Just hit a classic cost spike with my LangGraph conversational agent. It's designed for long sessions, but after an hour of back-and-forth, memory usage had doubled. My CloudWatch alarms went off! 🚨
I suspect it's state accumulation in the graph's memory object. My setup uses a `StateGraph` with a `MessagesState`. Are we supposed to manually prune the message list? Here's my basic checkpointer config:
```python
memory = MemorySaver()
graph = create_agent_chain(llm, tools)
app = graph.compile(checkpointer=memory)
```
I saw a `checkpointer` config for `MessageState` but the docs are light. Anyone have a working pattern for trimming old messages or clearing state between user sessions to keep costs predictable?
#savings
Ah, the "state accumulation in the memory object" suspicion. Classic. You've found the hidden subscription fee for these conversational graphs - they'll happily hoard every token you've ever sent them if you let them.
>Are we supposed to manually prune the message list?
You've hit the nail on the head. The docs are "light" because nobody wants to admit the default behavior is to let your memory object bloat until your bill looks like a phone number. Yes, you have to prune it yourself. That checkpointer config won't save you from history; it just saves the history for you, permanently.
I've seen teams bolt on a cron job to scan and clear old sessions, or add a custom node to truncate the message list after a certain count. It's all duct tape. The real pattern is accepting that "long sessions" with unchecked state are a one-way ticket to unpredictable costs. Good luck finding the right trim trigger without breaking the agent's context.
Test your rollback first
You're correct to suspect the `MessagesState` accumulation. The `MemorySaver` persists everything by design for full conversational recall, which is where the bloat happens. You do need to prune it yourself.
The workaround isn't in the checkpointer config, it's in the graph definition. You can add a conditional edge or a dedicated node that triggers a state truncation. For instance, after a certain number of exchanges or before a new session, you can splice the message list. Here's a minimal node example:
```python
def trim_messages(state: MessagesState):
state["messages"] = state["messages"][-20:] # keep last 10 exchanges
return state
```
Add that as a node and conditionally route to it. It's an extra step, but it keeps memory predictable. The docs should be more explicit about this.
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