58bc6efd4b
- Replace LLM-driven triage with code-based directory scan and project fetch - Two-step LLM approach: Step 1 classifies file→project+domains, Step 2 processes with tools - Add domain descriptions to Step 1 prompt for better extraction accuracy - Add _running_agents set for per-agent concurrency guard (one running instance per agent) - Return 409 from route before DB write when agent already running - Remove is_approved from task_agent create/update tools and system prompt - Remove is_approved from timeline_agent create/update tools and system prompt Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
1002 lines
37 KiB
Python
1002 lines
37 KiB
Python
"""Agent run orchestrator.
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Drives two agent types:
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* **Local directory agent** — two-step execution per file:
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Step 1 (Classification) uses code to fetch all projects and asks the LLM
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to identify which project the file belongs to and which domains are relevant.
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Step 2 (Processing) fetches existing entities for that project/domains via
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code and runs an LLM with tools — existing data in context enforces
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update-first naturally.
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* **Cloud connector agent** — fetches data from third-party APIs (Gmail,
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Teams, Outlook) and pushes extracted items to Electron.
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Usage
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-----
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Background tasks are spawned with ``asyncio.create_task()``::
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asyncio.create_task(run_local_agent(user_id, config, run_log, device_manager))
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asyncio.create_task(trigger_pending_runs(user_id, device_id, device_manager))
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The ``trigger_pending_runs`` function is called by the device WS endpoint
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when Electron sends ``device_hello``, so any overdue runs fire immediately
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when the device reconnects.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import uuid
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from datetime import datetime, timedelta, timezone
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from typing import Any
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from croniter import croniter
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
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from sqlalchemy import select
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from app.agents.filesystem_agent import FILESYSTEM_TOOLS
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from app.agents.note_agent import NOTE_TOOLS
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from app.agents.project_agent import PROJECT_TOOLS
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from app.agents.task_agent import TASK_TOOLS
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from app.agents.timeline_agent import TIMELINE_TOOLS
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from app.core.device_manager import DeviceConnectionManager
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from app.core.llm import get_llm
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from app.core.ws_context import clear_client_executor, execute_on_client, set_client_executor
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from app.db import async_session
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from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
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logger = logging.getLogger(__name__)
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# ── Concurrency guard ─────────────────────────────────────────────────────
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# Tracks agent IDs that currently have a run in progress.
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# Prevents multiple simultaneous runs of the same agent within a single process.
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_running_agents: set[str] = set()
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def is_agent_running(agent_id: str) -> bool:
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"""Return ``True`` if *agent_id* already has a run in progress."""
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return agent_id in _running_agents
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# ── Timeouts ───────────────────────────────────────────────────────────────
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# Max seconds to wait for a single tool-call round-trip (FE → BE).
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_TOOL_CALL_TIMEOUT: int = 30
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# Max LLM reasoning steps for Step 2 processing.
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_MAX_PROCESSING_STEPS: int = 12
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# Max directory recursion depth during scan.
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_MAX_SCAN_DEPTH: int = 5
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# ── Data-type to tool mapping ─────────────────────────────────────────────
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_DATA_TYPE_TOOLS: dict[str, list[Any]] = {
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"tasks": TASK_TOOLS,
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"projects": PROJECT_TOOLS,
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"notes": NOTE_TOOLS,
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"timelines": TIMELINE_TOOLS,
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}
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# ── Step 1: Classification prompt ─────────────────────────────────────────
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_DOMAIN_DESCRIPTIONS: dict[str, str] = {
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"tasks": (
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"Action items, to-dos, deliverables — anything that describes work to be done, "
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"assigned to someone, or tracked with a due date or status."
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),
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"notes": (
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"Documentation, meeting notes, summaries, reference material — "
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"written content meant to be read and referenced rather than acted on."
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),
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"timelines": (
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"Project milestones, deadlines, scheduled events — "
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"specific dates that mark a point in the progress of a project."
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),
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"projects": (
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"High-level project entities — only relevant if the file clearly introduces "
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"a new project or updates the scope of an existing one."
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),
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}
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_STEP1_SYSTEM_PROMPT = """\
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You are a file classifier for a freelance project management tool.
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Given a file's content and a list of existing projects, your job is to:
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1. Identify which project this file belongs to (or "standalone" if none match).
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2. Identify which data domains are relevant to extract from this file,
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limited to the allowed domains listed below.
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Domain definitions (only consider domains in the allowed list):
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{domain_definitions}
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Respond ONLY with a JSON object — no markdown, no explanation:
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{{"project_id": "<uuid> or standalone", "domains": ["tasks", "notes"]}}
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Existing projects:
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{projects_list}
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"""
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# ── Step 2: Processing prompt ─────────────────────────────────────────────
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_PROCESSING_SYSTEM_PROMPT = """\
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You are a data extraction assistant for a freelance project management tool.
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Your task is to read the file content provided and create or update records
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using the available tools.
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IMPORTANT — update-first rules:
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The existing records below are the source of truth.
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If an existing record semantically matches the content (by title, topic,
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or context), update it instead of creating a duplicate.
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Only create a new record when no existing match is found.
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Set isAiSuggested=1 on all new records.
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{existing_context}
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Project context: {project_context}
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Target domains: {data_types}
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{custom_prompt_section}
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"""
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# ── Cloud processing prompt (kept separate for cloud agent) ───────────────
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_CLOUD_PROCESSING_PROMPT = """\
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You are a data extraction and management assistant for a freelance project
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management tool.
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Available tools:
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Filesystem : read_file_content, list_directory, get_file_metadata
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Tasks : list_tasks, create_task, update_task, add_task_comment
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Notes : list_notes, get_note, create_note, update_note
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Timelines : list_timelines, create_timeline, update_timeline
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Projects : list_all_projects, get_project, create_project, update_project
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Your task:
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1. Read the full content of each file below using read_file_content.
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2. For each piece of information found, ALWAYS try to match and update an
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existing record before creating a new one.
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3. ONLY act on these entity types: {data_types}.
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4. Do NOT invent data. Only extract what is clearly present in the files.
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5. If a file contains no relevant data for the target entity types, skip it.
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{project_context}
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Files to process:
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{file_list}
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{custom_prompt_section}
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After processing all files, respond with a brief summary of what you updated
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and what you created.
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"""
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# ── Cron helper ────────────────────────────────────────────────────────────
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def _is_overdue(schedule_cron: str, last_run_at: datetime | None) -> bool:
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"""Return ``True`` if the next scheduled run time has already passed.
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Always validates the cron expression first — an invalid expression returns
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``False`` (fail-safe: never trigger an unparseable schedule).
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"""
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try:
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now = datetime.now(timezone.utc)
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if last_run_at is None:
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croniter(schedule_cron, now)
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return True
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ts = last_run_at
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if ts.tzinfo is None:
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ts = ts.replace(tzinfo=timezone.utc)
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cron = croniter(schedule_cron, ts)
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next_run: datetime = cron.get_next(datetime)
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return now >= next_run
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except Exception as exc:
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logger.warning("agent_runner: cannot parse cron %r: %s", schedule_cron, exc)
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return False
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# ── WS executor for agent context ─────────────────────────────────────────
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def _make_agent_executor(
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user_id: str,
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device_mgr: DeviceConnectionManager,
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run_context: dict | None = None,
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) -> Any:
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"""Create a WS callback for ``set_client_executor()`` so that all tools
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can use ``execute_on_client()`` during an agent run.
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If *run_context* is provided it is attached to every ``tool_call`` frame
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so the Electron client can attribute actions to the correct agent run.
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"""
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async def _executor(payload: dict) -> dict:
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payload["type"] = "tool_call"
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if run_context:
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payload["run_context"] = run_context
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call_id = payload["id"]
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fut = device_mgr.create_pending_call(user_id, call_id)
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await device_mgr.send_frame(user_id, payload)
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return await asyncio.wait_for(fut, timeout=_TOOL_CALL_TIMEOUT)
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return _executor
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# ── LLM tool-calling loop ─────────────────────────────────────────────────
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def _as_text(content: Any) -> str:
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts: list[str] = []
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for item in content:
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if isinstance(item, str):
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parts.append(item)
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elif isinstance(item, dict):
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text = item.get("text")
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if isinstance(text, str):
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parts.append(text)
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return "".join(parts)
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return str(content)
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async def _run_agent_with_tools(
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*,
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system_prompt: str,
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user_message: str,
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tools: list[Any],
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max_steps: int,
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) -> str:
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"""Run an LLM agent with tool-calling, returning the final text response."""
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llm = get_llm()
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llm_with_tools = llm.bind_tools(tools)
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messages: list[Any] = [
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SystemMessage(content=system_prompt),
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HumanMessage(content=user_message),
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]
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tool_map = {tool_def.name: tool_def for tool_def in tools}
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for _ in range(max_steps):
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response: AIMessage = await llm_with_tools.ainvoke(messages)
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messages.append(response)
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if not response.tool_calls:
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return _as_text(response.content)
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for call in response.tool_calls:
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call_id = str(call.get("id", ""))
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call_name = str(call.get("name", ""))
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call_args = call.get("args", {})
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logger.info(
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"agent_runner: tool_call name=%s args=%s",
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call_name,
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json.dumps(call_args, ensure_ascii=True)[:800],
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)
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tool_fn = tool_map.get(call_name)
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if tool_fn is None:
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tool_output = f"Unknown tool: {call_name}"
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else:
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tool_output = await tool_fn.ainvoke(call_args)
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logger.info(
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"agent_runner: tool_result name=%s output=%s",
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call_name,
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str(tool_output)[:200],
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)
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messages.append(ToolMessage(content=str(tool_output), tool_call_id=call["id"]))
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final = await llm.ainvoke(messages)
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return _as_text(final.content)
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# ── Tool list builder ─────────────────────────────────────────────────────
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def _build_processing_tools(data_types: list[str]) -> list[Any]:
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"""Build the tool list for processing based on user's data_types selection."""
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tools: list[Any] = list(FILESYSTEM_TOOLS)
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for dt in data_types:
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dt_tools = _DATA_TYPE_TOOLS.get(dt)
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if dt_tools:
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tools.extend(dt_tools)
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return tools
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# ── Code-based directory scanner ─────────────────────────────────────────
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async def _scan_directories(
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paths: list[str],
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extensions: list[str],
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last_run_at: datetime | None,
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) -> list[str]:
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"""Walk directories via WS tool calls and return filtered file paths.
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Recursion is capped at ``_MAX_SCAN_DEPTH``. Files are filtered by
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extension (if configured) and by modification date (if ``last_run_at``
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is set). Fails open: if metadata cannot be read, the file is included.
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"""
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all_files: list[str] = []
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ext_set = {e.lstrip(".").lower() for e in extensions} if extensions else set()
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async def _walk(path: str, depth: int) -> None:
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if depth > _MAX_SCAN_DEPTH:
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return
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try:
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result = await execute_on_client(action="list_directory", data={"path": path})
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except Exception as exc:
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logger.warning("agent_runner: list_directory failed %r: %s", path, exc)
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return
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for entry in result.get("entries", []):
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entry_path = entry.get("path", "")
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if not entry_path:
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continue
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if entry.get("type") == "directory":
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await _walk(entry_path, depth + 1)
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elif entry.get("type") == "file":
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if ext_set:
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dot_pos = entry_path.rfind(".")
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file_ext = entry_path[dot_pos + 1:].lower() if dot_pos != -1 else ""
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if file_ext not in ext_set:
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continue
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all_files.append(entry_path)
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for root in paths:
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await _walk(root, depth=0)
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if last_run_at is None:
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return all_files
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# Filter by modification date.
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last_run_ms = int(last_run_at.timestamp() * 1000)
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filtered: list[str] = []
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for file_path in all_files:
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try:
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meta = await execute_on_client(action="get_file_metadata", data={"path": file_path})
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modified_at = meta.get("modifiedAt")
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if modified_at is None:
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filtered.append(file_path)
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continue
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if isinstance(modified_at, (int, float)):
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mod_ms = int(modified_at)
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else:
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mod_ms = int(datetime.fromisoformat(str(modified_at)).timestamp() * 1000)
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if mod_ms > last_run_ms:
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filtered.append(file_path)
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except Exception:
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filtered.append(file_path) # fail-open
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return filtered
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# ── Code-based entity fetchers ────────────────────────────────────────────
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async def _fetch_projects() -> list[dict]:
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"""Fetch all projects from the Electron client via WS."""
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try:
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result = await execute_on_client(action="select", table="projects")
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return result.get("rows", [])
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except Exception as exc:
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logger.warning("agent_runner: failed to fetch projects: %s", exc)
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return []
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_DOMAIN_TABLE: dict[str, str] = {
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"tasks": "tasks",
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"notes": "notes",
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"timelines": "timelines",
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"projects": "projects",
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}
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async def _fetch_domain_entities(domain: str, project_id: str) -> list[dict]:
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"""Fetch existing rows for a domain, scoped to a project where applicable."""
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table = _DOMAIN_TABLE.get(domain)
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if not table:
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return []
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filters: dict[str, Any] = {}
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if project_id != "standalone" and domain != "projects":
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filters["projectId"] = project_id
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try:
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result = await execute_on_client(
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action="select",
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table=table,
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filters=filters if filters else None,
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)
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return result.get("rows", [])
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except Exception as exc:
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logger.warning("agent_runner: failed to fetch %s: %s", domain, exc)
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return []
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def _format_entities_for_context(domain: str, rows: list[dict]) -> str:
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"""Format existing entity rows as a readable context block for the LLM.
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Includes enough detail per record for the LLM to make a confident
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update-vs-create decision without overwhelming the context.
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Note content is truncated to 200 chars to stay within token budget.
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"""
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if not rows:
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return f"No existing {domain}."
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lines: list[str] = []
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for r in rows:
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if domain == "tasks":
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desc = r.get("description") or ""
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desc_part = f" — {desc[:120]}" if desc else ""
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assignee = r.get("assignee") or r.get("assignees") or ""
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due = r.get("dueDate") or r.get("due_date") or ""
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meta = ", ".join(filter(None, [
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f"priority: {r.get('priority', '')}" if r.get("priority") else "",
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f"assignee: {assignee}" if assignee else "",
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f"due: {due}" if due else "",
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]))
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lines.append(
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f" - [{r.get('status', '?')}] {r.get('title', '')}{desc_part}"
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f" ({meta}, id: {r['id']})"
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)
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elif domain == "notes":
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snippet = (r.get("content") or "")[:200].replace("\n", " ")
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snippet_part = f"\n Preview: {snippet}" if snippet else ""
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lines.append(
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f" - {r.get('title', '')} (id: {r['id']}){snippet_part}"
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)
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elif domain == "timelines":
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lines.append(
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f" - {r.get('title', '')} date={r.get('date', '')} (id: {r['id']})"
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)
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elif domain == "projects":
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summary = (r.get("aiSummary") or r.get("ai_summary") or "")[:120]
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summary_part = f" — {summary}" if summary else ""
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lines.append(
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f" - {r.get('name', '')} [{r.get('status', '')}]{summary_part}"
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f" (id: {r['id']})"
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)
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return f"Existing {domain}:\n" + "\n".join(lines)
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# ── Step 1: LLM file classifier ───────────────────────────────────────────
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async def _classify_file(
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file_path: str,
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file_content: str,
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projects: list[dict],
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config_data_types: list[str],
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) -> tuple[str, list[str]]:
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"""Call the LLM to classify a file by project and relevant domains.
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Returns ``(project_id_or_"standalone", domains)``.
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Falls back to ``("standalone", config_data_types)`` on any error.
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"""
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fallback = ("standalone", list(config_data_types))
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if not file_content.strip():
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return fallback
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projects_list = "\n".join(
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f" - {p.get('name', '')} (id: {p['id']}, status: {p.get('status', '')})"
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for p in projects
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) or " (none — all files are standalone)"
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domain_definitions = "\n".join(
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f" - {d}: {_DOMAIN_DESCRIPTIONS[d]}"
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for d in config_data_types
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if d in _DOMAIN_DESCRIPTIONS
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)
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system = _STEP1_SYSTEM_PROMPT.format(
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domain_definitions=domain_definitions,
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projects_list=projects_list,
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)
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||
|
||
llm = get_llm()
|
||
try:
|
||
response = await llm.ainvoke([
|
||
SystemMessage(content=system),
|
||
HumanMessage(content=f"File: {file_path}\n\nContent:\n{file_content[:4000]}"),
|
||
])
|
||
raw = _as_text(response.content).strip()
|
||
# Strip markdown fences if the model wraps the JSON.
|
||
if raw.startswith("```"):
|
||
raw = raw.split("```")[1]
|
||
if raw.startswith("json"):
|
||
raw = raw[4:]
|
||
parsed = json.loads(raw.strip())
|
||
project_id: str = str(parsed.get("project_id") or "standalone")
|
||
domains: list[str] = [
|
||
d for d in parsed.get("domains", [])
|
||
if d in config_data_types
|
||
]
|
||
if not domains:
|
||
domains = list(config_data_types)
|
||
return project_id, domains
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"agent_runner: step1 classification failed for %r: %s", file_path, exc
|
||
)
|
||
return fallback
|
||
|
||
|
||
# ── Local agent runner (two-step per file) ────────────────────────────────
|
||
|
||
|
||
async def run_local_agent(
|
||
user_id: str,
|
||
config: LocalAgentConfig,
|
||
run_log: AgentRunLog,
|
||
device_mgr: DeviceConnectionManager,
|
||
run_context: dict | None = None,
|
||
) -> None:
|
||
"""Execute a local directory agent run using a two-step approach per file.
|
||
|
||
Step 1 — Classification (code + 1 LLM call per file, no tools):
|
||
Code scans directories and fetches all projects via WS.
|
||
For each file, LLM identifies the project and relevant domains.
|
||
|
||
Step 2 — Processing (code + 1 LLM call per file, with tools):
|
||
Code fetches existing entities for the identified project/domains.
|
||
LLM receives file content + existing entities in context and uses
|
||
tools to update existing records or create new ones.
|
||
"""
|
||
run_id = run_log.id
|
||
agent_id = (run_context or {}).get("agent_id") or config.id
|
||
_running_agents.add(agent_id)
|
||
|
||
# ── Device online check ─────────────────────────────────────────
|
||
target_device_id = config.device_id.strip() if isinstance(config.device_id, str) else ""
|
||
is_online = (
|
||
device_mgr.is_online(user_id, target_device_id)
|
||
if target_device_id
|
||
else device_mgr.is_online(user_id)
|
||
)
|
||
|
||
if not is_online:
|
||
logger.info(
|
||
"agent_runner: skip run=%s — device %r offline for user=%s",
|
||
run_id,
|
||
target_device_id or "<any>",
|
||
user_id,
|
||
)
|
||
await _finalize_run(
|
||
run_log,
|
||
status="error",
|
||
errors=[f"Device {target_device_id or '<any>'!r} is not connected"],
|
||
)
|
||
return
|
||
|
||
# ── Set up WS executor for tools ────────────────────────────────
|
||
executor = _make_agent_executor(user_id, device_mgr, run_context)
|
||
set_client_executor(executor)
|
||
|
||
errors: list[str] = []
|
||
items_processed = 0
|
||
items_created = 0
|
||
|
||
custom_section = (
|
||
f"User instructions:\n{config.prompt_template}"
|
||
if config.prompt_template
|
||
else ""
|
||
)
|
||
|
||
try:
|
||
# ── Code: scan directories ───────────────────────────────────
|
||
logger.info("agent_runner: run=%s scanning directories user=%s", run_id, user_id)
|
||
file_paths = await _scan_directories(
|
||
paths=config.directory_paths,
|
||
extensions=config.file_extensions or [],
|
||
last_run_at=config.last_run_at,
|
||
)
|
||
logger.info(
|
||
"agent_runner: run=%s found %d file(s) after filtering", run_id, len(file_paths)
|
||
)
|
||
|
||
if not file_paths:
|
||
await _finalize_run(run_log, status="success", items_processed=0, items_created=0)
|
||
return
|
||
|
||
# ── Code: fetch all projects once ────────────────────────────
|
||
projects = await _fetch_projects()
|
||
|
||
# ── Per-file processing ──────────────────────────────────────
|
||
processing_tools = _build_processing_tools(config.data_types)
|
||
|
||
for file_path in file_paths:
|
||
try:
|
||
# Read file content via code.
|
||
file_result = await execute_on_client(
|
||
action="read_file_content", data={"path": file_path}
|
||
)
|
||
file_content: str = file_result.get("content", "")
|
||
if not file_content:
|
||
logger.debug("agent_runner: run=%s skipping empty file %r", run_id, file_path)
|
||
continue
|
||
|
||
items_processed += 1
|
||
|
||
# Step 1 — classify file.
|
||
project_id, domains = await _classify_file(
|
||
file_path=file_path,
|
||
file_content=file_content,
|
||
projects=projects,
|
||
config_data_types=config.data_types,
|
||
)
|
||
logger.info(
|
||
"agent_runner: run=%s file=%r → project=%s domains=%s",
|
||
run_id,
|
||
file_path,
|
||
project_id,
|
||
domains,
|
||
)
|
||
|
||
# Step 2 — fetch existing entities for this project + domains.
|
||
existing_blocks: list[str] = []
|
||
for domain in domains:
|
||
rows = await _fetch_domain_entities(domain, project_id)
|
||
existing_blocks.append(_format_entities_for_context(domain, rows))
|
||
|
||
existing_context = "\n\n".join(existing_blocks)
|
||
|
||
if project_id == "standalone":
|
||
project_context = "This file is not associated with any existing project."
|
||
else:
|
||
project_context = (
|
||
f"This file belongs to project ID: {project_id}. "
|
||
"Use this project_id when creating records."
|
||
)
|
||
|
||
system_prompt = _PROCESSING_SYSTEM_PROMPT.format(
|
||
existing_context=existing_context,
|
||
project_context=project_context,
|
||
data_types=", ".join(domains),
|
||
custom_prompt_section=custom_section,
|
||
)
|
||
|
||
result_text = await _run_agent_with_tools(
|
||
system_prompt=system_prompt,
|
||
user_message=(
|
||
f"Process this file and extract relevant information.\n\n"
|
||
f"File: {file_path}\n\nContent:\n{file_content}"
|
||
),
|
||
tools=processing_tools,
|
||
max_steps=_MAX_PROCESSING_STEPS,
|
||
)
|
||
logger.info(
|
||
"agent_runner: run=%s file=%r result=%s",
|
||
run_id,
|
||
file_path,
|
||
result_text[:200],
|
||
)
|
||
|
||
except Exception as exc:
|
||
errors.append(f"Error processing '{file_path}': {exc}")
|
||
logger.error(
|
||
"agent_runner: run=%s file=%r failed: %s", run_id, file_path, exc
|
||
)
|
||
|
||
except Exception as exc:
|
||
errors.append(f"Agent run failed: {exc}")
|
||
logger.error("agent_runner: run=%s failed: %s", run_id, exc)
|
||
finally:
|
||
_running_agents.discard(agent_id)
|
||
clear_client_executor()
|
||
|
||
# ── Finalise ────────────────────────────────────────────────────
|
||
if errors and items_processed == 0:
|
||
final_status = "error"
|
||
elif errors:
|
||
final_status = "partial"
|
||
else:
|
||
final_status = "success"
|
||
|
||
await _finalize_run(
|
||
run_log,
|
||
status=final_status,
|
||
items_processed=items_processed,
|
||
items_created=items_created,
|
||
errors=errors,
|
||
)
|
||
logger.info(
|
||
"agent_runner: run=%s done status=%s processed=%d errors=%d",
|
||
run_id,
|
||
final_status,
|
||
items_processed,
|
||
len(errors),
|
||
)
|
||
|
||
# Notify Electron that the run is complete.
|
||
if run_context and device_mgr.is_online(user_id):
|
||
try:
|
||
await device_mgr.send_frame(user_id, {
|
||
"type": "run_complete",
|
||
"run_context": run_context,
|
||
"status": final_status,
|
||
})
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"agent_runner: run=%s failed to send run_complete: %s", run_id, exc
|
||
)
|
||
|
||
|
||
# ── Cloud agent runner ─────────────────────────────────────────────────────
|
||
|
||
_CLOUD_DEFAULT_LOOKBACK_DAYS: int = 7
|
||
|
||
|
||
async def run_cloud_agent(
|
||
user_id: str,
|
||
config: CloudAgentConfig,
|
||
run_log: AgentRunLog,
|
||
device_mgr: DeviceConnectionManager,
|
||
) -> None:
|
||
"""Execute a cloud connector agent run end-to-end.
|
||
|
||
Steps:
|
||
|
||
1. Verify the user's device is online.
|
||
2. Decrypt the stored OAuth token from ``config.oauth_token_encrypted``.
|
||
3. Instantiate the provider client (Gmail or MS Graph).
|
||
4. Fetch messages/emails since ``config.last_run_at`` (or 7 days ago for
|
||
the first run) applying ``config.filter_config`` filters.
|
||
5. For each message/email call the LLM to extract structured items.
|
||
6. Push each item to Electron as an ``insert`` tool-call.
|
||
7. If the provider refreshed its access token, re-encrypt and write it
|
||
back to ``config.oauth_token_encrypted``.
|
||
8. Persist the run outcome via ``_finalize_run``.
|
||
"""
|
||
run_id = run_log.id
|
||
|
||
# ── 1. Device online check ─────────────────────────────────────────
|
||
if not device_mgr.is_online(user_id):
|
||
logger.info(
|
||
"agent_runner: skip cloud run=%s — no device online for user=%s",
|
||
run_id,
|
||
user_id,
|
||
)
|
||
await _finalize_run(
|
||
run_log,
|
||
status="error",
|
||
errors=["No connected device — cloud agent results cannot be delivered"],
|
||
)
|
||
return
|
||
|
||
# ── 2. Decrypt OAuth token ─────────────────────────────────────────
|
||
from app.integrations import decrypt_token, encrypt_token, get_provider
|
||
|
||
if not config.oauth_token_encrypted:
|
||
await _finalize_run(
|
||
run_log,
|
||
status="error",
|
||
errors=[f"No OAuth token stored for cloud agent '{config.name}'"],
|
||
)
|
||
return
|
||
|
||
try:
|
||
credentials_info = decrypt_token(config.oauth_token_encrypted)
|
||
except ValueError as exc:
|
||
logger.error("agent_runner: failed to decrypt OAuth token for agent %s: %s", config.id, exc)
|
||
await _finalize_run(
|
||
run_log,
|
||
status="error",
|
||
errors=[f"Failed to decrypt OAuth token: {exc}"],
|
||
)
|
||
return
|
||
|
||
# ── 3. Instantiate provider client ────────────────────────────────
|
||
try:
|
||
provider = get_provider(config.provider, credentials_info)
|
||
except ValueError as exc:
|
||
await _finalize_run(run_log, status="error", errors=[str(exc)])
|
||
return
|
||
|
||
# ── 4. Fetch messages ─────────────────────────────────────────────
|
||
since: datetime | None = config.last_run_at
|
||
if since is None:
|
||
since = datetime.now(timezone.utc) - timedelta(days=_CLOUD_DEFAULT_LOOKBACK_DAYS)
|
||
if since.tzinfo is None:
|
||
since = since.replace(tzinfo=timezone.utc)
|
||
|
||
errors: list[str] = []
|
||
items_processed = 0
|
||
items_created = 0
|
||
|
||
try:
|
||
if config.provider == "gmail":
|
||
raw_messages = await provider.fetch_messages( # type: ignore[union-attr]
|
||
filter_config=config.filter_config,
|
||
since=since,
|
||
)
|
||
elif config.provider == "outlook":
|
||
raw_messages = await provider.fetch_emails( # type: ignore[union-attr]
|
||
filter_config=config.filter_config,
|
||
since=since,
|
||
)
|
||
elif config.provider == "teams":
|
||
raw_messages = await provider.fetch_messages( # type: ignore[union-attr]
|
||
filter_config=config.filter_config,
|
||
since=since,
|
||
)
|
||
else:
|
||
raw_messages = []
|
||
except RuntimeError as exc:
|
||
logger.error(
|
||
"agent_runner: provider fetch failed for cloud agent %s: %s", config.id, exc
|
||
)
|
||
await _finalize_run(
|
||
run_log,
|
||
status="error",
|
||
errors=[f"Provider fetch failed: {exc}"],
|
||
update_config_last_run=True,
|
||
config_id=config.id,
|
||
config_type="cloud",
|
||
)
|
||
return
|
||
|
||
logger.info(
|
||
"agent_runner: cloud agent %s fetched %d item(s) from %s for user=%s",
|
||
config.id,
|
||
len(raw_messages),
|
||
config.provider,
|
||
user_id,
|
||
)
|
||
|
||
# ── 5–6. Extract + insert via LLM with tools ─────────────────────
|
||
executor = _make_agent_executor(user_id, device_mgr)
|
||
set_client_executor(executor)
|
||
|
||
try:
|
||
processing_tools = _build_processing_tools(config.data_types)
|
||
custom_section = (
|
||
f"User instructions:\n{config.prompt_template}"
|
||
if config.prompt_template
|
||
else ""
|
||
)
|
||
|
||
for msg in raw_messages:
|
||
content_text = msg.as_text
|
||
if not content_text:
|
||
continue
|
||
items_processed += 1
|
||
|
||
processing_prompt = _CLOUD_PROCESSING_PROMPT.format(
|
||
data_types=", ".join(config.data_types),
|
||
project_context="Determine the appropriate project from the message context.",
|
||
file_list=f"Message from {config.provider} (id: {msg.id})",
|
||
custom_prompt_section=custom_section,
|
||
)
|
||
|
||
try:
|
||
await _run_agent_with_tools(
|
||
system_prompt=processing_prompt,
|
||
user_message=f"Process this message content:\n\n{content_text[:8000]}",
|
||
tools=processing_tools,
|
||
max_steps=_MAX_PROCESSING_STEPS,
|
||
)
|
||
except Exception as exc:
|
||
errors.append(f"LLM processing error for message {msg.id!r}: {exc}")
|
||
finally:
|
||
clear_client_executor()
|
||
|
||
# ── 7. Persist refreshed token (if any) ───────────────────────────
|
||
refreshed = getattr(provider, "refreshed_credentials", None)
|
||
if refreshed:
|
||
try:
|
||
new_encrypted = encrypt_token(refreshed)
|
||
async with async_session() as db:
|
||
cfg_result = await db.execute(
|
||
select(CloudAgentConfig).where(CloudAgentConfig.id == config.id)
|
||
)
|
||
cfg_row = cfg_result.scalar_one_or_none()
|
||
if cfg_row:
|
||
cfg_row.oauth_token_encrypted = new_encrypted
|
||
await db.commit()
|
||
logger.debug("agent_runner: refreshed OAuth token persisted for agent %s", config.id)
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"agent_runner: failed to persist refreshed token for agent %s: %s",
|
||
config.id,
|
||
exc,
|
||
)
|
||
|
||
# ── 8. Finalise ────────────────────────────────────────────────────
|
||
if errors and items_created == 0:
|
||
final_status = "error"
|
||
elif errors:
|
||
final_status = "partial"
|
||
else:
|
||
final_status = "success"
|
||
|
||
await _finalize_run(
|
||
run_log,
|
||
status=final_status,
|
||
items_processed=items_processed,
|
||
items_created=items_created,
|
||
errors=errors,
|
||
update_config_last_run=True,
|
||
config_id=config.id,
|
||
config_type="cloud",
|
||
)
|
||
logger.info(
|
||
"agent_runner: cloud run=%s done status=%s processed=%d created=%d errors=%d",
|
||
run_id,
|
||
final_status,
|
||
items_processed,
|
||
items_created,
|
||
len(errors),
|
||
)
|
||
|
||
|
||
# ── Pending-run trigger ─────────────────────────────────────────────────────
|
||
|
||
|
||
async def trigger_pending_runs(
|
||
user_id: str,
|
||
device_id: str,
|
||
device_mgr: DeviceConnectionManager,
|
||
) -> None:
|
||
"""Dispatch any overdue agent runs after an Electron device connects.
|
||
|
||
Called as a background task from the device WS endpoint on ``device_hello``.
|
||
"""
|
||
logger.info(
|
||
"agent_runner: pending-run scan skipped for user=%s device=%s (client-owned agent config)",
|
||
user_id,
|
||
device_id,
|
||
)
|
||
return
|
||
|
||
|
||
# ── Internal helper ─────────────────────────────────────────────────────────
|
||
|
||
|
||
async def _finalize_run(
|
||
run_log: AgentRunLog,
|
||
*,
|
||
status: str,
|
||
items_processed: int = 0,
|
||
items_created: int = 0,
|
||
errors: list[str] | None = None,
|
||
update_config_last_run: bool = False,
|
||
config_id: str | None = None,
|
||
config_type: str | None = None,
|
||
) -> None:
|
||
"""Persist the run outcome and optionally update ``last_run_at`` on the config."""
|
||
now = datetime.now(timezone.utc)
|
||
try:
|
||
async with async_session() as db:
|
||
managed = await db.merge(run_log)
|
||
managed.status = status
|
||
managed.items_processed = items_processed
|
||
managed.items_created = items_created
|
||
managed.errors = errors or []
|
||
managed.completed_at = now
|
||
|
||
if update_config_last_run and config_id:
|
||
if config_type == "local":
|
||
cfg_result = await db.execute(
|
||
select(LocalAgentConfig).where(LocalAgentConfig.id == config_id)
|
||
)
|
||
cfg = cfg_result.scalar_one_or_none()
|
||
if cfg:
|
||
cfg.last_run_at = now
|
||
elif config_type == "cloud":
|
||
cfg_result = await db.execute(
|
||
select(CloudAgentConfig).where(CloudAgentConfig.id == config_id)
|
||
)
|
||
cfg = cfg_result.scalar_one_or_none()
|
||
if cfg:
|
||
cfg.last_run_at = now
|
||
|
||
await db.commit()
|
||
except Exception as exc:
|
||
logger.error(
|
||
"agent_runner: failed to finalize run_log=%s: %s", run_log.id, exc
|
||
)
|