🐛 fix(loading.py): replace spaces with underscores in tool names to improve consistency and avoid issues

🐛 fix(base.py): catch and log exceptions when fixing memory inputs to prevent crashes
🐛 fix(process.py): check if memory_key attribute exists before accessing it to prevent AttributeError
🐛 fix(memories.py): hide memory_key field for ConversationEntityMemory to improve user experience

src/backend/langflow/interface/initialize/loading.py

@@ -68,7 +68,11 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
     elif base_type == "prompts":
         return instantiate_prompt(node_type, class_object, params)
     elif base_type == "tools":
-        return instantiate_tool(node_type, class_object, params)
+        tool = instantiate_tool(node_type, class_object, params)
+        if hasattr(tool, "name") and isinstance(tool, BaseTool):
+            # tool name shouldn't contain spaces
+            tool.name = tool.name.replace(" ", "_")
+        return tool
     elif base_type == "toolkits":
         return instantiate_toolkit(node_type, class_object, params)
     elif base_type == "embeddings":
@@ -133,6 +137,9 @@ def instantiate_llm(node_type, class_object, params: Dict):
 def instantiate_memory(node_type, class_object, params):
     # process input_key and output_key to remove them if
     # they are empty strings
+    if node_type == "ConversationEntityMemory":
+        params.pop("memory_key", None)
+
     for key in ["input_key", "output_key"]:
         if key in params and (params[key] == "" or not params[key]):
             params.pop(key)

src/backend/langflow/processing/base.py

@@ -19,8 +19,11 @@ async def get_result_and_steps(langchain_object, inputs: Union[dict, str], **kwa
             # Deactivating until we have a frontend solution
             # to display intermediate steps
             langchain_object.return_intermediate_steps = True
+        try:
+            fix_memory_inputs(langchain_object)
+        except Exception as exc:
+            logger.error(exc)
 
-        fix_memory_inputs(langchain_object)
         try:
             async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
             output = await langchain_object.acall(inputs, callbacks=async_callbacks)

src/backend/langflow/processing/process.py

@@ -22,7 +22,10 @@ def fix_memory_inputs(langchain_object):
     if not hasattr(langchain_object, "memory") or langchain_object.memory is None:
         return
     try:
-        if langchain_object.memory.memory_key in langchain_object.input_variables:
+        if (
+            hasattr(langchain_object.memory, "memory_key")
+            and langchain_object.memory.memory_key in langchain_object.input_variables
+        ):
             return
     except AttributeError:
         input_variables = (

src/backend/langflow/template/frontend_node/memories.py

@@ -90,6 +90,9 @@ class MemoryFrontendNode(FrontendNode):
             field.show = True
         if field.name == "entity_store":
             field.show = False
+        if name == "ConversationEntityMemory" and field.name == "memory_key":
+            field.show = False
+            field.required = False
 
 
 class PostgresChatMessageHistoryFrontendNode(MemoryFrontendNode):