"""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 not supported through 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 logging from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Literal if TYPE_CHECKING: from langchain_core.messages import HumanMessage from deerflow.runtime.serialization import serialize from deerflow.runtime.stream_bridge import StreamBridge from .manager import RunManager, RunRecord from .schemas import RunStatus logger = logging.getLogger(__name__) # Valid stream_mode values for LangGraph's graph.astream() _VALID_LG_MODES = {"values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"} @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_meta_repo: Any | None = field(default=None) follow_up_to_run_id: str | None = field(default=None) 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_meta_repo = ctx.thread_meta_repo follow_up_to_run_id = ctx.follow_up_to_run_id run_id = record.run_id thread_id = record.thread_id requested_modes: set[str] = set(stream_modes or ["values"]) # Initialize RunJournal for event capture journal = None 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), ) # Write human_message event (model_dump format, aligned with checkpoint) human_msg = _extract_human_message(graph_input) if human_msg is not None: msg_metadata = {} if follow_up_to_run_id: msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id await event_store.put( thread_id=thread_id, run_id=run_id, event_type="human_message", category="message", content=human_msg.model_dump(), metadata=msg_metadata or None, ) content = human_msg.content journal.set_first_human_message(content if isinstance(content, str) else str(content)) # Track whether "events" was requested but skipped if "events" in requested_modes: logger.info( "Run %s: 'events' stream_mode not supported in gateway (requires astream_events + checkpoint callbacks). Skipping.", run_id, ) try: # 1. Mark running await run_manager.set_status(run_id, RunStatus.running) # Record pre-run checkpoint_id to support rollback (Phase 2). pre_run_checkpoint_id = None try: config_for_check = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}} ckpt_tuple = await checkpointer.aget_tuple(config_for_check) if ckpt_tuple is not None: pre_run_checkpoint_id = getattr(ckpt_tuple, "config", {}).get("configurable", {}).get("checkpoint_id") except Exception: logger.debug("Could not get pre-run checkpoint_id for run %s", run_id) # 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 can access thread_id # (langgraph-cli does this automatically; we must do it manually) runtime = Runtime(context={"thread_id": thread_id}, store=store) # If the caller already set a ``context`` key (LangGraph >= 0.6.0 # prefers it over ``configurable`` for thread-level data), make # sure ``thread_id`` is available there too. if "context" in config and isinstance(config["context"], dict): config["context"].setdefault("thread_id", thread_id) 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) runnable_config = RunnableConfig(**config) agent = agent_factory(config=runnable_config) # 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 # 6. Build LangGraph stream_mode list # "events" is NOT a valid astream mode — skip it # "messages-tuple" maps to LangGraph's "messages" mode lg_modes: list[str] = [] for m in requested_modes: if m == "messages-tuple": lg_modes.append("messages") elif m == "events": # Skipped — see log above continue elif m in _VALID_LG_MODES: lg_modes.append(m) if not lg_modes: lg_modes = ["values"] # Deduplicate while preserving order seen: set[str] = set() deduped: list[str] = [] for m in lg_modes: if m not in seen: seen.add(m) deduped.append(m) lg_modes = deduped logger.info("Run %s: streaming with modes %s (requested: %s)", run_id, lg_modes, requested_modes) # 7. Stream using graph.astream 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(graph_input, config=runnable_config, stream_mode=single_mode): if record.abort_event.is_set(): logger.info("Run %s abort requested — stopping", run_id) break sse_event = _lg_mode_to_sse_event(single_mode) await bridge.publish(run_id, sse_event, serialize(chunk, mode=single_mode)) else: # Multiple modes or subgraphs: astream yields tuples async for item in agent.astream( graph_input, config=runnable_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 = _unpack_stream_item(item, lg_modes, stream_subgraphs) if mode is None: continue sse_event = _lg_mode_to_sse_event(mode) await bridge.publish(run_id, sse_event, serialize(chunk, mode=mode)) # 8. Final status if record.abort_event.is_set(): action = record.abort_action if action == "rollback": await run_manager.set_status(run_id, RunStatus.error, error="Rolled back by user") # TODO(Phase 2): Implement full checkpoint rollback. # Use pre_run_checkpoint_id to revert the thread's checkpoint # to the state before this run started. Requires a # checkpointer.adelete() or equivalent API. try: if checkpointer is not None and pre_run_checkpoint_id is not None: # Phase 2: roll back to pre_run_checkpoint_id pass logger.info("Run %s rolled back", run_id) except Exception: logger.warning("Failed to rollback checkpoint for run %s", run_id) else: await run_manager.set_status(run_id, RunStatus.interrupted) else: await run_manager.set_status(run_id, RunStatus.success) except asyncio.CancelledError: action = record.abort_action if action == "rollback": await run_manager.set_status(run_id, RunStatus.error, error="Rolled back by user") logger.info("Run %s was cancelled (rollback)", run_id) else: await run_manager.set_status(run_id, RunStatus.interrupted) logger.info("Run %s was cancelled", run_id) except Exception as exc: error_msg = f"{exc}" logger.exception("Run %s failed: %s", run_id, error_msg) await run_manager.set_status(run_id, RunStatus.error, error=error_msg) await bridge.publish( run_id, "error", { "message": error_msg, "name": type(exc).__name__, }, ) finally: # Flush any buffered journal events and persist completion data if journal is not None: try: await journal.flush() except Exception: logger.warning("Failed to flush journal for run %s", run_id, exc_info=True) # 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) # Sync title from checkpoint to threads_meta.display_name if checkpointer 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_meta_repo.update_display_name(thread_id, title) except Exception: logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id) # Update threads_meta status based on run outcome try: final_status = "idle" if record.status == RunStatus.success else record.status.value await thread_meta_repo.update_status(thread_id, final_status) except Exception: logger.debug("Failed to update thread_meta status for %s (non-fatal)", thread_id) await bridge.publish_end(run_id) asyncio.create_task(bridge.cleanup(run_id, delay=60)) # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- 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 _extract_human_message(graph_input: dict) -> HumanMessage | None: """Extract or construct a HumanMessage from graph_input for event recording. Returns a LangChain HumanMessage so callers can use .model_dump() to get the checkpoint-aligned serialization format. """ from langchain_core.messages import HumanMessage messages = graph_input.get("messages") if not messages: return None last = messages[-1] if isinstance(messages, list) else messages if isinstance(last, HumanMessage): return last if isinstance(last, str): return HumanMessage(content=last) if last else None if hasattr(last, "content"): content = last.content return HumanMessage(content=content) if isinstance(last, dict): content = last.get("content", "") return HumanMessage(content=content) if content else None return None def _unpack_stream_item( item: Any, lg_modes: list[str], stream_subgraphs: bool, ) -> tuple[str | None, Any]: """Unpack a multi-mode or subgraph stream item into (mode, chunk). Returns ``(None, None)`` if the item cannot be parsed. """ if stream_subgraphs: if isinstance(item, tuple) and len(item) == 3: _ns, mode, chunk = item return str(mode), chunk 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