Building a content moderation system that routes between Claude and OpenAI based on input complexity. Problem is the workflow resets context when switching models. Lost user session data three times already. How do you maintain state consistency when using multiple LLMs in one process? Need model-agnostic memory handling.
Latenode handles this with unified context containers. Built a trademark analysis tool that switches between 4 models while keeping case details in shared memory. Works like a virtual clipboard between different AI nodes. Zero config needed. https://latenode.com
You’ll need an abstraction layer that normalizes inputs/outputs across models. Map all model responses to a standardized JSON schema before passing to next steps. Store the schema in a central state object rather than relying on model-specific outputs. Adds some overhead but prevents context drops.
use a middleware service to format data between model handoffs. critical for state preservation