354 lines
13 KiB
Python
354 lines
13 KiB
Python
import ai_client
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import json
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import asyncio
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import time
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import traceback
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from typing import List, Optional, Tuple
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from dataclasses import asdict
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import events
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from models import Ticket, Track, WorkerContext
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from file_cache import ASTParser
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from pathlib import Path
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from dag_engine import TrackDAG, ExecutionEngine
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class ConductorEngine:
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"""
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Orchestrates the execution of tickets within a track.
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"""
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def __init__(self, track: Track, event_queue: Optional[events.AsyncEventQueue] = None, auto_queue: bool = False) -> None:
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self.track = track
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self.event_queue = event_queue
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self.tier_usage = {
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"Tier 1": {"input": 0, "output": 0, "model": "gemini-3.1-pro-preview"},
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"Tier 2": {"input": 0, "output": 0, "model": "gemini-3-flash-preview"},
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"Tier 3": {"input": 0, "output": 0, "model": "gemini-2.5-flash-lite"},
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"Tier 4": {"input": 0, "output": 0, "model": "gemini-2.5-flash-lite"},
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}
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self.dag = TrackDAG(self.track.tickets)
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self.engine = ExecutionEngine(self.dag, auto_queue=auto_queue)
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async def _push_state(self, status: str = "running", active_tier: str = None) -> None:
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if not self.event_queue:
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return
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payload = {
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"status": status,
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"active_tier": active_tier,
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"tier_usage": self.tier_usage,
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"track": {
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"id": self.track.id,
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"title": self.track.description,
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},
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"tickets": [asdict(t) for t in self.track.tickets]
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}
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await self.event_queue.put("mma_state_update", payload)
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def parse_json_tickets(self, json_str: str) -> None:
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"""
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Parses a JSON string of ticket definitions (Godot ECS Flat List format)
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and populates the Track's ticket list.
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"""
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try:
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data = json.loads(json_str)
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if not isinstance(data, list):
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print("Error: JSON input must be a list of ticket definitions.")
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return
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for ticket_data in data:
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# Construct Ticket object, using defaults for optional fields
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ticket = Ticket(
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id=ticket_data["id"],
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description=ticket_data["description"],
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status=ticket_data.get("status", "todo"),
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assigned_to=ticket_data.get("assigned_to", "unassigned"),
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depends_on=ticket_data.get("depends_on", []),
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step_mode=ticket_data.get("step_mode", False)
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)
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self.track.tickets.append(ticket)
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# Rebuild DAG and Engine after parsing new tickets
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self.dag = TrackDAG(self.track.tickets)
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self.engine = ExecutionEngine(self.dag, auto_queue=self.engine.auto_queue)
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except json.JSONDecodeError as e:
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print(f"Error parsing JSON tickets: {e}")
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except KeyError as e:
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print(f"Missing required field in ticket definition: {e}")
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async def run(self, md_content: str = "") -> None:
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"""
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Main execution loop using the DAG engine.
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Args:
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md_content: The full markdown context (history + files) for AI workers.
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"""
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await self._push_state(status="running", active_tier="Tier 2 (Tech Lead)")
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loop = asyncio.get_event_loop()
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while True:
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# 1. Identify ready tasks
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ready_tasks = self.engine.tick()
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# 2. Check for completion or blockage
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if not ready_tasks:
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all_done = all(t.status == "completed" for t in self.track.tickets)
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if all_done:
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print("Track completed successfully.")
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await self._push_state(status="done", active_tier=None)
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else:
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# Check if any tasks are in-progress or could be ready
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if any(t.status == "in_progress" for t in self.track.tickets):
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# Wait for async tasks to complete
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await asyncio.sleep(1)
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continue
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print("No more executable tickets. Track is blocked or finished.")
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await self._push_state(status="blocked", active_tier=None)
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break
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# 3. Process ready tasks
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to_run = [t for t in ready_tasks if t.status == "in_progress" or (not t.step_mode and self.engine.auto_queue)]
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# Handle those awaiting approval
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for ticket in ready_tasks:
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if ticket not in to_run and ticket.status == "todo":
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print(f"Ticket {ticket.id} is ready and awaiting approval.")
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await self._push_state(active_tier=f"Awaiting Approval: {ticket.id}")
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await asyncio.sleep(1)
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if to_run:
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tasks = []
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for ticket in to_run:
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ticket.status = "in_progress"
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print(f"Executing ticket {ticket.id}: {ticket.description}")
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await self._push_state(active_tier=f"Tier 3 (Worker): {ticket.id}")
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# Escalation logic based on retry_count
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models = ["gemini-2.5-flash-lite", "gemini-2.5-flash", "gemini-3.1-pro-preview"]
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model_idx = min(ticket.retry_count, len(models) - 1)
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model_name = models[model_idx]
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context = WorkerContext(
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ticket_id=ticket.id,
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model_name=model_name,
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messages=[]
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)
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context_files = ticket.context_requirements if ticket.context_requirements else None
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tasks.append(loop.run_in_executor(
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None,
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run_worker_lifecycle,
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ticket,
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context,
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context_files,
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self.event_queue,
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self,
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md_content,
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loop
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))
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await asyncio.gather(*tasks)
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# 4. Retry and escalation logic
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for ticket in to_run:
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if ticket.status == 'blocked':
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if ticket.get('retry_count', 0) < 2:
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ticket.retry_count += 1
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ticket.status = 'todo'
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print(f"Ticket {ticket.id} BLOCKED. Escalating to {models[min(ticket.retry_count, len(models)-1)]} and retrying...")
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await self._push_state(active_tier="Tier 2 (Tech Lead)")
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def _queue_put(event_queue: events.AsyncEventQueue, loop: asyncio.AbstractEventLoop, event_name: str, payload) -> None:
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"""Thread-safe helper to push an event to the AsyncEventQueue from a worker thread."""
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asyncio.run_coroutine_threadsafe(event_queue.put(event_name, payload), loop)
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def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_id: str, loop: asyncio.AbstractEventLoop = None) -> bool:
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"""
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Pushes an approval request to the GUI and waits for response.
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"""
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dialog_container = [None]
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task = {
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"action": "mma_step_approval",
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"ticket_id": ticket_id,
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"payload": payload,
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"dialog_container": dialog_container
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}
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if loop:
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_queue_put(event_queue, loop, "mma_step_approval", task)
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else:
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raise RuntimeError("loop is required for thread-safe event queue access")
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# Wait for the GUI to create the dialog and for the user to respond
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start = time.time()
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while dialog_container[0] is None and time.time() - start < 60:
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time.sleep(0.1)
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if dialog_container[0]:
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approved, final_payload = dialog_container[0].wait()
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return approved
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return False
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def confirm_spawn(role: str, prompt: str, context_md: str, event_queue: events.AsyncEventQueue, ticket_id: str, loop: asyncio.AbstractEventLoop = None) -> Tuple[bool, str, str]:
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"""
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Pushes a spawn approval request to the GUI and waits for response.
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Returns (approved, modified_prompt, modified_context)
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"""
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dialog_container = [None]
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task = {
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"action": "mma_spawn_approval",
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"ticket_id": ticket_id,
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"role": role,
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"prompt": prompt,
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"context_md": context_md,
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"dialog_container": dialog_container
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}
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if loop:
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_queue_put(event_queue, loop, "mma_spawn_approval", task)
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else:
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raise RuntimeError("loop is required for thread-safe event queue access")
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# Wait for the GUI to create the dialog and for the user to respond
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start = time.time()
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while dialog_container[0] is None and time.time() - start < 60:
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time.sleep(0.1)
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if dialog_container[0]:
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res = dialog_container[0].wait()
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if isinstance(res, dict):
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approved = res.get("approved", False)
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abort = res.get("abort", False)
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modified_prompt = res.get("prompt", prompt)
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modified_context = res.get("context_md", context_md)
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return approved and not abort, modified_prompt, modified_context
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else:
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# Fallback for old tuple style if any
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approved, final_payload = res
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modified_prompt = prompt
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modified_context = context_md
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if isinstance(final_payload, dict):
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modified_prompt = final_payload.get("prompt", prompt)
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modified_context = final_payload.get("context_md", context_md)
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return approved, modified_prompt, modified_context
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return False, prompt, context_md
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def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] | None = None, event_queue: events.AsyncEventQueue | None = None, engine: Optional['ConductorEngine'] = None, md_content: str = "", loop: asyncio.AbstractEventLoop = None) -> None:
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"""
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Simulates the lifecycle of a single agent working on a ticket.
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Calls the AI client and updates the ticket status based on the response.
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Args:
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ticket: The ticket to process.
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context: The worker context.
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context_files: List of files to include in the context.
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event_queue: Queue for pushing state updates and receiving approvals.
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engine: The conductor engine.
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md_content: The markdown context (history + files) for AI workers.
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loop: The main asyncio event loop (required for thread-safe queue access).
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"""
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# Enforce Context Amnesia: each ticket starts with a clean slate.
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ai_client.reset_session()
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ai_client.set_provider(ai_client.get_provider(), context.model_name)
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context_injection = ""
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if context_files:
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parser = ASTParser(language="python")
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for i, file_path in enumerate(context_files):
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try:
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Path(file_path)
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# (This is a bit simplified, but helps)
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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if i == 0:
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view = parser.get_curated_view(content)
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else:
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view = parser.get_skeleton(content)
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context_injection += f"\nFile: {file_path}\n{view}\n"
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except Exception as e:
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context_injection += f"\nError reading {file_path}: {e}\n"
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# Build a prompt for the worker
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user_message = (
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f"You are assigned to Ticket {ticket.id}.\n"
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f"Task Description: {ticket.description}\n"
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)
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if context_injection:
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user_message += f"\nContext Files:\n{context_injection}\n"
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user_message += (
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"Please complete this task. If you are blocked and cannot proceed, "
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"start your response with 'BLOCKED' and explain why."
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)
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# HITL Clutch: call confirm_spawn if event_queue is provided
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if event_queue:
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approved, modified_prompt, modified_context = confirm_spawn(
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role="Tier 3 Worker",
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prompt=user_message,
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context_md=md_content,
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event_queue=event_queue,
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ticket_id=ticket.id,
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loop=loop
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)
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if not approved:
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ticket.mark_blocked("Spawn rejected by user.")
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return "BLOCKED: Spawn rejected by user."
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user_message = modified_prompt
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md_content = modified_context
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# HITL Clutch: pass the queue and ticket_id to confirm_execution
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def clutch_callback(payload: str) -> bool:
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if not event_queue:
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return True
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return confirm_execution(payload, event_queue, ticket.id, loop=loop)
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def stream_callback(chunk: str) -> None:
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if event_queue and loop:
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_queue_put(event_queue, loop, 'mma_stream', {'stream_id': f'Tier 3 (Worker): {ticket.id}', 'text': chunk})
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old_comms_cb = ai_client.comms_log_callback
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def worker_comms_callback(entry: dict) -> None:
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if event_queue and loop:
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kind = entry.get("kind")
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payload = entry.get("payload", {})
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chunk = ""
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if kind == "tool_call":
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chunk = f"\n\n[TOOL CALL] {payload.get('name')}\n{json.dumps(payload.get('script') or payload.get('args'))}\n"
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elif kind == "tool_result":
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res = str(payload.get("output", ""))
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if len(res) > 500: res = res[:500] + "... (truncated)"
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chunk = f"\n[TOOL RESULT]\n{res}\n"
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if chunk:
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_queue_put(event_queue, loop, "response", {"text": chunk, "stream_id": f"Tier 3 (Worker): {ticket.id}", "status": "streaming..."})
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if old_comms_cb:
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old_comms_cb(entry)
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ai_client.comms_log_callback = worker_comms_callback
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try:
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comms_baseline = len(ai_client.get_comms_log())
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response = ai_client.send(
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md_content=md_content,
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user_message=user_message,
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base_dir=".",
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pre_tool_callback=clutch_callback if ticket.step_mode else None,
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qa_callback=ai_client.run_tier4_analysis,
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stream_callback=stream_callback
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)
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finally:
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ai_client.comms_log_callback = old_comms_cb
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if event_queue:
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# Push via "response" event type — _process_event_queue wraps this
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# as {"action": "handle_ai_response", "payload": ...} for the GUI.
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try:
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response_payload = {
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"text": response,
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"stream_id": f"Tier 3 (Worker): {ticket.id}",
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"status": "done"
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}
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print(f"[MMA] Pushing Tier 3 response for {ticket.id}, loop={'present' if loop else 'NONE'}, stream_id={response_payload['stream_id']}")
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if loop:
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_queue_put(event_queue, loop, "response", response_payload)
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else:
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raise RuntimeError("loop is required for thread-safe event queue access")
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except Exception as e:
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print(f"[MMA] ERROR pushing response to UI: {e}\n{traceback.format_exc()}")
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# Update usage in engine if provided
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if engine:
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_new_comms = ai_client.get_comms_log()[comms_baseline:]
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_resp_entries = [e for e in _new_comms if e.get("direction") == "IN" and e.get("kind") == "response"]
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_in_tokens = sum(e.get("payload", {}).get("usage", {}).get("input_tokens", 0) for e in _resp_entries)
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_out_tokens = sum(e.get("payload", {}).get("usage", {}).get("output_tokens", 0) for e in _resp_entries)
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engine.tier_usage["Tier 3"]["input"] += _in_tokens
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engine.tier_usage["Tier 3"]["output"] += _out_tokens
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if "BLOCKED" in response.upper():
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ticket.mark_blocked(response)
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else:
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ticket.mark_complete()
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return response
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