Hello LangChain Team!
I’m currently building a long-running agent using langgraph and langmem and am focusing on making it reliable across application restarts.
My goal is to ensure that background tasks scheduled with langmem.reflection.LocalReflectionExecutor are not lost if the application reboots. My observation is that if the application restarts, any pending task is lost. My understanding is that this happens because the new Executor instance initializes with an empty in-memory queue and doesn’t automatically check the persistent store for tasks that were missed.
My primary question is: could you please confirm if my understanding is correct? Or is there an existing mechanism for this recovery that I might have missed?
To help frame the discussion, I’d also love to understand the design philosophy here. For example, was the LocalReflectionExecutor intentionally designed to be ephemeral (for a single application lifecycle), with persistence and recovery being a responsibility left to the application developer? Understanding the intended scope would be incredibly helpful.
Assuming this recovery feature doesn’t yet exist, I’d like to propose a potential solution for discussion.
I believe a recovery mechanism is a crucial feature for building robust, production-ready agents. It would prevent silent data loss and make the long-term learning of agents truly resilient.
My proposal is to create a decoupled, optional utility function, with a name that reflects its purpose within the ecosystem, something like resume_pending_reflections(executor, store). This keeps the components clean and gives users control over the recovery process (e.g., by calling it once at startup).
Here is a conceptual example of how it could be used:
# At application startup
store = PostgresStore(...)
manager = create_memory_store_manager(...)
executor = LocalReflectionExecutor(manager)
# The new recovery step, called once on startup
resume_pending_reflections(executor, store)
# Continue with the application's main logic
...
I would be grateful to hear the community’s thoughts on this.
Thank you!