mirror/nanobot
1
0
Fork 0
mirror of https://github.com/HKUDS/nanobot.git synced 2026-08-06 17:38:35 +00:00
Code Issues Packages Projects Releases Wiki Activity
nanobot/nanobot/agent/loop.py

2297 lines
98 KiB
Python
Raw Blame History

"""Agent loop: the core processing engine."""
# pyright: reportPrivateUsage=false
from __future__ import annotations
import asyncio
import dataclasses
import inspect
import os
import time
import weakref
from collections.abc import Coroutine, Iterable, Mapping
from contextlib import AbstractContextManager, ExitStack, nullcontext, suppress
from dataclasses import dataclass, field
from enum import Enum, auto
from functools import partial
from pathlib import Path
from typing import TYPE_CHECKING, Any, Awaitable, Callable, TypeVar, cast
from loguru import logger
from nanobot.agent import context as agent_context
from nanobot.agent import model_presets as preset_helpers
from nanobot.agent.autocompact import AutoCompact
from nanobot.agent.automation_turns import publish_next_deferred_turn
from nanobot.agent.context import ContextBuilder
from nanobot.agent.cron_turns import CronTurnCoordinator
from nanobot.agent.hook import AgentHook, AgentTurnHookFactory
from nanobot.agent.memory import Consolidator
from nanobot.agent.model_runtime import ModelRuntimeResolver
from nanobot.agent.runner import _MAX_INJECTIONS_PER_TURN, AgentRunner, AgentRunSpec
from nanobot.agent.subagent import SubagentManager
from nanobot.agent.tools.context import RequestContext, bind_request_context, reset_request_context
from nanobot.agent.tools.exec_session import ExecSessionManager
from nanobot.agent.tools.file_state import FileStateStore, bind_file_states, reset_file_states
from nanobot.agent.tools.message import MessageTool
from nanobot.agent.tools.registry import ToolRegistry
from nanobot.agent.tools.self import MyTool
from nanobot.agent.turn_delivery import (
TurnDelivery,
TurnDeliveryFactory,
)
from nanobot.agent.turn_delivery import TurnRoute as TurnRoute
from nanobot.agent.turn_hooks import AgentTurnHookSpec, build_agent_turn_hook
from nanobot.bus.events import InboundMessage, OutboundMessage
from nanobot.bus.outbound_events import StreamedResponseEvent
from nanobot.bus.queue import MessageBus
from nanobot.bus.runtime_events import RuntimeEventBus
from nanobot.command import CommandContext, CommandRouter, register_builtin_commands
from nanobot.config.schema import AgentDefaults, ModelPresetConfig
from nanobot.providers.base import LLMProvider, ProviderConversationState
from nanobot.providers.factory import ProviderSnapshot
from nanobot.runtime_context import (
RUNTIME_CONTEXT_HISTORY_META,
RUNTIME_CONTEXT_MESSAGE_META,
RuntimeContextBlock,
RuntimeContextProvider,
append_runtime_context,
resolve_runtime_context,
runtime_context_blocks_from_metadata,
)
from nanobot.security.workspace_access import (
WorkspaceScopeResolver,
bind_workspace_scope,
reset_workspace_scope,
)
from nanobot.session import turn_continuation
from nanobot.session.automation_turns import automation_history_overrides
from nanobot.session.goal_state import (
goal_state_runtime_lines,
runner_wall_llm_timeout_s,
sustained_goal_active,
)
from nanobot.session.history_visibility import HIDDEN_HISTORY_META
from nanobot.session.keys import UNIFIED_SESSION_KEY, remember_last_channel
from nanobot.session.manager import (
Session,
SessionManager,
replay_max_messages_for_context,
)
from nanobot.session.model_selection import (
SESSION_MODEL_PRESET_METADATA_KEY,
model_preset_from_metadata,
)
from nanobot.triggers.local_turns import LocalTriggerTurnCoordinator
from nanobot.utils.cancellation import task_is_cancelling
from nanobot.utils.document import reference_non_image_attachments
from nanobot.utils.helpers import image_placeholder_text
from nanobot.utils.helpers import truncate_text as truncate_text_fn
from nanobot.utils.llm_runtime import LLMRuntime
from nanobot.utils.runtime import (
EMPTY_FINAL_RESPONSE_MESSAGE,
)
if TYPE_CHECKING:
from nanobot.agent.tools.mcp import MCPConnection
from nanobot.config.schema import (
ChannelsConfig,
Config,
MCPServerConfig,
ProviderConfig,
ToolsConfig,
)
from nanobot.cron.service import CronService
from nanobot.triggers.local_store import LocalTriggerStore
_T = TypeVar("_T")
_SUBAGENT_PROVIDER_TASK_META = "subagent_provider_task_id"
class TurnKind(Enum):
USER = auto()
SYSTEM = auto()
@dataclass
class TurnContext:
msg: InboundMessage
session_key: str
turn_id: str
runtime: LLMRuntime | None
kind: TurnKind
delivery: TurnDelivery
original_user_text: str | None = None
session: Session | None = None
history: list[dict[str, Any]] = field(default_factory=list)
initial_messages: list[dict[str, Any]] = field(default_factory=list)
provider_state: ProviderConversationState | None = field(default=None, repr=False)
request_context: RequestContext | None = None
runtime_context_blocks: list[RuntimeContextBlock] = field(default_factory=list)
attributes: dict[str, Any] = field(default_factory=dict)
final_content: str | None = None
all_messages: list[dict[str, Any]] = field(default_factory=list)
stop_reason: str = ""
had_injections: bool = False
streamed_content: bool = False
input_persisted_early: bool = False
save_skip: int = 0
outbound: OutboundMessage | None = None
suppress_response: bool = False
on_progress: Callable[..., Awaitable[None]] | None = None
on_stream: Callable[[str], Awaitable[None]] | None = None
on_stream_end: Callable[..., Awaitable[None]] | None = None
on_runtime_admitted: Callable[[LLMRuntime], Awaitable[None]] | None = None
on_retry_wait: Callable[[str], Awaitable[None]] | None = None
pending_queue: asyncio.Queue[InboundMessage] | None = None
pending_summary: str | None = None
ephemeral: bool = False
run_extra_hooks_for_ephemeral: bool = False
hooks: list[AgentHook] = field(default_factory=list)
hook_factories: list[AgentTurnHookFactory] = field(default_factory=list)
turn_scopes: list[AbstractContextManager[Any]] = field(default_factory=list)
tools: ToolRegistry | None = None
turn_wall_started_at: float = field(default_factory=time.time)
visible_run_started_at: float | None = None
turn_latency_ms: int | None = None
def require_runtime(self) -> LLMRuntime:
"""Return the runtime established by the BUILD stage."""
if self.runtime is None:
raise RuntimeError("turn runtime is not initialized; BUILD must run before this stage")
return self.runtime
def require_session(self) -> Session:
"""Return the session established by the RESTORE stage."""
if self.session is None:
raise RuntimeError("turn session is not initialized; RESTORE must run before this stage")
return self.session
class AgentLoop:
"""
The agent loop is the core processing engine.
It:
1. Receives messages from the bus
2. Builds context with history, memory, skills
3. Calls the LLM
4. Executes tool calls
5. Sends responses back
"""
@property
def current_iteration(self) -> int:
return self._current_iteration
@property
def tool_names(self) -> list[str]:
return self.tools.tool_names
@property
def provider(self) -> LLMProvider:
"""Provider selected for future turn admissions."""
return self.runtime_resolver.runtime.provider
@property
def model(self) -> str:
"""Model selected for future turn admissions."""
return self.runtime_resolver.runtime.model
@property
def context_window_tokens(self) -> int:
"""Context limit selected for future turn admissions."""
return self.runtime_resolver.runtime.context_window_tokens
@property
def model_presets(self) -> Mapping[str, ModelPresetConfig]:
"""Configured model presets exposed for selection and display."""
return self.runtime_resolver.model_presets
@property
def model_preset(self) -> str | None:
return self.runtime_resolver.model_preset
@model_preset.setter
def model_preset(self, name: str | None) -> None:
self.set_model_preset(name)
def llm_runtime(self) -> LLMRuntime:
"""Resolve the immutable default used to admit the next turn."""
previous = self.runtime_resolver.runtime
runtime = self.runtime_resolver.admit()
if (
runtime.model != previous.model
or runtime.model_preset != previous.model_preset
or runtime.snapshot_signature != previous.snapshot_signature
):
self._publish_runtime_selection(runtime)
return runtime
def dream_runtime(self) -> LLMRuntime | None:
"""Resolve the optional preset used for Dream without changing defaults."""
if not self.dream_model_preset:
return None
return self.runtime_resolver.resolve_preset(self.dream_model_preset)
_RUNTIME_CHECKPOINT_KEY = "runtime_checkpoint"
_PENDING_USER_TURN_KEY = "pending_user_turn"
_PROVIDER_STATE_CHECKPOINT_VERSION_KEY = "provider_state_checkpoint_version"
_PROVIDER_STATE_CHECKPOINT_VERSION = "v1"
def __init__(
self,
bus: MessageBus,
provider: LLMProvider,
workspace: Path,
model: str | None = None,
max_iterations: int | None = None,
max_concurrent_subagents: int | None = None,
context_window_tokens: int | None = None,
context_block_limit: int | None = None,
max_tool_result_chars: int | None = None,
fail_on_tool_error: bool | None = None,
provider_retry_mode: str = "standard",
tool_hint_max_length: int | None = None,
cron_service: CronService | None = None,
restrict_to_workspace: bool = False,
session_manager: SessionManager | None = None,
mcp_servers: dict[str, MCPServerConfig] | None = None,
channels_config: ChannelsConfig | None = None,
timezone: str | None = None,
session_ttl_minutes: int = 0,
consolidation_ratio: float = 0.5,
hooks: list[AgentHook] | None = None,
hook_factories: list[AgentTurnHookFactory] | None = None,
unified_session: bool = False,
disabled_skills: list[str] | None = None,
tools_config: ToolsConfig | None = None,
image_generation_provider_config: ProviderConfig | None = None,
image_generation_provider_configs: dict[str, ProviderConfig] | None = None,
provider_snapshot_loader: Callable[..., ProviderSnapshot] | None = None,
provider_signature: tuple[object, ...] | None = None,
model_presets: dict[str, ModelPresetConfig] | None = None,
preset_catalog_loader: preset_helpers.PresetCatalogLoader | None = None,
model_preset: str | None = None,
dream_model_preset: str | None = None,
preset_snapshot_loader: preset_helpers.PresetSnapshotLoader | None = None,
runtime_events: RuntimeEventBus | None = None,
turn_delivery_factory: TurnDeliveryFactory | None = None,
runtime_model_publisher: Callable[[str, str | None], None] | None = None,
restart_mode: str = "auto",
local_trigger_store: LocalTriggerStore | None = None,
idle_compact_check_interval_seconds: int = 0,
):
from nanobot.config.schema import ToolsConfig
_tc = tools_config or ToolsConfig()
defaults = AgentDefaults()
self.bus = bus
if turn_delivery_factory is not None:
if turn_delivery_factory.bus is not bus:
raise ValueError("turn delivery factory must use the agent message bus")
if (
runtime_events is not None
and turn_delivery_factory.runtime_events is not runtime_events
):
raise ValueError("turn delivery factory must use the agent runtime event bus")
self.turn_delivery_factory = turn_delivery_factory
self.runtime_events = turn_delivery_factory.runtime_events
else:
self.runtime_events = runtime_events or RuntimeEventBus()
self.turn_delivery_factory = TurnDeliveryFactory(bus, self.runtime_events)
self.runtime_event_publisher = self.turn_delivery_factory.runtime_event_publisher
self.channels_config = channels_config
self.restart_mode = restart_mode
self._runtime_model_publisher = runtime_model_publisher
self.workspace = workspace
initial_model = model or provider.get_default_model()
self.max_iterations = (
max_iterations if max_iterations is not None else defaults.max_tool_iterations
)
initial_context_window = (
context_window_tokens
if context_window_tokens is not None
else defaults.context_window_tokens
)
configured_presets = model_presets or {}
self.runtime_resolver = ModelRuntimeResolver(
LLMRuntime.capture(
provider,
initial_model,
context_window_tokens=initial_context_window,
snapshot_signature=provider_signature,
),
model_presets=configured_presets,
preset_catalog_loader=preset_catalog_loader,
configured_default_preset=model_preset,
provider_snapshot_loader=provider_snapshot_loader,
preset_snapshot_loader=preset_snapshot_loader,
)
self.dream_model_preset = dream_model_preset
self.context_block_limit = context_block_limit
self.max_tool_result_chars = (
max_tool_result_chars
if max_tool_result_chars is not None
else defaults.max_tool_result_chars
)
self.provider_retry_mode = provider_retry_mode
self.tool_hint_max_length = (
tool_hint_max_length if tool_hint_max_length is not None
else defaults.tool_hint_max_length
)
self.tools_config = _tc
self.web_config = _tc.web
self.exec_config = _tc.exec
self._image_generation_provider_configs = dict(image_generation_provider_configs or {})
if (
image_generation_provider_config is not None
and "openrouter" not in self._image_generation_provider_configs
):
self._image_generation_provider_configs["openrouter"] = image_generation_provider_config
self.cron_service = cron_service
self.local_trigger_store = local_trigger_store
self.restrict_to_workspace = restrict_to_workspace
self.workspace_scopes = WorkspaceScopeResolver(
default_workspace=workspace,
default_restrict_to_workspace=restrict_to_workspace,
)
self._start_time = time.time()
self._last_usage: dict[str, int] = {}
self._extra_hooks: list[AgentHook] = hooks or []
self._hook_factories: list[AgentTurnHookFactory] = hook_factories or []
self.context = ContextBuilder(workspace, timezone=timezone, disabled_skills=disabled_skills)
self.sessions = session_manager or SessionManager(workspace)
self.sessions.set_file_cap_archiver(self.context.memory.raw_archive)
self.tools = ToolRegistry()
# One file-read/write tracker per logical session. The tool registry is
# shared by this loop, so tools resolve the active state via contextvars.
self._file_state_store = FileStateStore()
self._exec_session_manager = ExecSessionManager()
self.runner = AgentRunner()
self.subagents = SubagentManager(
workspace=workspace,
bus=bus,
tools_config=_tc,
max_tool_result_chars=self.max_tool_result_chars,
restrict_to_workspace=restrict_to_workspace,
disabled_skills=disabled_skills,
max_iterations=self.max_iterations,
max_concurrent_subagents=max_concurrent_subagents,
fail_on_tool_error=fail_on_tool_error,
llm_wall_timeout_for_session=lambda sk: runner_wall_llm_timeout_s(self.sessions, sk),
)
self._unified_session = unified_session
self._running = False
self._mcp_servers = mcp_servers or {}
self._mcp_stacks: dict[str, MCPConnection] = {}
self._mcp_connecting = False
self._runtime_context_providers: list[RuntimeContextProvider] = []
self._active_tasks: dict[str, set[asyncio.Task[Any]]] = {}
self._background_tasks: set[asyncio.Task[Any]] = set()
self._close_mcp_lock = asyncio.Lock()
self._session_locks: weakref.WeakValueDictionary[str, asyncio.Lock] = (
weakref.WeakValueDictionary()
)
# Per-session pending queues for mid-turn message injection.
# When a session has an active task, new messages for that session
# are routed here instead of creating a new task.
self._pending_queues: dict[str, asyncio.Queue[InboundMessage]] = {}
self._deferred_automation_turns: dict[str, list[InboundMessage]] = {}
self._cron_turns = CronTurnCoordinator(
publish_inbound=self.bus.publish_inbound,
dispatch=self._dispatch,
is_running=lambda: self._running,
deferred_queues=self._deferred_automation_turns,
)
self._local_trigger_turns = LocalTriggerTurnCoordinator(
publish_inbound=self.bus.publish_inbound,
dispatch=self._dispatch,
is_running=lambda: self._running,
deferred_queues=self._deferred_automation_turns,
)
self._automation_turn_coordinators = (
("cron", self._cron_turns),
("local trigger", self._local_trigger_turns),
)
# NANOBOT_MAX_CONCURRENT_REQUESTS: <=0 means unlimited; default 3.
_max = int(os.environ.get("NANOBOT_MAX_CONCURRENT_REQUESTS", "3"))
self._concurrency_gate: asyncio.Semaphore | None = (
asyncio.Semaphore(_max) if _max > 0 else None
)
self.consolidator = Consolidator(
store=self.context.memory,
sessions=self.sessions,
build_messages=self.context.build_messages,
get_tool_definitions=self.tools.get_definitions,
consolidation_ratio=consolidation_ratio,
unified_session=unified_session,
)
self.auto_compact = AutoCompact(
sessions=self.sessions,
consolidator=self.consolidator,
session_ttl_minutes=session_ttl_minutes,
)
self._idle_compact_check_interval_s = idle_compact_check_interval_seconds
self._next_idle_compact_check_at = time.monotonic()
if model_preset:
self.set_model_preset(model_preset, publish_update=False)
self._register_default_tools(provider_snapshot_loader=provider_snapshot_loader)
self._runtime_vars: dict[str, Any] = {}
self._current_iteration: int = 0
self.commands = CommandRouter()
register_builtin_commands(self.commands)
@classmethod
def from_config(
cls,
config: Config,
bus: MessageBus | None = None,
**extra: Any,
) -> AgentLoop:
"""Create an AgentLoop from config with the common parameter set.
Extra keyword arguments are forwarded to ``AgentLoop.__init__``,
allowing callers to override or extend the standard config-derived
parameters (e.g. ``cron_service``, ``session_manager``).
"""
from nanobot.providers.factory import make_provider
if bus is None:
bus = MessageBus()
defaults = config.agents.defaults
provider = extra.pop("provider", None) or make_provider(config)
resolved = config.resolve_preset()
model = extra.pop("model", None) or resolved.model
context_window_tokens = extra.pop("context_window_tokens", None) or resolved.context_window_tokens
provider_snapshot_loader = extra.pop("provider_snapshot_loader", None)
preset_snapshot_loader = extra.pop("preset_snapshot_loader", None) or preset_helpers.make_preset_snapshot_loader(
config,
provider_snapshot_loader,
)
return cls(
bus=bus,
provider=provider,
workspace=config.workspace_path,
model=model,
max_iterations=defaults.max_tool_iterations,
max_concurrent_subagents=defaults.max_concurrent_subagents,
context_window_tokens=context_window_tokens,
context_block_limit=defaults.context_block_limit,
max_tool_result_chars=defaults.max_tool_result_chars,
fail_on_tool_error=defaults.fail_on_tool_error,
provider_retry_mode=defaults.provider_retry_mode,
tool_hint_max_length=defaults.tool_hint_max_length,
restrict_to_workspace=config.tools.restrict_to_workspace,
mcp_servers=config.tools.mcp_servers,
channels_config=config.channels,
timezone=defaults.timezone,
unified_session=defaults.unified_session,
disabled_skills=defaults.disabled_skills,
session_ttl_minutes=defaults.session_ttl_minutes,
idle_compact_check_interval_seconds=defaults.idle_compact_check_interval_seconds,
consolidation_ratio=defaults.consolidation_ratio,
tools_config=config.tools,
model_presets=preset_helpers.configured_model_presets(config),
model_preset=defaults.model_preset,
dream_model_preset=defaults.dream.model_override,
restart_mode=config.gateway.restart_mode,
provider_snapshot_loader=provider_snapshot_loader,
preset_snapshot_loader=preset_snapshot_loader,
**extra,
)
def _sync_subagent_runtime_limits(self) -> None:
"""Keep subagent runtime limits aligned with mutable loop settings."""
self.subagents.max_iterations = self.max_iterations
def invalidate_runtime_config(self) -> None:
"""Invalidate runtime config and notify clients to refresh its catalog."""
self.runtime_resolver.invalidate()
self._publish_runtime_selection(self.runtime_resolver.runtime)
def runtime_for_session(
self,
session: Session,
*,
recover_removed: bool = True,
) -> LLMRuntime:
"""Resolve the immutable runtime selected by one session."""
name = model_preset_from_metadata(session.metadata)
if name is None:
return self.llm_runtime()
try:
return self.runtime_resolver.resolve_preset(name)
except KeyError:
if not recover_removed or name in self.runtime_resolver.model_presets:
raise
logger.warning(
"Session '{}' references removed model preset '{}'; falling back to default",
session.key,
name,
)
session.metadata.pop(SESSION_MODEL_PRESET_METADATA_KEY, None)
self.sessions.save(session)
return self.llm_runtime()
def set_session_model_preset(
self,
session_key: str,
name: str,
) -> LLMRuntime:
"""Validate and persist one session's preset selection."""
runtime = self.runtime_resolver.resolve_preset(name)
session = self.sessions.get_or_create(session_key)
session.metadata[SESSION_MODEL_PRESET_METADATA_KEY] = runtime.model_preset
self.sessions.save(session)
return runtime
def _publish_runtime_selection(
self,
runtime: LLMRuntime,
*,
publish_update: bool = True,
) -> None:
if not publish_update:
return
if self._runtime_model_publisher is not None:
self._runtime_model_publisher(runtime.model, runtime.model_preset)
self.runtime_event_publisher.runtime_model_changed(
runtime.model,
runtime.model_preset,
)
def set_model_preset(
self,
name: str | None,
*,
publish_update: bool = True,
) -> LLMRuntime:
"""Select a named default runtime for future turns."""
old_model = self.model
runtime = self.runtime_resolver.select_preset(name)
self._publish_runtime_selection(runtime, publish_update=publish_update)
logger.info(
"Runtime model switched for next turn: {} -> {}",
old_model,
runtime.model,
)
return runtime
def set_runtime_model(self, model: str) -> LLMRuntime:
"""Select a model on the current provider for future turns."""
return self.runtime_resolver.select_model(model)
def set_runtime_context_window(self, context_window_tokens: int) -> LLMRuntime:
"""Select a context limit for future turns."""
return self.runtime_resolver.select_context_window(context_window_tokens)
def _register_default_tools(
self,
*,
provider_snapshot_loader: Callable[..., ProviderSnapshot] | None,
) -> None:
"""Register the default set of tools via plugin loader."""
from nanobot.agent.tools.context import ToolContext
from nanobot.agent.tools.loader import ToolLoader
ctx = ToolContext(
config=self.tools_config,
workspace=str(self.workspace),
bus=self.bus,
subagent_manager=self.subagents,
cron_service=self.cron_service,
exec_session_manager=self._exec_session_manager,
sessions=self.sessions,
provider_snapshot_loader=provider_snapshot_loader,
image_generation_provider_configs=self._image_generation_provider_configs,
timezone=self.context.timezone or "UTC",
workspace_sandbox=self.workspace_scopes.sandbox_status,
runtime_events=self.runtime_events,
)
loader = ToolLoader()
registered = loader.load(ctx, self.tools)
# MyTool needs runtime state reference — manual registration
if self.tools_config.my.enable:
self.tools.register(
MyTool(runtime_state=self, modify_allowed=self.tools_config.my.allow_set)
)
registered.append("my")
logger.info("Registered {} tools: {}", len(registered), registered)
async def _connect_mcp(self) -> None:
"""Connect configured MCP servers."""
await agent_context.connect_mcp(self, self.tools)
def register_runtime_context_provider(
self,
provider: RuntimeContextProvider,
) -> Callable[[], None]:
"""Register a per-turn context provider and return an unsubscribe callback."""
if provider in self._runtime_context_providers:
return lambda: None
self._runtime_context_providers.append(provider)
def _unsubscribe() -> None:
with suppress(ValueError):
self._runtime_context_providers.remove(provider)
return _unsubscribe
async def submit_cron_turn(self, msg: InboundMessage) -> OutboundMessage | None:
return await self._cron_turns.submit(msg)
async def submit_local_trigger_turn(self, msg: InboundMessage) -> OutboundMessage | None:
return await self._local_trigger_turns.submit(msg)
def pending_cron_job_ids_for_session(self, session_key: str) -> set[str]:
return self._cron_turns.pending_job_ids_for_session(session_key)
def pending_local_trigger_ids_for_session(self, session_key: str) -> set[str]:
return self._local_trigger_turns.pending_trigger_ids_for_session(session_key)
async def _publish_next_deferred_automation_turn(self, session_key: str) -> None:
await publish_next_deferred_turn(
deferred_queues=self._deferred_automation_turns,
publish_inbound=self.bus.publish_inbound,
session_key=session_key,
)
def _persist_user_message_early(
self,
msg: InboundMessage,
session: Session,
runtime_context_blocks: list[RuntimeContextBlock] | None = None,
**kwargs: Any,
) -> bool:
"""Persist the triggering user message before the turn starts.
Returns True if the message was persisted.
"""
if not turn_continuation.should_persist_user_message(msg.metadata):
return False
media_paths = [
path
for path in (msg.media or [])
if isinstance(cast(object, path), str) and path
]
content_value = cast(object, msg.content)
has_text = isinstance(content_value, str) and content_value.strip()
if has_text or media_paths or runtime_context_blocks:
extra: dict[str, Any] = ({"media": list(media_paths)} if media_paths else {}) | agent_context.session_extra(msg.metadata)
extra.update(kwargs)
text = content_value if isinstance(content_value, str) else ""
text_override, automation_extra = automation_history_overrides(msg.metadata)
if text_override is not None:
text = text_override
extra.update(automation_extra)
text, runtime_context_meta = append_runtime_context(
text,
runtime_context_blocks or (),
)
if runtime_context_meta is not None:
extra[RUNTIME_CONTEXT_HISTORY_META] = runtime_context_meta
session.add_message("user", text, **extra)
self._mark_pending_user_turn(session)
self.sessions.save(session)
return True
return False
def _build_initial_messages(self, ctx: TurnContext) -> list[dict[str, Any]]:
"""Build the initial message list for the LLM turn."""
assert ctx.session is not None
scope = self.workspace_scopes.for_message(ctx.msg, ctx.session.metadata)
return self.context.build_messages(
history=ctx.history,
current_message=ctx.msg.content,
media=ctx.msg.media if ctx.kind is TurnKind.USER and ctx.msg.media else None,
channel=ctx.delivery.route.channel,
session_summary=ctx.pending_summary,
workspace=scope.project_path,
runtime_context_blocks=ctx.runtime_context_blocks,
include_memory_recent_history=not ctx.ephemeral,
session_key=ctx.session.key,
unified_session=self._unified_session,
)
def _request_context_for_turn(self, ctx: TurnContext) -> RequestContext:
assert ctx.session is not None
scope = self.workspace_scopes.for_turn(
channel=ctx.delivery.route.channel,
message_metadata=ctx.msg.metadata,
session_metadata=ctx.session.metadata,
)
return RequestContext(
channel=ctx.delivery.route.channel,
chat_id=ctx.delivery.route.chat_id,
message_id=ctx.msg.metadata.get("message_id"),
session_key=ctx.session_key,
original_user_text=ctx.original_user_text,
runtime=ctx.runtime,
metadata=dict(ctx.msg.metadata or {}),
attributes=dict(ctx.attributes),
sender_id=ctx.msg.sender_id,
turn_id=ctx.turn_id,
workspace=scope.project_path,
)
async def _resolve_runtime_context_for_turn(
self,
ctx: TurnContext,
) -> list[RuntimeContextBlock]:
assert ctx.request_context is not None
return await self._resolve_runtime_context_for_request(
ctx.request_context,
ctx.tools or self.tools,
)
async def _resolve_runtime_context_for_request(
self,
request: RequestContext,
tools: ToolRegistry,
) -> list[RuntimeContextBlock]:
providers = [
*tools.get_runtime_context_providers(),
*self._runtime_context_providers,
]
blocks = runtime_context_blocks_from_metadata(request.metadata)
blocks.extend(await resolve_runtime_context(providers, request))
return blocks
async def _dispatch_command_inline(
self,
msg: InboundMessage,
key: str,
raw: str,
dispatch_fn: Callable[[CommandContext], Awaitable[OutboundMessage | None]],
) -> None:
"""Dispatch a command directly from the run() loop and publish the result."""
ctx = CommandContext(msg=msg, session=None, key=key, raw=raw, loop=self)
result = await dispatch_fn(ctx)
if result:
await self.bus.publish_outbound(result)
else:
logger.warning("Command '{}' matched but dispatch returned None", raw)
async def _cancel_active_tasks(self, key: str) -> int:
"""Cancel and await all active tasks and subagents for *key*.
Returns the total number of cancelled tasks + subagents.
"""
tasks = tuple(self._active_tasks.pop(key, set()))
cancelled = sum(1 for t in tasks if not t.done() and t.cancel())
for t in tasks:
with suppress(asyncio.CancelledError, Exception):
await t
sub_cancelled = await self.subagents.cancel_by_session(key)
return cancelled + sub_cancelled
def _effective_session_key(self, msg: InboundMessage) -> str:
"""Return the session key used for task routing and mid-turn injections."""
if self._unified_session and not msg.session_key_override:
return UNIFIED_SESSION_KEY
return msg.session_key
def _remember_unified_session_route(
self,
session: Session,
msg: InboundMessage,
*,
is_user_turn: bool,
) -> None:
"""Remember the latest user-facing route for unified-session delivery."""
if (
not self._unified_session
or session.key != UNIFIED_SESSION_KEY
or not is_user_turn
or msg.channel in {"cli", "system"}
or msg.sender_id == "subagent"
):
return
_, automation_metadata = automation_history_overrides(msg.metadata)
if automation_metadata:
return
remember_last_channel(session.metadata, msg.channel, msg.chat_id)
@staticmethod
def _replay_token_budget(runtime: LLMRuntime) -> int:
"""Derive a token budget for session history replay from the context window."""
if runtime.context_window_tokens <= 0:
return 0
max_output = runtime.generation.max_tokens
try:
reserved_output = int(max_output)
except (TypeError, ValueError):
reserved_output = 4096
budget = runtime.context_window_tokens - max(1, reserved_output) - 1024
return budget if budget > 0 else max(128, runtime.context_window_tokens // 2)
async def _run_agent_loop(
self,
initial_messages: list[dict[str, Any]],
on_progress: Callable[..., Awaitable[None]] | None = None,
on_stream: Callable[[str], Awaitable[None]] | None = None,
on_stream_end: Callable[..., Awaitable[None]] | None = None,
on_retry_wait: Callable[[str], Awaitable[None]] | None = None,
*,
runtime: LLMRuntime,
session: Session | None = None,
channel: str = "cli",
chat_id: str = "direct",
message_id: str | None = None,
metadata: dict[str, Any] | None = None,
session_key: str | None = None,
original_user_text: str | None = None,
pending_queue: asyncio.Queue[InboundMessage] | None = None,
ephemeral: bool = False,
run_extra_hooks_for_ephemeral: bool = False,
hooks: list[AgentHook] | None = None,
hook_factories: list[AgentTurnHookFactory] | None = None,
turn_scopes: list[AbstractContextManager[Any]] | None = None,
tools: ToolRegistry | None = None,
request_context: RequestContext | None = None,
provider_state: ProviderConversationState | None = None,
) -> tuple[str | None, list[str], list[dict[str, Any]], str, bool]:
"""Run the agent iteration loop.
*on_stream*: called with each content delta during streaming.
*on_stream_end(resuming, merge_next)*: called when a streaming session finishes.
``resuming=True`` means the active turn continues. ``merge_next=True`` means
the next text segment belongs to the same user-visible assistant message.
Returns (final_content, tools_used, messages, stop_reason, had_injections).
"""
self._sync_subagent_runtime_limits()
async def _checkpoint(payload: dict[str, Any]) -> None:
if session is None:
return
public_payload = dict(payload)
private_state = public_payload.pop("provider_state", None)
public_payload.pop(self._PROVIDER_STATE_CHECKPOINT_VERSION_KEY, None)
if "provider_state" in payload and (
private_state is None
or isinstance(private_state, ProviderConversationState)
):
session.provider_state = private_state
public_payload[self._PROVIDER_STATE_CHECKPOINT_VERSION_KEY] = (
self._PROVIDER_STATE_CHECKPOINT_VERSION
)
self._set_runtime_checkpoint(session, public_payload)
async def _drain_pending(*, limit: int = _MAX_INJECTIONS_PER_TURN) -> list[dict[str, Any]]:
"""Drain follow-up messages from the pending queue.
When no messages are immediately available but sub-agents
spawned in this dispatch are still running, blocks until at
least one result arrives (or timeout). This keeps the runner
loop alive so subsequent sub-agent completions are consumed
in-order rather than dispatched separately.
"""
if pending_queue is None:
return []
async def _to_user_message(pending_msg: InboundMessage) -> dict[str, Any]:
content = pending_msg.content
image_paths = pending_msg.media if pending_msg.media else None
if image_paths:
content, image_paths = reference_non_image_attachments(
content,
image_paths,
)
image_paths = image_paths or None
user_content = self.context.build_user_content(
content,
image_paths=image_paths,
)
row: dict[str, Any] = {"role": "user", "content": user_content}
metadata_value = cast(object, pending_msg.metadata)
metadata = (
pending_msg.metadata
if isinstance(metadata_value, dict)
else {}
)
if pending_msg.channel != "system":
scope = self.workspace_scopes.for_turn(
channel=pending_msg.channel,
message_metadata=metadata,
session_metadata=session.metadata if session is not None else None,
)
pending_request = RequestContext(
channel=pending_msg.channel,
chat_id=pending_msg.chat_id,
message_id=metadata.get("message_id"),
session_key=active_session_key,
original_user_text=pending_msg.content,
runtime=runtime,
metadata=dict(metadata),
attributes=dict(request_ctx.attributes),
sender_id=pending_msg.sender_id,
turn_id=request_ctx.turn_id,
workspace=scope.project_path,
)
blocks = await self._resolve_runtime_context_for_request(
pending_request,
effective_tools,
)
row["content"], runtime_marker = append_runtime_context(
user_content,
blocks,
)
if runtime_marker is not None:
row["_meta"] = {
RUNTIME_CONTEXT_MESSAGE_META: runtime_marker,
}
if (
pending_msg.sender_id == "subagent"
and metadata.get("injected_event") == "subagent_result"
):
subagent_marker: dict[str, Any] = {"kind": "subagent_result"}
task_id = metadata.get("subagent_task_id")
if isinstance(task_id, str) and task_id:
subagent_marker["subagent_task_id"] = task_id
row["subagent_task_id"] = task_id
row[HIDDEN_HISTORY_META] = subagent_marker
row["injected_event"] = "subagent_result"
return row
items: list[dict[str, Any]] = []
while len(items) < limit:
try:
items.append(await _to_user_message(pending_queue.get_nowait()))
except asyncio.QueueEmpty:
break
# Block if nothing drained but sub-agents spawned in this dispatch
# are still running. Keeps the runner loop alive so subsequent
# completions are injected in-order rather than dispatched separately.
if (not items
and session is not None
and self.subagents.get_running_count_by_session(session.key) > 0):
try:
msg = await asyncio.wait_for(pending_queue.get(), timeout=300)
except asyncio.TimeoutError:
logger.warning(
"Timeout waiting for sub-agent completion in session {}",
session.key,
)
return items
items.append(await _to_user_message(msg))
while len(items) < limit:
try:
items.append(await _to_user_message(pending_queue.get_nowait()))
except asyncio.QueueEmpty:
break
return items
active_session_key = session.key if session else session_key
effective_scope = self.workspace_scopes.for_turn(
channel=channel,
message_metadata=metadata,
session_metadata=session.metadata if session is not None else None,
)
effective_tools = tools or self.tools
request_ctx = request_context or RequestContext(
channel=channel,
chat_id=chat_id,
message_id=message_id,
session_key=active_session_key,
original_user_text=original_user_text,
runtime=runtime,
metadata=dict(metadata or {}),
workspace=effective_scope.project_path,
)
file_state_token = bind_file_states(self._file_state_store.for_session(active_session_key))
request_token = bind_request_context(request_ctx)
workspace_token = bind_workspace_scope(effective_scope)
turn_scope_stack = ExitStack()
# Compute lazily because create_goal may create goal metadata during this run.
def _goal_continue() -> str | None:
_goal_lines = goal_state_runtime_lines(session.metadata if session is not None else None)
if not _goal_lines:
return None
return (
"You have an active sustained goal:\n\n"
+ "\n".join(_goal_lines)
+ "\n\nPlease continue working toward the objective using your tools, "
"or call update_goal with action='complete' if the work is truly finished."
)
session_metadata = session.metadata if session is not None else None
try:
for scope in turn_scopes or ():
turn_scope_stack.enter_context(scope)
hook = build_agent_turn_hook(AgentTurnHookSpec(
on_progress=on_progress,
on_stream=on_stream,
on_stream_end=on_stream_end,
channel=channel,
chat_id=chat_id,
message_id=message_id,
metadata=metadata,
attributes=dict(request_ctx.attributes),
session_key=active_session_key,
workspace=effective_scope.project_path,
tool_hint_max_length=self.tool_hint_max_length,
on_iteration=lambda iteration: setattr(self, "_current_iteration", iteration),
registered_hook_factories=self._hook_factories,
turn_hook_factories=list(hook_factories or []),
registered_hooks=self._extra_hooks,
turn_hooks=list(hooks or []),
ephemeral=ephemeral,
run_extra_hooks_for_ephemeral=run_extra_hooks_for_ephemeral,
))
result = await self.runner.run(AgentRunSpec(
initial_messages=initial_messages,
tools=effective_tools,
runtime=runtime,
max_iterations=self.max_iterations,
max_tool_result_chars=self.max_tool_result_chars,
hook=hook,
error_message="Sorry, I encountered an error calling the AI model.",
concurrent_tools=True,
workspace=effective_scope.project_path,
session_key=session.key if session else None,
context_block_limit=self.context_block_limit,
provider_retry_mode=self.provider_retry_mode,
progress_callback=on_progress,
stream_progress_deltas=on_stream is not None,
retry_wait_callback=on_retry_wait,
checkpoint_callback=_checkpoint,
injection_callback=_drain_pending,
# Sustained goals may legitimately exceed NANOBOT_LLM_TIMEOUT_S; idle stall
# is still capped by NANOBOT_STREAM_IDLE_TIMEOUT_S in streaming providers.
llm_timeout_s=runner_wall_llm_timeout_s(
self.sessions,
session.key if session is not None else session_key,
metadata=session_metadata,
message_metadata=metadata,
),
goal_active_predicate=lambda: sustained_goal_active(session.metadata) if session is not None else False,
goal_continue_message=_goal_continue,
finalize_on_max_iterations=turn_continuation.should_finalize_on_max_iterations(
pending_queue_available=pending_queue is not None and session is not None,
session_metadata=session_metadata,
message_metadata=metadata,
),
provider_state=provider_state,
))
finally:
turn_scope_stack.close()
reset_workspace_scope(workspace_token)
reset_request_context(request_token)
reset_file_states(file_state_token)
self._last_usage = result.usage
if session is not None and not ephemeral:
session.provider_state = result.provider_state
if result.stop_reason == "max_iterations":
logger.warning("Max iterations ({}) reached", self.max_iterations)
should_stream = turn_continuation.should_stream_budget_response(
stop_reason=result.stop_reason,
pending_queue_available=pending_queue is not None and session is not None,
session_metadata=session_metadata,
message_metadata=metadata,
)
# Push final content through stream so streaming channels (e.g. Feishu)
# update the card instead of leaving it empty.
if on_stream and on_stream_end and should_stream:
stream_content = (
result.pending_stream_content
if result.pending_stream_content is not None
else result.final_content or ""
)
await on_stream(stream_content)
await on_stream_end(resuming=False)
elif result.stop_reason == "error":
logger.error("LLM returned error: {}", (result.final_content or "")[:200])
return result.final_content, result.tools_used, result.messages, result.stop_reason, result.had_injections
def _check_expired_sessions_if_due(self) -> None:
"""Scan idle sessions no more often than the configured interval."""
now = time.monotonic()
if now < self._next_idle_compact_check_at:
return
self._next_idle_compact_check_at = now + self._idle_compact_check_interval_s
self.auto_compact.check_expired(
self.schedule_background,
self.runtime_for_session,
active_session_keys=self._pending_queues.keys(),
)
async def run(self) -> None:
"""Run the agent loop, dispatching messages as tasks to stay responsive to /stop."""
self._running = True
try:
await self._connect_mcp()
logger.info("Agent loop started")
while self._running:
try:
msg = await asyncio.wait_for(self.bus.consume_inbound(), timeout=1.0)
except asyncio.TimeoutError:
self._check_expired_sessions_if_due()
continue
except asyncio.CancelledError:
# Preserve real task cancellation so shutdown can complete cleanly.
# Only ignore non-task CancelledError signals that may leak from integrations.
if not self._running or task_is_cancelling():
raise
logger.warning(
"Ignoring leaked CancelledError while consuming inbound messages"
)
continue
except Exception as e:
logger.warning("Error consuming inbound message: {}, continuing...", e)
continue
raw = msg.content.strip()
effective_key = self._effective_session_key(msg)
if await agent_context.handle_runtime_control(self, msg, self.tools):
continue
if self.commands.is_priority(raw):
await self._dispatch_command_inline(
msg, effective_key, raw,
self.commands.dispatch_priority,
)
continue
deferred = False
for label, coordinator in self._automation_turn_coordinators:
if coordinator.defer_if_active(
msg,
session_key=effective_key,
active_session_keys=self._pending_queues.keys(),
):
logger.info(
"Deferred {} turn for active session {}",
label,
effective_key,
)
deferred = True
break
if deferred:
continue
# If this session already has an active pending queue (i.e. a task
# is processing this session), route the message there for mid-turn
# injection instead of creating a competing task.
if effective_key in self._pending_queues:
# Non-priority commands must not be queued for injection;
# dispatch them directly (same pattern as priority commands).
if self.commands.is_dispatchable_command(raw):
await self._dispatch_command_inline(
msg, effective_key, raw,
self.commands.dispatch,
)
continue
pending_msg = msg
if effective_key != msg.session_key:
pending_msg = dataclasses.replace(
msg,
session_key_override=effective_key,
)
try:
self._pending_queues[effective_key].put_nowait(pending_msg)
except asyncio.QueueFull:
logger.warning(
"Pending queue full for session {}, falling back to queued task",
effective_key,
)
else:
logger.info(
"Routed follow-up message to pending queue for session {}",
effective_key,
)
continue
# Compute the effective session key before dispatching
# This ensures /stop command can find tasks correctly when unified session is enabled
task = asyncio.create_task(self._dispatch(msg))
active_tasks = self._active_tasks.setdefault(effective_key, set())
active_tasks.add(task)
task.add_done_callback(active_tasks.discard)
finally:
# MCP stdio transports use AnyIO cancel scopes; close them from the task that opened them.
await self.close_mcp()
async def _dispatch(self, msg: InboundMessage) -> None:
"""Process a message: per-session serial, cross-session concurrent."""
session_key = self._effective_session_key(msg)
if session_key != msg.session_key:
msg = dataclasses.replace(msg, session_key_override=session_key)
lock = self._get_session_lock(session_key)
gate = self._concurrency_gate or nullcontext()
delivery = self.turn_delivery_factory.unrouted(msg, session_key)
pending: asyncio.Queue[InboundMessage] | None = None
try:
async with lock, gate:
# Only the task that owns the session lock may publish the
# active mid-turn injection queue for this session.
pending = asyncio.Queue(maxsize=20)
self._pending_queues[session_key] = pending
try:
delivery = self.turn_delivery_factory.create(
msg,
session_key,
enable_stream=True,
)
response = await self._process_message(
msg,
on_stream=delivery.on_stream,
on_stream_end=delivery.on_stream_end,
pending_queue=pending,
delivery=delivery,
)
continuing = turn_continuation.internal_continuation_pending(msg.metadata)
await delivery.complete(
response,
publish_completion=not continuing,
)
for _, coordinator in self._automation_turn_coordinators:
coordinator.complete(msg, response=response)
except asyncio.CancelledError:
for _, coordinator in self._automation_turn_coordinators:
coordinator.complete(msg, error=asyncio.CancelledError())
logger.info("Task cancelled for session {}", session_key)
try:
await delivery.abort_stream()
except Exception:
logger.debug(
"Could not close stream for cancelled session {}",
session_key,
exc_info=True,
)
# Preserve partial context from the interrupted turn so
# the user does not lose tool results and assistant
# messages accumulated before /stop. The checkpoint was
# already persisted to session metadata by
# _emit_checkpoint during tool execution; materializing
# it into session history now makes it visible in the
# next conversation turn.
try:
key = self._effective_session_key(msg)
session = self.sessions.get_or_create(key)
if self._restore_runtime_checkpoint(session):
self._clear_pending_user_turn(session)
self.sessions.save(session)
logger.info(
"Restored partial context for cancelled session {}",
key,
)
except Exception:
logger.debug(
"Could not restore checkpoint for cancelled session {}",
session_key,
exc_info=True,
)
raise
except Exception as exc:
logger.exception("Error processing message for session {}", session_key)
await delivery.fail(
publish_completion=not turn_continuation.internal_continuation_pending(
msg.metadata
)
)
for _, coordinator in self._automation_turn_coordinators:
coordinator.complete(msg, error=exc)
finally:
# Drain any messages still in the pending queue and re-publish
# them to the bus so they are processed as fresh inbound messages
# rather than silently lost. Only remove our own queue; a
# later task waiting on the lock must not be able to steal
# cleanup ownership.
queue = None
if self._pending_queues.get(session_key) is pending:
queue = self._pending_queues.pop(session_key, None)
else:
queue = pending
if queue is not None:
leftover = 0
while True:
try:
item = queue.get_nowait()
except asyncio.QueueEmpty:
break
await self.bus.publish_inbound(item)
leftover += 1
if leftover:
logger.info(
"Re-published {} leftover message(s) to bus for session {}",
leftover, session_key,
)
if not turn_continuation.internal_continuation_pending(msg.metadata):
await delivery.idle()
await self._publish_next_deferred_automation_turn(session_key)
finally:
if pending is None:
await delivery.idle()
await self._publish_next_deferred_automation_turn(session_key)
async def close_mcp(self) -> None:
"""Stop active work, then close exec, subagent, and MCP resources.
Resource teardown must still run if cancellation interrupts task draining.
Gateway shutdown deliberately bounds this coroutine, so keeping the cleanup
phase in ``finally`` prevents a timed-out background task from leaving
subprocess transports alive after the event loop closes.
"""
# The agent loop closes itself from ``run()`` while gateway shutdown also
# performs a guaranteed final close. Serialize those owners so they cannot
# tear down the same subprocess transports concurrently.
close_lock = getattr(self, "_close_mcp_lock", None)
if close_lock is None:
close_lock = self._close_mcp_lock = asyncio.Lock()
async with close_lock:
await self._close_mcp_unlocked()
async def _close_mcp_unlocked(self) -> None:
errors: list[BaseException] = []
active_task_groups = getattr(self, "_active_tasks", {})
active_tasks = tuple({task for tasks in active_task_groups.values() for task in tasks})
active_task_groups.clear()
current_task = asyncio.current_task()
active_tasks = tuple(task for task in active_tasks if task is not current_task)
for task in active_tasks:
if not task.done():
task.cancel()
try:
if active_tasks:
await asyncio.gather(*active_tasks, return_exceptions=True)
if self._background_tasks:
await asyncio.gather(*self._background_tasks, return_exceptions=True)
except BaseException as exc:
errors.append(exc)
finally:
self._background_tasks.clear()
cleanup_steps = (
self.subagents.close,
self._exec_session_manager.close_all,
lambda: agent_context.close_mcp(self),
)
for cleanup in cleanup_steps:
try:
await cleanup()
except BaseException as exc:
errors.append(exc)
if len(errors) == 1:
raise errors[0]
if errors:
raise BaseExceptionGroup("failed to close agent resources", errors)
def schedule_background(self, coro: Coroutine[Any, Any, Any]) -> None:
"""Schedule a coroutine as a tracked background task (drained on shutdown)."""
task = asyncio.create_task(coro)
self._background_tasks.add(task)
task.add_done_callback(self._background_tasks.discard)
def stop(self) -> None:
"""Stop the agent loop."""
self._running = False
logger.info("Agent loop stopping")
async def _process_message(
self,
msg: InboundMessage,
session_key: str | None = None,
on_progress: Callable[..., Awaitable[None]] | None = None,
on_stream: Callable[[str], Awaitable[None]] | None = None,
on_stream_end: Callable[..., Awaitable[None]] | None = None,
pending_queue: asyncio.Queue[InboundMessage] | None = None,
ephemeral: bool = False,
run_extra_hooks_for_ephemeral: bool = False,
hooks: list[AgentHook] | None = None,
hook_factories: list[AgentTurnHookFactory] | None = None,
tools: ToolRegistry | None = None,
runtime: LLMRuntime | None = None,
delivery: TurnDelivery | None = None,
on_runtime_admitted: Callable[[LLMRuntime], Awaitable[None]] | None = None,
attributes: Mapping[str, Any] | None = None,
) -> OutboundMessage | None:
"""Process a single inbound message and return the response."""
kind = TurnKind.SYSTEM if msg.channel == "system" else TurnKind.USER
if kind is TurnKind.SYSTEM:
destination = (
msg.chat_id.split(":", 1) if ":" in msg.chat_id else ("cli", msg.chat_id)
)
key = session_key or msg.session_key_override or f"{destination[0]}:{destination[1]}"
else:
key = session_key or msg.session_key
if delivery is None:
delivery = self.turn_delivery_factory.create(msg, key)
elif delivery.session_key != key:
raise ValueError("turn delivery session does not match the processing session")
if on_stream is None:
on_stream = delivery.on_stream
if on_stream_end is None:
on_stream_end = delivery.on_stream_end
t0 = time.time()
ctx = TurnContext(
msg=msg,
session=None,
session_key=key,
turn_id=f"{key}:{time.time_ns()}",
runtime=runtime,
kind=kind,
delivery=delivery,
original_user_text=(
None
if kind is TurnKind.SYSTEM
or turn_continuation.internal_continuation_inbound(msg.metadata)
else msg.content
),
turn_wall_started_at=t0,
visible_run_started_at=turn_continuation.internal_continuation_run_started_at(
msg.metadata,
),
on_progress=on_progress,
on_stream=on_stream,
on_stream_end=on_stream_end,
on_runtime_admitted=on_runtime_admitted,
pending_queue=pending_queue,
ephemeral=ephemeral,
run_extra_hooks_for_ephemeral=run_extra_hooks_for_ephemeral,
hooks=list(hooks or []),
hook_factories=list(hook_factories or []),
tools=tools,
attributes=dict(attributes or {}),
)
# A streaming callback may be present even when the final text comes from a
# non-streaming recovery. Only the last completed segment can suppress the
# regular outbound message.
if ctx.on_stream is not None:
stream_callback = ctx.on_stream
stream_end_callback = ctx.on_stream_end
stream_end_accepts_merge_next = False
if stream_end_callback is not None:
try:
stream_end_signature = inspect.signature(stream_end_callback)
stream_end_accepts_merge_next = (
"merge_next" in stream_end_signature.parameters
or any(
parameter.kind is inspect.Parameter.VAR_KEYWORD
for parameter in stream_end_signature.parameters.values()
)
)
except (TypeError, ValueError):
pass
segment_streamed_content = False
async def _tracked_stream(delta: str) -> None:
nonlocal segment_streamed_content
if delta:
segment_streamed_content = True
await stream_callback(delta)
async def _tracked_stream_end(
*,
resuming: bool = False,
merge_next: bool = False,
) -> None:
nonlocal segment_streamed_content
ctx.streamed_content = segment_streamed_content
segment_streamed_content = False
if stream_end_callback is not None:
if merge_next and stream_end_accepts_merge_next:
await stream_end_callback(resuming=resuming, merge_next=True)
else:
await stream_end_callback(resuming=resuming)
ctx.on_stream = _tracked_stream
ctx.on_stream_end = _tracked_stream_end
await self._run_turn_stage(ctx, "restore", self._restore_turn)
await self._run_turn_stage(ctx, "compact", self._compact_session)
if await self._run_turn_stage(ctx, "command", self._dispatch_command):
return ctx.outbound
await self._run_turn_stage(ctx, "build", self._build_turn)
await self._run_turn_stage(ctx, "run", self._run_turn)
await self._run_turn_stage(ctx, "save", self._persist_turn)
await self._run_turn_stage(ctx, "respond", self._prepare_outbound)
return ctx.outbound
async def _run_turn_stage(
self,
ctx: TurnContext,
name: str,
handler: Callable[[TurnContext], Awaitable[_T]],
) -> _T:
started_at = time.perf_counter()
try:
result = await handler(ctx)
except Exception:
duration_ms = (time.perf_counter() - started_at) * 1000
logger.debug(
"[turn {}] Stage {} failed after {:.1f}ms",
ctx.turn_id,
name,
duration_ms,
)
raise
duration_ms = (time.perf_counter() - started_at) * 1000
logger.debug(
"[turn {}] Stage {} completed in {:.1f}ms",
ctx.turn_id,
name,
duration_ms,
)
return result
def _assemble_outbound(
self,
msg: InboundMessage,
final_content: str,
stop_reason: str,
had_injections: bool,
streamed_content: bool,
*,
turn_latency_ms: int | None = None,
) -> OutboundMessage | None:
"""Assemble the final outbound message from turn results."""
# MessageTool suppression
if (mt := self.tools.get("message")) and isinstance(mt, MessageTool) and mt._sent_in_turn:
if not had_injections or stop_reason == "empty_final_response":
return None
preview = final_content[:120] + "..." if len(final_content) > 120 else final_content
logger.info("Response to {}:{}: {}", msg.channel, msg.sender_id, preview)
event = None
meta = dict(msg.metadata or {})
if streamed_content and stop_reason not in {"error", "tool_error"}:
event = StreamedResponseEvent()
if turn_latency_ms is not None:
meta["latency_ms"] = int(turn_latency_ms)
return OutboundMessage(
channel=msg.channel,
chat_id=msg.chat_id,
content=final_content,
event=event,
metadata=meta,
)
async def _restore_turn(self, ctx: TurnContext) -> None:
"""Restore checkpoint / pending user turn; reference non-image attachments."""
msg = ctx.msg
if ctx.kind is TurnKind.USER and msg.media:
new_content, image_paths = reference_non_image_attachments(
msg.content,
msg.media,
)
ctx.msg = dataclasses.replace(msg, content=new_content, media=image_paths)
msg = ctx.msg
preview = msg.content[:80] + "..." if len(msg.content) > 80 else msg.content
if ctx.kind is TurnKind.SYSTEM:
logger.info("Processing system message from {}", msg.sender_id)
else:
logger.info("Processing message from {}:{}: {}", msg.channel, msg.sender_id, preview)
# Session is already fetched by the caller (_process_message) but
# ensure it exists in case this handler is invoked independently.
if ctx.session is None:
ctx.session = self.sessions.get_or_create(ctx.session_key)
session = ctx.session
self._remember_unified_session_route(
session,
msg,
is_user_turn=ctx.original_user_text is not None,
)
await ctx.delivery.started()
if ctx.kind is TurnKind.USER:
self.workspace_scopes.persist_message_scope(session, msg)
if self._restore_runtime_checkpoint(session):
self.sessions.save(session)
if self._restore_pending_user_turn(session):
self.sessions.save(session)
async def _compact_session(self, ctx: TurnContext) -> None:
session = ctx.require_session()
ctx.session, pending = self.auto_compact.prepare_session(
session,
ctx.session_key,
)
ctx.pending_summary = pending
async def _dispatch_command(self, ctx: TurnContext) -> bool:
if ctx.kind is TurnKind.SYSTEM:
return False
session = ctx.require_session()
raw = ctx.msg.content.strip()
_, automation_metadata = automation_history_overrides(ctx.msg.metadata)
is_user_turn = (
ctx.original_user_text is not None
and not automation_metadata
and ctx.msg.channel != "system"
and ctx.msg.sender_id != "subagent"
)
cmd_ctx = CommandContext(
msg=ctx.msg,
session=session,
key=ctx.session_key,
raw=raw,
loop=self,
runtime=ctx.runtime,
is_user_turn=is_user_turn,
turn_scopes=ctx.turn_scopes,
)
result = await self.commands.dispatch(cmd_ctx)
if result is not None:
ctx.outbound = result
# Shortcut commands skip BUILD and SAVE, so we must persist the
# turn here so WebUI history hydration after _turn_end sees the
# message. Mark messages with _command so get_history can filter
# them out of LLM context. /new is excluded because it
# intentionally clears the session.
if cmd_ctx.raw.lower() != "/new":
ctx.input_persisted_early = self._persist_user_message_early(
ctx.msg, session, _command=True
)
session.add_message(
"assistant", result.content, _command=True
)
self._clear_pending_user_turn(session)
self.sessions.save(session)
if not ctx.ephemeral:
await self.runtime_event_publisher.session_turn_persisted(
ctx.msg,
ctx.session_key,
turn_id=ctx.turn_id,
attributes=ctx.attributes,
)
return True
return False
async def _build_turn(self, ctx: TurnContext) -> None:
session = ctx.require_session()
runtime = ctx.runtime
if runtime is None:
runtime = self.runtime_for_session(session)
ctx.runtime = runtime
if ctx.session_key.startswith("dream:"):
logger.info(
"Dream run using model={} (preset={})",
runtime.model,
runtime.model_preset or "default",
)
if ctx.on_runtime_admitted is not None:
await ctx.on_runtime_admitted(runtime)
replay_max_messages = replay_max_messages_for_context(
runtime.context_window_tokens
)
if not ctx.ephemeral:
await self.consolidator.maybe_consolidate_by_tokens(
session,
runtime=runtime,
replay_max_messages=replay_max_messages,
)
is_subagent = ctx.kind is TurnKind.SYSTEM and ctx.msg.sender_id == "subagent"
if ctx.kind is TurnKind.USER and (message_tool := self.tools.get("message")):
if isinstance(message_tool, MessageTool):
message_tool.start_turn()
_hist_kwargs: dict[str, Any] = {
"max_messages": replay_max_messages,
"max_tokens": self._replay_token_budget(runtime),
"extend_to_user": is_subagent,
}
ctx.history = session.get_history(**_hist_kwargs)
stored_state = session.provider_state
subagent_followup_persisted = False
if is_subagent:
# Keep the durable internal delivery as an assistant record, but
# present this completion to the model as fresh follow-up input.
# Providers without assistant-prefill support drop trailing
# assistant messages, so using the persisted record as the current
# prompt would hide an independently dispatched subagent result.
subagent_followup_persisted = self._persist_subagent_followup(
session,
ctx.msg,
)
if subagent_followup_persisted:
logger.debug("Subagent result persisted for session {}", ctx.session_key)
# Establish a durable, replay-safe baseline before any fallible
# provider compatibility or prompt assembly work. A compatible
# staged state replaces this in a second atomic save below.
session.provider_state = None
self.sessions.save(session)
ctx.input_persisted_early = True
ctx.delivery.record_runtime(runtime)
ctx.request_context = self._request_context_for_turn(ctx)
if ctx.kind is TurnKind.USER:
ctx.runtime_context_blocks = await self._resolve_runtime_context_for_turn(ctx)
staged_provider_state = False
if stored_state is not None and runtime.provider.can_resume_conversation_state(
stored_state,
runtime.model,
):
current_provider_message = self.context.build_current_message(
ctx.msg.content,
media=ctx.msg.media if ctx.kind is TurnKind.USER and ctx.msg.media else None,
runtime_context_blocks=ctx.runtime_context_blocks,
)
task_id = ctx.msg.metadata.get("subagent_task_id") if is_subagent else None
already_staged = False
if isinstance(task_id, str) and task_id:
internal_meta = current_provider_message.get("_meta")
current_provider_message["_meta"] = {
**(
cast(dict[str, Any], internal_meta)
if isinstance(internal_meta, dict)
else {}
),
_SUBAGENT_PROVIDER_TASK_META: task_id,
}
already_staged = any(
isinstance(message.get("_meta"), dict)
and cast(dict[str, Any], message["_meta"]).get(
_SUBAGENT_PROVIDER_TASK_META
)
== task_id
for message in stored_state.pending_messages
)
ctx.provider_state = (
stored_state
if already_staged
else stored_state.with_pending_messages([
*stored_state.pending_messages,
current_provider_message,
])
)
if (
not ctx.ephemeral
and (ctx.kind is TurnKind.USER or subagent_followup_persisted)
):
session.provider_state = ctx.provider_state
staged_provider_state = True
elif stored_state is not None:
session.provider_state = None
if ctx.kind is TurnKind.USER:
ctx.input_persisted_early = self._persist_user_message_early(
ctx.msg,
session,
runtime_context_blocks=ctx.runtime_context_blocks,
)
if staged_provider_state and not ctx.input_persisted_early:
session.provider_state = stored_state
elif subagent_followup_persisted and staged_provider_state:
# Upgrade the replay-safe baseline to the resumable state before
# prompt assembly and the first model checkpoint.
self.sessions.save(session)
ctx.initial_messages = self._build_initial_messages(ctx)
if ctx.on_progress is None:
ctx.on_progress = ctx.delivery.progress_callback()
if ctx.on_retry_wait is None:
ctx.on_retry_wait = ctx.delivery.retry_wait_callback()
async def _run_turn(self, ctx: TurnContext) -> None:
runtime = ctx.require_runtime()
if ctx.visible_run_started_at is None:
ctx.visible_run_started_at = time.time()
await ctx.delivery.running(started_at=ctx.visible_run_started_at)
result = await self._run_agent_loop(
ctx.initial_messages,
runtime=runtime,
on_progress=ctx.on_progress,
on_stream=ctx.on_stream,
on_stream_end=ctx.on_stream_end,
on_retry_wait=ctx.on_retry_wait,
session=ctx.session,
channel=ctx.delivery.route.channel,
chat_id=ctx.delivery.route.chat_id,
message_id=ctx.msg.metadata.get("message_id"),
metadata=ctx.msg.metadata,
session_key=ctx.session_key,
original_user_text=ctx.original_user_text,
pending_queue=ctx.pending_queue,
ephemeral=ctx.ephemeral,
run_extra_hooks_for_ephemeral=ctx.run_extra_hooks_for_ephemeral,
hooks=ctx.hooks,
hook_factories=ctx.hook_factories,
turn_scopes=ctx.turn_scopes,
tools=ctx.tools,
request_context=ctx.request_context,
provider_state=ctx.provider_state,
)
final_content, _, all_msgs, stop_reason, had_injections = result
ctx.final_content = final_content
ctx.all_messages = all_msgs
ctx.stop_reason = stop_reason
ctx.had_injections = had_injections
if ctx.kind is TurnKind.USER:
await turn_continuation.maybe_continue_turn(ctx)
async def _persist_turn(self, ctx: TurnContext) -> None:
runtime = ctx.require_runtime()
session = ctx.require_session()
turn_continuation.prepare_save_boundary(ctx)
if (
ctx.kind is TurnKind.USER
and (ctx.final_content is None or not ctx.final_content.strip())
and not ctx.suppress_response
):
ctx.final_content = EMPTY_FINAL_RESPONSE_MESSAGE
latency_started_at = (
ctx.visible_run_started_at
if (
ctx.kind is TurnKind.SYSTEM
or turn_continuation.internal_continuation_inbound(ctx.msg.metadata)
)
and ctx.visible_run_started_at is not None
else ctx.turn_wall_started_at
)
ctx.turn_latency_ms = max(0, int((time.time() - latency_started_at) * 1000))
self._save_turn(
session, ctx.all_messages, ctx.save_skip,
turn_latency_ms=ctx.turn_latency_ms,
)
ctx.delivery.record_latency(ctx.turn_latency_ms)
if not ctx.ephemeral:
session.enforce_file_cap(
on_archive=partial(self.context.memory.raw_archive, session_key=ctx.session_key)
)
self.schedule_background(
self.consolidator.maybe_consolidate_by_tokens(
session,
runtime=runtime,
replay_max_messages=replay_max_messages_for_context(
runtime.context_window_tokens
),
)
)
self._clear_pending_user_turn(session)
self._clear_runtime_checkpoint(session)
self.sessions.save(session)
if not ctx.ephemeral:
await self.runtime_event_publisher.session_turn_persisted(
ctx.msg,
ctx.session_key,
turn_id=ctx.turn_id,
attributes=ctx.attributes,
)
async def _prepare_outbound(self, ctx: TurnContext) -> None:
if ctx.suppress_response:
ctx.outbound = None
return
if ctx.kind is TurnKind.SYSTEM:
ctx.outbound = ctx.delivery.background_response(
ctx.final_content,
stop_reason=ctx.stop_reason,
streamed=ctx.streamed_content,
latency_ms=ctx.turn_latency_ms,
)
return
ctx.outbound = self._assemble_outbound(
ctx.msg,
cast(str, ctx.final_content),
ctx.stop_reason,
ctx.had_injections,
ctx.streamed_content,
turn_latency_ms=ctx.turn_latency_ms,
)
if ctx.ephemeral and ctx.outbound is not None:
ctx.outbound.metadata["_stop_reason"] = ctx.stop_reason
def _sanitize_persisted_blocks(
self,
content: list[object],
*,
should_truncate_text: bool = False,
) -> list[object]:
"""Strip volatile multimodal payloads before writing session history."""
filtered: list[object] = []
for block in content:
if not isinstance(block, dict):
filtered.append(block)
continue
block_data = cast(dict[str, Any], block)
image_url = cast(dict[str, Any], block_data.get("image_url", {}))
if block_data.get("type") == "image_url" and str(
image_url.get("url", "")
).startswith("data:image/"):
internal_meta = cast(dict[str, Any], block_data.get("_meta") or {})
path = cast(str, internal_meta.get("path", ""))
filtered.append(
{"type": "text", "text": image_placeholder_text(path)}
)
continue
if block_data.get("type") == "text" and isinstance(
block_data.get("text"),
str,
):
text = cast(str, block_data["text"])
if should_truncate_text and len(text) > self.max_tool_result_chars:
text = truncate_text_fn(text, self.max_tool_result_chars)
filtered.append({**block_data, "text": text})
continue
filtered.append(block_data)
return filtered
def _save_turn(
self,
session: Session,
messages: list[dict[str, Any]],
skip: int,
*,
turn_latency_ms: int | None = None,
) -> None:
"""Save new-turn messages into session, truncating large tool results."""
from datetime import datetime
declared_tool_call_ids = {
str(tc["id"])
for m in session.messages
if m.get("role") == "assistant"
for tc_value in cast(Iterable[object], m.get("tool_calls") or [])
if isinstance(tc_value, dict)
for tc in (cast(dict[str, Any], tc_value),)
if tc.get("id")
}
fulfilled_tool_call_ids = {
str(m["tool_call_id"])
for m in session.messages
if m.get("role") == "tool" and m.get("tool_call_id")
}
last_assistant_idx: int | None = None
for m in messages[skip:]:
entry = dict(m)
internal_meta = cast(object, entry.pop("_meta", None))
runtime_context_meta = (
cast(dict[str, Any], internal_meta).get(
RUNTIME_CONTEXT_MESSAGE_META
)
if isinstance(internal_meta, dict)
else None
)
role, content = entry.get("role"), entry.get("content")
if role == "assistant" and not content and not entry.get("tool_calls"):
continue # skip empty assistant messages — they poison session context
if role == "tool":
tool_call_id = entry.get("tool_call_id")
tool_call_id_str = str(tool_call_id) if tool_call_id else ""
if (
not tool_call_id_str
or tool_call_id_str not in declared_tool_call_ids
or tool_call_id_str in fulfilled_tool_call_ids
):
# Undeclared tool results corrupt future provider requests.
logger.warning(
"Dropping invalid tool result {} from session {} during persistence",
tool_call_id_str or "(missing id)",
session.key,
)
continue
fulfilled_tool_call_ids.add(tool_call_id_str)
if isinstance(content, str) and len(content) > self.max_tool_result_chars:
entry["content"] = truncate_text_fn(content, self.max_tool_result_chars)
elif isinstance(content, list):
filtered = self._sanitize_persisted_blocks(
cast(list[object], content),
should_truncate_text=True,
)
if not filtered:
# Preserve the tool_call/result pair after block filtering.
filtered = [
{"type": "text", "text": "[tool result omitted during persistence]"}
]
entry["content"] = filtered
elif role == "user":
if isinstance(content, list):
filtered = self._sanitize_persisted_blocks(
cast(list[object], content),
)
if not filtered:
continue
entry["content"] = filtered
if isinstance(runtime_context_meta, dict):
entry[RUNTIME_CONTEXT_HISTORY_META] = runtime_context_meta
entry.setdefault("timestamp", datetime.now().isoformat())
session.messages.append(entry)
if role == "assistant":
last_assistant_idx = len(session.messages) - 1
declared_tool_call_ids.update(
str(tc["id"])
for tc_value in cast(
Iterable[object],
entry.get("tool_calls") or [],
)
if isinstance(tc_value, dict)
for tc in (cast(dict[str, Any], tc_value),)
if tc.get("id")
)
if turn_latency_ms is not None and last_assistant_idx is not None:
session.messages[last_assistant_idx]["latency_ms"] = int(turn_latency_ms)
session.updated_at = datetime.now()
def _persist_subagent_followup(self, session: Session, msg: InboundMessage) -> bool:
"""Persist subagent follow-ups before prompt assembly so history stays durable.
Returns True if a new entry was appended; False if the follow-up was
deduped (same ``subagent_task_id`` already in session) or carries no
content worth persisting.
"""
if not msg.content:
return False
metadata_value = cast(object, msg.metadata)
task_id = (
msg.metadata.get("subagent_task_id")
if isinstance(metadata_value, dict)
else None
)
if task_id and any(
m.get("injected_event") == "subagent_result" and m.get("subagent_task_id") == task_id
for m in session.messages
):
return False
session.add_message(
"assistant",
msg.content,
sender_id=msg.sender_id,
injected_event="subagent_result",
subagent_task_id=task_id,
)
return True
def _set_runtime_checkpoint(self, session: Session, payload: dict[str, Any]) -> None:
"""Persist the latest in-flight turn state into session metadata."""
session.metadata[self._RUNTIME_CHECKPOINT_KEY] = payload
self.sessions.save(session)
def _mark_pending_user_turn(self, session: Session) -> None:
session.metadata[self._PENDING_USER_TURN_KEY] = True
def _clear_pending_user_turn(self, session: Session) -> None:
session.metadata.pop(self._PENDING_USER_TURN_KEY, None)
def _clear_runtime_checkpoint(self, session: Session) -> None:
if self._RUNTIME_CHECKPOINT_KEY in session.metadata:
session.metadata.pop(self._RUNTIME_CHECKPOINT_KEY, None)
@staticmethod
def _checkpoint_message_key(message: dict[str, Any]) -> tuple[Any, ...]:
return (
message.get("role"),
message.get("content"),
message.get("tool_call_id"),
message.get("name"),
message.get("tool_calls"),
message.get("reasoning_content"),
message.get("thinking_blocks"),
)
def _restore_runtime_checkpoint(self, session: Session) -> bool:
"""Materialize an unfinished turn into session history before a new request."""
from datetime import datetime
checkpoint = cast(
object,
session.metadata.get(self._RUNTIME_CHECKPOINT_KEY),
)
if not isinstance(checkpoint, dict):
return False
checkpoint_data = cast(dict[str, Any], checkpoint)
assistant_message = cast(object, checkpoint_data.get("assistant_message"))
completed_tool_results = cast(
Iterable[object],
checkpoint_data.get("completed_tool_results") or [],
)
pending_tool_calls = cast(
Iterable[object],
checkpoint_data.get("pending_tool_calls") or [],
)
restored_messages: list[dict[str, Any]] = []
if isinstance(assistant_message, dict):
restored = dict(cast(dict[str, Any], assistant_message))
restored.setdefault("timestamp", datetime.now().isoformat())
restored_messages.append(restored)
for message in completed_tool_results:
if isinstance(message, dict):
restored = dict(cast(dict[str, Any], message))
restored.setdefault("timestamp", datetime.now().isoformat())
restored_messages.append(restored)
for tool_call in pending_tool_calls:
if not isinstance(tool_call, dict):
continue
tool_call_data = cast(dict[str, Any], tool_call)
tool_id = tool_call_data.get("id")
function_data = cast(
dict[str, Any],
tool_call_data.get("function") or {},
)
name = function_data.get("name") or "tool"
restored_messages.append(
{
"role": "tool",
"tool_call_id": tool_id,
"name": name,
"content": "Error: Task interrupted before this tool finished.",
"timestamp": datetime.now().isoformat(),
}
)
overlap = 0
max_overlap = min(len(session.messages), len(restored_messages))
for size in range(max_overlap, 0, -1):
existing = session.messages[-size:]
restored = restored_messages[:size]
if all(
self._checkpoint_message_key(left) == self._checkpoint_message_key(right)
for left, right in zip(existing, restored)
):
overlap = size
break
appended_messages = restored_messages[overlap:]
session.messages.extend(appended_messages)
assistant_message_data = (
cast(dict[str, Any], assistant_message)
if isinstance(assistant_message, dict)
else None
)
provider_state_is_synchronized = (
checkpoint_data.get(self._PROVIDER_STATE_CHECKPOINT_VERSION_KEY)
== self._PROVIDER_STATE_CHECKPOINT_VERSION
)
phase = checkpoint_data.get("phase")
exact_final_response = (
phase == "final_response"
and assistant_message_data is not None
and assistant_message_data.get("role") == "assistant"
and not bool(checkpoint_data.get("completed_tool_results"))
and not bool(checkpoint_data.get("pending_tool_calls"))
)
exact_completed_tools = (
phase == "tools_completed"
and assistant_message_data is not None
and assistant_message_data.get("role") == "assistant"
and not bool(checkpoint_data.get("pending_tool_calls"))
)
if not (
provider_state_is_synchronized
and (exact_final_response or exact_completed_tools)
):
session.provider_state = None
self._clear_pending_user_turn(session)
self._clear_runtime_checkpoint(session)
return True
def _restore_pending_user_turn(self, session: Session) -> bool:
"""Close a turn that only persisted the user message before crashing."""
from datetime import datetime
if not session.metadata.get(self._PENDING_USER_TURN_KEY):
return False
if session.messages and session.messages[-1].get("role") == "user":
session.messages.append(
{
"role": "assistant",
"content": "Error: Task interrupted before a response was generated.",
"timestamp": datetime.now().isoformat(),
}
)
session.provider_state = None
session.updated_at = datetime.now()
self._clear_pending_user_turn(session)
return True
async def process_direct(
self,
content: str,
session_key: str = "cli:direct",
channel: str = "cli",
chat_id: str = "direct",
sender_id: str = "user",
media: list[str] | None = None,
on_progress: Callable[..., Awaitable[None]] | None = None,
on_stream: Callable[[str], Awaitable[None]] | None = None,
on_stream_end: Callable[..., Awaitable[None]] | None = None,
ephemeral: bool = False,
_run_extra_hooks_for_ephemeral: bool = False,
hooks: list[AgentHook] | None = None,
hook_factories: list[AgentTurnHookFactory] | None = None,
tools: ToolRegistry | None = None,
persist_user_message: bool = True,
runtime: LLMRuntime | None = None,
on_runtime_admitted: Callable[[LLMRuntime], Awaitable[None]] | None = None,
attributes: Mapping[str, Any] | None = None,
) -> OutboundMessage | None:
"""Process an external message directly and return the outbound payload."""
if channel == "system":
raise ValueError("channel 'system' is reserved for internal messages")
await self._connect_mcp()
metadata: dict[str, Any] = {}
if not persist_user_message:
metadata[turn_continuation.SKIP_USER_PERSIST_META] = True
msg = InboundMessage(
channel=channel, sender_id=sender_id, chat_id=chat_id,
content=content, media=media or [], metadata=metadata,
)
# Share the dispatch lock so direct calls serialize with bus turns.
lock = self._get_session_lock(session_key)
try:
async with lock:
kwargs: dict[str, Any] = {
"session_key": session_key,
"on_progress": on_progress,
"on_stream": on_stream,
"on_stream_end": on_stream_end,
"ephemeral": ephemeral,
}
if _run_extra_hooks_for_ephemeral:
kwargs["run_extra_hooks_for_ephemeral"] = True
if hooks is not None:
kwargs["hooks"] = hooks
if hook_factories is not None:
kwargs["hook_factories"] = hook_factories
if tools is not None:
kwargs["tools"] = tools
if runtime is not None:
kwargs["runtime"] = runtime
if on_runtime_admitted is not None:
kwargs["on_runtime_admitted"] = on_runtime_admitted
if attributes is not None:
kwargs["attributes"] = dict(attributes)
return await self._process_message(
msg,
**kwargs,
)
finally:
await self.runtime_event_publisher.run_status_changed(msg, session_key, "idle")
self.runtime_event_publisher.clear_turn(session_key)
def _get_session_lock(self, session_key: str) -> asyncio.Lock:
"""Return the shared lock while allowing idle session entries to expire."""
lock = self._session_locks.get(session_key)
if lock is None:
lock = asyncio.Lock()
self._session_locks[session_key] = lock
return lock
Reference in New Issue View Git Blame Copy Permalink