[open-swe] fix: Handle invalid tool calls and prevent AttributeError in extraction

tests/unit_tests/test_extraction.py

@@ -724,3 +724,60 @@ def foo() -> None:
             enable_updates=False,
             enable_deletes=False,
         )
+
+
+async def test_invalid_tool_call_handling():
+    """Test that invalid tool calls in additional_kwargs are handled gracefully.
+
+    This reproduces the issue where LLM returns invalid tool calls (e.g., due to token limits)
+    that result in empty tool_calls array but invalid tool call info in additional_kwargs.
+    Without proper handling, this would cause AttributeError: 'ExtractionState' object has no attribute 'tool_call_id'.
+    """
+
+    # Create a simple schema for testing
+    class TestSchema(BaseModel):
+        name: str
+        value: int
+
+    # Create an AIMessage that simulates the invalid tool call scenario from the JSON file
+    # This mimics what happens when LLM hits token limits and returns malformed JSON
+    invalid_tool_call_message = AIMessage(
+        content="",  # Empty content like in the JSON file
+        tool_calls=[],  # Empty tool_calls array - this is the key issue
+        additional_kwargs={
+            "tool_calls": [
+                {
+                    "id": "call_invalid_test_123",
+                    "function": {
+                        "name": "TestSchema",
+                        "arguments": '{"name": "test", "value": "invalid_json_here...',  # Malformed JSON
+                    },
+                    "type": "invalid_tool_call",  # This indicates parsing failure
+                    "error": "Unterminated string starting at: line 1 column 64 (char 63)",
+                }
+            ],
+            "finish_reason": "length",  # Indicates token limit was hit
+        },
+    )
+
+    # Create a fake LLM that returns the invalid tool call message
+    fake_llm = FakeExtractionModel(
+        responses=[invalid_tool_call_message],
+        backup_responses=[invalid_tool_call_message] * 3,
+    )
+
+    # Create extractor with the test schema
+    extractor = create_extractor(
+        llm=fake_llm,
+        tools=[TestSchema],
+        enable_inserts=True,
+        enable_updates=True,
+    )
+
+    # This should not raise AttributeError: 'ExtractionState' object has no attribute 'tool_call_id'
+    # Instead, it should handle the invalid tool call gracefully
+    result = await extractor.ainvoke("Extract a test schema")
+
+    # The result should be empty since the tool call was invalid and couldn't be processed
+    assert len(result["responses"]) == 0
+    assert result["attempts"] > 0  # Should have attempted to process