Fix handle malformed entity dicts and None LLM response in memgraph_memory (#4238)

mem0/memory/memgraph_memory.py

@@ -219,6 +219,7 @@ class MemoryGraph:
                 if tool_call["name"] != "extract_entities":
                     continue
                 for item in tool_call["arguments"]["entities"]:
+                    if "entity" in item and "entity_type" in item:
                         entity_type_map[item["entity"]] = item["entity_type"]
         except Exception as e:
             logger.exception(
@@ -263,8 +264,8 @@ class MemoryGraph:
         )
 
         entities = []
-        if extracted_entities["tool_calls"]:
-            entities = extracted_entities["tool_calls"][0]["arguments"]["entities"]
+        if extracted_entities and extracted_entities.get("tool_calls"):
+            entities = extracted_entities["tool_calls"][0].get("arguments", {}).get("entities", [])
 
         entities = self._remove_spaces_from_entities(entities)
         logger.debug(f"Extracted entities: {entities}")

tests/memory/test_memgraph_memory.py

@@ -0,0 +1,96 @@
+from unittest.mock import MagicMock, Mock, patch
+
+# langchain_memgraph and rank_bm25 are optional deps — mock them so tests run without install
+_memgraph_mock = Mock()
+patch.dict("sys.modules", {
+    "langchain_memgraph": _memgraph_mock,
+    "langchain_memgraph.graphs": _memgraph_mock,
+    "langchain_memgraph.graphs.memgraph": _memgraph_mock,
+    "rank_bm25": Mock(),
+}).start()
+
+from mem0.memory.memgraph_memory import MemoryGraph as MemgraphMemoryGraph  # noqa: E402
+MemoryGraph = MemgraphMemoryGraph
+
+
+def _make_instance():
+    with patch.object(MemoryGraph, "__init__", return_value=None):
+        instance = MemoryGraph.__new__(MemoryGraph)
+        instance.llm_provider = "openai"
+        instance.llm = MagicMock()
+        instance.embedding_model = MagicMock()
+        instance.config = MagicMock()
+        instance.config.graph_store.custom_prompt = None
+        return instance
+
+
+class TestRetrieveNodesFromData:
+    """Tests for _retrieve_nodes_from_data in MemoryGraph."""
+
+    def test_normal_entities_extracted(self):
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = {
+            "tool_calls": [{"name": "extract_entities", "arguments": {"entities": [
+                {"entity": "Alice", "entity_type": "person"},
+                {"entity": "hiking", "entity_type": "activity"},
+            ]}}]
+        }
+        result = instance._retrieve_nodes_from_data("Alice loves hiking", {"user_id": "u1"})
+        assert result == {"alice": "person", "hiking": "activity"}
+
+    def test_malformed_entity_missing_entity_type_is_skipped(self):
+        """LLM returns entity dict without entity_type — should skip it, keep valid ones.
+        Reproduces the exact data from issue #4055."""
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = {
+            "tool_calls": [{"name": "extract_entities", "arguments": {"entities": [
+                {"entity": "matrix multiplication", "entity_type": "task"},
+                {"entity": "task"},
+                {"entity": "ReLU", "entity_type": "task"},
+            ]}}]
+        }
+        result = instance._retrieve_nodes_from_data("some text", {"user_id": "u1"})
+        assert "matrix_multiplication" in result
+        assert "relu" in result
+        assert "task" not in result
+
+    def test_none_tool_calls_returns_empty(self):
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = {"tool_calls": None}
+        result = instance._retrieve_nodes_from_data("hello world", {"user_id": "u1"})
+        assert result == {}
+
+
+class TestEstablishNodesRelationsFromData:
+    """Tests for _establish_nodes_relations_from_data in MemoryGraph."""
+
+    def test_none_response_does_not_crash(self):
+        """openai_structured returns None when no relations found — must not crash.
+        Exact crash from issue #4055: TypeError: 'NoneType' object is not subscriptable."""
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = None
+        result = instance._establish_nodes_relations_from_data(
+            "Hello world", {"user_id": "u1"}, {}
+        )
+        assert result == []
+
+    def test_empty_tool_calls_returns_empty(self):
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = {"tool_calls": []}
+        result = instance._establish_nodes_relations_from_data(
+            "Hello world", {"user_id": "u1"}, {}
+        )
+        assert result == []
+
+    def test_valid_entities_returned(self):
+        instance = _make_instance()
+        instance.llm.generate_response.return_value = {
+            "tool_calls": [{"name": "add_entities", "arguments": {"entities": [
+                {"source": "alice", "relationship": "loves", "destination": "hiking"}
+            ]}}]
+        }
+        result = instance._establish_nodes_relations_from_data(
+            "Alice loves hiking", {"user_id": "u1"}, {"alice": "person"}
+        )
+        assert len(result) == 1
+        assert result[0]["source"] == "alice"