From ad7845cc6aca8fe0e5a2549cf19c392a6e8b105e Mon Sep 17 00:00:00 2001
From: Yunxiao Xu <xuyunxiao2001@gmail.com>
Date: Mon, 23 Feb 2026 05:51:03 -0800
Subject: [PATCH] test(orchestrator): Add integration tests for the
 Orchestrator-Workers loop

---
 backend/tests/test_orchestrator_loop.py | 86 +++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 backend/tests/test_orchestrator_loop.py

diff --git a/backend/tests/test_orchestrator_loop.py b/backend/tests/test_orchestrator_loop.py
new file mode 100644
index 0000000..3c8b73f
--- /dev/null
+++ b/backend/tests/test_orchestrator_loop.py
@@ -0,0 +1,86 @@
+import pytest
+from unittest.mock import MagicMock
+from ea_chatbot.graph.workflow import create_workflow
+from ea_chatbot.graph.state import AgentState
+from langchain_core.messages import AIMessage, HumanMessage
+
+def test_orchestrator_full_flow():
+    """Verify the full Orchestrator-Workers flow via direct node injection."""
+    
+    mock_analyzer = MagicMock()
+    mock_planner = MagicMock()
+    mock_delegate = MagicMock()
+    mock_worker = MagicMock()
+    mock_reflector = MagicMock()
+    mock_synthesizer = MagicMock()
+    mock_summarize_conv = MagicMock()
+    
+    # 1. Analyzer: Proceed to planning
+    mock_analyzer.return_value = {"next_action": "plan"}
+    
+    # 2. Planner: Generate checklist
+    mock_planner.return_value = {
+        "checklist": [{"task": "T1", "worker": "data_analyst"}],
+        "current_step": 0
+    }
+    
+    # 3. Delegate: Route to data_analyst
+    mock_delegate.side_effect = [
+        {"next_action": "data_analyst"}, # First call
+        {"next_action": "summarize"}    # Second call (after reflector)
+    ]
+    
+    # 4. Worker: Success
+    mock_worker.return_value = {
+        "messages": [AIMessage(content="Worker result")],
+        "vfs": {"res.txt": "data"}
+    }
+    
+    # 5. Reflector: Advance
+    mock_reflector.return_value = {
+        "current_step": 1,
+        "next_action": "delegate"
+    }
+    
+    # 6. Synthesizer: Final answer
+    mock_synthesizer.return_value = {
+        "messages": [AIMessage(content="Final synthesized answer")],
+        "next_action": "end"
+    }
+    
+    # 7. Summarize Conv: End
+    mock_summarize_conv.return_value = {"summary": "Done"}
+    
+    # Create workflow with injected mocks
+    app = create_workflow(
+        query_analyzer=mock_analyzer,
+        planner=mock_planner,
+        delegate=mock_delegate,
+        data_analyst_worker=mock_worker,
+        reflector=mock_reflector,
+        synthesizer=mock_synthesizer,
+        summarize_conversation=mock_summarize_conv
+    )
+    
+    initial_state = AgentState(
+        messages=[HumanMessage(content="Explain results")],
+        question="Explain results",
+        analysis={},
+        next_action="",
+        iterations=0,
+        checklist=[],
+        current_step=0,
+        vfs={},
+        plots=[],
+        dfs={}
+    )
+    
+    final_state = app.invoke(initial_state)
+    
+    assert mock_analyzer.called
+    assert mock_planner.called
+    assert mock_delegate.call_count == 2
+    assert mock_worker.called
+    assert mock_reflector.called
+    assert mock_synthesizer.called
+    assert "Final synthesized answer" in [m.content for m in final_state["messages"]]