When my API call to OpenAI times out or fails during graph processing, the execution stops and the thread gets stuck on the current node. I want to restart the workflow from the beginning node when a new message arrives.
Both approaches aren’t giving me the expected results. The graph still doesn’t reset to the starting position.
Has anyone successfully implemented this kind of node reset functionality? What’s the correct way to force the graph execution back to the initial node?
Clear the entire checkpoint before restarting - I had this same problem and update_state didn’t fix it. Delete the thread’s checkpoint data first, then use invoke() to start fresh instead of picking up where it crashed. The graph keeps track of its position even after you update the state.
Yeah, this is a super common issue - I’ve run into it tons of times building production workflows.
LangGraph keeps its execution pointer separate from state data. So even when you update messages, the graph still “remembers” where it was during the timeout.
Here’s what actually works:
# Get the current checkpoint
checkpoint = self.graph_bot.get_state(thread_config)
# Create a new thread config with different thread_id
new_thread_config = {"configurable": {"thread_id": f"{original_thread_id}_restart"}}
# Invoke with fresh config
result = self.graph_bot.invoke({"messages": msg_list}, new_thread_config)
Or if you want to keep the same thread ID, manually clear the checkpoint:
# Clear the checkpoint
self.graph_bot.checkpointer.delete(thread_config)
# Then invoke normally
result = self.graph_bot.invoke({"messages": msg_list}, thread_config)
Learned this the hard way when our support bot kept getting stuck on failed API calls. You’re not just updating state - you’re abandoning the failed execution path entirely.
I encountered a similar problem when dealing with API timeouts during graph processing. The key point to understand is that using update_state only modifies the data without changing the execution flow of the graph. What resolved my issue was implementing the stream method with a fresh configuration. Upon detection of a timeout, I catch the exception and initiate a new stream execution using the updated message list. This effectively restarts the graph from the beginning. Additionally, consider developing a custom interrupt handler that resets the thread’s checkpoint data before proceeding. Alternatively, you can directly manipulate the checkpointer to clear the stored state for the specific thread ID to ensure a complete reset. Remember, LangGraph distinguishes between execution state and data state; merely updating the messages won’t rectify the execution process.
Hit this exact problem last month building a customer service bot that kept hanging on OpenAI timeouts. LangGraph keeps an internal execution state that sticks around even after you update the graph state data. When your API call fails, the graph just sits there mid-execution waiting for that node to finish. Updating the state won’t reset this execution context. I tried implementing a timeout handler that catches the exception, then uses the checkpointer’s aget_tuple method to check the current checkpoint before deleting it completely and restarting. But the most reliable fix was creating a wrapper that detects timeouts and automatically switches to a new thread config while keeping the conversation history. This completely avoids the stuck execution state and keeps things smooth for the user.