Revert "fix(memory): accept llm kwarg in sync Memory.add()/_create_procedural_memory (#5911)" (#5990)

mem0/memory/main.py

@@ -726,7 +726,6 @@ class Memory(MemoryBase):
         infer: bool = True,
         memory_type: Optional[str] = None,
         prompt: Optional[str] = None,
-        llm=None,
     ):
         """
         Create a new memory.
@@ -803,7 +802,7 @@ class Memory(MemoryBase):
             )
 
         if agent_id is not None and memory_type == MemoryType.PROCEDURAL.value:
-            results = self._create_procedural_memory(messages, metadata=processed_metadata, prompt=prompt, llm=llm)
+            results = self._create_procedural_memory(messages, metadata=processed_metadata, prompt=prompt)
             scale_threshold_notice = detect_scale_threshold_from_add_result(self, results)
             if temporal_usage_notice:
                 display_temporal_usage_notice(self, "sync", "add", *temporal_usage_notice)
@@ -1911,27 +1910,15 @@ class Memory(MemoryBase):
         )
         return memory_id
 
-    def _create_procedural_memory(self, messages, metadata=None, llm=None, prompt=None):
+    def _create_procedural_memory(self, messages, metadata=None, prompt=None):
         """
         Create a procedural memory
 
         Args:
             messages (list): List of messages to create a procedural memory from.
             metadata (dict): Metadata to create a procedural memory from.
-            llm (llm, optional): LLM to use for the procedural memory creation. Defaults to None.
             prompt (str, optional): Prompt to use for the procedural memory creation. Defaults to None.
         """
-        if llm is not None:
-            try:
-                from langchain_core.messages.utils import (
-                    convert_to_messages,  # type: ignore
-                )
-            except Exception:
-                logger.error(
-                    "Import error while loading langchain-core. Please install 'langchain-core' to use procedural memory."
-                )
-                raise
-
         logger.info("Creating procedural memory")
 
         parsed_messages = [
@@ -1944,10 +1931,6 @@ class Memory(MemoryBase):
         ]
 
         try:
-            if llm is not None:
-                response = llm.invoke(input=convert_to_messages(parsed_messages))
-                procedural_memory = remove_code_blocks(response.content)
-            else:
             procedural_memory = self.llm.generate_response(messages=parsed_messages)
             procedural_memory = remove_code_blocks(procedural_memory)
         except Exception as e:

tests/test_memory.py

@@ -1529,85 +1529,3 @@ async def test_async_procedural_memory_langchain_strips_code_blocks(mock_llm_fac
     insert_call = memory.vector_store.insert.call_args
     stored_data = insert_call[1]["payloads"][0]["data"]
     assert "```" not in stored_data
-
-
-@patch("mem0.memory.main.VectorStoreFactory")
-@patch("mem0.memory.main.EmbedderFactory")
-@patch("mem0.memory.main.LlmFactory")
-def test_sync_procedural_memory_accepts_llm_kwarg_and_strips_code_blocks(
-    mock_llm_factory, mock_emb, mock_vs
-):
-    """Regression #5911: sync Memory.add()/Memory._create_procedural_memory must
-    accept the same `llm` kwarg as the async path and route through llm.invoke()."""
-    mock_vs.return_value = MagicMock()
-    mock_emb.return_value = MagicMock()
-    mock_emb.return_value.embed.return_value = [0.1] * 1536
-    mock_llm_factory.return_value = MagicMock()
-
-    from mem0.memory.main import Memory
-
-    config = MemoryConfig()
-    memory = Memory(config)
-    memory.vector_store = MagicMock()
-    memory.vector_store.insert = MagicMock()
-
-    mock_langchain_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = '```json\n{"key": "value"}\n```'
-    mock_langchain_llm.invoke.return_value = mock_response
-
-    messages = [{"role": "user", "content": "test"}]
-    metadata = {"user_id": "test_user"}
-
-    memory._create_procedural_memory(messages, metadata=metadata, llm=mock_langchain_llm)
-
-    mock_langchain_llm.invoke.assert_called_once()
-    insert_call = memory.vector_store.insert.call_args
-    stored_data = insert_call[1]["payloads"][0]["data"]
-    assert "```" not in stored_data
-
-
-@patch("mem0.memory.main.VectorStoreFactory")
-@patch("mem0.memory.main.EmbedderFactory")
-@patch("mem0.memory.main.LlmFactory")
-def test_sync_memory_add_forwards_llm_to_procedural_memory(
-    mock_llm_factory, mock_emb, mock_vs
-):
-    """Regression #5911: Memory.add() must forward `llm` to _create_procedural_memory."""
-    mock_vs.return_value = MagicMock()
-    mock_emb.return_value = MagicMock()
-    mock_emb.return_value.embed.return_value = [0.1] * 1536
-    mock_llm_factory.return_value = MagicMock()
-
-    from mem0.memory.main import Memory
-
-    config = MemoryConfig()
-    memory = Memory(config)
-    memory.vector_store = MagicMock()
-    memory.vector_store.insert = MagicMock()
-
-    mock_langchain_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = "procedural summary"
-    mock_langchain_llm.invoke.return_value = mock_response
-
-    forwarded = {"called": False}
-    original_create = memory._create_procedural_memory
-
-    def spy(*args, **kwargs):
-        forwarded["called"] = True
-        forwarded["llm"] = kwargs.get("llm")
-        return original_create(*args, **kwargs)
-
-    memory._create_procedural_memory = spy
-
-    memory.add(
-        [{"role": "user", "content": "Always verify inputs before processing."}],
-        agent_id="agent-1",
-        memory_type="procedural_memory",
-        llm=mock_langchain_llm,
-    )
-
-    assert forwarded["called"] is True
-    assert forwarded["llm"] is mock_langchain_llm
-    mock_langchain_llm.invoke.assert_called_once()