294 lines
10 KiB
Python
294 lines
10 KiB
Python
import ai_client
|
|
import json
|
|
import asyncio
|
|
import threading
|
|
import time
|
|
from typing import List, Optional, Tuple
|
|
from dataclasses import asdict
|
|
import events
|
|
from models import Ticket, Track, WorkerContext
|
|
from file_cache import ASTParser
|
|
from pathlib import Path
|
|
|
|
from dag_engine import TrackDAG, ExecutionEngine
|
|
|
|
class ConductorEngine:
|
|
"""
|
|
Orchestrates the execution of tickets within a track.
|
|
"""
|
|
|
|
def __init__(self, track: Track, event_queue: Optional[events.AsyncEventQueue] = None, auto_queue: bool = False):
|
|
self.track = track
|
|
self.event_queue = event_queue
|
|
self.tier_usage = {
|
|
"Tier 1": {"input": 0, "output": 0},
|
|
"Tier 2": {"input": 0, "output": 0},
|
|
"Tier 3": {"input": 0, "output": 0},
|
|
"Tier 4": {"input": 0, "output": 0},
|
|
}
|
|
self.dag = TrackDAG(self.track.tickets)
|
|
self.engine = ExecutionEngine(self.dag, auto_queue=auto_queue)
|
|
|
|
async def _push_state(self, status: str = "running", active_tier: str = None):
|
|
if not self.event_queue:
|
|
return
|
|
payload = {
|
|
"status": status,
|
|
"active_tier": active_tier,
|
|
"tier_usage": self.tier_usage,
|
|
"track": {
|
|
"id": self.track.id,
|
|
"title": self.track.description,
|
|
},
|
|
"tickets": [asdict(t) for t in self.track.tickets]
|
|
}
|
|
await self.event_queue.put("mma_state_update", payload)
|
|
|
|
def parse_json_tickets(self, json_str: str):
|
|
"""
|
|
Parses a JSON string of ticket definitions (Godot ECS Flat List format)
|
|
and populates the Track's ticket list.
|
|
"""
|
|
try:
|
|
data = json.loads(json_str)
|
|
if not isinstance(data, list):
|
|
print("Error: JSON input must be a list of ticket definitions.")
|
|
return
|
|
for ticket_data in data:
|
|
# Construct Ticket object, using defaults for optional fields
|
|
ticket = Ticket(
|
|
id=ticket_data["id"],
|
|
description=ticket_data["description"],
|
|
status=ticket_data.get("status", "todo"),
|
|
assigned_to=ticket_data.get("assigned_to", "unassigned"),
|
|
depends_on=ticket_data.get("depends_on", []),
|
|
step_mode=ticket_data.get("step_mode", False)
|
|
)
|
|
self.track.tickets.append(ticket)
|
|
# Rebuild DAG and Engine after parsing new tickets
|
|
self.dag = TrackDAG(self.track.tickets)
|
|
self.engine = ExecutionEngine(self.dag, auto_queue=self.engine.auto_queue)
|
|
except json.JSONDecodeError as e:
|
|
print(f"Error parsing JSON tickets: {e}")
|
|
except KeyError as e:
|
|
print(f"Missing required field in ticket definition: {e}")
|
|
|
|
async def run(self, md_content: str = ""):
|
|
"""
|
|
Main execution loop using the DAG engine.
|
|
Args:
|
|
md_content: The full markdown context (history + files) for AI workers.
|
|
"""
|
|
await self._push_state(status="running", active_tier="Tier 2 (Tech Lead)")
|
|
while True:
|
|
# 1. Identify ready tasks
|
|
ready_tasks = self.engine.tick()
|
|
# 2. Check for completion or blockage
|
|
if not ready_tasks:
|
|
all_done = all(t.status == "completed" for t in self.track.tickets)
|
|
if all_done:
|
|
print("Track completed successfully.")
|
|
await self._push_state(status="done", active_tier=None)
|
|
else:
|
|
# Check if any tasks are in-progress or could be ready
|
|
if any(t.status == "in_progress" for t in self.track.tickets):
|
|
# Wait for async tasks to complete
|
|
await asyncio.sleep(1)
|
|
continue
|
|
print("No more executable tickets. Track is blocked or finished.")
|
|
await self._push_state(status="blocked", active_tier=None)
|
|
break
|
|
# 3. Process ready tasks
|
|
loop = asyncio.get_event_loop()
|
|
for ticket in ready_tasks:
|
|
# If auto_queue is on and step_mode is off, engine.tick() already marked it 'in_progress'
|
|
# but we need to verify and handle the lifecycle.
|
|
if ticket.status == "in_progress" or (not ticket.step_mode and self.engine.auto_queue):
|
|
ticket.status = "in_progress"
|
|
print(f"Executing ticket {ticket.id}: {ticket.description}")
|
|
await self._push_state(active_tier=f"Tier 3 (Worker): {ticket.id}")
|
|
context = WorkerContext(
|
|
ticket_id=ticket.id,
|
|
model_name="gemini-2.5-flash-lite",
|
|
messages=[]
|
|
)
|
|
# Offload the blocking lifecycle call to a thread to avoid blocking the async event loop.
|
|
# We pass the md_content so the worker has full context.
|
|
context_files = ticket.context_requirements if ticket.context_requirements else None
|
|
await loop.run_in_executor(
|
|
None,
|
|
run_worker_lifecycle,
|
|
ticket,
|
|
context,
|
|
context_files,
|
|
self.event_queue,
|
|
self,
|
|
md_content
|
|
)
|
|
await self._push_state(active_tier="Tier 2 (Tech Lead)")
|
|
elif ticket.status == "todo" and (ticket.step_mode or not self.engine.auto_queue):
|
|
# Task is ready but needs approval
|
|
print(f"Ticket {ticket.id} is ready and awaiting approval.")
|
|
await self._push_state(active_tier=f"Awaiting Approval: {ticket.id}")
|
|
# In a real UI, this would wait for a user event.
|
|
# For now, we'll treat it as a pause point if not auto-queued.
|
|
pass
|
|
|
|
def confirm_execution(payload: str, event_queue: events.AsyncEventQueue, ticket_id: str) -> bool:
|
|
"""
|
|
Pushes an approval request to the GUI and waits for response.
|
|
"""
|
|
# We use a list container so the GUI can inject the actual Dialog object back to us
|
|
# since the dialog is created in the GUI thread.
|
|
dialog_container = [None]
|
|
task = {
|
|
"action": "mma_step_approval",
|
|
"ticket_id": ticket_id,
|
|
"payload": payload,
|
|
"dialog_container": dialog_container
|
|
}
|
|
# Push to queue
|
|
try:
|
|
loop = asyncio.get_event_loop()
|
|
if loop.is_running():
|
|
asyncio.run_coroutine_threadsafe(event_queue.put("mma_step_approval", task), loop)
|
|
else:
|
|
event_queue._queue.put_nowait(("mma_step_approval", task))
|
|
except Exception:
|
|
# Fallback if no loop
|
|
event_queue._queue.put_nowait(("mma_step_approval", task))
|
|
# Wait for the GUI to create the dialog and for the user to respond
|
|
start = time.time()
|
|
while dialog_container[0] is None and time.time() - start < 60:
|
|
time.sleep(0.1)
|
|
if dialog_container[0]:
|
|
approved, final_payload = dialog_container[0].wait()
|
|
return approved
|
|
return False
|
|
|
|
def confirm_spawn(role: str, prompt: str, context_md: str, event_queue: events.AsyncEventQueue, ticket_id: str) -> Tuple[bool, str, str]:
|
|
"""
|
|
Pushes a spawn approval request to the GUI and waits for response.
|
|
Returns (approved, modified_prompt, modified_context)
|
|
"""
|
|
dialog_container = [None]
|
|
task = {
|
|
"action": "mma_spawn_approval",
|
|
"ticket_id": ticket_id,
|
|
"role": role,
|
|
"prompt": prompt,
|
|
"context_md": context_md,
|
|
"dialog_container": dialog_container
|
|
}
|
|
# Push to queue
|
|
try:
|
|
loop = asyncio.get_event_loop()
|
|
if loop.is_running():
|
|
asyncio.run_coroutine_threadsafe(event_queue.put("mma_spawn_approval", task), loop)
|
|
else:
|
|
event_queue._queue.put_nowait(("mma_spawn_approval", task))
|
|
except Exception:
|
|
# Fallback if no loop
|
|
event_queue._queue.put_nowait(("mma_spawn_approval", task))
|
|
# Wait for the GUI to create the dialog and for the user to respond
|
|
start = time.time()
|
|
while dialog_container[0] is None and time.time() - start < 60:
|
|
time.sleep(0.1)
|
|
if dialog_container[0]:
|
|
res = dialog_container[0].wait()
|
|
if isinstance(res, dict):
|
|
approved = res.get("approved", False)
|
|
abort = res.get("abort", False)
|
|
modified_prompt = res.get("prompt", prompt)
|
|
modified_context = res.get("context_md", context_md)
|
|
return approved and not abort, modified_prompt, modified_context
|
|
else:
|
|
# Fallback for old tuple style if any
|
|
approved, final_payload = res
|
|
modified_prompt = prompt
|
|
modified_context = context_md
|
|
if isinstance(final_payload, dict):
|
|
modified_prompt = final_payload.get("prompt", prompt)
|
|
modified_context = final_payload.get("context_md", context_md)
|
|
return approved, modified_prompt, modified_context
|
|
return False, prompt, context_md
|
|
|
|
def run_worker_lifecycle(ticket: Ticket, context: WorkerContext, context_files: List[str] = None, event_queue: events.AsyncEventQueue = None, engine: Optional['ConductorEngine'] = None, md_content: str = ""):
|
|
"""
|
|
Simulates the lifecycle of a single agent working on a ticket.
|
|
Calls the AI client and updates the ticket status based on the response.
|
|
Args:
|
|
ticket: The ticket to process.
|
|
context: The worker context.
|
|
context_files: List of files to include in the context.
|
|
event_queue: Queue for pushing state updates and receiving approvals.
|
|
engine: The conductor engine.
|
|
md_content: The markdown context (history + files) for AI workers.
|
|
"""
|
|
# Enforce Context Amnesia: each ticket starts with a clean slate.
|
|
ai_client.reset_session()
|
|
context_injection = ""
|
|
if context_files:
|
|
parser = ASTParser(language="python")
|
|
for i, file_path in enumerate(context_files):
|
|
try:
|
|
abs_path = Path(file_path)
|
|
# (This is a bit simplified, but helps)
|
|
with open(file_path, 'r', encoding='utf-8') as f:
|
|
content = f.read()
|
|
if i == 0:
|
|
view = parser.get_curated_view(content)
|
|
else:
|
|
view = parser.get_skeleton(content)
|
|
context_injection += f"\nFile: {file_path}\n{view}\n"
|
|
except Exception as e:
|
|
context_injection += f"\nError reading {file_path}: {e}\n"
|
|
# Build a prompt for the worker
|
|
user_message = (
|
|
f"You are assigned to Ticket {ticket.id}.\n"
|
|
f"Task Description: {ticket.description}\n"
|
|
)
|
|
if context_injection:
|
|
user_message += f"\nContext Files:\n{context_injection}\n"
|
|
user_message += (
|
|
"Please complete this task. If you are blocked and cannot proceed, "
|
|
"start your response with 'BLOCKED' and explain why."
|
|
)
|
|
# HITL Clutch: call confirm_spawn if event_queue is provided
|
|
if event_queue:
|
|
approved, modified_prompt, modified_context = confirm_spawn(
|
|
role="Tier 3 Worker",
|
|
prompt=user_message,
|
|
context_md=md_content,
|
|
event_queue=event_queue,
|
|
ticket_id=ticket.id
|
|
)
|
|
if not approved:
|
|
ticket.mark_blocked("Spawn rejected by user.")
|
|
return "BLOCKED: Spawn rejected by user."
|
|
user_message = modified_prompt
|
|
md_content = modified_context
|
|
# HITL Clutch: pass the queue and ticket_id to confirm_execution
|
|
|
|
def clutch_callback(payload: str) -> bool:
|
|
if not event_queue:
|
|
return True
|
|
return confirm_execution(payload, event_queue, ticket.id)
|
|
response = ai_client.send(
|
|
md_content=md_content,
|
|
user_message=user_message,
|
|
base_dir=".",
|
|
pre_tool_callback=clutch_callback if ticket.step_mode else None,
|
|
qa_callback=ai_client.run_tier4_analysis
|
|
)
|
|
# Update usage in engine if provided
|
|
if engine:
|
|
stats = {} # ai_client.get_token_stats() is not available
|
|
engine.tier_usage["Tier 3"]["input"] += stats.get("prompt_tokens", 0)
|
|
engine.tier_usage["Tier 3"]["output"] += stats.get("candidates_tokens", 0)
|
|
if "BLOCKED" in response.upper():
|
|
ticket.mark_blocked(response)
|
|
else:
|
|
ticket.mark_complete()
|
|
return response
|