Support for sync and async lambda function

README.md

@@ -133,8 +133,11 @@ Check `output/stories.json` for the full generated content.
 
 ## SyGra Studio
 
-**SyGra Studio** is a visual workflow builder that replaces manual YAML editing with an interactive drag-and-drop interface:
+**SyGra Studio** is a visual workflow builder that replaces manual YAML editing with an interactive drag-and-drop interface. It also allows you to execute a task, monitor during execution and view the result along with metadata like latency, token usage etc.
 
+![SyGraStudio](https://raw.githubusercontent.com/ServiceNow/SyGra/refs/heads/main/docs/resources/videos/studio_create_new_flow.gif)
+
+**Studio Features:**
 - **Visual Graph Builder** — Drag-and-drop nodes, connect them visually, configure with forms
 - **Real-time Execution** — Watch your workflow run with live node status and streaming logs
 - **Rich Analytics** — Track usage, tokens, latency, and success rates across runs

docs/concepts/nodes/lambda_node.md

@@ -29,6 +29,9 @@ lambda_function_node:
 - **`node_state`**:  
   Optional. Node-specific state key.
 
+- **`node_name`**:  
+  Optional. Node name used in the studio UI.
+
 ### Example Lambda Implementation
 
 You can implement a lambda either as a class or a function:
@@ -38,6 +41,7 @@ You can implement a lambda either as a class or a function:
 from sygra.core.graph.functions.lambda_function import LambdaFunction
 from sygra.core.graph.sygra_state import SygraState
 
+# Lambda function with sync apply(), to use async flow use AsyncLambdaFunction
 class TestLambda(LambdaFunction):
     def apply(lambda_node_dict: dict, state: SygraState):
         state["return_key1"] = "hello world"
@@ -56,7 +60,111 @@ def lambda_function(lambda_node_dict: dict, state: SygraState):
 - Lambda nodes give you full control over data transformation, allowing you to bridge, preprocess, or postprocess state as needed.
 - All keys you want accessible in the next node should be listed in `output_keys`.
 - Use lambda nodes for any custom task, especially when built-in nodes do not cover your use case.
+- If you have async programming in your lambda function, use `AsyncLambdaFunction` instead of `LambdaFunction`. In this case, the `apply` function is async in nature, and you can call async function with `await` keyword.
+
+----
+### Example workflow with sync and async lambda function:
+
+YAML configuration
+```yaml
+data_config:
+  source:
+    type: hf
+    repo_id: openai/gsm8k
+    config_name: main
+    split: train
+graph_config:
+  nodes:
+    lambda_1:
+      node_type: lambda
+      lambda: tasks.examples.lambda_test.task_executor.Lambda1Function
+      node_name: Sync Node
+    lambda_2:
+      node_type: lambda
+      lambda: tasks.examples.lambda_test.task_executor.Lambda2Function
+      node_name: Async Node
+  edges:
+  - from: START
+    to: lambda_1
+  - from: lambda_1
+    to: lambda_2
+  - from: lambda_2
+    to: END
+```
 
+Task Executor Code:
+```python
+"""
+Task executor for lambda test workflow having sync and async implementation.
+"""
+import asyncio
+import time
+from sygra.core.graph.functions.lambda_function import LambdaFunction, AsyncLambdaFunction
+from sygra.core.graph.sygra_state import SygraState
+from sygra.logger.logger_config import logger
+
+async def count_async():
+    print("One")
+    logger.info("One...")
+    await asyncio.sleep(1)
+    print("Two")
+    logger.info("Two...")
+    await asyncio.sleep(1)
+
+def count_sync(count:int):
+    print("One")
+    logger.info("One...")
+    time.sleep(1)
+    print("Two")
+    logger.info("Two...")
+    time.sleep(1)
+    logger.info("Count..." + str(count))
+
+async def wrapper_count_sync(count:int):
+    return count_sync(count)
+
+# sync lambda function
+class Lambda1Function(LambdaFunction):
+    """Execute custom logic on workflow state."""
+
+    @staticmethod
+    def apply(lambda_node_dict: dict, state: SygraState) -> SygraState:
+        """Implement this method to apply lambda function.
+
+        Args:
+            lambda_node_dict: configuration dictionary
+            state: current state of the graph
+
+        Returns:
+            SygraState: the updated state object
+        """
+        logger.info("sync function calling.......class1...")
+
+        count_sync(2)
+
+        logger.info("task done")
+        return state
+
+#async lambda function
+class Lambda2Function(AsyncLambdaFunction):
+    """Execute custom logic on workflow state."""
+
+    @staticmethod
+    async def apply(lambda_node_dict: dict, state: SygraState) -> SygraState:
+        """Implement this method to apply lambda function.
+
+        Args:
+            lambda_node_dict: configuration dictionary
+            state: current state of the graph
+
+        Returns:
+            SygraState: the updated state object
+        """
+        logger.info("async function calling.......class2...")
+        await count_async()
+        return state
+
+```
 ---
 
 **Tip:** Keep your lambda logic modular and reusable across tasks for maximum flexibility.

docs/getting_started/create_task_ui.md

@@ -3,6 +3,7 @@
 **Visual workflow builder and execution platform for SyGra synthetic data pipelines**
 
 ---
+![SyGraStudio](https://raw.githubusercontent.com/ServiceNow/SyGra/refs/heads/main/docs/resources/videos/studio_create_new_flow.gif)
 
 ## Why This Exists
 

sygra/core/graph/backend_factory.py

@@ -11,12 +11,13 @@ class BackendFactory(ABC):
     """
 
     @abstractmethod
-    def create_lambda_runnable(self, exec_wrapper):
+    def create_lambda_runnable(self, exec_wrapper, async_func=True):
         """
         Abstract method to create a Lambda runnable.
 
         Args:
             exec_wrapper: Async function to execute
+            async_func: True if the function is async
 
         Returns:
             Any: backend specific runnable object like Runnable for backend=Langgraph

sygra/core/graph/functions/lambda_function.py

@@ -21,3 +21,23 @@ def apply(lambda_node_dict: dict, state: SygraState):
             SygraState: the updated state object
         """
         pass
+
+
+class AsyncLambdaFunction(ABC):
+    """
+    This is a function class represent Async Lambda Function class.
+    Implement async apply() method for lambda function to be called by graph node.
+    """
+
+    @staticmethod
+    @abstractmethod
+    async def apply(lambda_node_dict: dict, state: SygraState):
+        """
+        Implement this method to apply lambda function
+        Args:
+            lambda_node_dict: configuration dictionary
+            state: current state of the graph
+        Returns:
+            SygraState: the updated state object
+        """
+        pass

sygra/core/graph/langgraph/langgraph_factory.py

@@ -22,17 +22,21 @@ class LangGraphFactory(BackendFactory):
     A factory class to convert Nodes into Runnable objects which LangGraph framework can execute.
     """
 
-    def create_lambda_runnable(self, exec_wrapper):
+    def create_lambda_runnable(self, exec_wrapper, async_func=True):
         """
         Abstract method to create a Lambda runnable.
 
         Args:
-            exec_wrapper: Async function to execute
+            exec_wrapper: Async/sync function to execute
+            async_func: True if the function is async
 
         Returns:
             Any: backend specific runnable object like Runnable for backend=Langgraph
         """
-        return RunnableLambda(lambda x: x, afunc=exec_wrapper)
+        if async_func:
+            return RunnableLambda(lambda x: x, afunc=exec_wrapper)
+        else:
+            return RunnableLambda(func=exec_wrapper)
 
     def create_llm_runnable(self, exec_wrapper):
         """

sygra/core/graph/nodes/lambda_node.py

@@ -1,3 +1,4 @@
+import asyncio
 import time
 from inspect import isclass
 from typing import Any
@@ -23,8 +24,35 @@ def __init__(self, node_name: str, config: dict):
         self.func_to_execute = utils.get_func_from_str(self.node_config["lambda"])
         if isclass(self.func_to_execute):
             self.func_to_execute = self.func_to_execute.apply
+        # deduce if the function type is sync or async
+        if asyncio.iscoroutinefunction(self.func_to_execute):
+            self.func_type = "async"
+        else:
+            self.func_type = "sync"
 
-    async def _exec_wrapper(self, state: dict[str, Any]) -> dict[str, Any]:
+    async def _async_exec_wrapper(self, state: dict[str, Any]) -> dict[str, Any]:
+        """
+        Wrapper to track lambda node execution.
+
+        Args:
+            state: State of the node.
+
+        Returns:
+            Updated state
+        """
+        start_time = time.time()
+        success = True
+
+        try:
+            result: dict[str, Any] = await self.func_to_execute(self.node_config, state)
+            return result
+        except Exception:
+            success = False
+            raise
+        finally:
+            self._record_execution_metadata(start_time, success)
+
+    def _sync_exec_wrapper(self, state: dict[str, Any]) -> dict[str, Any]:
         """
         Wrapper to track lambda node execution.
 
@@ -53,7 +81,15 @@ def to_backend(self) -> Any:
         Returns:
              Any: platform specific runnable object like Runnable in LangGraph.
         """
-        return utils.backend_factory.create_lambda_runnable(self._exec_wrapper)
+        if self.func_type == "sync":
+            return utils.backend_factory.create_lambda_runnable(
+                self._sync_exec_wrapper, async_func=False
+            )
+        elif self.func_type == "async":
+            # default to async function as old behavior(default async_func is True)
+            return utils.backend_factory.create_lambda_runnable(self._async_exec_wrapper)
+        else:
+            raise Exception("Invalid function type")
 
     def validate_node(self):
         """