mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-09-17 10:06:18 +00:00
7389331e65
* feat(extensions): observe task lifecycle and system model calls PR 1 (#4636) gave extensions a middleware chain, and a middleware only sees what passes through the agent graph. Two runtime surfaces stay invisible to it: when a lead run or a subagent begins and ends, and the DeerFlow-owned model calls made outside the graph. This slice adds both, with no new Gateway surface -- routers, services, and the reference extension stay in PR 3. Contract (deerflow-extension-api 0.1.1) --------------------------------------- Two contribution kinds join `middlewares` on the registry: `task_lifecycle` (`on_task_start` / `on_task_stop`, receiving a `TaskInfo` and a conservative `TaskOutcome` of completed / aborted / failed) and `system_model_observer` (`on_system_model_call`, receiving a `SystemOperationKind`, a `SystemModelRequest` snapshot, and a `SystemModelResult` carrying either the response or the provider exception plus a duration). `SystemModelRequest.messages` normalizes to a tuple at construction. Goal evaluation and memory extraction pass a message list while title generation and summarization pass one prompt string, and a bare `str` already satisfies `Sequence` -- without normalization an observer iterating `request.messages` would silently walk characters. Copying also makes the frozen snapshot immutable in fact rather than only by declaration, since observations may run after the call site returns and keeps mutating its own list. Registry marks and rollbacks become per-bucket and positional, so an `install()` that fails after registering two different kinds cannot leave one of them behind. `needs_task_store` now covers all three kinds: a deployment that registers only lifecycle hooks still gets a task store. Task lifecycle -------------- The lead worker notifies start after the run has started and stop after completion persistence and the completion hook, but before clearing the finalizing barrier and publishing the stream end -- holding the barrier across stop is what keeps a same-thread replacement run from overlapping this task's lifecycle. Cancellation raised out of the stop notification is deferred, not propagated in place, so a cancelled run still clears the barrier and emits its end frame. A subagent with a parent `run_id` wraps its execution in the same pair inside `finally`, reporting `parent_task_id` so a delegation tree is reconstructable; a subagent without a `run_id` (embedded client, standalone LangGraph Server) logs and skips rather than inventing a parent. Contributors run in registration order inside one shared 3s budget and every failure is logged and failed open. System model calls ------------------ Four kinds cover the model calls the middleware chain cannot see: goal evaluation, memory extraction, title generation, and summarization. Each site reports both terminal paths without changing the provider exception the host observes, short-circuits on `has_system_model_observers`, and passes the live task store when the runtime has one (detached work gets an isolated store). The sync summarization half stays unobserved on purpose -- it and its only host caller are the sync side of an async-only runtime, so notifying there would block a thread on a call site the host never reaches; the reason is recorded at the call site. The DeerMem backend must stay vendorable and cannot import the extension API, so it reports through a new `MemoryCallbacks.on_memory_llm_result` host hook that the DeerFlow-side callbacks translate into an observation. Notification loop ----------------- Extension resources must be touched on the loop that created them, but subagents can execute on isolated loops and DeerMem runs on a worker thread. The Gateway registers its serving loop before any runtime dependency starts and resets it last through the exit stack, so every startup-failure and cancellation path is covered. Awaited hooks raised on another loop are dispatched across with `run_coroutine_threadsafe` and awaited under the same budget; synchronous sites submit fire-and-forget work. Shutdown stops accepting detached observations before the memory flush -- that flush runs on a worker thread and can emit memory observations -- while keeping the loop alive for awaited task hooks until run and subagent drain completes. Tests ----- `test_extension_task_lifecycle.py`, `test_extension_subagent_lifecycle.py`, and `test_extension_system_model_calls.py` cover ordering, fail-open, budget exhaustion, snapshot binding under a concurrent singleton replacement, the loop-dispatch and shutdown-suspension paths, and both terminal paths at every call site. `test_gateway_run_drain_shutdown.py` pins the stop-before-barrier and drain ordering. * fix(extensions): decide notification fail-open by origin, observe cancellation `_notify_each` only guarded `Exception`, so a contributor letting a `CancelledError` escape — an extension implementing an internal timeout with cancellation, say — skipped its successors and reached the worker's deferred-interrupt path, ending an otherwise successful run as cancelled. Fail-open is about where a failure came from, not its base class: only a genuine cancellation of the host task increments `Task.cancelling()`, so propagate on that and contain everything else. `KeyboardInterrupt` / `SystemExit` still propagate. `observe_system_model_call` skipped observers on cancellation for the same base-class reason, leaving goal / title / summarization silent on a terminal path that is routine — interrupt/rollback admission and shutdown both cancel the run task, with the provider tokens already spent. Awaiting observers there is unreliable (a repeated cancel interrupts that await before any of them runs), so report through the same non-blocking submission the synchronous memory bridge uses, then propagate the cancellation untouched. DeerMem keeps `BaseException` around its provider call, now with the reason recorded: that path runs on a worker thread, where cancelling the awaiting side never interrupts the running thread, so `CancelledError` cannot arrive at all. Its host-hook wrapper narrows to `Exception` — only the hook's own failures are non-fatal, and an observability path must not swallow a process teardown signal. * fix(extensions): warn on budget exhaustion, scope observer logs by task, propagate teardown Review response on #4684: - The memory observation bridge caught BaseException, which would swallow a teardown signal raised while dispatching; it now catches Exception, matching the boundary the DeerMem-side call site documents and tests. - A notification-budget timeout raised mid-hook fell into the generic hook-failure path and logged an asyncio-internal traceback; it now logs a warning like the pre-hook budget skip, while a TimeoutError a contributor raises on its own stays classified as a hook failure. - System model observer logs passed the operation kind as the task id, so log lines said "task goal/title/..."; they now carry the task scope id alongside the kind.
2424 lines
108 KiB
Python
2424 lines
108 KiB
Python
"""Background agent execution.
|
|
|
|
Runs an agent graph inside an ``asyncio.Task``, publishing events to
|
|
a :class:`StreamBridge` as they are produced.
|
|
|
|
Uses ``graph.astream(stream_mode=[...])`` which gives correct full-state
|
|
snapshots for ``values`` mode, proper ``{node: writes}`` for ``updates``,
|
|
and ``(chunk, metadata)`` tuples for ``messages`` mode.
|
|
|
|
Note: ``events`` mode is rejected by the gateway — it requires
|
|
``graph.astream_events()`` which cannot simultaneously produce ``values``
|
|
snapshots. The JS open-source LangGraph API server works around this via
|
|
internal checkpoint callbacks that are not exposed in the Python public API.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import copy
|
|
import inspect
|
|
import logging
|
|
import os
|
|
import sys
|
|
import threading
|
|
import weakref
|
|
from collections.abc import AsyncIterator
|
|
from contextlib import asynccontextmanager
|
|
from dataclasses import dataclass, field
|
|
from datetime import datetime
|
|
from functools import lru_cache
|
|
from typing import Any, Literal, cast
|
|
|
|
from langgraph.checkpoint.base import empty_checkpoint
|
|
from langgraph.types import Overwrite
|
|
|
|
from deerflow.agents.goal_state import GoalEvaluation, GoalState
|
|
from deerflow.config.app_config import AppConfig
|
|
from deerflow.config.database_config import CheckpointChannelMode
|
|
from deerflow.constants import TOOL_RESULTS_DIRNAME
|
|
from deerflow.runtime.checkpoint_mode import (
|
|
aensure_checkpoint_mode_compatible,
|
|
inject_checkpoint_mode,
|
|
)
|
|
from deerflow.runtime.checkpoint_state import (
|
|
CheckpointStateAccessor,
|
|
build_state_mutation_graph,
|
|
graph_reducer_channels,
|
|
graph_state_schema,
|
|
graph_writable_channels,
|
|
)
|
|
from deerflow.runtime.context_keys import CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY
|
|
from deerflow.runtime.goal import (
|
|
DEFAULT_MAX_GOAL_CONTINUATIONS,
|
|
DEFAULT_MAX_NO_PROGRESS_CONTINUATIONS,
|
|
GoalWriteConflict,
|
|
_call_checkpointer_method,
|
|
_is_visible_message,
|
|
_message_type,
|
|
attach_goal_evaluation,
|
|
compute_no_progress_count,
|
|
create_goal_evaluator_model,
|
|
evaluate_goal_completion,
|
|
goal_thread_lock,
|
|
latest_visible_assistant_signature,
|
|
make_goal_continuation_message,
|
|
read_thread_goal,
|
|
should_continue_goal,
|
|
visible_conversation_signature,
|
|
write_thread_goal,
|
|
)
|
|
from deerflow.runtime.serialization import serialize
|
|
from deerflow.runtime.stream_bridge import StreamBridge
|
|
from deerflow.runtime.stream_modes import normalize_stream_modes, to_langgraph_stream_modes
|
|
from deerflow.runtime.user_context import get_effective_user_id, resolve_runtime_user_id
|
|
from deerflow.trace_context import (
|
|
DEERFLOW_TRACE_METADATA_KEY,
|
|
is_trace_id_from_request_header,
|
|
resolve_deerflow_trace_id,
|
|
)
|
|
from deerflow.tracing import inject_langfuse_metadata
|
|
from deerflow.utils.messages import message_to_text
|
|
from deerflow.workspace_changes import capture_workspace_snapshot, get_changed_output_paths, record_workspace_changes
|
|
from deerflow.workspace_changes.types import WorkspaceSnapshot
|
|
|
|
from .manager import RunManager, RunRecord, RunStartOutcome
|
|
from .naming import resolve_root_run_name
|
|
from .schemas import RunStatus
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
_checkpoint_locks_guard = threading.Lock()
|
|
_checkpoint_locks_by_loop: weakref.WeakKeyDictionary[asyncio.AbstractEventLoop, dict[str, asyncio.Lock]] = weakref.WeakKeyDictionary()
|
|
|
|
|
|
@asynccontextmanager
|
|
async def _checkpoint_thread_lock(thread_id: str) -> AsyncIterator[None]:
|
|
"""Serialize checkpoint mutations for one thread without blocking goal commands."""
|
|
loop = asyncio.get_running_loop()
|
|
with _checkpoint_locks_guard:
|
|
locks = _checkpoint_locks_by_loop.get(loop)
|
|
if locks is None:
|
|
locks = {}
|
|
_checkpoint_locks_by_loop[loop] = locks
|
|
lock = locks.get(thread_id)
|
|
if lock is None:
|
|
lock = asyncio.Lock()
|
|
locks[thread_id] = lock
|
|
|
|
async with lock:
|
|
yield
|
|
|
|
|
|
_DELIVERY_RECEIPT_RETRY_DELAYS_SECONDS = (0.1, 0.5)
|
|
_EXTENSION_TASK_NOTIFY_TIMEOUT_SECONDS = 3.0
|
|
|
|
|
|
async def _persist_delivery_receipt(
|
|
event_store: Any,
|
|
*,
|
|
thread_id: str,
|
|
run_id: str,
|
|
content: dict[str, Any],
|
|
) -> bool:
|
|
"""Persist a terminal receipt with short bounded retries.
|
|
|
|
The owning worker still knows the real terminal outcome and renews its
|
|
lease while this coroutine runs. Retrying here handles transient event
|
|
store failures without handing a successful run to orphan recovery, which
|
|
cannot reconstruct either the terminal status or the detailed receipt.
|
|
"""
|
|
attempts = len(_DELIVERY_RECEIPT_RETRY_DELAYS_SECONDS) + 1
|
|
for attempt in range(attempts):
|
|
try:
|
|
await event_store.put_if_absent(
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
event_type="run.delivery",
|
|
category="outputs",
|
|
content=content,
|
|
)
|
|
return True
|
|
except Exception:
|
|
if attempt == attempts - 1:
|
|
logger.warning(
|
|
"Failed to persist delivery receipt for run %s after %d attempts; applying terminal delivery semantics without a receipt",
|
|
run_id,
|
|
attempts,
|
|
exc_info=True,
|
|
)
|
|
return False
|
|
delay = _DELIVERY_RECEIPT_RETRY_DELAYS_SECONDS[attempt]
|
|
logger.warning(
|
|
"Failed to persist delivery receipt for run %s (attempt %d/%d); retrying in %.1fs",
|
|
run_id,
|
|
attempt + 1,
|
|
attempts,
|
|
delay,
|
|
exc_info=True,
|
|
)
|
|
await asyncio.sleep(delay)
|
|
|
|
return False # pragma: no cover - loop always returns
|
|
|
|
|
|
_DELIVERY_INCOMPLETE_ERROR = "Artifact delivery incomplete: no produced output artifact was presented"
|
|
_DELIVERY_RECEIPT_FAILED_ERROR = "Artifact delivery verification failed: terminal delivery receipt could not be persisted"
|
|
|
|
|
|
def _empty_delivery_content() -> dict[str, Any]:
|
|
return {"presented": 0, "paths": [], "by_tool": {}}
|
|
|
|
|
|
def _presented_path_covers_output(presented_path: str, produced_path: str) -> bool:
|
|
presented_path = presented_path.rstrip("/")
|
|
return bool(presented_path) and (produced_path == presented_path or produced_path.startswith(f"{presented_path}/"))
|
|
|
|
|
|
def _delivery_content_with_outputs(
|
|
content: dict[str, Any],
|
|
produced_paths: list[str],
|
|
) -> dict[str, Any]:
|
|
"""Attach a delivery verdict when this run created or modified outputs."""
|
|
if not produced_paths:
|
|
return content
|
|
|
|
presented_paths = content.get("by_tool", {}).get("present_files", [])
|
|
matched_paths = [produced_path for produced_path in produced_paths if any(_presented_path_covers_output(presented_path, produced_path) for presented_path in presented_paths)]
|
|
satisfied = bool(matched_paths)
|
|
return {
|
|
**content,
|
|
"verification": {
|
|
"source": "outputs_changed",
|
|
"requirement": "present_files_matches_produced_output",
|
|
},
|
|
"produced_paths": produced_paths,
|
|
"presented_paths": presented_paths,
|
|
"matched_paths": matched_paths,
|
|
"stage": "presented" if satisfied else ("mismatched" if presented_paths else "not_started"),
|
|
"satisfied": satisfied,
|
|
}
|
|
|
|
|
|
def _delivery_error(content: dict[str, Any]) -> str | None:
|
|
"""Return the terminal error when no changed output was presented."""
|
|
if not content.get("produced_paths") or content.get("satisfied") is True:
|
|
return None
|
|
return _DELIVERY_INCOMPLETE_ERROR
|
|
|
|
|
|
def _workspace_excluded_dir_names(app_config: AppConfig | None) -> frozenset[str]:
|
|
"""Directory names workspace snapshots must skip for this deployment.
|
|
|
|
The tool-output budget middleware externalizes oversized tool outputs into
|
|
a storage subdir under outputs (default ``.tool-results``). Those files are
|
|
process feedback referenced from the budget preview via ``read_file``, not
|
|
deliverables: counting them as produced artifacts would fail run delivery
|
|
verification for any run that externalized a tool output without also
|
|
presenting a real artifact. The default name is excluded by the scanner
|
|
itself; a custom ``tool_output.storage_subdir`` (a single-segment name,
|
|
enforced by ``ToolOutputConfig`` so the scanner's dir-name pruning always
|
|
matches) is threaded through the snapshot capture here so before/after
|
|
diffs stay consistent.
|
|
"""
|
|
storage_subdir = app_config.tool_output.storage_subdir if app_config is not None else TOOL_RESULTS_DIRNAME
|
|
return frozenset({storage_subdir})
|
|
|
|
|
|
async def _produced_output_paths(
|
|
before: WorkspaceSnapshot | None,
|
|
*,
|
|
thread_id: str,
|
|
user_id: str | None,
|
|
extra_excluded_dir_names: frozenset[str] | None = None,
|
|
) -> list[str]:
|
|
"""Detect regular output files created or modified by this run."""
|
|
if before is None:
|
|
return []
|
|
try:
|
|
after = await capture_workspace_snapshot(thread_id, user_id=user_id, include_text=False, extra_excluded_dir_names=extra_excluded_dir_names)
|
|
return get_changed_output_paths(before, after)
|
|
except Exception:
|
|
logger.warning("Could not detect produced output artifacts for run thread %s", thread_id, exc_info=True)
|
|
return []
|
|
|
|
|
|
# Keep this streaming policy separate from middleware write-authorization sets.
|
|
_LARGE_FILE_TOOL_NAMES = frozenset({"str_replace", "write_file"})
|
|
_LARGE_FILE_TOOL_BATCH_SIZE = 32
|
|
|
|
|
|
@dataclass
|
|
class _LargeFileToolChunkBatcher:
|
|
"""Batch file-body argument deltas to avoid quadratic browser parsing.
|
|
|
|
Normal assistant text and non-file tool calls remain token-streamed. Large
|
|
file arguments still update progressively, but in bounded batches instead
|
|
of forcing the browser to reparse the growing JSON on every model token.
|
|
"""
|
|
|
|
batch_size: int = _LARGE_FILE_TOOL_BATCH_SIZE
|
|
tool_names: dict[tuple[str, str, str], str] = field(default_factory=dict)
|
|
pending_identity: tuple[str, str, str] | None = None
|
|
pending_message: Any | None = None
|
|
pending_metadata: dict[str, Any] = field(default_factory=dict)
|
|
pending_count: int = 0
|
|
|
|
def push(self, chunk: Any) -> list[Any]:
|
|
if not isinstance(chunk, tuple) or len(chunk) != 2:
|
|
return [*self.flush(), chunk]
|
|
|
|
message, metadata = chunk
|
|
message_id = getattr(message, "id", None)
|
|
tool_call_chunks = getattr(message, "tool_call_chunks", None)
|
|
if not isinstance(message_id, str) or not message_id or not isinstance(tool_call_chunks, list) or len(tool_call_chunks) != 1:
|
|
return [*self.flush(), chunk]
|
|
|
|
tool_chunk = tool_call_chunks[0]
|
|
if not isinstance(tool_chunk, dict):
|
|
return [*self.flush(), chunk]
|
|
index = tool_chunk.get("index")
|
|
tool_call_id = tool_chunk.get("id")
|
|
if isinstance(index, int):
|
|
discriminator = f"index:{index}"
|
|
elif isinstance(tool_call_id, str) and tool_call_id:
|
|
discriminator = f"id:{tool_call_id}"
|
|
else:
|
|
discriminator = "single"
|
|
raw_namespace = None
|
|
if isinstance(metadata, dict):
|
|
raw_namespace = metadata.get("langgraph_checkpoint_ns") or metadata.get("checkpoint_ns")
|
|
namespace = raw_namespace if isinstance(raw_namespace, str) else ""
|
|
identity = (namespace, message_id, discriminator)
|
|
name_fragment = tool_chunk.get("name")
|
|
tool_name = self.tool_names.get(identity, "")
|
|
if tool_name not in _LARGE_FILE_TOOL_NAMES and isinstance(name_fragment, str) and name_fragment:
|
|
tool_name += name_fragment
|
|
if any(candidate.startswith(tool_name) for candidate in _LARGE_FILE_TOOL_NAMES):
|
|
self.tool_names[identity] = tool_name
|
|
else:
|
|
self.tool_names.pop(identity, None)
|
|
# Batching starts only after the accumulated name matches; split or
|
|
# incomplete name fragments stream per-chunk until then.
|
|
if tool_name not in _LARGE_FILE_TOOL_NAMES:
|
|
return [*self.flush(), chunk]
|
|
|
|
model_copy = getattr(message, "model_copy", None)
|
|
if not callable(model_copy):
|
|
return [*self.flush(), chunk]
|
|
additional_kwargs = getattr(message, "additional_kwargs", None)
|
|
sanitized_additional_kwargs = additional_kwargs
|
|
if isinstance(additional_kwargs, dict) and ("function_call" in additional_kwargs or "tool_calls" in additional_kwargs):
|
|
sanitized_additional_kwargs = {key: value for key, value in additional_kwargs.items() if key not in {"function_call", "tool_calls"}}
|
|
has_non_tool_payload = bool(getattr(message, "content", None) or sanitized_additional_kwargs or getattr(message, "usage_metadata", None) or getattr(message, "response_metadata", None))
|
|
outputs: list[Any] = []
|
|
if self.pending_identity is not None and self.pending_identity != identity:
|
|
outputs.extend(self.flush())
|
|
if has_non_tool_payload:
|
|
visible_message = model_copy(
|
|
update={
|
|
"additional_kwargs": sanitized_additional_kwargs,
|
|
"invalid_tool_calls": [],
|
|
"tool_call_chunks": [],
|
|
"tool_calls": [],
|
|
}
|
|
)
|
|
outputs.append((visible_message, metadata))
|
|
|
|
tool_only_message = model_copy(
|
|
update={
|
|
"additional_kwargs": {},
|
|
"content": "",
|
|
"invalid_tool_calls": [],
|
|
"response_metadata": {},
|
|
"tool_calls": [],
|
|
"usage_metadata": None,
|
|
}
|
|
)
|
|
self.pending_identity = identity
|
|
self.pending_message = tool_only_message if self.pending_message is None else self.pending_message + tool_only_message
|
|
if isinstance(metadata, dict):
|
|
self.pending_metadata.update(metadata)
|
|
self.pending_count += 1
|
|
if self.pending_count >= self.batch_size:
|
|
outputs.extend(self.flush())
|
|
return outputs
|
|
|
|
def flush(self) -> list[Any]:
|
|
if self.pending_message is None:
|
|
return []
|
|
chunk = (self.pending_message, self.pending_metadata)
|
|
self.pending_identity = None
|
|
self.pending_message = None
|
|
self.pending_metadata = {}
|
|
self.pending_count = 0
|
|
return [chunk]
|
|
|
|
def finish(self) -> list[Any]:
|
|
"""Flush and release identities at a values or end-of-stream boundary.
|
|
|
|
A regular batch-size or interleaved-mode flush must retain identities
|
|
because continuation chunks commonly omit the tool name.
|
|
"""
|
|
chunks = self.flush()
|
|
self.tool_names.clear()
|
|
return chunks
|
|
|
|
|
|
def _build_runtime_context(
|
|
thread_id: str,
|
|
run_id: str,
|
|
caller_context: Any | None,
|
|
app_config: AppConfig | None = None,
|
|
task_store: Any | None = None,
|
|
extensions: Any | None = None,
|
|
) -> dict[str, Any]:
|
|
"""Build the dict that becomes ``ToolRuntime.context`` for the run.
|
|
|
|
Always includes ``thread_id`` and ``run_id``. Additional keys from the caller's
|
|
``config['context']`` (e.g. ``agent_name`` for the bootstrap flow — issue #2677)
|
|
are merged in but never override ``thread_id``/``run_id``. The resolved
|
|
``AppConfig`` is added by the worker so tools can consume it without ambient
|
|
global lookups.
|
|
|
|
langgraph 1.1+ surfaces this as ``runtime.context`` via the parent runtime stored
|
|
under ``config['configurable']['__pregel_runtime']`` — see
|
|
``langgraph.pregel.main`` where ``parent_runtime.merge(...)`` is invoked.
|
|
"""
|
|
runtime_ctx: dict[str, Any] = {"thread_id": thread_id, "run_id": run_id}
|
|
if isinstance(caller_context, dict):
|
|
for key, value in caller_context.items():
|
|
if key == CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY:
|
|
continue
|
|
runtime_ctx.setdefault(key, value)
|
|
if app_config is not None:
|
|
runtime_ctx["app_config"] = app_config
|
|
if task_store is not None:
|
|
from deerflow_extension_api import EXTENSION_TASK_STORE_KEY
|
|
|
|
runtime_ctx[EXTENSION_TASK_STORE_KEY] = task_store
|
|
# Publish the run's extension snapshot so work dispatched during graph
|
|
# execution (task delegation) binds the same generation the lead agent was
|
|
# built with, instead of re-reading a singleton that may have been replaced
|
|
# mid-run. Written after the caller merge and popped when absent, because a
|
|
# caller-supplied value for this host-internal key is never authoritative.
|
|
from deerflow.extensions import EXTENSION_SNAPSHOT_CONTEXT_KEY
|
|
|
|
if extensions is not None:
|
|
runtime_ctx[EXTENSION_SNAPSHOT_CONTEXT_KEY] = extensions
|
|
else:
|
|
runtime_ctx.pop(EXTENSION_SNAPSHOT_CONTEXT_KEY, None)
|
|
return runtime_ctx
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class RunContext:
|
|
"""Infrastructure dependencies for a single agent run.
|
|
|
|
Groups checkpointer, store, and persistence-related singletons so that
|
|
``run_agent`` (and any future callers) receive one object instead of a
|
|
growing list of keyword arguments.
|
|
"""
|
|
|
|
checkpointer: Any
|
|
store: Any | None = field(default=None)
|
|
event_store: Any | None = field(default=None)
|
|
run_events_config: Any | None = field(default=None)
|
|
thread_store: Any | None = field(default=None)
|
|
app_config: AppConfig | None = field(default=None)
|
|
extensions: Any | None = field(default=None)
|
|
checkpoint_channel_mode: CheckpointChannelMode = "full"
|
|
# Delta snapshot cadence frozen at startup; ``None`` means "not frozen in
|
|
# this process" (embedded/tests) and resolves to the config default.
|
|
checkpoint_snapshot_frequency: int | None = None
|
|
on_run_completed: Any | None = field(default=None)
|
|
|
|
|
|
def _install_runtime_context(config: dict, runtime_context: dict[str, Any]) -> None:
|
|
existing_context = config.get("context")
|
|
if isinstance(existing_context, dict):
|
|
existing_context.setdefault("thread_id", runtime_context["thread_id"])
|
|
existing_context.setdefault("run_id", runtime_context["run_id"])
|
|
if DEERFLOW_TRACE_METADATA_KEY in runtime_context:
|
|
existing_context.setdefault(DEERFLOW_TRACE_METADATA_KEY, runtime_context[DEERFLOW_TRACE_METADATA_KEY])
|
|
if "app_config" in runtime_context:
|
|
existing_context["app_config"] = runtime_context["app_config"]
|
|
if CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY in runtime_context:
|
|
existing_context[CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY] = runtime_context[CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY]
|
|
return
|
|
|
|
config["context"] = dict(runtime_context)
|
|
|
|
|
|
def _compute_agent_factory_supports_app_config(agent_factory: Any) -> bool:
|
|
try:
|
|
return "app_config" in inspect.signature(agent_factory).parameters
|
|
except (TypeError, ValueError):
|
|
return False
|
|
|
|
|
|
@lru_cache(maxsize=128)
|
|
def _cached_agent_factory_supports_app_config(agent_factory: Any) -> bool:
|
|
return _compute_agent_factory_supports_app_config(agent_factory)
|
|
|
|
|
|
def _agent_factory_supports_app_config(agent_factory: Any) -> bool:
|
|
try:
|
|
return _cached_agent_factory_supports_app_config(agent_factory)
|
|
except TypeError:
|
|
# Some callable instances are unhashable; fall back to a direct check.
|
|
return _compute_agent_factory_supports_app_config(agent_factory)
|
|
|
|
|
|
class _SubagentEventBuffer:
|
|
"""Buffer subagent ``task_*`` step events and flush them in one locked batch (#3779).
|
|
|
|
The live SSE bridge already forwards these events for real-time display; this
|
|
additionally writes them so the subtask card's step history survives a reload.
|
|
|
|
``RunEventStore.put`` is documented as a low-frequency path — on Postgres each
|
|
call opens its own transaction and takes a per-thread advisory lock. A deep
|
|
subagent (``general-purpose`` runs up to ``max_turns=150``) emits hundreds of
|
|
``task_running`` steps on the hot stream loop, so persisting each with
|
|
``put()`` would serialize against the run's own message-batch writer. This
|
|
accumulates recognized subagent events and writes them with ``put_batch``,
|
|
which acquires the lock once per batch, honoring the store's contract.
|
|
|
|
Best-effort: a missing store (run_events not configured) or an unrecognized
|
|
chunk is a no-op, flush failures are logged but never propagate into the
|
|
stream loop, and terminal ``subagent.end`` events flush eagerly so a completed
|
|
subagent's step history is durable promptly rather than only at run end.
|
|
"""
|
|
|
|
#: Flush once this many events are buffered, bounding memory and reload lag on
|
|
#: a single deep subagent without paying a per-step lock.
|
|
FLUSH_THRESHOLD = 25
|
|
|
|
def __init__(self, event_store: Any | None, thread_id: str, run_id: str) -> None:
|
|
self._event_store = event_store
|
|
self._thread_id = thread_id
|
|
self._run_id = run_id
|
|
self._pending: list[dict[str, Any]] = []
|
|
|
|
async def add(self, chunk: Any) -> None:
|
|
"""Buffer one custom stream chunk; flush on a terminal event or threshold."""
|
|
if self._event_store is None:
|
|
return
|
|
# Lazy import: importing deerflow.subagents at module load triggers its
|
|
# package __init__ (executor → agents → tools → task_tool), which imports
|
|
# back from deerflow.subagents and deadlocks at gateway startup. Deferring
|
|
# it to call time (after all modules are loaded) breaks that cycle.
|
|
from deerflow.subagents.step_events import subagent_run_event
|
|
|
|
record = subagent_run_event(chunk)
|
|
if record is None:
|
|
return
|
|
self._pending.append({"thread_id": self._thread_id, "run_id": self._run_id, **record})
|
|
if record["event_type"] == "subagent.end" or len(self._pending) >= self.FLUSH_THRESHOLD:
|
|
await self.flush()
|
|
|
|
async def flush(self) -> None:
|
|
"""Persist buffered events in one ``put_batch`` call; swallow store errors."""
|
|
if self._event_store is None or not self._pending:
|
|
return
|
|
batch = self._pending
|
|
self._pending = []
|
|
try:
|
|
await self._event_store.put_batch(batch)
|
|
except Exception:
|
|
# Re-buffer the failed batch (ahead of any events queued since) so a
|
|
# transient store error does not silently drop subagent step events.
|
|
self._pending = batch + self._pending
|
|
logger.warning("Run %s: failed to persist %d subagent step event(s)", self._run_id, len(batch), exc_info=True)
|
|
|
|
|
|
async def run_agent(
|
|
bridge: StreamBridge,
|
|
run_manager: RunManager,
|
|
record: RunRecord,
|
|
*,
|
|
ctx: RunContext,
|
|
agent_factory: Any,
|
|
graph_input: dict,
|
|
config: dict,
|
|
stream_modes: list[str] | None = None,
|
|
stream_subgraphs: bool = False,
|
|
interrupt_before: list[str] | Literal["*"] | None = None,
|
|
interrupt_after: list[str] | Literal["*"] | None = None,
|
|
) -> None:
|
|
"""Execute an agent in the background, publishing events to *bridge*."""
|
|
|
|
# Unpack infrastructure dependencies from RunContext.
|
|
checkpointer = ctx.checkpointer
|
|
store = ctx.store
|
|
event_store = ctx.event_store
|
|
run_events_config = ctx.run_events_config
|
|
thread_store = ctx.thread_store
|
|
terminal_status_kwargs = {"persist": False} if event_store is not None else {}
|
|
|
|
run_id = record.run_id
|
|
thread_id = record.thread_id
|
|
|
|
from deerflow_extension_api import ExtensionData, TaskInfo
|
|
|
|
from deerflow.extensions import get_loaded_extensions
|
|
from deerflow.extensions.notify import (
|
|
lead_task_id,
|
|
lead_task_outcome,
|
|
notify_task_start,
|
|
notify_task_stop,
|
|
)
|
|
|
|
extensions = ctx.extensions if ctx.extensions is not None else get_loaded_extensions()
|
|
task_store: ExtensionData | None = None
|
|
task_info: TaskInfo | None = None
|
|
deferred_stop_interrupt: BaseException | None = None
|
|
pre_run_checkpoint_id: str | None = None
|
|
pre_run_workspace_snapshot: WorkspaceSnapshot | None = None
|
|
workspace_changes_user_id: str | None = None
|
|
workspace_excluded_dir_names: frozenset[str] | None = None
|
|
snapshot_capture_failed = False
|
|
llm_error_fallback_message: str | None = None
|
|
checkpoint_rollback_completed = False
|
|
# Message ids checkpointed *before* this run started. The stream loop uses
|
|
# this set to mask out ``deerflow_error_fallback`` markers that belong to
|
|
# earlier runs on the same thread — without it, one stale fallback in
|
|
# history would mark every subsequent run on this thread as ``error``.
|
|
pre_existing_message_ids: set[str] = set()
|
|
|
|
# Bound agent graph accessor + captured pre-run rollback point; assigned
|
|
# inside the try block so the finally rollback path can fork the pre-run
|
|
# checkpoint lineage (see below).
|
|
accessor: CheckpointStateAccessor | None = None
|
|
rollback_point: RollbackPoint | None = None
|
|
journal = None
|
|
delivery_content: dict[str, Any] | None = None
|
|
produced_output_paths: list[str] | None = None
|
|
# Journal construction moved ahead of preflight so every terminal run can
|
|
# emit a receipt. Completion persistence keeps its prior boundary: before
|
|
# #4272 the journal did not exist until preflight had succeeded, so early
|
|
# checkpoint failures / cancellation while waiting did not write an empty
|
|
# completion snapshot into RunStore.
|
|
persist_completion = False
|
|
# Buffers subagent step events for batched persistence (#3779); assigned once
|
|
# streaming starts and flushed in the finally block. Pre-bound to None so the
|
|
# finally is safe even if an exception fires before streaming begins.
|
|
subagent_events: _SubagentEventBuffer | None = None
|
|
started = False
|
|
|
|
async def _finish_cancellation(
|
|
action: str,
|
|
*,
|
|
restore_checkpoint: bool = True,
|
|
) -> None:
|
|
nonlocal checkpoint_rollback_completed
|
|
await run_manager.set_finalizing(run_id, True)
|
|
if action == "rollback":
|
|
await run_manager.set_status(
|
|
run_id,
|
|
RunStatus.error,
|
|
error="Rolled back by user",
|
|
**terminal_status_kwargs,
|
|
)
|
|
if not restore_checkpoint:
|
|
return
|
|
try:
|
|
checkpoint_rollback_completed = await _rollback_to_pre_run_checkpoint(
|
|
accessor=accessor,
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
rollback_point=rollback_point,
|
|
snapshot_capture_failed=snapshot_capture_failed,
|
|
)
|
|
logger.info(
|
|
"Run %s rolled back to pre-run checkpoint %s",
|
|
run_id,
|
|
pre_run_checkpoint_id,
|
|
)
|
|
except Exception:
|
|
logger.warning(
|
|
"Run %s cancellation rollback failed",
|
|
run_id,
|
|
exc_info=True,
|
|
)
|
|
else:
|
|
await run_manager.set_status(
|
|
run_id,
|
|
RunStatus.interrupted,
|
|
**terminal_status_kwargs,
|
|
)
|
|
logger.info("Run %s was cancelled", run_id)
|
|
|
|
try:
|
|
normalized_stream_modes = normalize_stream_modes(stream_modes)
|
|
requested_modes: set[str] = set(normalized_stream_modes)
|
|
lg_modes = to_langgraph_stream_modes(normalized_stream_modes)
|
|
# Initialize the run-scoped journal before any fallible or cancellable
|
|
# preflight work. Every terminal run with an event store must reach the
|
|
# shared finally block with a journal available for its run.delivery
|
|
# receipt, including checkpoint validation failures and cancellation
|
|
# while waiting for an earlier run to finish finalizing.
|
|
if event_store is not None:
|
|
from deerflow.runtime.journal import RunJournal
|
|
|
|
journal = RunJournal(
|
|
run_id=run_id,
|
|
thread_id=thread_id,
|
|
event_store=event_store,
|
|
track_token_usage=getattr(run_events_config, "track_token_usage", True),
|
|
progress_reporter=lambda snapshot: run_manager.update_run_progress(run_id, **snapshot),
|
|
)
|
|
|
|
await run_manager.wait_for_prior_finalizing(
|
|
thread_id,
|
|
run_id,
|
|
abort_event=record.abort_event,
|
|
)
|
|
|
|
start_outcome = await run_manager.try_start(run_id)
|
|
if start_outcome is not RunStartOutcome.started:
|
|
if record.abort_event.is_set():
|
|
await _finish_cancellation(
|
|
record.abort_action,
|
|
restore_checkpoint=False,
|
|
)
|
|
return
|
|
started = True
|
|
|
|
task_id = lead_task_id(run_id)
|
|
if extensions.needs_task_store:
|
|
task_store = ExtensionData(task_id)
|
|
|
|
if extensions.has_task_lifecycle:
|
|
task_info = TaskInfo(
|
|
task_id=task_id,
|
|
run_id=run_id,
|
|
thread_id=thread_id,
|
|
kind="lead",
|
|
agent_name=record.assistant_id,
|
|
)
|
|
assert task_store is not None
|
|
await notify_task_start(
|
|
extensions,
|
|
task_store,
|
|
task_info,
|
|
timeout=_EXTENSION_TASK_NOTIFY_TIMEOUT_SECONDS,
|
|
)
|
|
|
|
if not record.ownership_lost and thread_store is not None:
|
|
try:
|
|
await thread_store.update_status(thread_id, "running")
|
|
except Exception:
|
|
logger.debug("Failed to update thread_meta status for %s (non-fatal)", thread_id)
|
|
mode = ctx.checkpoint_channel_mode
|
|
inject_checkpoint_mode(config, mode)
|
|
checkpoint_config = {
|
|
"configurable": {
|
|
"thread_id": thread_id,
|
|
"checkpoint_ns": "",
|
|
}
|
|
}
|
|
if checkpointer is not None:
|
|
await aensure_checkpoint_mode_compatible(
|
|
checkpointer,
|
|
checkpoint_config,
|
|
mode,
|
|
)
|
|
configurable = config["configurable"]
|
|
selected_configurable = {
|
|
"thread_id": thread_id,
|
|
"checkpoint_ns": configurable.get("checkpoint_ns", ""),
|
|
}
|
|
for selector_key in ("checkpoint_id", "checkpoint_map"):
|
|
if selector_key in configurable:
|
|
selected_configurable[selector_key] = configurable[selector_key]
|
|
selected_checkpoint_config = {
|
|
"configurable": selected_configurable,
|
|
}
|
|
if selected_checkpoint_config != checkpoint_config:
|
|
await aensure_checkpoint_mode_compatible(
|
|
checkpointer,
|
|
selected_checkpoint_config,
|
|
mode,
|
|
)
|
|
|
|
persist_completion = True
|
|
|
|
if event_store is not None:
|
|
workspace_changes_user_id = get_effective_user_id()
|
|
# Resolved once per run so the pre-run snapshot, the post-run
|
|
# delivery scan, and the workspace-changes scan all agree on the
|
|
# same exclusion set.
|
|
workspace_excluded_dir_names = _workspace_excluded_dir_names(ctx.app_config)
|
|
try:
|
|
pre_run_workspace_snapshot = await capture_workspace_snapshot(
|
|
thread_id,
|
|
user_id=workspace_changes_user_id,
|
|
extra_excluded_dir_names=workspace_excluded_dir_names,
|
|
)
|
|
except Exception:
|
|
logger.warning("Could not capture pre-run workspace snapshot for run %s", run_id, exc_info=True)
|
|
|
|
# 2. Publish metadata — useStream needs both run_id AND thread_id
|
|
await bridge.publish(
|
|
run_id,
|
|
"metadata",
|
|
{
|
|
"run_id": run_id,
|
|
"thread_id": thread_id,
|
|
},
|
|
)
|
|
|
|
# 3. Build the agent
|
|
from langchain_core.runnables import RunnableConfig
|
|
from langgraph.runtime import Runtime
|
|
|
|
# Inject runtime context so middlewares and tools (via ToolRuntime.context) can
|
|
# access thread-level data. langgraph-cli does this automatically; we must do it
|
|
# manually here because we drive the graph through ``agent.astream(config=...)``
|
|
# without passing the official ``context=`` parameter.
|
|
runtime_ctx = _build_runtime_context(
|
|
thread_id,
|
|
run_id,
|
|
config.get("context"),
|
|
ctx.app_config,
|
|
task_store,
|
|
extensions,
|
|
)
|
|
incoming_metadata = config.get("metadata") if isinstance(config.get("metadata"), dict) else {}
|
|
deerflow_trace_id = resolve_deerflow_trace_id(incoming_metadata.get(DEERFLOW_TRACE_METADATA_KEY))
|
|
if deerflow_trace_id:
|
|
runtime_ctx[DEERFLOW_TRACE_METADATA_KEY] = deerflow_trace_id
|
|
if is_trace_id_from_request_header():
|
|
merged_metadata = dict(incoming_metadata)
|
|
merged_metadata[DEERFLOW_TRACE_METADATA_KEY] = deerflow_trace_id
|
|
config["metadata"] = merged_metadata
|
|
# Expose the run-scoped journal under a sentinel key so middleware can
|
|
# write audit events (e.g. SafetyFinishReasonMiddleware recording
|
|
# suppressed tool calls). Double-underscore prefix marks it as a
|
|
# runtime-internal channel; user code must not depend on the key name.
|
|
if journal is not None:
|
|
runtime_ctx["__run_journal"] = journal
|
|
_install_runtime_context(config, runtime_ctx)
|
|
runtime = Runtime(context=cast(Any, runtime_ctx), store=store)
|
|
config.setdefault("configurable", {})["__pregel_runtime"] = runtime
|
|
|
|
# Inject RunJournal as a LangChain callback handler.
|
|
# on_llm_end captures token usage; on_chain_start/end captures lifecycle.
|
|
if journal is not None:
|
|
config.setdefault("callbacks", []).append(journal)
|
|
|
|
# Inject Langfuse trace-attribute metadata so the langchain CallbackHandler
|
|
# can lift session_id / user_id / trace_name / tags onto the root trace.
|
|
# Shared helper with ``DeerFlowClient.stream`` so both entry points stay
|
|
# in sync; caller-provided metadata wins via setdefault inside the helper.
|
|
inject_langfuse_metadata(
|
|
config,
|
|
thread_id=thread_id,
|
|
user_id=resolve_runtime_user_id(runtime),
|
|
assistant_id=record.assistant_id,
|
|
model_name=record.model_name,
|
|
environment=os.environ.get("DEER_FLOW_ENV") or os.environ.get("ENVIRONMENT"),
|
|
deerflow_trace_id=deerflow_trace_id,
|
|
)
|
|
|
|
# Resolve after runtime context installation so context/configurable reflect
|
|
# the agent name that this run will actually execute.
|
|
config.setdefault("run_name", resolve_root_run_name(config, record.assistant_id))
|
|
initial_runnable_config = RunnableConfig(**config)
|
|
|
|
def _continuation_runnable_config() -> RunnableConfig:
|
|
continuation_config = dict(config)
|
|
configurable = dict(continuation_config.get("configurable", {}) or {})
|
|
configurable["checkpoint_ns"] = ""
|
|
configurable.pop("checkpoint_id", None)
|
|
configurable.pop("checkpoint_map", None)
|
|
continuation_config["configurable"] = configurable
|
|
return RunnableConfig(**continuation_config)
|
|
|
|
agent_factory_kwargs: dict[str, Any] = {"config": initial_runnable_config}
|
|
if ctx.app_config is not None and _agent_factory_supports_app_config(agent_factory):
|
|
agent_factory_kwargs["app_config"] = ctx.app_config
|
|
from deerflow.extensions import bind_agent_build_extensions
|
|
|
|
with bind_agent_build_extensions(extensions):
|
|
agent = agent_factory(**agent_factory_kwargs)
|
|
|
|
accessor = CheckpointStateAccessor.bind(
|
|
agent,
|
|
checkpointer,
|
|
store=store,
|
|
mode=mode,
|
|
)
|
|
|
|
# Capture the pre-run rollback point (materialized state + raw pending
|
|
# writes) before this run mutates the thread. Raw checkpoint blobs
|
|
# cannot reconstruct Delta-channel messages (their checkpoints omit
|
|
# channel_values), so rollback forks the pre-run lineage through the
|
|
# graph and needs the materialized messages up front. Any capture
|
|
# failure disables rollback: restoring an empty or partial message
|
|
# history would silently truncate the thread.
|
|
if checkpointer is not None:
|
|
# A previous successful run may still be persisting duration
|
|
# metadata after its active admission slot is released. Share its
|
|
# checkpoint lock so the rollback snapshot and any resume rewrite
|
|
# are one uninterrupted read/write sequence against the head.
|
|
async with _checkpoint_thread_lock(thread_id):
|
|
try:
|
|
rollback_point = await _capture_rollback_point(accessor, checkpointer, checkpoint_config)
|
|
except Exception:
|
|
snapshot_capture_failed = True
|
|
logger.warning("Could not capture pre-run checkpoint snapshot for run %s", run_id, exc_info=True)
|
|
if rollback_point is not None:
|
|
pre_run_checkpoint_id = rollback_point.config.get("configurable", {}).get("checkpoint_id")
|
|
pre_existing_message_ids = _collect_pre_existing_message_ids({"messages": list(rollback_point.messages)})
|
|
|
|
# Resuming from an older checkpoint is a fork, and a delta fork
|
|
# materializes the abandoned sibling's writes back into state
|
|
# (#4458). Rewrite it as a linear head write *after* the rollback
|
|
# point is captured, so cancel-with-rollback still restores the
|
|
# real pre-run head rather than the rolled-back one.
|
|
resumed_messages = await _linearize_delta_checkpoint_resume(
|
|
accessor=accessor,
|
|
checkpointer=checkpointer,
|
|
config=config,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
)
|
|
if resumed_messages is not None:
|
|
# The graph now starts from the selected state, so the
|
|
# current-run message boundary is that state, not the head we
|
|
# captured for rollback.
|
|
pre_existing_message_ids = _collect_pre_existing_message_ids({"messages": list(resumed_messages)})
|
|
initial_runnable_config = RunnableConfig(**config)
|
|
|
|
runtime_ctx[CURRENT_RUN_PRE_EXISTING_MESSAGE_IDS_KEY] = frozenset(pre_existing_message_ids)
|
|
_install_runtime_context(config, runtime_ctx)
|
|
|
|
# Capture the effective (resolved) model name from the agent's metadata.
|
|
# _resolve_model_name in agent.py may return the default model if the
|
|
# requested name is not in the allowlist — this update ensures the
|
|
# persisted model_name reflects the actual model used.
|
|
if record.model_name is not None:
|
|
resolved = getattr(agent, "metadata", {}) or {}
|
|
if isinstance(resolved, dict):
|
|
effective = resolved.get("model_name")
|
|
if effective and effective != record.model_name:
|
|
await run_manager.update_model_name(record.run_id, effective)
|
|
|
|
# 4. Attach checkpointer and store
|
|
if checkpointer is not None:
|
|
agent.checkpointer = checkpointer
|
|
if store is not None:
|
|
agent.store = store
|
|
|
|
# 5. Set interrupt nodes
|
|
if interrupt_before:
|
|
agent.interrupt_before_nodes = interrupt_before
|
|
if interrupt_after:
|
|
agent.interrupt_after_nodes = interrupt_after
|
|
|
|
logger.info("Run %s: streaming with modes %s (requested: %s)", run_id, lg_modes, requested_modes)
|
|
|
|
# Buffer subagent step events and persist them in batches (#3779) instead
|
|
# of one low-frequency put() per step on the hot stream loop. Flushed in
|
|
# the finally block so buffered steps survive abort/exception paths too.
|
|
subagent_events = _SubagentEventBuffer(event_store, thread_id, run_id)
|
|
|
|
goal_evaluator_model: Any | None = None
|
|
|
|
def _get_goal_evaluator_model() -> Any:
|
|
nonlocal goal_evaluator_model
|
|
if goal_evaluator_model is None:
|
|
goal_evaluator_model = create_goal_evaluator_model(
|
|
model_name=record.model_name,
|
|
app_config=ctx.app_config,
|
|
)
|
|
return goal_evaluator_model
|
|
|
|
async def _stream_once(input_payload: Any, stream_config: RunnableConfig) -> None:
|
|
nonlocal llm_error_fallback_message
|
|
file_tool_chunk_batcher = _LargeFileToolChunkBatcher() if "values" in requested_modes else None
|
|
try:
|
|
async with _checkpoint_thread_lock(thread_id):
|
|
if len(lg_modes) == 1 and not stream_subgraphs:
|
|
# Single mode, no subgraphs: astream yields raw chunks
|
|
single_mode = lg_modes[0]
|
|
async for chunk in agent.astream(input_payload, config=stream_config, stream_mode=single_mode):
|
|
if record.abort_event.is_set():
|
|
logger.info("Run %s abort requested — stopping", run_id)
|
|
break
|
|
llm_error_fallback_message = llm_error_fallback_message or _extract_llm_error_fallback_message(chunk, pre_existing_message_ids)
|
|
sse_event = _lg_mode_to_sse_event(single_mode)
|
|
await bridge.publish(run_id, sse_event, serialize(chunk, mode=single_mode))
|
|
if single_mode == "custom":
|
|
await subagent_events.add(chunk)
|
|
return
|
|
# Multiple modes or subgraphs: astream yields tuples
|
|
async for item in agent.astream(
|
|
input_payload,
|
|
config=stream_config,
|
|
stream_mode=lg_modes,
|
|
subgraphs=stream_subgraphs,
|
|
):
|
|
if record.abort_event.is_set():
|
|
logger.info("Run %s abort requested — stopping", run_id)
|
|
break
|
|
|
|
mode, chunk, namespace = _unpack_stream_item(item, lg_modes, stream_subgraphs)
|
|
if mode is None:
|
|
continue
|
|
|
|
if not namespace:
|
|
# Only root-graph frames may decide the parent run's error
|
|
# fallback: a delegated subagent's marked fallback is the
|
|
# executor's to map (task_failed), not this run's.
|
|
llm_error_fallback_message = llm_error_fallback_message or _extract_llm_error_fallback_message(chunk, pre_existing_message_ids)
|
|
await _publish_stream_item(
|
|
bridge=bridge,
|
|
run_id=run_id,
|
|
mode=mode,
|
|
chunk=chunk,
|
|
namespace=namespace,
|
|
file_tool_chunk_batcher=file_tool_chunk_batcher,
|
|
subagent_events=subagent_events,
|
|
)
|
|
finally:
|
|
stream_error = sys.exception()
|
|
if file_tool_chunk_batcher is not None:
|
|
try:
|
|
for publish_chunk in file_tool_chunk_batcher.finish():
|
|
await bridge.publish(run_id, "messages", serialize(publish_chunk, mode="messages"))
|
|
except Exception:
|
|
if stream_error is None:
|
|
raise
|
|
logger.debug("Could not flush pending file-tool chunks for run %s", run_id, exc_info=True)
|
|
|
|
# 7. Stream the requested turn, then optionally continue hidden goal turns.
|
|
# Clear any stale stop_reason before the first (user-visible) turn only.
|
|
# Continuation turns preserve a cap reason from the user turn: a run that
|
|
# hits a cap during the user turn IS capped even if hidden goal-evaluator
|
|
# turns complete cleanly afterward (#4176 review).
|
|
if isinstance(runtime.context, dict):
|
|
runtime.context.pop("stop_reason", None)
|
|
await _stream_once(graph_input, initial_runnable_config)
|
|
while not record.abort_event.is_set() and not llm_error_fallback_message and (journal is None or not journal.had_llm_error_fallback):
|
|
continuation_input = await _prepare_goal_continuation_input(
|
|
bridge=bridge,
|
|
accessor=accessor,
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
model_name=record.model_name,
|
|
app_config=ctx.app_config,
|
|
evaluator_model_factory=_get_goal_evaluator_model,
|
|
abort_event=record.abort_event,
|
|
user_id=resolve_runtime_user_id(runtime),
|
|
deerflow_trace_id=deerflow_trace_id,
|
|
task_store=task_store,
|
|
extensions=extensions,
|
|
)
|
|
if continuation_input is None or record.abort_event.is_set():
|
|
break
|
|
await _stream_once(continuation_input, _continuation_runnable_config())
|
|
|
|
# 8. Final status
|
|
if record.abort_event.is_set():
|
|
await _finish_cancellation(record.abort_action)
|
|
elif llm_error_fallback_message or (journal is not None and journal.had_llm_error_fallback):
|
|
error_msg = llm_error_fallback_message
|
|
if error_msg is None and journal is not None:
|
|
error_msg = journal.llm_error_fallback_message
|
|
error_msg = error_msg or "LLM provider failed after retries"
|
|
await _ensure_finalizing_before_edit_failure(run_manager, record)
|
|
cancel_action = await run_manager.set_status_if_not_cancelled(
|
|
run_id,
|
|
RunStatus.error,
|
|
error=error_msg,
|
|
**terminal_status_kwargs,
|
|
)
|
|
if cancel_action is not None:
|
|
await _finish_cancellation(cancel_action)
|
|
else:
|
|
runtime_context = runtime.context if isinstance(runtime.context, dict) else None
|
|
# Guard middlewares that hard-stop a run by stripping tool_calls
|
|
# stamp stop_reason into runtime.context so the worker can surface
|
|
# it on the run record:
|
|
# loop_detection -> "loop_capped"
|
|
# token_budget -> "token_capped"
|
|
# safety_finish_reason -> "safety_capped"
|
|
# subagent_limit -> "subagent_limit_capped"
|
|
# model_length_finish_reason -> "model_length_capped"
|
|
#
|
|
# If more guards grow stop_reason semantics, consider a publish/
|
|
# collect pattern (e.g. each guard middleware publishes its cap
|
|
# reason to a dedicated runtime.context channel, and the worker
|
|
# collects the most severe / first / all reasons) instead of each
|
|
# guard writing directly to the same key.
|
|
stop_reason = runtime_context.get("stop_reason") if runtime_context is not None else None
|
|
produced_output_paths = await _produced_output_paths(
|
|
pre_run_workspace_snapshot,
|
|
thread_id=thread_id,
|
|
user_id=workspace_changes_user_id,
|
|
extra_excluded_dir_names=workspace_excluded_dir_names,
|
|
)
|
|
delivery_content = _delivery_content_with_outputs(
|
|
journal.get_delivery_content() if journal is not None else _empty_delivery_content(),
|
|
produced_output_paths,
|
|
)
|
|
delivery_error = _delivery_error(delivery_content)
|
|
cancel_action = await run_manager.set_status_if_not_cancelled(
|
|
run_id,
|
|
RunStatus.error if delivery_error else RunStatus.success,
|
|
error=delivery_error,
|
|
stop_reason=stop_reason,
|
|
**terminal_status_kwargs,
|
|
)
|
|
if cancel_action is not None:
|
|
await _finish_cancellation(cancel_action)
|
|
|
|
except asyncio.CancelledError:
|
|
await _finish_cancellation(record.abort_action)
|
|
|
|
except Exception as exc:
|
|
error_msg = f"{exc}"
|
|
logger.exception("Run %s failed: %s", run_id, error_msg)
|
|
await _ensure_finalizing_before_edit_failure(run_manager, record)
|
|
cancel_action = await run_manager.set_status_if_not_cancelled(
|
|
run_id,
|
|
RunStatus.error,
|
|
error=error_msg,
|
|
**terminal_status_kwargs,
|
|
)
|
|
if cancel_action is not None:
|
|
await _finish_cancellation(cancel_action)
|
|
else:
|
|
await bridge.publish(
|
|
run_id,
|
|
"error",
|
|
{
|
|
"message": error_msg,
|
|
"name": type(exc).__name__,
|
|
},
|
|
)
|
|
|
|
finally:
|
|
if record.ownership_lost:
|
|
logger.warning(
|
|
"Skipping durable finalization for run %s because this worker no longer owns its lease",
|
|
run_id,
|
|
)
|
|
|
|
if not record.ownership_lost and _is_edit_replay_run(record) and record.status != RunStatus.success:
|
|
if not record.finalizing:
|
|
await run_manager.set_finalizing(run_id, True)
|
|
try:
|
|
if not checkpoint_rollback_completed:
|
|
checkpoint_rollback_completed = await _rollback_to_pre_run_checkpoint(
|
|
accessor=accessor,
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
rollback_point=rollback_point,
|
|
snapshot_capture_failed=snapshot_capture_failed,
|
|
)
|
|
if checkpoint_rollback_completed:
|
|
await _publish_restored_checkpoint_values(
|
|
bridge=bridge,
|
|
run_id=run_id,
|
|
accessor=accessor,
|
|
thread_id=thread_id,
|
|
)
|
|
logger.info("Run %s edit replay restored pre-run checkpoint %s", run_id, pre_run_checkpoint_id)
|
|
except Exception:
|
|
logger.warning("Run %s edit replay rollback failed", run_id, exc_info=True)
|
|
|
|
# Persist any subagent step events still buffered (#3779) — including on
|
|
# abort/exception paths, where the stream loop broke before its own flush.
|
|
if not record.ownership_lost and subagent_events is not None:
|
|
await subagent_events.flush()
|
|
|
|
if not record.ownership_lost and event_store is not None and pre_run_workspace_snapshot is not None:
|
|
try:
|
|
await record_workspace_changes(
|
|
event_store,
|
|
thread_id,
|
|
run_id,
|
|
pre_run_workspace_snapshot,
|
|
user_id=workspace_changes_user_id,
|
|
extra_excluded_dir_names=workspace_excluded_dir_names,
|
|
)
|
|
except Exception:
|
|
logger.warning("Failed to record workspace changes for run %s", run_id, exc_info=True)
|
|
|
|
# Flush buffered journal events before the terminal receipt. The
|
|
# receipt uses a run-scoped idempotent write shared with recovery, then
|
|
# the staged terminal status is persisted. This ordering closes the
|
|
# crash window where a terminal run could otherwise outlive its receipt.
|
|
# A fenced worker leaves receipt recovery to the peer that claimed it.
|
|
if not record.ownership_lost and journal is not None:
|
|
try:
|
|
await journal.flush()
|
|
except Exception:
|
|
logger.warning("Failed to flush journal for run %s", run_id, exc_info=True)
|
|
|
|
if delivery_content is None:
|
|
if produced_output_paths is None:
|
|
produced_output_paths = await _produced_output_paths(
|
|
pre_run_workspace_snapshot,
|
|
thread_id=thread_id,
|
|
user_id=workspace_changes_user_id,
|
|
extra_excluded_dir_names=workspace_excluded_dir_names,
|
|
)
|
|
delivery_content = _delivery_content_with_outputs(journal.get_delivery_content(), produced_output_paths)
|
|
receipt_persisted = await _persist_delivery_receipt(
|
|
event_store,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
content=delivery_content,
|
|
)
|
|
if produced_output_paths and record.status == RunStatus.success and not receipt_persisted:
|
|
await run_manager.set_status(
|
|
run_id,
|
|
RunStatus.error,
|
|
error=_DELIVERY_RECEIPT_FAILED_ERROR,
|
|
persist=False,
|
|
)
|
|
|
|
if not record.ownership_lost and event_store is not None:
|
|
try:
|
|
# Even after bounded receipt retries are exhausted, persist the
|
|
# real worker outcome. Leaving a successful row inflight would
|
|
# let lease recovery rewrite it as an error with a synthetic
|
|
# zero receipt.
|
|
if record.abort_event.is_set():
|
|
await run_manager.persist_current_status(run_id)
|
|
else:
|
|
cancel_action = await run_manager.set_status_if_not_cancelled(
|
|
run_id,
|
|
record.status,
|
|
error=record.error,
|
|
stop_reason=record.stop_reason,
|
|
)
|
|
if cancel_action is not None:
|
|
await _finish_cancellation(cancel_action)
|
|
await run_manager.persist_current_status(run_id)
|
|
except Exception:
|
|
logger.warning("Failed to persist terminal status for run %s after delivery receipt attempts", run_id, exc_info=True)
|
|
|
|
if not record.ownership_lost and journal is not None and persist_completion:
|
|
try:
|
|
# Persist token usage + convenience fields to RunStore
|
|
completion = journal.get_completion_data()
|
|
await run_manager.update_run_completion(run_id, status=record.status.value, **completion)
|
|
except Exception:
|
|
logger.warning("Failed to persist run completion for %s (non-fatal)", run_id, exc_info=True)
|
|
|
|
if started and not record.ownership_lost and checkpointer is not None and record.status == RunStatus.interrupted and not _is_edit_replay_run(record):
|
|
try:
|
|
await run_manager.wait_for_prior_finalizing(thread_id, run_id)
|
|
if not await run_manager.has_later_started_run(thread_id, run_id):
|
|
await _ensure_interrupted_title(checkpointer=checkpointer, thread_id=thread_id, app_config=ctx.app_config, graph_input=graph_input)
|
|
except Exception:
|
|
logger.debug("Failed to generate interrupted title for thread %s (non-fatal)", thread_id)
|
|
|
|
# Sync title from checkpoint to threads_meta.display_name
|
|
if started and not record.ownership_lost and checkpointer is not None and thread_store is not None:
|
|
try:
|
|
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
|
ckpt_tuple = await checkpointer.aget_tuple(ckpt_config)
|
|
if ckpt_tuple is not None:
|
|
ckpt = getattr(ckpt_tuple, "checkpoint", {}) or {}
|
|
title = ckpt.get("channel_values", {}).get("title")
|
|
if title:
|
|
await thread_store.update_display_name(thread_id, title)
|
|
except Exception:
|
|
logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id)
|
|
|
|
# Persist run duration to checkpoint metadata so history reads
|
|
# don't need to correlate runs and events.
|
|
if started and not record.ownership_lost and checkpointer is not None and record.status == RunStatus.success:
|
|
try:
|
|
created = datetime.fromisoformat(record.created_at.replace("Z", "+00:00"))
|
|
updated = datetime.fromisoformat(record.updated_at.replace("Z", "+00:00"))
|
|
# Match legacy history semantics: turn_duration is the whole
|
|
# RunRecord lifetime in integer seconds, including admission
|
|
# delay. Persist zero for sub-second successful turns.
|
|
duration = max(0, int((updated - created).total_seconds()))
|
|
await _persist_run_duration(
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
duration_seconds=duration,
|
|
)
|
|
except Exception:
|
|
logger.debug("Failed to persist run duration for thread %s run %s (non-fatal)", thread_id, run_id)
|
|
|
|
# Update threads_meta status based on run outcome
|
|
if started and not record.ownership_lost and thread_store is not None:
|
|
try:
|
|
final_status = "idle" if record.status == RunStatus.success else record.status.value
|
|
await thread_store.update_status(thread_id, final_status)
|
|
except Exception:
|
|
logger.debug("Failed to update thread_meta status for %s (non-fatal)", thread_id)
|
|
|
|
if not record.ownership_lost and ctx.on_run_completed is not None:
|
|
try:
|
|
await ctx.on_run_completed(record)
|
|
except Exception:
|
|
logger.warning("Run completion hook failed for %s (non-fatal)", run_id, exc_info=True)
|
|
|
|
if task_info is not None and task_store is not None:
|
|
# Keep the finalizing barrier held until stop observers finish, so
|
|
# a same-thread replacement cannot overlap this task's lifecycle.
|
|
try:
|
|
await notify_task_stop(
|
|
extensions,
|
|
task_store,
|
|
task_info,
|
|
lead_task_outcome(
|
|
aborted=(record.abort_event.is_set() or record.status == RunStatus.interrupted),
|
|
succeeded=record.status == RunStatus.success,
|
|
),
|
|
timeout=_EXTENSION_TASK_NOTIFY_TIMEOUT_SECONDS,
|
|
)
|
|
except Exception:
|
|
logger.warning(
|
|
"Extension task-stop notification failed for run %s (non-fatal)",
|
|
run_id,
|
|
exc_info=True,
|
|
)
|
|
except BaseException as exc:
|
|
# Cancellation here must not strand the finalizing barrier or
|
|
# leave stream consumers waiting for the end frame.
|
|
deferred_stop_interrupt = exc
|
|
logger.warning(
|
|
"Extension task-stop notification interrupted for run %s; completing cleanup first",
|
|
run_id,
|
|
)
|
|
if record.finalizing:
|
|
await run_manager.set_finalizing(run_id, False)
|
|
|
|
await bridge.publish_end(run_id)
|
|
asyncio.create_task(bridge.cleanup(run_id, delay=60))
|
|
|
|
if deferred_stop_interrupt is not None:
|
|
raise deferred_stop_interrupt
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helpers
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def _checkpoint_id(checkpoint_tuple: Any) -> str | None:
|
|
config = getattr(checkpoint_tuple, "config", {}) or {}
|
|
configurable = config.get("configurable", {}) if isinstance(config, dict) else {}
|
|
checkpoint_id = configurable.get("checkpoint_id") if isinstance(configurable, dict) else None
|
|
if isinstance(checkpoint_id, str):
|
|
return checkpoint_id
|
|
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
|
if isinstance(checkpoint, dict) and isinstance(checkpoint.get("id"), str):
|
|
return checkpoint["id"]
|
|
return None
|
|
|
|
|
|
def _goal_instance_matches(left: GoalState | None, right: GoalState | None) -> bool:
|
|
if not left or not right:
|
|
return False
|
|
same_status = left.get("status") == right.get("status") == "active"
|
|
same_objective = left.get("objective") == right.get("objective")
|
|
same_created_at = left.get("created_at") == right.get("created_at")
|
|
return same_status and same_objective and same_created_at
|
|
|
|
|
|
async def _materialized_checkpoint_messages(accessor: CheckpointStateAccessor, thread_id: str) -> list[Any]:
|
|
"""Read ``messages`` through the mode-matched accessor.
|
|
|
|
Raw ``channel_values`` reads see a sentinel in delta mode; only a
|
|
materialized read reconstructs the list. Raw checkpoint tuples remain
|
|
valid for tuple-level metadata (checkpoint id, ``pending_writes``).
|
|
"""
|
|
snapshot = await accessor.aget({"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}})
|
|
values = getattr(snapshot, "values", None) or {}
|
|
messages = values.get("messages") if isinstance(values, dict) else None
|
|
return list(messages) if isinstance(messages, list) else []
|
|
|
|
|
|
def _read_checkpoint_goal(checkpoint_tuple: Any) -> GoalState | None:
|
|
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
|
|
channel_values = checkpoint.get("channel_values", {}) if isinstance(checkpoint, dict) else {}
|
|
raw_goal = channel_values.get("goal") if isinstance(channel_values, dict) else None
|
|
return copy.deepcopy(raw_goal) if isinstance(raw_goal, dict) else None
|
|
|
|
|
|
def _has_durable_goal_turn_receipt(checkpoint_tuple: Any, messages: list[Any]) -> bool:
|
|
"""Return true when a completed visible assistant turn is safely checkpointed.
|
|
|
|
``pending_writes`` is the durability signal: a ``CheckpointTuple`` carries no
|
|
``tasks`` field (those live on a ``StateSnapshot``), so the presence of any
|
|
queued writes is what tells us the turn is still in flight.
|
|
"""
|
|
if _checkpoint_id(checkpoint_tuple) is None:
|
|
return False
|
|
if getattr(checkpoint_tuple, "pending_writes", None):
|
|
return False
|
|
visible_messages = []
|
|
for message in messages:
|
|
if _is_visible_message(message) and message_to_text(message).strip():
|
|
visible_messages.append(message)
|
|
if not visible_messages:
|
|
return False
|
|
return _message_type(visible_messages[-1]) == "ai"
|
|
|
|
|
|
def _stand_down_reason(goal: GoalState, evaluation: GoalEvaluation, no_progress_count: int) -> str | None:
|
|
if evaluation["satisfied"]:
|
|
return None
|
|
if evaluation["blocker"] != "goal_not_met_yet":
|
|
return f"blocked:{evaluation['blocker']}"
|
|
# Default caps mirror should_continue_goal so the two gate functions agree on
|
|
# a goal dict that is missing these fields.
|
|
if int(goal.get("continuation_count", 0)) >= int(goal.get("max_continuations", DEFAULT_MAX_GOAL_CONTINUATIONS)):
|
|
return "max_continuations_reached"
|
|
if no_progress_count >= int(goal.get("max_no_progress_continuations", DEFAULT_MAX_NO_PROGRESS_CONTINUATIONS)):
|
|
return "no_progress_detected"
|
|
return None
|
|
|
|
|
|
async def _persist_goal_evaluation(
|
|
*,
|
|
bridge: StreamBridge,
|
|
checkpointer: Any,
|
|
thread_id: str,
|
|
run_id: str,
|
|
goal: GoalState,
|
|
evaluation: GoalEvaluation,
|
|
no_progress_count: int,
|
|
continuation_count: int | None = None,
|
|
stand_down_reason: str | None = None,
|
|
evidence_signature: str = "",
|
|
) -> GoalState | None:
|
|
try:
|
|
async with goal_thread_lock(thread_id):
|
|
checkpoint_tuple = await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aget_tuple",
|
|
"get_tuple",
|
|
{"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}},
|
|
)
|
|
if checkpoint_tuple is None:
|
|
return None
|
|
current_goal = _read_checkpoint_goal(checkpoint_tuple)
|
|
if current_goal is None or not _goal_instance_matches(goal, current_goal):
|
|
return None
|
|
# Defensive: compute continuation_count from the fresh current_goal
|
|
# inside the lock. The caller computed it from a possibly-stale goal
|
|
# snapshot; a racing continuation may have already bumped the count.
|
|
if continuation_count is not None:
|
|
current_count = int(current_goal.get("continuation_count", 0))
|
|
continuation_count = max(continuation_count, current_count + 1)
|
|
expected_checkpoint_id = _checkpoint_id(checkpoint_tuple)
|
|
updated_goal = attach_goal_evaluation(
|
|
current_goal,
|
|
evaluation,
|
|
run_id=run_id,
|
|
continuation_count=continuation_count,
|
|
no_progress_count=no_progress_count,
|
|
stand_down_reason=stand_down_reason,
|
|
evidence_signature=evidence_signature,
|
|
)
|
|
values = await write_thread_goal(
|
|
checkpointer,
|
|
thread_id,
|
|
updated_goal,
|
|
as_node="goal_evaluator",
|
|
expected_checkpoint_id=expected_checkpoint_id,
|
|
)
|
|
await bridge.publish(run_id, "values", serialize(values, mode="values"))
|
|
return updated_goal
|
|
except GoalWriteConflict:
|
|
return None
|
|
except Exception:
|
|
logger.warning("Could not persist goal evaluation for thread %s", thread_id, exc_info=True)
|
|
return None
|
|
|
|
|
|
async def _reread_goal_and_checkpoint(checkpointer: Any, thread_id: str) -> tuple[GoalState | None, Any]:
|
|
"""Re-read the goal and latest checkpoint together for a concurrency re-check."""
|
|
goal = await read_thread_goal(checkpointer, thread_id)
|
|
checkpoint_tuple = await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aget_tuple",
|
|
"get_tuple",
|
|
{"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}},
|
|
)
|
|
return goal, checkpoint_tuple
|
|
|
|
|
|
async def _prepare_goal_continuation_input(
|
|
*,
|
|
bridge: StreamBridge,
|
|
accessor: CheckpointStateAccessor,
|
|
checkpointer: Any,
|
|
thread_id: str,
|
|
run_id: str,
|
|
model_name: str | None,
|
|
app_config: AppConfig | None,
|
|
evaluator_model_factory: Any | None = None,
|
|
abort_event: asyncio.Event | None = None,
|
|
user_id: str | None = None,
|
|
deerflow_trace_id: str | None = None,
|
|
task_store: Any | None = None,
|
|
extensions: Any | None = None,
|
|
) -> dict[str, Any] | None:
|
|
"""Evaluate the active goal and return a hidden continuation input if needed.
|
|
|
|
NOTE: The re-reads below catch a racing user message or ``/goal clear``
|
|
before we queue a continuation. Goal writes then serialize per thread and
|
|
pass the checkpoint id they read from, so stale evaluator writes stand down
|
|
instead of clobbering a newer goal change.
|
|
"""
|
|
if checkpointer is None:
|
|
return None
|
|
if abort_event is not None and abort_event.is_set():
|
|
return None
|
|
|
|
try:
|
|
goal = await read_thread_goal(checkpointer, thread_id)
|
|
except Exception:
|
|
logger.warning("Could not read goal for thread %s after run %s", thread_id, run_id, exc_info=True)
|
|
return None
|
|
if not goal or goal.get("status") != "active":
|
|
return None
|
|
|
|
async def _persist(
|
|
goal: GoalState,
|
|
evaluation: GoalEvaluation,
|
|
no_progress_count: int,
|
|
*,
|
|
stand_down_reason: str | None = None,
|
|
continuation_count: int | None = None,
|
|
) -> GoalState | None:
|
|
"""Record the evaluation against the still-current goal instance."""
|
|
return await _persist_goal_evaluation(
|
|
bridge=bridge,
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
run_id=run_id,
|
|
goal=goal,
|
|
evaluation=evaluation,
|
|
no_progress_count=no_progress_count,
|
|
continuation_count=continuation_count,
|
|
stand_down_reason=stand_down_reason,
|
|
evidence_signature=evidence_signature,
|
|
)
|
|
|
|
try:
|
|
checkpoint_tuple = await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aget_tuple",
|
|
"get_tuple",
|
|
{"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}},
|
|
)
|
|
if checkpoint_tuple is None:
|
|
return None
|
|
checkpoint_id_before = _checkpoint_id(checkpoint_tuple)
|
|
messages = await _materialized_checkpoint_messages(accessor, thread_id)
|
|
conversation_signature_before = visible_conversation_signature(messages)
|
|
evidence_signature = latest_visible_assistant_signature(messages)
|
|
|
|
if not _has_durable_goal_turn_receipt(checkpoint_tuple, messages):
|
|
evaluation = GoalEvaluation(
|
|
satisfied=False,
|
|
blocker="run_failed",
|
|
reason="No durable assistant end-of-turn receipt was available.",
|
|
evidence_summary="",
|
|
)
|
|
no_progress_count = compute_no_progress_count(goal, evaluation, evidence_signature=evidence_signature)
|
|
await _persist(goal, evaluation, no_progress_count, stand_down_reason="no_durable_end_of_turn")
|
|
return None
|
|
|
|
if abort_event is not None and abort_event.is_set():
|
|
return None
|
|
evaluator_model = evaluator_model_factory() if evaluator_model_factory is not None else None
|
|
evaluation = await evaluate_goal_completion(
|
|
goal,
|
|
messages,
|
|
model=evaluator_model,
|
|
model_name=model_name,
|
|
app_config=app_config,
|
|
thread_id=thread_id,
|
|
user_id=user_id,
|
|
deerflow_trace_id=deerflow_trace_id,
|
|
task_store=task_store,
|
|
extensions=extensions,
|
|
)
|
|
if abort_event is not None and abort_event.is_set():
|
|
return None
|
|
except Exception:
|
|
logger.warning("Goal evaluator failed for thread %s after run %s", thread_id, run_id, exc_info=True)
|
|
return None
|
|
|
|
no_progress_count = compute_no_progress_count(goal, evaluation, evidence_signature=evidence_signature)
|
|
|
|
# Re-check that neither the goal nor the visible conversation changed while the
|
|
# evaluator ran — a user message or /goal clear racing the evaluation must win.
|
|
try:
|
|
current_goal, current_checkpoint_tuple = await _reread_goal_and_checkpoint(checkpointer, thread_id)
|
|
except Exception:
|
|
logger.warning("Could not re-check goal state for thread %s after evaluation", thread_id, exc_info=True)
|
|
return None
|
|
|
|
if not _goal_instance_matches(goal, current_goal) or current_checkpoint_tuple is None:
|
|
return None
|
|
|
|
checkpoint_changed = _checkpoint_id(current_checkpoint_tuple) != checkpoint_id_before
|
|
messages_changed = visible_conversation_signature(await _materialized_checkpoint_messages(accessor, thread_id)) != conversation_signature_before
|
|
if checkpoint_changed or messages_changed:
|
|
await _persist(current_goal, evaluation, no_progress_count, stand_down_reason="thread_changed_after_evaluation")
|
|
return None
|
|
|
|
if evaluation["satisfied"]:
|
|
try:
|
|
async with goal_thread_lock(thread_id):
|
|
latest_checkpoint_tuple = await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aget_tuple",
|
|
"get_tuple",
|
|
{"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}},
|
|
)
|
|
if latest_checkpoint_tuple is None:
|
|
return None
|
|
latest_goal = _read_checkpoint_goal(latest_checkpoint_tuple)
|
|
if latest_goal is None or not _goal_instance_matches(goal, latest_goal):
|
|
return None
|
|
values = await write_thread_goal(
|
|
checkpointer,
|
|
thread_id,
|
|
None,
|
|
as_node="goal_evaluator",
|
|
expected_checkpoint_id=_checkpoint_id(latest_checkpoint_tuple),
|
|
)
|
|
await bridge.publish(run_id, "values", serialize(values, mode="values"))
|
|
except GoalWriteConflict:
|
|
return None
|
|
except Exception:
|
|
logger.warning("Could not clear satisfied goal for thread %s", thread_id, exc_info=True)
|
|
return None
|
|
|
|
stand_down_reason = _stand_down_reason(goal, evaluation, no_progress_count)
|
|
if stand_down_reason is not None or not should_continue_goal(goal, evaluation, no_progress_count=no_progress_count):
|
|
await _persist(goal, evaluation, no_progress_count, stand_down_reason=stand_down_reason)
|
|
return None
|
|
|
|
next_count = int(goal.get("continuation_count", 0)) + 1
|
|
updated_goal = await _persist(goal, evaluation, no_progress_count, continuation_count=next_count)
|
|
if updated_goal is None:
|
|
return None
|
|
|
|
# Final guard: the persist above bumped the checkpoint id, so only the visible
|
|
# conversation signature is meaningful for detecting a racing user turn here.
|
|
try:
|
|
latest_goal, latest_checkpoint_tuple = await _reread_goal_and_checkpoint(checkpointer, thread_id)
|
|
except Exception:
|
|
logger.warning("Could not verify queued goal continuation for thread %s", thread_id, exc_info=True)
|
|
return None
|
|
if not _goal_instance_matches(updated_goal, latest_goal) or latest_checkpoint_tuple is None:
|
|
return None
|
|
if visible_conversation_signature(await _materialized_checkpoint_messages(accessor, thread_id)) != conversation_signature_before:
|
|
# Do not pass continuation_count here: the persist above already
|
|
# committed it (as next_count). Re-passing next_count would make
|
|
# _persist_goal_evaluation's race guard (#4088) see that same write as
|
|
# a "current_count" bump and add another +1 on top of it, silently
|
|
# double-counting this single continuation attempt against the
|
|
# continuation budget even though it is being stood down, not
|
|
# delivered. Omitting it leaves the already-committed count untouched,
|
|
# matching every other stand-down call site in this function.
|
|
await _persist(
|
|
latest_goal,
|
|
evaluation,
|
|
no_progress_count,
|
|
stand_down_reason="thread_changed_before_continuation",
|
|
)
|
|
return None
|
|
|
|
logger.info(
|
|
"Run %s continuing thread %s for active goal (%d/%d)",
|
|
run_id,
|
|
thread_id,
|
|
updated_goal.get("continuation_count", next_count),
|
|
updated_goal.get("max_continuations", 0),
|
|
)
|
|
return {"messages": [make_goal_continuation_message(updated_goal, evaluation)]}
|
|
|
|
|
|
def _is_edit_replay_run(record: RunRecord) -> bool:
|
|
metadata = record.metadata or {}
|
|
return metadata.get("replay_kind") == "edit"
|
|
|
|
|
|
async def _ensure_finalizing_before_edit_failure(run_manager: RunManager, record: RunRecord) -> None:
|
|
if _is_edit_replay_run(record) and not record.finalizing:
|
|
await run_manager.set_finalizing(record.run_id, True)
|
|
|
|
|
|
async def _publish_restored_checkpoint_values(
|
|
*,
|
|
bridge: StreamBridge,
|
|
run_id: str,
|
|
accessor: CheckpointStateAccessor | None,
|
|
thread_id: str,
|
|
) -> None:
|
|
if accessor is None:
|
|
return
|
|
snapshot = await accessor.aget({"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}})
|
|
values = getattr(snapshot, "values", None)
|
|
if isinstance(values, dict):
|
|
await bridge.publish(run_id, "values", serialize(values, mode="values"))
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class RollbackPoint:
|
|
"""Materialized pre-run state used to restore the thread after cancellation.
|
|
|
|
Raw checkpoint blobs cannot reconstruct Delta-channel messages (their
|
|
checkpoints omit the materialized value), so rollback preserves those
|
|
messages plus delta mode's materialized non-message state in addition to
|
|
the raw pending writes.
|
|
"""
|
|
|
|
config: dict[str, Any]
|
|
state_values: dict[str, Any]
|
|
messages: tuple[Any, ...]
|
|
metadata: dict[str, Any]
|
|
pending_writes: tuple[tuple[str, str, Any], ...]
|
|
|
|
|
|
async def _capture_rollback_point(
|
|
accessor: CheckpointStateAccessor,
|
|
checkpointer: Any,
|
|
read_config: dict[str, Any],
|
|
) -> RollbackPoint | None:
|
|
"""Materialize the pre-run checkpoint state and its raw pending writes.
|
|
|
|
Returns ``None`` when the thread has no checkpoint yet; the caller keeps
|
|
the existing delete/reset rollback contract for that case.
|
|
"""
|
|
snapshot = await accessor.aget(read_config)
|
|
snapshot_config = getattr(snapshot, "config", None) or {}
|
|
configurable = snapshot_config.get("configurable") or {}
|
|
if not configurable.get("checkpoint_id"):
|
|
return None
|
|
checkpoint_tuple = await _call_checkpointer_method(checkpointer, "aget_tuple", "get_tuple", snapshot_config)
|
|
raw_values = getattr(snapshot, "values", None) or {}
|
|
messages = raw_values.get("messages") if isinstance(raw_values, dict) else None
|
|
state_values = copy.deepcopy({key: value for key, value in raw_values.items() if key != "messages"}) if accessor.mode == "delta" and isinstance(raw_values, dict) else {}
|
|
return RollbackPoint(
|
|
config={
|
|
"configurable": {
|
|
"thread_id": configurable.get("thread_id"),
|
|
"checkpoint_ns": configurable.get("checkpoint_ns") or "",
|
|
"checkpoint_id": configurable.get("checkpoint_id"),
|
|
}
|
|
},
|
|
state_values=state_values,
|
|
messages=tuple(messages or ()),
|
|
metadata=dict(getattr(snapshot, "metadata", None) or {}),
|
|
pending_writes=tuple(getattr(checkpoint_tuple, "pending_writes", ()) or ()),
|
|
)
|
|
|
|
|
|
def _complete_state_replacement_values(
|
|
*,
|
|
mutation_graph: Any,
|
|
selected_values: dict[str, Any],
|
|
current_values: dict[str, Any],
|
|
run_id: str,
|
|
operation: str,
|
|
) -> dict[str, Any]:
|
|
"""Build a whole-state replacement through the graph's effective schema."""
|
|
writable_fields = graph_writable_channels(mutation_graph)
|
|
reducer_fields = graph_reducer_channels(mutation_graph)
|
|
if writable_fields is None or reducer_fields is None:
|
|
raise RuntimeError(f"Run {run_id} could not inspect the state schema for {operation}")
|
|
|
|
replacement_values: dict[str, Any] = {}
|
|
for field_name in writable_fields:
|
|
if field_name in selected_values:
|
|
replacement = copy.deepcopy(selected_values[field_name])
|
|
elif field_name in current_values:
|
|
# LangGraph has no public "unset channel" update. A fresh channel
|
|
# exposes its schema default when one exists (for example [] / {});
|
|
# optional and otherwise-unconstructible channels reset to None.
|
|
channel = mutation_graph.channels.get(field_name)
|
|
replacement = copy.deepcopy(channel.get()) if channel is not None and channel.is_available() else None
|
|
else:
|
|
continue
|
|
replacement_values[field_name] = Overwrite(replacement) if field_name in reducer_fields else replacement
|
|
return replacement_values
|
|
|
|
|
|
async def _linearize_delta_checkpoint_resume(
|
|
*,
|
|
accessor: CheckpointStateAccessor,
|
|
checkpointer: Any,
|
|
config: dict[str, Any],
|
|
thread_id: str,
|
|
run_id: str,
|
|
) -> list[Any] | None:
|
|
"""Replace a delta-mode checkpoint fork with an equivalent linear write.
|
|
|
|
Resuming from an older checkpoint forks the lineage, and in ``delta`` mode
|
|
the fork's state cannot be materialized correctly: the delta history walk
|
|
collects **every** ``pending_writes`` entry stored on each on-path
|
|
ancestor, but a shared parent also carries the writes of the sibling child
|
|
that was abandoned. Those writes are replayed into the fork, so the run
|
|
starts from a message list that still contains the answer it was supposed
|
|
to replace — regenerating in a branched thread surfaced this as the old
|
|
assistant message reappearing beside the new one after a reload (#4458).
|
|
Reproduced on postgres, sqlite, and the in-memory saver; ``full`` mode is
|
|
unaffected because its checkpoints carry complete ``channel_values`` and
|
|
need no replay.
|
|
|
|
The upstream contract (`BaseCheckpointSaver.get_delta_channel_history` and
|
|
the savers overriding it) is where write-to-child ownership belongs, so
|
|
this does not reimplement it. Instead the fork is expressed as what it
|
|
means: materialize the requested checkpoint's state and write it with
|
|
replace semantics on the **current head**, which has no other children,
|
|
then run linearly. Every materialized channel is restored; channels that
|
|
exist only on the newer head are reset to their schema default (or
|
|
``None`` when the channel has no constructible default). The abandoned
|
|
turn stays in checkpoint history as the rewritten head's ancestry.
|
|
|
|
Returns the materialized messages when the resume was linearized, or
|
|
``None`` when there was nothing to do (full mode, no checkpoint selector,
|
|
a non-root namespace, or a selector that already names the head). Failures
|
|
propagate: silently falling back to the fork would persist the corrupted
|
|
history this exists to prevent. The worker call site holds
|
|
``_checkpoint_thread_lock`` across rollback capture and this rewrite; do
|
|
not reacquire that non-reentrant lock inside this helper.
|
|
"""
|
|
if checkpointer is None or accessor.mode != "delta":
|
|
return None
|
|
configurable = config.get("configurable")
|
|
if not isinstance(configurable, dict):
|
|
return None
|
|
checkpoint_id = configurable.get("checkpoint_id")
|
|
if not isinstance(checkpoint_id, str) or not checkpoint_id:
|
|
return None
|
|
if configurable.get("checkpoint_ns"):
|
|
# Subgraph namespaces have their own lineage; the Gateway only selects
|
|
# root checkpoints, so leave anything else untouched.
|
|
return None
|
|
|
|
head_config: dict[str, Any] = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
|
head = await accessor.aget(head_config)
|
|
if _checkpoint_id(head) == checkpoint_id:
|
|
# Selecting the head is already linear — no sibling can exist yet.
|
|
return None
|
|
|
|
source_config: dict[str, Any] = {"configurable": {"thread_id": thread_id, "checkpoint_ns": "", "checkpoint_id": checkpoint_id}}
|
|
snapshot = await accessor.aget(source_config)
|
|
values = getattr(snapshot, "values", None) or {}
|
|
messages = values.get("messages") if isinstance(values, dict) else None
|
|
if not isinstance(messages, list):
|
|
raise RuntimeError(f"Run {run_id} could not materialize resume checkpoint {checkpoint_id}")
|
|
|
|
# Write through the thread's effective schema so every application and
|
|
# middleware channel can be restored. Reducer channels need Overwrite to
|
|
# replace their already-aggregated value instead of merging it again.
|
|
mutation_graph = build_state_mutation_graph("checkpoint_resume", accessor.mode, graph_state_schema(getattr(accessor, "graph", None)))
|
|
selected_values = dict(values)
|
|
head_values = getattr(head, "values", None) or {}
|
|
head_values = dict(head_values) if isinstance(head_values, dict) else {}
|
|
replacement_values = _complete_state_replacement_values(
|
|
mutation_graph=mutation_graph,
|
|
selected_values=selected_values,
|
|
current_values=head_values,
|
|
run_id=run_id,
|
|
operation="checkpoint resume",
|
|
)
|
|
|
|
mutation_accessor = CheckpointStateAccessor.bind(mutation_graph, checkpointer, mode=accessor.mode)
|
|
await mutation_accessor.aupdate(head_config, replacement_values, as_node="checkpoint_resume")
|
|
configurable.pop("checkpoint_id", None)
|
|
configurable.pop("checkpoint_map", None)
|
|
logger.info("Run %s linearized a delta-mode resume of checkpoint %s onto thread %s", run_id, checkpoint_id, thread_id)
|
|
return list(messages)
|
|
|
|
|
|
async def _rollback_to_pre_run_checkpoint(
|
|
*,
|
|
accessor: CheckpointStateAccessor | None,
|
|
checkpointer: Any,
|
|
thread_id: str,
|
|
run_id: str,
|
|
rollback_point: RollbackPoint | None,
|
|
snapshot_capture_failed: bool,
|
|
) -> bool:
|
|
"""Restore the complete pre-run state and report whether it completed.
|
|
|
|
Full mode forks the captured pre-run checkpoint and overwrites messages;
|
|
all other channels inherit from that parent. Delta mode cannot safely fork
|
|
once the cancelled path has attached writes to the same parent, so it
|
|
replaces every captured channel on the current head instead. Both writes
|
|
use a state-only mutation graph whose synthetic ``rollback_restore`` node
|
|
finishes immediately and schedules no agent work.
|
|
"""
|
|
if checkpointer is None:
|
|
logger.info("Run %s rollback requested but no checkpointer is configured", run_id)
|
|
return False
|
|
|
|
if snapshot_capture_failed:
|
|
logger.warning("Run %s rollback skipped: pre-run checkpoint capture failed", run_id)
|
|
return False
|
|
|
|
if rollback_point is None:
|
|
await _call_checkpointer_method(checkpointer, "adelete_thread", "delete_thread", thread_id)
|
|
logger.info("Run %s rollback reset thread %s to empty state", run_id, thread_id)
|
|
return True
|
|
|
|
configurable = rollback_point.config.get("configurable", {})
|
|
if not configurable.get("checkpoint_id"):
|
|
logger.warning("Run %s rollback skipped: pre-run checkpoint has no checkpoint id", run_id)
|
|
return False
|
|
|
|
if accessor is None:
|
|
# Unreachable in practice: a rollback point can only be captured
|
|
# through the bound accessor. Stay fail-closed.
|
|
logger.warning("Run %s rollback skipped: agent accessor unavailable", run_id)
|
|
return False
|
|
|
|
# Compile with the thread's effective schema so middleware-contributed
|
|
# channels survive (the base ThreadState fallback would silently drop
|
|
# them).
|
|
mutation_graph = build_state_mutation_graph("rollback_restore", accessor.mode, graph_state_schema(getattr(accessor, "graph", None)))
|
|
mutation_accessor = CheckpointStateAccessor.bind(mutation_graph, checkpointer, mode=accessor.mode)
|
|
if accessor.mode == "delta":
|
|
# A delta rollback fork has the same write-ownership problem as a
|
|
# checkpoint resume: the captured parent now carries writes from the
|
|
# cancelled sibling. Restore linearly on the current head instead.
|
|
restore_config: dict[str, Any] = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
|
current = await accessor.aget(restore_config)
|
|
raw_current_values = getattr(current, "values", None) or {}
|
|
current_values = dict(raw_current_values) if isinstance(raw_current_values, dict) else {}
|
|
selected_values = copy.deepcopy(rollback_point.state_values)
|
|
selected_values["messages"] = list(rollback_point.messages)
|
|
replacement_values = _complete_state_replacement_values(
|
|
mutation_graph=mutation_graph,
|
|
selected_values=selected_values,
|
|
current_values=current_values,
|
|
run_id=run_id,
|
|
operation="rollback",
|
|
)
|
|
else:
|
|
restore_config = rollback_point.config
|
|
replacement_values = {"messages": Overwrite(list(rollback_point.messages))}
|
|
|
|
restored_config = await mutation_accessor.aupdate(
|
|
restore_config,
|
|
replacement_values,
|
|
as_node="rollback_restore",
|
|
)
|
|
if not isinstance(restored_config, dict):
|
|
raise RuntimeError(f"Run {run_id} rollback restore returned invalid config: expected dict")
|
|
restored_configurable = restored_config.get("configurable", {})
|
|
if not isinstance(restored_configurable, dict):
|
|
raise RuntimeError(f"Run {run_id} rollback restore returned invalid config payload")
|
|
restored_checkpoint_id = restored_configurable.get("checkpoint_id")
|
|
if not restored_checkpoint_id:
|
|
raise RuntimeError(f"Run {run_id} rollback restore did not return checkpoint_id")
|
|
|
|
pending_writes = rollback_point.pending_writes
|
|
if not pending_writes:
|
|
return True
|
|
|
|
writes_by_task: dict[str, list[tuple[str, Any]]] = {}
|
|
for item in pending_writes:
|
|
if not isinstance(item, (tuple, list)) or len(item) != 3:
|
|
raise RuntimeError(f"Run {run_id} rollback failed: pending_write is not a 3-tuple: {item!r}")
|
|
task_id, channel, value = item
|
|
if not isinstance(channel, str):
|
|
raise RuntimeError(f"Run {run_id} rollback failed: pending_write has non-string channel: task_id={task_id!r}, channel={channel!r}")
|
|
writes_by_task.setdefault(str(task_id), []).append((channel, value))
|
|
|
|
for task_id, writes in writes_by_task.items():
|
|
await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aput_writes",
|
|
"put_writes",
|
|
restored_config,
|
|
writes,
|
|
task_id=task_id,
|
|
)
|
|
return True
|
|
|
|
|
|
def _new_checkpoint_marker() -> dict[str, str]:
|
|
marker = empty_checkpoint()
|
|
return {"id": marker["id"], "ts": marker["ts"]}
|
|
|
|
|
|
def _bump_channel_version(checkpointer: Any, current_version: Any) -> Any:
|
|
"""Return a strictly-different next version for a checkpoint channel.
|
|
|
|
DB-backed LangGraph savers (PostgresSaver / v4 SqliteSaver blob layout)
|
|
persist channel blobs keyed by ``channel_versions[<channel>]``, so the
|
|
new value MUST differ from the prior value. We delegate to the
|
|
checkpointer's ``get_next_version`` when available — that is the canonical
|
|
versioning scheme each saver picks (int, monotonic float, or
|
|
UUID-shaped string). When the checkpointer doesn't expose it (or it
|
|
returns ``None``/an unchanged value), fall back to a defensive bump that
|
|
still guarantees inequality.
|
|
"""
|
|
get_next_version = getattr(checkpointer, "get_next_version", None)
|
|
if callable(get_next_version):
|
|
try:
|
|
next_version = get_next_version(current_version, None)
|
|
except Exception:
|
|
next_version = None
|
|
if next_version is not None and next_version != current_version:
|
|
return next_version
|
|
# fall through to defensive bump
|
|
|
|
if isinstance(current_version, bool):
|
|
# ``bool`` is a subclass of ``int``; treat True/False as 1/0 instead of
|
|
# adding to the boolean itself, which would produce an int anyway but
|
|
# via a path that surprises readers.
|
|
return int(current_version) + 1
|
|
if isinstance(current_version, int):
|
|
return current_version + 1
|
|
if isinstance(current_version, float):
|
|
# Match LangGraph's default float versioning (monotonic increment).
|
|
return current_version + 1.0
|
|
if isinstance(current_version, str):
|
|
try:
|
|
return str(int(current_version) + 1)
|
|
except ValueError:
|
|
return f"{current_version}.1"
|
|
return 1
|
|
|
|
|
|
def _checkpoint_identity(ckpt_tuple: Any | None, checkpoint: dict[str, Any]) -> str | None:
|
|
tuple_config = getattr(ckpt_tuple, "config", {}) or {}
|
|
tuple_configurable = tuple_config.get("configurable", {}) if isinstance(tuple_config, dict) else {}
|
|
if isinstance(tuple_configurable, dict):
|
|
checkpoint_id = tuple_configurable.get("checkpoint_id")
|
|
if isinstance(checkpoint_id, str) and checkpoint_id:
|
|
return checkpoint_id
|
|
checkpoint_id = checkpoint.get("id")
|
|
return checkpoint_id if isinstance(checkpoint_id, str) and checkpoint_id else None
|
|
|
|
|
|
def _checkpoint_namespace(ckpt_tuple: Any | None) -> str:
|
|
tuple_config = getattr(ckpt_tuple, "config", {}) or {}
|
|
tuple_configurable = tuple_config.get("configurable", {}) if isinstance(tuple_config, dict) else {}
|
|
checkpoint_ns = tuple_configurable.get("checkpoint_ns", "") if isinstance(tuple_configurable, dict) else ""
|
|
return checkpoint_ns if isinstance(checkpoint_ns, str) else ""
|
|
|
|
|
|
def _graph_input_messages(graph_input: Any | None) -> list[Any]:
|
|
if not isinstance(graph_input, dict):
|
|
return []
|
|
messages = graph_input.get("messages")
|
|
if isinstance(messages, list):
|
|
return messages
|
|
if isinstance(messages, tuple):
|
|
return list(messages)
|
|
return []
|
|
|
|
|
|
def _title_generation_state(channel_values: dict[str, Any], graph_input: Any | None) -> dict[str, Any]:
|
|
state = dict(channel_values)
|
|
messages = state.get("messages")
|
|
if not messages:
|
|
fallback_messages = _graph_input_messages(graph_input)
|
|
if fallback_messages:
|
|
state["messages"] = fallback_messages
|
|
return state
|
|
|
|
|
|
def valid_duration_entry(run_id: Any, duration_seconds: Any) -> bool:
|
|
"""Check that (run_id, duration_seconds) is a well-formed duration entry."""
|
|
return isinstance(run_id, str) and bool(run_id) and isinstance(duration_seconds, int) and not isinstance(duration_seconds, bool)
|
|
|
|
|
|
async def persist_run_durations(
|
|
*,
|
|
checkpointer: Any,
|
|
thread_id: str,
|
|
durations: dict[str, int],
|
|
) -> bool:
|
|
"""Merge validated run durations into a metadata-only checkpoint.
|
|
|
|
Durations accumulate so the history fast path can serve every known turn
|
|
from the latest checkpoint. Per-entry overhead is negligible (~50 bytes
|
|
per run_id) compared to the messages channel blob written on every graph
|
|
checkpoint, so no pruning is needed.
|
|
"""
|
|
updates = {run_id: max(0, duration_seconds) for run_id, duration_seconds in durations.items() if valid_duration_entry(run_id, duration_seconds)}
|
|
if not updates:
|
|
return False
|
|
|
|
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
|
async with _checkpoint_thread_lock(thread_id):
|
|
for _attempt in range(3):
|
|
ckpt_tuple = await _call_checkpointer_method(checkpointer, "aget_tuple", "get_tuple", ckpt_config)
|
|
if ckpt_tuple is None:
|
|
return False
|
|
|
|
checkpoint = dict(getattr(ckpt_tuple, "checkpoint", {}) or {})
|
|
metadata = dict(getattr(ckpt_tuple, "metadata", {}) or {})
|
|
raw_run_durations = metadata.get("run_durations")
|
|
run_durations = {key: value for key, value in raw_run_durations.items() if valid_duration_entry(key, value)} if isinstance(raw_run_durations, dict) else {}
|
|
changed_durations = {run_id: duration for run_id, duration in updates.items() if run_durations.get(run_id) != duration}
|
|
if not changed_durations:
|
|
return False
|
|
|
|
run_durations.update(changed_durations)
|
|
parent_checkpoint_id = _checkpoint_identity(ckpt_tuple, checkpoint)
|
|
latest_tuple = await _call_checkpointer_method(checkpointer, "aget_tuple", "get_tuple", ckpt_config)
|
|
latest_checkpoint = dict(getattr(latest_tuple, "checkpoint", {}) or {}) if latest_tuple is not None else {}
|
|
if _checkpoint_identity(latest_tuple, latest_checkpoint) != parent_checkpoint_id:
|
|
continue
|
|
|
|
checkpoint.update(_new_checkpoint_marker())
|
|
metadata["source"] = "update"
|
|
prev_step = metadata.get("step")
|
|
metadata["step"] = (prev_step + 1) if isinstance(prev_step, int) else 1
|
|
metadata["run_durations"] = run_durations
|
|
metadata["writes"] = {"runtime_run_duration": {"run_ids": sorted(changed_durations)}}
|
|
|
|
checkpoint_ns = _checkpoint_namespace(ckpt_tuple)
|
|
write_config = {
|
|
"configurable": {
|
|
"thread_id": thread_id,
|
|
"checkpoint_ns": checkpoint_ns,
|
|
"checkpoint_id": parent_checkpoint_id,
|
|
}
|
|
}
|
|
await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aput",
|
|
"put",
|
|
write_config,
|
|
checkpoint,
|
|
metadata,
|
|
{},
|
|
)
|
|
return True
|
|
return False
|
|
|
|
|
|
async def _persist_run_duration(
|
|
*,
|
|
checkpointer: Any,
|
|
thread_id: str,
|
|
run_id: str,
|
|
duration_seconds: int,
|
|
) -> None:
|
|
"""Persist one completed run duration in the thread checkpoint metadata."""
|
|
await persist_run_durations(
|
|
checkpointer=checkpointer,
|
|
thread_id=thread_id,
|
|
durations={run_id: duration_seconds},
|
|
)
|
|
|
|
|
|
async def _ensure_interrupted_title(*, checkpointer: Any, thread_id: str, app_config: AppConfig | None, graph_input: Any | None = None) -> str | None:
|
|
"""Persist a local fallback title for interrupted first-turn runs.
|
|
|
|
Returns the title that is now persisted (existing or newly written), or
|
|
``None`` when no checkpoint is available or no title text can be derived.
|
|
Idempotent: re-invoking against a checkpoint that already carries a title
|
|
short-circuits without writing a new checkpoint.
|
|
"""
|
|
from deerflow.agents.middlewares.title_middleware import TitleMiddleware
|
|
|
|
middleware = TitleMiddleware(app_config=app_config) if app_config is not None else TitleMiddleware()
|
|
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
|
|
|
for _attempt in range(3):
|
|
ckpt_tuple = await _call_checkpointer_method(checkpointer, "aget_tuple", "get_tuple", ckpt_config)
|
|
checkpoint = copy.deepcopy(getattr(ckpt_tuple, "checkpoint", {}) or {}) if ckpt_tuple is not None else empty_checkpoint()
|
|
channel_values = dict(checkpoint.get("channel_values", {}) or {})
|
|
existing_title = channel_values.get("title")
|
|
if existing_title:
|
|
return existing_title
|
|
|
|
result = middleware._generate_title_result(_title_generation_state(channel_values, graph_input), allow_partial_exchange=True)
|
|
title = result.get("title") if isinstance(result, dict) else None
|
|
if not title:
|
|
return None
|
|
|
|
# ``empty_checkpoint()`` creates a fresh id every time; only real tuples
|
|
# carry an identity stable enough for the stale-snapshot comparison.
|
|
base_identity = _checkpoint_identity(ckpt_tuple, checkpoint) if ckpt_tuple is not None else None
|
|
latest_tuple = await _call_checkpointer_method(checkpointer, "aget_tuple", "get_tuple", ckpt_config)
|
|
latest_checkpoint = copy.deepcopy(getattr(latest_tuple, "checkpoint", {}) or {}) if latest_tuple is not None else empty_checkpoint()
|
|
latest_identity = _checkpoint_identity(latest_tuple, latest_checkpoint) if latest_tuple is not None else None
|
|
if base_identity is None:
|
|
if latest_identity is not None:
|
|
continue
|
|
elif latest_identity != base_identity:
|
|
continue
|
|
|
|
checkpoint = latest_checkpoint
|
|
channel_values = dict(checkpoint.get("channel_values", {}) or {})
|
|
existing_title = channel_values.get("title")
|
|
if existing_title:
|
|
return existing_title
|
|
|
|
channel_values["title"] = title
|
|
marker = _new_checkpoint_marker()
|
|
checkpoint.update({"id": marker["id"], "ts": marker["ts"], "channel_values": channel_values})
|
|
|
|
# Bump ``channel_versions["title"]`` and declare the bump in ``new_versions``
|
|
# so DB-backed savers (SqliteSaver v4 / PostgresSaver) actually persist the
|
|
# new blob — those savers strip inline ``channel_values`` from ``put`` and
|
|
# only write blobs for channels listed in ``new_versions``. The legacy
|
|
# single-table sqlite saver ignores ``new_versions`` and inlines the
|
|
# snapshot, so this path is correct for both layouts. Mirrors
|
|
# ``_rollback_to_pre_run_checkpoint`` in the same file.
|
|
channel_versions = dict(checkpoint.get("channel_versions", {}) or {})
|
|
next_title_version = _bump_channel_version(checkpointer, channel_versions.get("title"))
|
|
channel_versions["title"] = next_title_version
|
|
checkpoint["channel_versions"] = channel_versions
|
|
|
|
metadata = dict(getattr(latest_tuple, "metadata", {}) or {})
|
|
metadata["source"] = "update"
|
|
prev_step = metadata.get("step")
|
|
metadata["step"] = (prev_step + 1) if isinstance(prev_step, int) else 1
|
|
metadata["writes"] = {"runtime_interrupt_title": {"title": title}}
|
|
|
|
checkpoint_ns = _checkpoint_namespace(latest_tuple)
|
|
# Parent to the checkpoint this write was derived from - a parentless
|
|
# raw write would sever Delta-channel replay ancestry (and truncate
|
|
# full-mode history walks).
|
|
write_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": checkpoint_ns, "checkpoint_id": latest_identity}}
|
|
await _call_checkpointer_method(
|
|
checkpointer,
|
|
"aput",
|
|
"put",
|
|
write_config,
|
|
checkpoint,
|
|
metadata,
|
|
{"title": next_title_version},
|
|
)
|
|
return title
|
|
|
|
return None
|
|
|
|
|
|
def _lg_mode_to_sse_event(mode: str) -> str:
|
|
"""Map LangGraph internal stream_mode name to SSE event name.
|
|
|
|
LangGraph's ``astream(stream_mode="messages")`` produces message
|
|
tuples. The SSE protocol calls this ``messages-tuple`` when the
|
|
client explicitly requests it, but the default SSE event name used
|
|
by LangGraph Platform is simply ``"messages"``.
|
|
"""
|
|
# All LG modes map 1:1 to SSE event names — "messages" stays "messages"
|
|
return mode
|
|
|
|
|
|
def _error_fallback_message_from_metadata(metadata: dict[str, Any], content: Any) -> str:
|
|
detail = metadata.get("error_detail")
|
|
if isinstance(detail, str) and detail.strip():
|
|
return detail.strip()
|
|
reason = metadata.get("error_reason")
|
|
if isinstance(reason, str) and reason.strip():
|
|
return reason.strip()
|
|
if isinstance(content, str) and content.strip():
|
|
return content.strip()[:2000]
|
|
return "LLM provider failed after retries"
|
|
|
|
|
|
def _message_id(obj: Any) -> str | None:
|
|
"""Best-effort extraction of a stable message id from a message-like object."""
|
|
msg_id = getattr(obj, "id", None)
|
|
if isinstance(msg_id, str) and msg_id:
|
|
return msg_id
|
|
if isinstance(obj, dict):
|
|
raw = obj.get("id")
|
|
if isinstance(raw, str) and raw:
|
|
return raw
|
|
return None
|
|
|
|
|
|
def _try_extract_from_message(obj: Any, pre_existing_ids: set[str] | None = None) -> str | None:
|
|
"""Try to extract fallback marker from a single message object or dict.
|
|
|
|
Messages whose id appears in ``pre_existing_ids`` are skipped — those are
|
|
history checkpointed by a *prior* run on this thread and any fallback
|
|
marker on them was already accounted for when that earlier run finished.
|
|
Without this filter, a single past run that ended with a fallback marker
|
|
would mark every subsequent run on the same thread as ``error``, because
|
|
LangGraph replays the full message history through ``stream_mode="values"``.
|
|
"""
|
|
if pre_existing_ids:
|
|
msg_id = _message_id(obj)
|
|
if msg_id is not None and msg_id in pre_existing_ids:
|
|
return None
|
|
|
|
additional_kwargs = getattr(obj, "additional_kwargs", None)
|
|
if isinstance(additional_kwargs, dict) and additional_kwargs.get("deerflow_error_fallback"):
|
|
return _error_fallback_message_from_metadata(additional_kwargs, getattr(obj, "content", None))
|
|
|
|
if isinstance(obj, dict):
|
|
nested_kwargs = obj.get("additional_kwargs")
|
|
if isinstance(nested_kwargs, dict) and nested_kwargs.get("deerflow_error_fallback"):
|
|
return _error_fallback_message_from_metadata(nested_kwargs, obj.get("content"))
|
|
return None
|
|
|
|
|
|
def _extract_llm_error_fallback_message(value: Any, pre_existing_ids: set[str] | None = None) -> str | None:
|
|
"""Find LLM fallback markers in streamed LangGraph chunks.
|
|
|
|
Error fallback messages returned by model-call middleware are not guaranteed
|
|
to pass through LLM end callbacks, but they do appear in graph state chunks.
|
|
|
|
Messages whose id appears in ``pre_existing_ids`` are ignored — they are
|
|
history from prior runs on the same thread (LangGraph replays the full
|
|
messages channel in ``stream_mode="values"`` chunks), and any error
|
|
fallback in that history was already resolved when its run finished.
|
|
"""
|
|
# Fast path: large state chunks produced by stream_mode="values" have a
|
|
# top-level "messages" list. Scanning only that list avoids expensive deep
|
|
# recursion into large state dicts.
|
|
if isinstance(value, dict):
|
|
messages = value.get("messages")
|
|
if isinstance(messages, (list, tuple)):
|
|
for msg in messages:
|
|
result = _try_extract_from_message(msg, pre_existing_ids)
|
|
if result is not None:
|
|
return result
|
|
# Fallback marker is attached to an AI message in the messages
|
|
# channel; it will never appear elsewhere in a values chunk.
|
|
return None
|
|
# No top-level "messages" — this is likely an "updates" chunk (small
|
|
# dict keyed by node name). Fall through to deep walk, which is cheap
|
|
# for these payloads.
|
|
|
|
# Deep walk for updates / messages / tuple / list modes. Payloads are
|
|
# small, so full recursion is acceptable here.
|
|
seen: set[int] = set()
|
|
|
|
def walk(obj: Any) -> str | None:
|
|
oid = id(obj)
|
|
if oid in seen:
|
|
return None
|
|
seen.add(oid)
|
|
|
|
result = _try_extract_from_message(obj, pre_existing_ids)
|
|
if result is not None:
|
|
return result
|
|
|
|
if isinstance(obj, dict):
|
|
for item in obj.values():
|
|
result = walk(item)
|
|
if result is not None:
|
|
return result
|
|
return None
|
|
|
|
if isinstance(obj, (list, tuple, set)):
|
|
for item in obj:
|
|
result = walk(item)
|
|
if result is not None:
|
|
return result
|
|
return None
|
|
|
|
return walk(value)
|
|
|
|
|
|
def _collect_pre_existing_message_ids(values: Any) -> set[str]:
|
|
"""Collect stable message IDs from graph-materialized channel values."""
|
|
if not isinstance(values, dict):
|
|
return set()
|
|
messages = values.get("messages")
|
|
if not isinstance(messages, (list, tuple)):
|
|
return set()
|
|
return {message_id for message in messages if (message_id := _message_id(message)) is not None}
|
|
|
|
|
|
def _unpack_stream_item(
|
|
item: Any,
|
|
lg_modes: list[str],
|
|
stream_subgraphs: bool,
|
|
) -> tuple[str | None, Any, tuple[str, ...]]:
|
|
"""Unpack a multi-mode or subgraph stream item into (mode, chunk, namespace).
|
|
|
|
``namespace`` is the subgraph namespace tuple LangGraph prefixes onto each
|
|
frame when ``subgraphs=True``; it is empty for root-graph frames. Delegated
|
|
subagent graphs inherit the parent's checkpoint namespace (see
|
|
``subagents/executor.py``), so their frames arrive here with a non-empty
|
|
namespace and must not be mistaken for root frames.
|
|
|
|
Returns ``(None, None, ())`` if the item cannot be parsed.
|
|
"""
|
|
if stream_subgraphs:
|
|
if isinstance(item, tuple) and len(item) == 3:
|
|
ns, mode, chunk = item
|
|
namespace = tuple(str(part) for part in ns) if isinstance(ns, (list, tuple)) else (str(ns),)
|
|
return str(mode), chunk, namespace
|
|
if isinstance(item, tuple) and len(item) == 2:
|
|
mode, chunk = item
|
|
return str(mode), chunk, ()
|
|
return None, None, ()
|
|
|
|
if isinstance(item, tuple) and len(item) == 2:
|
|
mode, chunk = item
|
|
return str(mode), chunk, ()
|
|
|
|
# Fallback: single-element output from first mode
|
|
return lg_modes[0] if lg_modes else None, item, ()
|
|
|
|
|
|
def _compose_sse_event(sse_event: str, namespace: tuple[str, ...]) -> str:
|
|
"""Namespace-qualified SSE event name, LangGraph Platform style.
|
|
|
|
Root frames keep the bare event name; subgraph frames become
|
|
``mode|ns1|ns2`` so clients can tell them apart. The LangGraph SDK parses
|
|
exactly this shape (``event.split("|").slice(1)``) and routes
|
|
subagent-namespaced values away from the thread view.
|
|
"""
|
|
if not namespace:
|
|
return sse_event
|
|
return "|".join((sse_event, *namespace))
|
|
|
|
|
|
async def _publish_stream_item(
|
|
*,
|
|
bridge: Any,
|
|
run_id: str,
|
|
mode: str,
|
|
chunk: Any,
|
|
namespace: tuple[str, ...],
|
|
file_tool_chunk_batcher: Any,
|
|
subagent_events: Any,
|
|
) -> None:
|
|
"""Publish one stream frame, preserving the subgraph namespace.
|
|
|
|
A subgraph frame published under a bare event name impersonates the root
|
|
graph: a delegated subagent's ``values`` snapshot then replaces the whole
|
|
thread view in SDK clients and its token chunks flood the parent message
|
|
stream (#4399). Subgraph frames therefore keep their namespace in the event
|
|
name and bypass the root-only consumers (file-tool chunk batcher, subagent
|
|
event persistence — task_* lifecycle events are root frames already).
|
|
"""
|
|
sse_event = _compose_sse_event(_lg_mode_to_sse_event(mode), namespace)
|
|
if namespace:
|
|
await bridge.publish(run_id, sse_event, serialize(chunk, mode=mode))
|
|
return
|
|
if file_tool_chunk_batcher is not None and mode != "messages":
|
|
pending_chunks = file_tool_chunk_batcher.finish() if mode == "values" else file_tool_chunk_batcher.flush()
|
|
for publish_chunk in pending_chunks:
|
|
await bridge.publish(run_id, "messages", serialize(publish_chunk, mode="messages"))
|
|
chunks_to_publish = file_tool_chunk_batcher.push(chunk) if mode == "messages" and file_tool_chunk_batcher is not None else [chunk]
|
|
for publish_chunk in chunks_to_publish:
|
|
await bridge.publish(run_id, sse_event, serialize(publish_chunk, mode=mode))
|
|
if mode == "custom":
|
|
await subagent_events.add(chunk)
|