.

message is optional now. The nudge arms the tool with the text that was
delivered and says not to repeat it, so an agent that has said its piece can park
on it with an empty call. Anything it does want to add it passes normally.

Parking still cannot leave the user on silence: an empty call is refused unless
something was actually said, and the arming is single use - execution clears it
as soon as a turn ends any other way.

The interactive prompt now also says to answer and stop in one respond_to_user
call, which is what avoids the nudge in the first place.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* drop the worked example from the interactive prompt

"the user greeted you, asked something you can answer outright, or you need a
decision" was the run I had been reading, written into a rule that holds
whatever the reason. The rule is that replying and stopping is one call; listing
occasions only invites the model to check whether this is one of them.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* drop the arming flag; an empty message just waits

Passing the delivered text from execution into the tool, and refusing an empty
call without it, was machinery guarding against an agent parking having said
nothing. That leaves the user looking at "waiting for your reply" with a cursor
in front of them - they type. It does not need a mechanism.

What is left is the default on message, and the nudge saying the text already
landed.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* only offer waiting on words that were written

The nudge told every agent its text had already been delivered, but it fires
whenever a turn leaves the agent running, and a turn can end with no tool call
and no text at all - _final_output_preview has carried <none> and <empty>
branches all along. An agent that said nothing was being invited to wait on an
answer the user never received, leaving them at a bare prompt.

It now reads the turn: waiting on what was said is offered only when something
was, and otherwise the agent is told plainly that the user has read nothing and
to send its message.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* leave the continuation nudge alone

Rewording it meant asserting from the outside whether the agent had spoken, and
the nudge fires whenever a turn leaves the agent running - text or no text. The
agent knows which it did without being told, so the guidance belongs in its
prompt, where the condition is its own to read.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* say it in the nudge, where the agent is reading

An agent stranded by the nudge reasons off the nudge. Told only to call
respond_to_user, it supplies a message, and since it has just answered in plain
text that message is the same answer again. The system prompt saying otherwise
sits thousands of tokens earlier and loses.

The clause goes on the line the agent acts on: call respond_to_user, with no
message if it has already said it. That reads true whatever the turn did,
including one that produced no text, because the agent is the one who knows
which — nothing here has to work it out from the outside.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
2026-08-09 00:57:16 +03:00

1033 lines
37 KiB
Python

"""Execution loop for addressable SDK-backed Strix agents."""
from __future__ import annotations
import asyncio
import contextlib
import logging
import uuid
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, cast
import litellm
from agents import RunConfig, Runner
from agents.exceptions import AgentsException, MaxTurnsExceeded, UserError
from agents.sandbox.errors import ExecTransportError
from docker import errors as docker_errors # type: ignore[import-untyped, unused-ignore]
from openai import (
APIConnectionError,
APIError,
APITimeoutError,
)
from strix.config import codex
from strix.core.hooks import (
BudgetExceededError,
BudgetPausedError,
SubagentBudgetReservedError,
)
from strix.core.inputs import child_initial_input
from strix.core.sessions import (
enforce_image_budget,
open_agent_session,
replace_session_items,
seed_initial_input,
strip_all_images_from_session,
)
from strix.llm.compaction import is_context_overflow, maybe_compact
if TYPE_CHECKING:
from pathlib import Path
from agents.items import TResponseInputItem
from agents.lifecycle import RunHooks
from agents.memory import Session, SQLiteSession
from agents.result import RunResultBase
from strix.core.agents import AgentCoordinator, Status
logger = logging.getLogger(__name__)
StreamEventSink = Callable[[str, Any], None]
_INPUT_REJECTION_CODES = frozenset({400, 404, 422})
_MAX_COMPACTIONS_PER_CYCLE = 2
class ProviderRefusalError(AgentsException):
"""Raised when a provider returns a structured refusal instead of an exception."""
def _structured_provider_refusal(result: Any) -> str | None:
for item in getattr(result, "new_items", ()) or ():
raw_item = getattr(item, "raw_item", None)
for content in getattr(raw_item, "content", ()) or ():
if getattr(content, "type", None) != "refusal":
continue
refusal = getattr(content, "refusal", None)
if isinstance(refusal, str) and refusal.strip():
return refusal.strip()
return "The model provider refused this request."
return None
def _run_config_model(run_config: RunConfig) -> str | None:
return run_config.model if isinstance(run_config.model, str) else None
def _agent_instructions(agent: Any) -> str:
instructions = getattr(agent, "instructions", None)
return instructions if isinstance(instructions, str) else ""
def _agent_tools_text(agent: Any) -> str:
parts: list[str] = []
for tool in getattr(agent, "tools", []) or []:
name = getattr(tool, "name", "")
description = getattr(tool, "description", "") or ""
schema = getattr(tool, "params_json_schema", "") or ""
parts.append(f"{name} {description} {schema}")
return "\n".join(parts)
async def _compact_session(
agent: Any, session: Session, run_config: RunConfig, *, force: bool
) -> bool:
model = _run_config_model(run_config)
if session is None or model is None:
return False
return await maybe_compact(
session,
model=model,
instructions=_agent_instructions(agent),
tools_text=_agent_tools_text(agent),
force=force,
)
_MAX_TRANSIENT_MODEL_RETRIES = 5
_TRANSIENT_MODEL_RETRY_BASE_DELAY_S = 2.0
_TRANSIENT_MODEL_RETRY_MAX_DELAY_S = 90.0
def _model_error_status_code(exc: BaseException) -> int | None:
code = getattr(exc, "status_code", None)
return code if isinstance(code, int) else None
def _is_transient_model_error(exc: BaseException) -> bool:
if codex.is_content_guardrail_error(exc):
return False
if isinstance(
exc, APITimeoutError | APIConnectionError | TimeoutError | ConnectionError | OSError
):
return True
code = _model_error_status_code(exc)
if code is not None:
return bool(litellm._should_retry(code))
return isinstance(exc, APIError)
def _transient_model_retry_delay(attempt: int) -> float:
delay = _TRANSIENT_MODEL_RETRY_BASE_DELAY_S * float(2 ** (attempt - 1))
return min(delay, _TRANSIENT_MODEL_RETRY_MAX_DELAY_S)
async def _salvage_stream_to_session(
session: Session,
pre_run_items: list[Any],
stream: Any,
agent_id: str,
) -> None:
"""Persist a crashed run's full history so a revived agent loses no context."""
if stream is None:
return
try:
replay = list(stream.to_input_list())
except Exception:
logger.exception("could not build salvage history for %s", agent_id)
return
desired = list(pre_run_items) + replay
if len(desired) <= len(pre_run_items):
return
try:
await replace_session_items(session, desired)
except Exception:
logger.exception("salvaging crashed run history failed for %s", agent_id)
async def _seed_and_prepare_first_input(
session: Session | None, initial_input: Any, *, start_parked: bool
) -> Any:
"""Persist the opening input up front so it survives a first-turn crash."""
if initial_input and session is not None and not start_parked:
with contextlib.suppress(Exception):
if await seed_initial_input(session, initial_input):
return []
return initial_input
async def run_agent_loop(
*,
agent: Any,
initial_input: Any,
run_config: RunConfig,
context: dict[str, Any],
max_turns: int,
coordinator: AgentCoordinator,
agent_id: str,
interactive: bool,
session: Session | None = None,
start_parked: bool = False,
event_sink: StreamEventSink | None = None,
hooks: RunHooks[dict[str, Any]] | None = None,
) -> RunResultBase | None:
await coordinator.attach_runtime(
agent_id,
session=session,
interrupt_on_message=interactive,
)
result: RunResultBase | None = None
first_cycle_input = await _seed_and_prepare_first_input(
session, initial_input, start_parked=start_parked
)
budget_stopped = coordinator.budget_stopped
reserve_stopped = coordinator.reserve_stopped
if budget_stopped:
await coordinator.set_status(agent_id, "stopped")
raise BudgetExceededError("scan budget reached")
if reserve_stopped and context.get("parent_id") is not None:
await coordinator.set_status(agent_id, "stopped")
raise SubagentBudgetReservedError("scan reached the sub-agent budget reserve")
if reserve_stopped and start_parked and interactive and context.get("parent_id") is None:
await coordinator.send(agent_id, _reserve_notice())
if not (start_parked and interactive):
with contextlib.suppress(BudgetPausedError):
result = await _run_until_lifecycle(
agent,
coordinator,
agent_id,
initial_input=first_cycle_input,
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
interactive=interactive,
event_sink=event_sink,
hooks=hooks,
)
if not interactive:
return result
while True:
timeout = await _plain_waiting_timeout(coordinator, agent_id)
try:
woke = await coordinator.wait_for_message(agent_id, timeout=timeout)
except asyncio.CancelledError:
return result
if coordinator.budget_stopped:
await coordinator.set_status(agent_id, "stopped")
raise BudgetExceededError("scan budget reached")
if coordinator.reserve_stopped and context.get("parent_id") is not None:
await coordinator.set_status(agent_id, "stopped")
raise SubagentBudgetReservedError("scan reached the sub-agent budget reserve")
if woke:
# Real input is real progress, so the nudge budget starts over. A bare
# auto-resume is not: it must not hand a wedged agent a fresh budget.
await coordinator.reset_recovery(agent_id)
await coordinator.reset_idle_resumes(agent_id)
else:
idle_resumes = await coordinator.record_idle_resume(agent_id)
if idle_resumes >= _MAX_IDLE_AUTO_RESUMES:
logger.warning(
"agent %s auto-resumed %d times without hearing from anyone; "
"leaving it parked until a real message arrives",
agent_id,
idle_resumes,
)
await coordinator.park_waiting(agent_id, wait_kind="stalled")
await _notify_parent_on_stall(coordinator, agent_id)
continue
logger.info("agent %s reached its waiting timeout; auto-resuming", agent_id)
await coordinator.send(
agent_id,
{
"from": "system",
"type": "auto_resume",
"content": "Waiting timeout reached. Resuming execution.",
},
interrupt=False,
)
await coordinator.consume_pending(agent_id)
with contextlib.suppress(BudgetPausedError):
result = await _run_until_lifecycle(
agent,
coordinator,
agent_id,
initial_input=[],
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
interactive=True,
event_sink=event_sink,
hooks=hooks,
)
async def spawn_child_agent(
*,
coordinator: AgentCoordinator,
factory: Any,
agents_db_path: Path,
sessions_to_close: list[SQLiteSession],
run_config: RunConfig,
max_turns: int,
interactive: bool,
parent_ctx: dict[str, Any],
name: str,
task: str,
skills: list[str],
parent_history: list[Any],
event_sink: StreamEventSink | None = None,
hooks: RunHooks[dict[str, Any]] | None = None,
) -> dict[str, Any]:
parent_id = parent_ctx.get("agent_id")
if not isinstance(parent_id, str):
raise TypeError("Parent agent_id missing from context")
child_id = uuid.uuid4().hex[:8]
child_agent = factory(name=name, skills=skills)
await coordinator.register(
child_id,
name,
parent_id,
task=task,
skills=skills,
)
await _start_child_runner(
parent_ctx=parent_ctx,
coordinator=coordinator,
agents_db_path=agents_db_path,
sessions_to_close=sessions_to_close,
run_config=run_config,
max_turns=max_turns,
interactive=interactive,
child_agent=child_agent,
child_id=child_id,
name=name,
parent_id=parent_id,
task=task,
initial_input=child_initial_input(
name=name,
child_id=child_id,
parent_id=parent_id,
task=task,
parent_history=parent_history,
),
event_sink=event_sink,
hooks=hooks,
)
return {
"success": True,
"agent_id": child_id,
"name": name,
"parent_id": parent_id,
"message": f"Spawned '{name}' ({child_id}) running in parallel.",
}
async def respawn_subagents(
*,
coordinator: AgentCoordinator,
factory: Any,
agents_db_path: Path,
sessions_to_close: list[SQLiteSession],
run_config: RunConfig,
max_turns: int,
interactive: bool,
parent_ctx: dict[str, Any],
root_id: str,
event_sink: StreamEventSink | None = None,
hooks: RunHooks[dict[str, Any]] | None = None,
) -> None:
async with coordinator._lock:
agents_snapshot = [
(aid, status, dict(coordinator.metadata.get(aid, {})))
for aid, status in coordinator.statuses.items()
]
candidates: list[tuple[str, str, str | None, dict[str, Any]]] = []
for aid, status, md in agents_snapshot:
if not interactive and status not in {"running", "waiting"}:
continue
if coordinator.parent_of.get(aid) is None or aid == root_id:
continue
md["_restored_status"] = status
candidates.append(
(
aid,
coordinator.names.get(aid, aid),
coordinator.parent_of.get(aid),
md,
)
)
for child_id, name, parent_id, md in candidates:
try:
restored_status = str(md.get("_restored_status") or "running")
start_parked = interactive and restored_status != "running"
if start_parked:
logger.warning(
"respawn %s (%s): starting parked from status=%s",
child_id,
name,
restored_status,
)
child_skills = list(md.get("skills") or [])
child_agent = factory(name=name, skills=child_skills)
await _start_child_runner(
parent_ctx=parent_ctx,
coordinator=coordinator,
agents_db_path=agents_db_path,
sessions_to_close=sessions_to_close,
run_config=run_config,
max_turns=max_turns,
interactive=interactive,
child_agent=child_agent,
child_id=child_id,
name=name,
parent_id=parent_id,
task=str(md.get("task", "")),
initial_input=[],
start_parked=start_parked,
event_sink=event_sink,
hooks=hooks,
)
logger.info(
"respawned %s (%s) parent=%s task_len=%d",
child_id,
name,
parent_id or "-",
len(md.get("task", "")),
)
except Exception:
logger.exception("respawn %s failed; marking crashed", child_id)
with contextlib.suppress(Exception):
await coordinator.set_status(child_id, "crashed")
_INTERACTIVE_TOOL_RECOVERY_LIMIT = 3
async def _run_until_lifecycle(
agent: Any,
coordinator: AgentCoordinator,
agent_id: str,
*,
initial_input: Any,
run_config: RunConfig,
context: dict[str, Any],
max_turns: int,
session: Session | None,
interactive: bool,
event_sink: StreamEventSink | None,
hooks: RunHooks[dict[str, Any]] | None,
) -> RunResultBase | None:
"""Drive an agent until an explicit lifecycle tool settles its status.
A turn that ends without ``finish_scan``, ``agent_finish``,
``respond_to_user``, or ``wait_for_agents`` leaves the agent ``running``:
plain text never terminates a run and never yields to the user. Such a turn
is nudged back into a tool call, bounded by a recovery limit.
"""
result: RunResultBase | None = None
input_data: Any = initial_input
recovery_limit = _INTERACTIVE_TOOL_RECOVERY_LIMIT if interactive else max(1, max_turns)
while True:
if coordinator.budget_stopped:
await coordinator.set_status(agent_id, "stopped")
raise BudgetExceededError("scan budget reached")
if coordinator.reserve_stopped and context.get("parent_id") is not None:
await coordinator.set_status(agent_id, "stopped")
raise SubagentBudgetReservedError("scan reached the sub-agent budget reserve")
if interactive:
result = await _run_cycle_parked(
agent,
coordinator,
agent_id,
input_data=input_data,
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
event_sink=event_sink,
hooks=hooks,
)
else:
result = await _run_cycle(
agent,
coordinator,
agent_id,
input_data=input_data,
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
interactive=False,
event_sink=event_sink,
hooks=hooks,
)
status = await _agent_status(coordinator, agent_id)
if status != "running":
await coordinator.reset_recovery(agent_id)
return result
recoveries = await coordinator.record_recovery(agent_id)
logger.warning(
"agent %s ended a turn without a lifecycle tool call (interactive=%s); "
"forcing tool continuation (%d/%d): %s",
agent_id,
interactive,
recoveries,
recovery_limit,
_final_output_preview(result),
)
if recoveries >= recovery_limit:
return await _exhausted_recovery(coordinator, agent_id, result, interactive=interactive)
input_data = await _append_tool_required_message(
session=session,
context=context,
attempt=recoveries,
limit=recovery_limit,
interactive=interactive,
)
async def _exhausted_recovery(
coordinator: AgentCoordinator,
agent_id: str,
result: RunResultBase | None,
*,
interactive: bool,
) -> RunResultBase | None:
"""Settle an agent that never recovered into a tool call.
Interactive runs park instead of dying: a human is attached and can message
any agent, so the scan stays resumable. Autonomous runs have nobody to
resume them, so they fail loudly.
"""
if not interactive:
await coordinator.set_status(agent_id, "crashed")
await notify_parent_on_terminal(coordinator, agent_id, "crashed")
raise MaxTurnsExceeded(
"Agent exhausted recovery attempts without calling finish_scan or agent_finish."
)
logger.warning(
"agent %s exhausted tool-call recovery attempts; parking until a message arrives",
agent_id,
)
await coordinator.park_waiting(agent_id, wait_kind="stalled")
# A parked child owes its parent a completion report it can no longer send. The
# parent is an agent, not a watching human, so nothing else tells it to stop
# waiting and it burns its full timeout on a message that is never coming.
await _notify_parent_on_stall(coordinator, agent_id)
return result
_WAITING_AUTO_RESUME_TIMEOUT_S = 300.0
# An agent that parks again after every auto-resume makes no progress, so stop
# spending a model turn per timeout and leave it parked for a real message.
_MAX_IDLE_AUTO_RESUMES = 3
async def _plain_waiting_timeout(
coordinator: AgentCoordinator,
agent_id: str,
) -> float | None:
"""Auto-resume timeout for a parked agent; None waits until a message arrives.
Driven by what the agent is waiting on, not by where it sits in the graph:
the user can message any agent, so an agent awaiting a human parks
indefinitely whether or not it is the root. Only an agent awaiting other
agents is re-checked on a timer, and only until it has spent its idle
budget re-parking without hearing anything.
"""
async with coordinator._lock:
status = coordinator.statuses.get(agent_id)
has_error = agent_id in coordinator.errors
runtime = coordinator.runtimes.get(agent_id)
gated = runtime.user_wake_required if runtime is not None else False
wait_kind = coordinator.wait_kinds.get(agent_id)
idle_resumes = coordinator.idle_resume_counts.get(agent_id, 0)
if status != "waiting" or has_error or gated:
return None
if wait_kind != "agents" or idle_resumes >= _MAX_IDLE_AUTO_RESUMES:
return None
return _WAITING_AUTO_RESUME_TIMEOUT_S
async def _run_cycle_parked(
agent: Any,
coordinator: AgentCoordinator,
agent_id: str,
*,
input_data: Any,
run_config: RunConfig,
context: dict[str, Any],
max_turns: int,
session: Session | None,
event_sink: StreamEventSink | None,
hooks: RunHooks[dict[str, Any]] | None,
) -> RunResultBase | None:
"""Interactive run cycle that parks on any error instead of killing the runner."""
try:
return await _run_cycle(
agent,
coordinator,
agent_id,
input_data=input_data,
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
interactive=True,
event_sink=event_sink,
hooks=hooks,
)
except (BudgetExceededError, BudgetPausedError, SubagentBudgetReservedError):
raise
except Exception as exc:
logger.exception("error escaped the run cycle for %s; parking as failed", agent_id)
await coordinator.set_status(agent_id, "failed", error=str(exc) or type(exc).__name__)
await notify_parent_on_terminal(coordinator, agent_id, "failed")
return None
async def _run_cycle( # noqa: PLR0912, PLR0915
agent: Any,
coordinator: AgentCoordinator,
agent_id: str,
*,
input_data: Any,
run_config: RunConfig,
context: dict[str, Any],
max_turns: int,
session: Session | None,
interactive: bool,
event_sink: StreamEventSink | None,
hooks: RunHooks[dict[str, Any]] | None,
) -> RunResultBase | None:
image_strips = 0
compactions = 0
model_retries = 0
while True:
stream: Any = None
pre_run_items: list[Any] = []
try:
await coordinator.mark_running(agent_id)
if session is not None:
max_images = context.get("max_context_images")
if isinstance(max_images, int):
try:
await enforce_image_budget(session, max_images)
except Exception:
logger.exception("image-budget enforcement failed for %s", agent_id)
try:
await _compact_session(agent, session, run_config, force=False)
except Exception:
logger.exception("proactive compaction failed for %s", agent_id)
with contextlib.suppress(Exception):
pre_run_items = list(await session.get_items())
stream = Runner.run_streamed(
agent,
input=input_data,
run_config=run_config,
context=context,
max_turns=max_turns,
session=session,
hooks=hooks,
)
await coordinator.attach_stream(agent_id, stream)
try:
try:
async for event in stream.stream_events():
if event_sink is not None:
try:
event_sink(agent_id, event)
except Exception:
logger.exception("stream event sink failed for %s", agent_id)
if stream.run_loop_exception is not None:
raise stream.run_loop_exception
if refusal := _structured_provider_refusal(stream):
raise ProviderRefusalError(refusal)
except (BudgetExceededError, BudgetPausedError, SubagentBudgetReservedError):
raise
except RuntimeError as stream_exc:
if "after shutdown" not in str(stream_exc):
raise
logger.warning(
"Ignoring LiteLLM end-of-stream shutdown race for %s",
agent_id,
)
except (ExecTransportError, docker_errors.NotFound):
if not coordinator.is_shutting_down:
raise
logger.warning(
"Ignoring sandbox container error during teardown for %s",
agent_id,
exc_info=True,
)
finally:
await coordinator.detach_stream(agent_id, stream)
except BudgetPausedError as exc:
logger.info("agent %s paused at the scan budget limit: %s", agent_id, exc)
await coordinator.pause_for_budget(agent_id)
raise
except SubagentBudgetReservedError as exc:
logger.info("sub-agent %s stopped at the budget reserve: %s", agent_id, exc)
await coordinator.set_status(agent_id, "stopped")
await _notify_root_on_budget_reserve(coordinator)
raise
except BudgetExceededError as exc:
logger.info(
"agent %s reached the scan budget limit; stopping the scan: %s", agent_id, exc
)
await coordinator.set_status(agent_id, "stopped")
await coordinator.trigger_budget_stop()
raise
except Exception as exc:
if (
image_strips < 3
and session is not None
and getattr(exc, "status_code", None) in _INPUT_REJECTION_CODES
):
try:
stripped = await strip_all_images_from_session(session)
except Exception:
logger.exception("image-strip recovery failed for %s", agent_id)
stripped = False
if stripped:
image_strips += 1
logger.info(
"Stripped images from %s session after rejection; retrying (%d)",
agent_id,
image_strips,
)
input_data = []
continue
if (
compactions < _MAX_COMPACTIONS_PER_CYCLE
and session is not None
and is_context_overflow(exc)
):
try:
compacted = await _compact_session(agent, session, run_config, force=True)
except Exception:
logger.exception("overflow compaction recovery failed for %s", agent_id)
compacted = False
if compacted:
compactions += 1
logger.info(
"Compacted %s session after context overflow; retrying (%d)",
agent_id,
compactions,
)
input_data = []
continue
if model_retries < _MAX_TRANSIENT_MODEL_RETRIES and _is_transient_model_error(exc):
model_retries += 1
delay = _transient_model_retry_delay(model_retries)
logger.warning(
"transient model/provider error for %s; replaying turn "
"(attempt %d/%d, backoff %.1fs): %r",
agent_id,
model_retries,
_MAX_TRANSIENT_MODEL_RETRIES,
delay,
exc,
)
await asyncio.sleep(delay)
if session is not None:
input_data = []
continue
if session is not None:
await _salvage_stream_to_session(session, pre_run_items, stream, agent_id)
if isinstance(exc, ProviderRefusalError):
logger.warning("agent %s refused by the model provider: %s", agent_id, exc)
await coordinator.set_status(agent_id, "failed", error=str(exc))
await notify_parent_on_terminal(coordinator, agent_id, "failed")
return None
if isinstance(exc, MaxTurnsExceeded):
status: Status = "stopped"
elif isinstance(exc, UserError | AgentsException | APIError):
status = "failed"
else:
status = "crashed"
logger.exception("agent run failed for %s; marking %s", agent_id, status)
# Settle the status and wake the parent before the exception unwinds a
# non-interactive agent's task: a child that dies still owes its parent a
# report, and the parent would otherwise wait out its timeout on a message
# the dead child can no longer send.
await coordinator.set_status(agent_id, status, error=str(exc) or type(exc).__name__)
await notify_parent_on_terminal(coordinator, agent_id, status)
if not interactive:
raise
return None
else:
return cast("RunResultBase | None", stream)
async def _agent_status(coordinator: AgentCoordinator, agent_id: str) -> Status | None:
async with coordinator._lock:
return coordinator.statuses.get(agent_id)
def _final_output_preview(result: RunResultBase | None) -> str:
final_output = getattr(result, "final_output", None)
if final_output is None:
return "<none>"
text = str(final_output).replace("\n", " ").strip()
if not text:
return "<empty>"
return text[:300]
async def _append_tool_required_message(
*,
session: Session | None,
context: dict[str, Any],
attempt: int,
limit: int,
interactive: bool,
) -> list[dict[str, str]]:
finish_tool = "finish_scan" if context.get("parent_id") is None else "agent_finish"
if interactive:
message = (
"Your previous message ended a turn without a tool call. Plain text never ends "
"execution and never hands control to the user: it is shown to the user, and the "
"run continues. Continue immediately and call exactly one tool. "
"If you have something to tell the user and nothing to do until they reply, "
"call respond_to_user — with no message if you have already said it. "
"If you are blocked waiting for another agent, call wait_for_agents. "
f"If the whole engagement is complete, call {finish_tool}. "
"Otherwise use the appropriate execution or planning tool. "
f"This is recovery attempt {attempt}/{limit}."
)
else:
message = (
"Your previous response ended the autonomous run without a lifecycle tool "
"call. That is invalid in non-interactive mode; plain text final answers are "
"ignored. Continue immediately and call exactly one tool. "
f"If your work is complete, call {finish_tool}. "
"If you are blocked waiting for another agent, call wait_for_agents. "
"Otherwise use the appropriate execution or planning tool. "
f"This is recovery attempt {attempt}/{limit}."
)
item = {"role": "user", "content": message}
if session is None:
return [item]
await session.add_items([cast("TResponseInputItem", item)])
return []
_TERMINAL_NOTICE = {
"completed": (
"[Agent completed] {name} ({agent_id}) finished and is no longer running, but it "
"sent no completion report. Stop waiting on this child; ask it directly if you "
"need its results."
),
"crashed": (
"[Agent crash] {name} ({agent_id}) terminated unexpectedly. "
"Stop waiting on this child unless you want to message it again."
),
"failed": (
"[Agent failed] {name} ({agent_id}) stopped with an error and will not "
"send a completion report. Stop waiting on this child unless you want to "
"message it again."
),
"stopped": (
"[Agent stopped] {name} ({agent_id}) was stopped before finishing (turn limit "
"or an explicit stop). It will not send a completion report, so stop waiting "
"on this child; account for its unfinished subtask and continue."
),
}
_STALL_NOTICE = (
"[Agent stalled] {name} ({agent_id}) kept ending turns without a tool call and is "
"parked until it receives a message. It will not send a completion report on its "
"own: either message it with a concrete next step to unblock it, or stop waiting on "
"it and account for its unfinished subtask."
)
async def _notify_parent_on_stall(
coordinator: AgentCoordinator,
agent_id: str,
) -> None:
"""Tell the parent that a child parked mid-task, so it stops waiting blindly."""
async with coordinator._lock:
parent = coordinator.parent_of.get(agent_id)
name = coordinator.names.get(agent_id, agent_id)
if parent is None:
return
await coordinator.send(
parent,
{
"from": agent_id,
"type": "stalled",
"priority": "high",
"content": _STALL_NOTICE.format(name=name, agent_id=agent_id),
},
interrupt=False,
)
async def notify_parent_on_terminal(
coordinator: AgentCoordinator,
agent_id: str,
status: str,
) -> None:
template = _TERMINAL_NOTICE.get(status)
if template is None:
return
async with coordinator._lock:
parent = coordinator.parent_of.get(agent_id)
name = coordinator.names.get(agent_id, agent_id)
if parent is None:
return
if not await coordinator.claim_parent_notice(agent_id):
return
await coordinator.send(
parent,
{
"from": agent_id,
"type": status,
"priority": "high",
"content": template.format(name=name, agent_id=agent_id),
},
interrupt=False,
)
def _reserve_notice() -> dict[str, Any]:
return {
"from": "system",
"type": "budget_reserve_stop",
"priority": "high",
"content": (
"[Budget reserve] The scan has reached the sub-agent budget reserve: every "
"sub-agent is being force-stopped as soon as its in-flight turn completes, and "
"none will send a completion report. Their confirmed vulnerabilities are "
"already filed as they were found. Do not wait on any sub-agents and do not "
"spawn new ones — wrap up now and call finish_scan."
),
}
async def _notify_root_on_budget_reserve(coordinator: AgentCoordinator) -> None:
root = await coordinator.claim_reserve_notification()
if root is None:
return
await coordinator.send(root, _reserve_notice())
async def _notify_parent_on_exit(
coordinator: AgentCoordinator,
agent_id: str,
) -> None:
"""Backstop for a child whose loop ended without telling its parent.
Every terminal state counts, including ``completed``: a child that skips its
completion report leaves the parent waiting on a message nobody will send.
"""
status = await _agent_status(coordinator, agent_id)
if status is None:
return
await notify_parent_on_terminal(coordinator, agent_id, status)
async def _start_child_runner(
*,
parent_ctx: dict[str, Any],
coordinator: AgentCoordinator,
agents_db_path: Path,
sessions_to_close: list[SQLiteSession],
run_config: RunConfig,
max_turns: int,
interactive: bool,
child_agent: Any,
child_id: str,
name: str,
parent_id: str | None,
task: str,
initial_input: Any,
start_parked: bool = False,
event_sink: StreamEventSink | None = None,
hooks: RunHooks[dict[str, Any]] | None = None,
) -> None:
session = open_agent_session(child_id, agents_db_path)
sessions_to_close.append(session)
await coordinator.attach_runtime(child_id, session=session)
child_ctx: dict[str, Any] = dict(parent_ctx)
child_ctx["agent_id"] = child_id
child_ctx["parent_id"] = parent_id
child_ctx["task"] = task
async def _child_loop() -> None:
# A budget stop is a clean scan-wide shutdown, not a child failure: the
# child's status and parent notification are already settled in
# ``_run_cycle``. Swallow it here so the detached task does not surface a
# spurious "Task exception was never retrieved" warning. The root agent
# hits the same limit on its next call and tears the scan down.
try:
await run_agent_loop(
agent=child_agent,
initial_input=initial_input,
run_config=run_config,
context=child_ctx,
max_turns=max_turns,
coordinator=coordinator,
agent_id=child_id,
interactive=interactive,
session=session,
start_parked=start_parked,
event_sink=event_sink,
hooks=hooks,
)
except BudgetExceededError:
logger.info("child %s stopped after reaching the scan budget limit", child_id)
except SubagentBudgetReservedError:
logger.info("child %s stopped at the sub-agent budget reserve", child_id)
finally:
if not coordinator.is_shutting_down:
await _notify_parent_on_exit(coordinator, child_id)
task_handle = asyncio.create_task(_child_loop(), name=f"agent-{name}-{child_id}")
await coordinator.attach_runtime(child_id, task=task_handle)