Ed_
60396f03f8
Applied 236 return type annotations to functions with no return values across 100+ files (core modules, tests, scripts, simulations). Added Phase 4 to python_style_refactor track for remaining 597 items (untyped params, vars, and functions with return values). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
241 lines
10 KiB
Python
241 lines
10 KiB
Python
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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# 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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def test_conductor_engine_initialization() -> None:
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"""
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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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engine = ConductorEngine(track=track)
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assert engine.track == track
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@pytest.mark.asyncio
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async def test_conductor_engine_run_linear_executes_tickets_in_order(monkeypatch):
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"""
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Test that run_linear iterates through executable tickets and calls the worker lifecycle.
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"""
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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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engine = ConductorEngine(track=track)
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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 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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# 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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ticket.mark_complete()
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return "Success"
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mock_lifecycle.side_effect = side_effect
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await engine.run_linear()
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# Track.get_executable_tickets() should be called repeatedly until all are done
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# T1 should run first, then T2.
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assert mock_lifecycle.call_count == 2
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assert ticket1.status == "completed"
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assert ticket2.status == "completed"
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# Verify sequence: T1 before T2
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calls = mock_lifecycle.call_args_list
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assert calls[0][0][0].id == "T1"
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assert calls[1][0][0].id == "T2"
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@pytest.mark.asyncio
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async def test_run_worker_lifecycle_calls_ai_client_send(monkeypatch):
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"""
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Test that run_worker_lifecycle triggers the AI client and updates ticket status on success.
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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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# 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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mock_send.return_value = "Task complete. I have updated the file."
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result = run_worker_lifecycle(ticket, context)
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assert result == "Task complete. I have updated the file."
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assert ticket.status == "completed"
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mock_send.assert_called_once()
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# Check if description was passed to send()
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args, kwargs = mock_send.call_args
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# user_message is passed as a keyword argument
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assert ticket.description in kwargs["user_message"]
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@pytest.mark.asyncio
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async def test_run_worker_lifecycle_context_injection(monkeypatch):
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"""
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Test that run_worker_lifecycle can take a context_files list and injects AST views into the prompt.
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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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context_files = ["primary.py", "secondary.py"]
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from 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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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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"primary.py": "def primary(): pass",
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"secondary.py": "def secondary(): pass"
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}
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def mock_open_side_effect(file, *args, **kwargs):
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content = file_contents.get(file, "")
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mock_file = MagicMock()
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mock_file.read.return_value = content
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mock_file.__enter__.return_value = mock_file
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return mock_file
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mock_open.side_effect = mock_open_side_effect
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# Setup ASTParser mock
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mock_ast_parser = mock_ast_parser_class.return_value
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mock_ast_parser.get_curated_view.return_value = "CURATED VIEW"
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mock_ast_parser.get_skeleton.return_value = "SKELETON VIEW"
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mock_send.return_value = "Success"
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run_worker_lifecycle(ticket, context, context_files=context_files)
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# Verify ASTParser calls:
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# First file (primary) should get curated view, others (secondary) get skeleton
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mock_ast_parser.get_curated_view.assert_called_once_with("def primary(): pass")
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mock_ast_parser.get_skeleton.assert_called_once_with("def secondary(): pass")
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# Verify user_message contains the views
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_, kwargs = mock_send.call_args
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user_message = kwargs["user_message"]
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assert "CURATED VIEW" in user_message
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assert "SKELETON VIEW" in user_message
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assert "primary.py" in user_message
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assert "secondary.py" in user_message
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@pytest.mark.asyncio
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async def test_run_worker_lifecycle_handles_blocked_response(monkeypatch):
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"""
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Test that run_worker_lifecycle marks the ticket as blocked if the AI indicates it cannot proceed.
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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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# 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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# Simulate a response indicating a block
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mock_send.return_value = "I am BLOCKED because I don't have enough information."
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run_worker_lifecycle(ticket, context)
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assert ticket.status == "blocked"
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assert "BLOCKED" in ticket.blocked_reason
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@pytest.mark.asyncio
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async def test_run_worker_lifecycle_step_mode_confirmation(monkeypatch):
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"""
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Test that run_worker_lifecycle passes confirm_execution to ai_client.send when step_mode is True.
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Verify that if confirm_execution is called (simulated by mocking ai_client.send to call its callback),
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the flow works as expected.
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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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# 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_execution") as mock_confirm:
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# We simulate ai_client.send by making it call the pre_tool_callback it received
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def mock_send_side_effect(md_content, user_message, **kwargs):
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callback = kwargs.get("pre_tool_callback")
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if callback:
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# Simulate calling it with some payload
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callback('{"tool": "read_file", "args": {"path": "test.txt"}}')
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return "Success"
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mock_send.side_effect = mock_send_side_effect
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mock_confirm.return_value = True
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mock_event_queue = MagicMock()
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run_worker_lifecycle(ticket, context, event_queue=mock_event_queue)
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# Verify confirm_execution was called
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mock_confirm.assert_called_once()
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assert ticket.status == "completed"
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@pytest.mark.asyncio
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async def test_run_worker_lifecycle_step_mode_rejection(monkeypatch):
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"""
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Verify that if confirm_execution returns False, the logic (in ai_client, which we simulate here)
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would prevent execution. In run_worker_lifecycle, we just check if it's passed.
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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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# 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_execution") as mock_confirm:
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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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run_worker_lifecycle(ticket, context)
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# Verify it was passed to send
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args, kwargs = mock_send.call_args
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assert kwargs["pre_tool_callback"] is not None
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# Since we've already tested ai_client's implementation of pre_tool_callback (mentally or via other tests),
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# here we just verify the wiring.
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@pytest.mark.asyncio
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async def test_conductor_engine_dynamic_parsing_and_execution(monkeypatch):
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"""
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Test that parse_json_tickets correctly populates the track and run_linear 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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track = Track(id="dynamic_track", description="Dynamic Track")
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engine = ConductorEngine(track=track)
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tickets_json = json.dumps([
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{
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"id": "T1",
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"description": "Initial task",
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"status": "todo",
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"assigned_to": "worker1",
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"depends_on": []
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},
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{
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"id": "T2",
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"description": "Dependent task",
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"status": "todo",
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"assigned_to": "worker2",
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"depends_on": ["T1"]
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},
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{
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"id": "T3",
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"description": "Another initial task",
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"status": "todo",
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"assigned_to": "worker3",
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"depends_on": []
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}
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])
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engine.parse_json_tickets(tickets_json)
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assert len(engine.track.tickets) == 3
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assert engine.track.tickets[0].id == "T1"
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assert engine.track.tickets[1].id == "T2"
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assert engine.track.tickets[2].id == "T3"
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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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# 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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def side_effect(ticket, context, *args, **kwargs):
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ticket.mark_complete()
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return "Success"
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mock_lifecycle.side_effect = side_effect
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await engine.run_linear()
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assert mock_lifecycle.call_count == 3
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# Verify dependency order: T1 must be called before T2
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calls = [call[0][0].id for call in mock_lifecycle.call_args_list]
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t1_idx = calls.index("T1")
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t2_idx = calls.index("T2")
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assert t1_idx < t2_idx
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# T3 can be anywhere relative to T1 and T2, but T1 < T2 is mandatory
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assert "T3" in calls
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