diff --git a/pyproject.toml b/pyproject.toml
index 2856cd97a..7aed748cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.6.3a4"
+version = "0.6.3a5"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py
index df8418e97..99b7d5427 100644
--- a/src/backend/langflow/api/v1/endpoints.py
+++ b/src/backend/langflow/api/v1/endpoints.py
@@ -1,5 +1,5 @@
 from http import HTTPStatus
-from typing import Annotated, Optional, Union
+from typing import Annotated, List, Optional, Union
 
 import sqlalchemy as sa
 from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status
@@ -42,7 +42,7 @@ router = APIRouter(tags=["Base"])
 
 async def process_graph_data(
     graph_data: dict,
-    inputs: Optional[dict] = None,
+    inputs: Optional[Union[List[dict], dict]] = None,
     tweaks: Optional[dict] = None,
     clear_cache: bool = False,
     session_id: Optional[str] = None,
@@ -160,7 +160,7 @@ async def process_json(
 async def process(
     session: Annotated[Session, Depends(get_session)],
     flow_id: str,
-    inputs: Optional[dict] = None,
+    inputs: Optional[Union[List[dict], dict]] = None,
     tweaks: Optional[dict] = None,
     clear_cache: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
     session_id: Annotated[Union[None, str], Body(embed=True)] = None,  # noqa: F821
diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index 03587cd29..982ccc6f1 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -6,11 +6,12 @@ from langchain.chains.base import Chain
 from langchain.schema import AgentAction, Document
 from langchain.vectorstores.base import VectorStore
 from langchain_core.runnables.base import Runnable
+from loguru import logger
+from pydantic import BaseModel
+
 from langflow.components.custom_components import CustomComponent
 from langflow.interface.run import build_sorted_vertices, get_memory_key, update_memory_keys
 from langflow.services.deps import get_session_service
-from loguru import logger
-from pydantic import BaseModel
 
 
 def fix_memory_inputs(langchain_object):
@@ -118,7 +119,7 @@ def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
     return inputs
 
 
-async def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
+async def generate_result(langchain_object: Union[Chain, VectorStore, Runnable], inputs: Union[dict, List[dict]]):
     if isinstance(langchain_object, Chain):
         if inputs is None:
             raise ValueError("Inputs must be provided for a Chain")
@@ -131,12 +132,21 @@ async def generate_result(langchain_object: Union[Chain, VectorStore], inputs: d
     elif isinstance(langchain_object, Document):
         result = langchain_object.dict()
     elif isinstance(langchain_object, Runnable):
-        if isinstance(inputs, List):
-            call_func = langchain_object.abatch
-        elif isinstance(inputs, dict):
-            call_func = langchain_object.ainvoke
-        result = await call_func(inputs)
-        result = result.content if hasattr(result, "content") else result
+        # Define call_method as a coroutine function
+        # by default
+        if isinstance(inputs, List) and hasattr(langchain_object, "abatch"):
+            call_method = langchain_object.abatch
+        elif isinstance(inputs, dict) and hasattr(langchain_object, "ainvoke"):
+            call_method = langchain_object.ainvoke
+        else:
+            raise ValueError("Inputs must be provided for a Runnable")
+        result = await call_method(inputs)
+        if isinstance(result, list):
+            result = [r.content if hasattr(r, "content") else r for r in result]
+        elif hasattr(result, "content"):
+            result = result.content
+        else:
+            result = result
     elif hasattr(langchain_object, "run") and isinstance(langchain_object, CustomComponent):
         result = langchain_object.run(inputs)