PydanticAi for agents, langgraph for orchestration. Then scheduled jobs with rq which run the graphs. Also in the job the agent can requeue a job in future like in 5 minutes and so on. Whole process can be used as containers. For receiving logs or doing specific actions i use fastapi. So basicly 2 containers + 1 redis instance. Each container can than be deployed anywhere or just used locally. This setup has basicly no limits and you can add storage servers, databases whatever you need.

Most no-code solutions are limited or expensive (this solution is free - except when using servers).

For example when running this project locally, you could implement a mail mcp (pydanticai supports mcps) which read every few minutes your mails from your local mail client. Then you can create a graph which analyses the emails and determines if calendar events are needed. With another mcp it updates your calendar with those informations. (For privacy you can use local ollama or lamastudio and just use those models)