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import pytest
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import pytest
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from unittest.mock import MagicMock, patch
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from models import Ticket, Track, WorkerContext
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import ai_client
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from src.models import Ticket, Track, WorkerContext
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from src import ai_client
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# These tests define the expected interface for multi_agent_conductor.py
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# which will be implemented in the next phase of TDD.
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@@ -11,7 +11,7 @@ def test_conductor_engine_initialization() -> None:
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Test that ConductorEngine can be initialized with a Track.
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"""
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track = Track(id="test_track", description="Test Track")
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from multi_agent_conductor import ConductorEngine
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from src.multi_agent_conductor import ConductorEngine
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engine = ConductorEngine(track=track, auto_queue=True)
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assert engine.track == track
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@@ -23,7 +23,7 @@ async def test_conductor_engine_run_executes_tickets_in_order(monkeypatch: pytes
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ticket1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
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ticket2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker2", depends_on=["T1"])
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track = Track(id="track1", description="Track 1", tickets=[ticket1, ticket2])
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from multi_agent_conductor import ConductorEngine
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from src.multi_agent_conductor import ConductorEngine
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engine = ConductorEngine(track=track, auto_queue=True)
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vlogger.log_state("Ticket Count", 0, 2)
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@@ -34,7 +34,7 @@ async def test_conductor_engine_run_executes_tickets_in_order(monkeypatch: pytes
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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# We mock run_worker_lifecycle as it is expected to be in the same module
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with patch("multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
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with patch("src.multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
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# Mocking lifecycle to mark ticket as complete so dependencies can be resolved
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def side_effect(ticket, context, *args, **kwargs):
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@@ -64,7 +64,7 @@ async def test_run_worker_lifecycle_calls_ai_client_send(monkeypatch: pytest.Mon
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"""
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ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
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context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
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from multi_agent_conductor import run_worker_lifecycle
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from src.multi_agent_conductor import run_worker_lifecycle
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# Mock ai_client.send using monkeypatch
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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@@ -86,12 +86,12 @@ async def test_run_worker_lifecycle_context_injection(monkeypatch: pytest.Monkey
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ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
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context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
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context_files = ["primary.py", "secondary.py"]
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from multi_agent_conductor import run_worker_lifecycle
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from src.multi_agent_conductor import run_worker_lifecycle
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# Mock ai_client.send using monkeypatch
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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# We mock ASTParser which is expected to be imported in multi_agent_conductor
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with patch("multi_agent_conductor.ASTParser") as mock_ast_parser_class, \
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with patch("src.multi_agent_conductor.ASTParser") as mock_ast_parser_class, \
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patch("builtins.open", new_callable=MagicMock) as mock_open:
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# Setup open mock to return different content for different files
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file_contents = {
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@@ -131,7 +131,7 @@ async def test_run_worker_lifecycle_handles_blocked_response(monkeypatch: pytest
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"""
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ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
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context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
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from multi_agent_conductor import run_worker_lifecycle
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from src.multi_agent_conductor import run_worker_lifecycle
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# Mock ai_client.send using monkeypatch
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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@@ -150,14 +150,14 @@ async def test_run_worker_lifecycle_step_mode_confirmation(monkeypatch: pytest.M
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"""
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ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1", step_mode=True)
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context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
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from multi_agent_conductor import run_worker_lifecycle
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from src.multi_agent_conductor import run_worker_lifecycle
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# Mock ai_client.send using monkeypatch
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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# Important: confirm_spawn is called first if event_queue is present!
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with patch("multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("multi_agent_conductor.confirm_execution") as mock_confirm:
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with patch("src.multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("src.multi_agent_conductor.confirm_execution") as mock_confirm:
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mock_spawn.return_value = (True, "mock prompt", "mock context")
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mock_confirm.return_value = True
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@@ -186,12 +186,12 @@ async def test_run_worker_lifecycle_step_mode_rejection(monkeypatch: pytest.Monk
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"""
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ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1", step_mode=True)
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context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
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from multi_agent_conductor import run_worker_lifecycle
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from src.multi_agent_conductor import run_worker_lifecycle
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# Mock ai_client.send using monkeypatch
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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with patch("multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("multi_agent_conductor.confirm_execution") as mock_confirm:
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with patch("src.multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("src.multi_agent_conductor.confirm_execution") as mock_confirm:
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mock_spawn.return_value = (True, "mock prompt", "mock context")
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mock_confirm.return_value = False
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mock_send.return_value = "Task failed because tool execution was rejected."
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@@ -209,7 +209,7 @@ async def test_conductor_engine_dynamic_parsing_and_execution(monkeypatch: pytes
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Test that parse_json_tickets correctly populates the track and run executes them in dependency order.
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"""
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import json
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from multi_agent_conductor import ConductorEngine
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from src.multi_agent_conductor import ConductorEngine
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track = Track(id="dynamic_track", description="Dynamic Track")
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engine = ConductorEngine(track=track, auto_queue=True)
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tickets_json = json.dumps([
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@@ -246,7 +246,7 @@ async def test_conductor_engine_dynamic_parsing_and_execution(monkeypatch: pytes
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mock_send = MagicMock()
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monkeypatch.setattr(ai_client, 'send', mock_send)
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# Mock run_worker_lifecycle to mark tickets as complete
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with patch("multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
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with patch("src.multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
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def side_effect(ticket, context, *args, **kwargs):
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ticket.mark_complete()
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return "Success"
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@@ -279,9 +279,9 @@ def test_run_worker_lifecycle_pushes_response_via_queue(monkeypatch: pytest.Monk
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mock_send = MagicMock(return_value="Task complete.")
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monkeypatch.setattr(ai_client, 'send', mock_send)
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monkeypatch.setattr(ai_client, 'reset_session', MagicMock())
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from multi_agent_conductor import run_worker_lifecycle
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with patch("multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("multi_agent_conductor._queue_put") as mock_queue_put:
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from src.multi_agent_conductor import run_worker_lifecycle
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with patch("src.multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("src.multi_agent_conductor._queue_put") as mock_queue_put:
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mock_spawn.return_value = (True, "prompt", "context")
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run_worker_lifecycle(ticket, context, event_queue=mock_event_queue, loop=mock_loop)
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mock_queue_put.assert_called_once()
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@@ -309,14 +309,13 @@ def test_run_worker_lifecycle_token_usage_from_comms_log(monkeypatch: pytest.Mon
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[], # baseline call (before send)
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fake_comms, # after-send call
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]))
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from multi_agent_conductor import run_worker_lifecycle, ConductorEngine
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from models import Track
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from src.multi_agent_conductor import run_worker_lifecycle, ConductorEngine
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from src.models import Track
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track = Track(id="test_track", description="Test")
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engine = ConductorEngine(track=track, auto_queue=True)
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with patch("multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("multi_agent_conductor._queue_put"):
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with patch("src.multi_agent_conductor.confirm_spawn") as mock_spawn, \
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patch("src.multi_agent_conductor._queue_put"):
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mock_spawn.return_value = (True, "prompt", "ctx")
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run_worker_lifecycle(ticket, context, event_queue=MagicMock(), loop=MagicMock(), engine=engine)
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assert engine.tier_usage["Tier 3"]["input"] == 120
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assert engine.tier_usage["Tier 3"]["output"] == 45
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