I want to build an educational assistant using langchain and large language models. The main feature I need is to have the system periodically check in with users during conversations to ask about their assignment completion status. Depending on whether they say yes or no, the app should either stop asking or schedule another reminder for later. I’m not sure what approach to take for implementing this kind of recurring reminder functionality within a langchain application. Any guidance on the best way to handle this would be really helpful.
Honestly, just use APScheduler with LangChain - it’s way simpler than what everyone else is suggesting. When the user says no, schedule a job to call your LangChain agent later. If they say yes, cancel the job. Store reminder times in a dict or small database. I’ve been using this setup for months without any problems.
Had the same problem when I built a language learning reminder system. Here’s what worked for me: use langchain’s callback handlers with a simple job queue like Celery or RQ. Set up your langchain agent with custom callbacks that catch when users respond to reminder prompts. User says “no”? The callback automatically queues a delayed task for the next reminder. That queued task just calls your langchain chain again with the user’s context. This keeps everything in your existing langchain setup - no external workflow tools needed. Store reminder states right in langchain’s memory classes and let the job queue handle timing. One thing that bit me - you’ve got to persist conversation memory between reminder cycles. I used ConversationBufferWindowMemory with a backing store so users don’t lose context when reminders fire hours later. Makes conversations feel way more natural than starting over each time.
I built something like this for a work training app. The trick is separating reminders from your langchain conversations.
Don’t make your langchain app handle timers - use a separate automation tool for scheduling. Your app should just process responses and update reminder states.
Here’s what worked:
- User says “no” → langchain app updates their status in a database
- External scheduler checks database regularly
- Time for reminder → scheduler triggers your langchain assistant
- Assistant asks question, processes response, updates database
- Repeat until user says “yes”
The automation tool handles all timing stuff. You just need API endpoints for triggers and state updates.
This keeps your conversation logic clean and lets you tweak reminder intervals without redeploying.
This video helped a ton with the automation setup:
You absolutely need a database for persistent storage between conversations. SQLite works great for simple cases.
The tricky part with periodic reminders in langchain apps is you need something running outside your main conversation flow to handle scheduling and triggering.
Most people try cron jobs or task queues, but that gets messy fast with multiple users who have different reminder schedules.
You really need a workflow automation platform that handles timing logic while integrating with your langchain setup. I’ve solved this by using Latenode to create automated workflows that:
- Track user conversation states
- Schedule reminder checks based on user responses
- Trigger your langchain assistant at the right intervals
- Handle yes/no logic to continue or stop reminders
Latenode handles all the scheduling complexity while your langchain app focuses on conversation logic. You can set up conditional workflows that branch based on user responses and automatically manage reminder frequencies per user.
You also get proper logging and can easily modify reminder patterns without touching your main codebase.
Check it out: https://latenode.com
I hit this exact problem building a study bot last year. Split it into two systems that talk to each other instead of cramming everything into langchain.
Use langchain’s memory with webhook triggers from an external scheduler. When your bot gets a “no” response, it saves the user’s reminder preference and calls an API to schedule the next check-in. The external service hits your webhook when it’s reminder time.
This works because your langchain agent keeps full context on each user’s progress and personalizes reminder messages. You can tweak reminder timing based on user patterns without touching your conversation flows.
I threw Redis in there to cache user states between reminders - database calls were slowing down conversations. Stays snappy even with hundreds of users on different reminder schedules.