Hello everyone! I'm developing an AI consultant within Telegram and have encountered a slight hurdle with the workflow implementation.
My Current Task: I need to collect user messages for approximately 10-15 seconds before forwarding them to the AI model. The goals are to:
- Ensure comprehensive conversation context is gathered.
- Decrease the frequency of API calls.
- Enhance the quality of responses.
What I’ve Already Tried:
- Utilized the Telegram Trigger.
- Planned to use OpenAI/OpenRouter for generating responses.
- Intend to implement temporary message storage within the workflow.
Questions for the Community:
- What is the recommended method for implementing message buffering in callin.io?
- What are the best practices for managing sequential messages?
- Are there any existing templates or examples available for this type of workflow?
I would greatly appreciate any advice or guidance!
My Tech Stack:
- callin.io
- Telegram
- OpenAI/OpenRouter
What you’re describing (waiting 10–15s before sending messages to the model) isn’t natively supported in callin.io. The default flow is always “event → action → response” with no buffering.
Options that work today
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Make or Zapier
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Connect Telegram as the trigger.
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Add a Delay of 10–15 seconds.
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While the delay runs, store incoming messages in Storage (Make) or Storage by Zapier.
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Once the delay finishes, concatenate all stored messages and send them to your model (OpenAI/OpenRouter).
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Buffer management
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Use the Telegram
chat_id
as the storage key. -
Each new message gets appended to that list.
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When the delay is up, fetch the list, clear it, and forward it to the model.
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For clarity, add separators like “---” between messages so the model understands they’re sequential.
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Best practices
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Define an inactivity window (e.g., if >15s without new messages, flush the buffer).
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A 10–15s window usually balances context gathering with acceptable latency.
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Don’t concatenate endless text—keep it to coherent chunks for better model responses.
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