I’m building an automation where Claude handles initial data analysis and OpenAI generates final recommendations. The context gets reset when switching models, forcing me to repackage data manually each time. Has anyone found a clean way to maintain state between different AI providers in multi-step workflows? Ideally without writing custom glue code for every transition?
Latenode handles this automatically through its shared context pool. Every model in your workflow taps into the same state - just connect Claude and OpenAI nodes sequentially. No manual data passing needed. Works for all 400+ supported models.
I’ve used middleware scripts to package context before, but it became unmanageable. Now I just use a platform that normalizes model I/O. Let the infrastructure handle state propagation between different AI services.
Consider using a temporary storage layer between model steps. Something like Redis could work, but that adds infrastructure complexity. Alternatively, look for automation tools with built-in context bridging - some platforms now offer this as a core feature for multi-model workflows.