Cheaper, Faster, Accurate Answers with Memory Summarization & Dynamic Routing! - n8n Workflow | Neura Market
Cheaper, Faster, Accurate Answers with Memory Summarization & Dynamic Routing!
# Smart Telegram AI Assistant with Memory Summarization & Dynamic Model Selection
> **Optimize your AI workflows, cut costs, and get faster, more accurate answers.**
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## Description
Tired of expensive AI calls, slow responses, or bots that forget your context? This **Telegram AI Assistant template** is designed to **optimize cost, speed, and precision** in your AI-powered conversations. By combining **PostgreSQL chat memory**, **AI summarization**, and **dynamic model selection**, this workflow ensures you only pay for what you really need. Simple queries get routed to lightweight models, while complex requests automatically trigger more advanced ones. The result? **Smarter context, lower costs, and better answers.**
This template is perfect for anyone who wants to:
- **Save money** by using cheaper models for easy tasks.
- **Keep context relevant** with AI-powered summarization.
- **Respond faster** thanks to optimized chat memory storage.
- **Deliver better answers** directly inside Telegram.
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### Key Benefits
- **Cost Optimization**: Automatically routes simple requests to Gemini Flash Lite and reserves Gemini Pro only for complex reasoning.
- **Smarter Context**: Summarization ensures only the most relevant chat history is used.
- **Faster Workflows**: Storing user + agent messages in a single row reduces DB queries by half and saves ~0.3s per response.
- **Voice Message Support**: Convert Telegram voice notes to text and reply intelligently.
- **Error-Proof Formatting**: Safe MarkdownV2 ensures Telegram-ready answers.
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## Use Case
This template is for **anyone who needs an AI chatbot on Telegram** that balances **cost, performance, and intelligence**.
- Customer support teams can **reduce expenses** by using lightweight models for FAQs.
- Freelancers and consultants can **offer faster AI-powered chats** without losing context.
- Power users can **handle voice + text seamlessly** while keeping conversations memory-aware.
Whether you're scaling a business or just want a smarter assistant, this workflow adapts to your needs and budget.
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## Example Interactions
- **Quick Q&A** Routed to Gemini Flash Lite for fast, low-cost answers.
- **Complex problem-solving** Sent to Gemini Pro for in-depth reasoning.
- **Voice messages** Automatically transcribed, summarized, and answered.
- **Long conversations** Context is summarized, ensuring precise and efficient replies.
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## Required Credentials
- **Telegram Bot API** (Bot Token)
- **PostgreSQL** (Database connection)
- **Google Gemini API** (Flash Lite, Flash, Pro)
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## Setup Instructions
1. **Create the PostgreSQL table** (`chat_memory`) from the Gray section SQL.
2. Configure the **Telegram Trigger** with your bot token.
3. Connect your **Gemini API credentials**.
4. Set up PostgreSQL nodes with your DB details.
5. Activate the workflow and start chatting with your AI-powered Telegram bot.
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## Tags
`telegram` `ai-assistant` `chatbot` `postgresql` `summarization` `memory` `gemini` `dynamic-routing` `workflow-optimization` `cost-saving` `voice-to-text`
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## Acknowledgement
A special thank you to Davide for the inspiration behind this template. His work on the [**AI Orchestrator that dynamically selects models based on input type**](https://n8n.io/workflows/7004-ai-orchestrator-dynamically-selects-models-based-on-input-type/) served as a foundational guide for this architecture.
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## Need Assistance?
Want to customize this workflow for your business or project? Let's connect:
- Email: [johnsilva11031@gmail.com](mailto:johnsilva11031@gmail.com)
- LinkedIn: [John Alejandro Silva Rodríguez](https://www.linkedin.com/in/john-alejandro-silva-rodriguez-48093526b/)
Platform
n8n
Category
AI
Price
Free
Creator
John Alejandro SIlva
set
code
switch
postgres
telegram
aggregate
stickyNote
manualTrigger
agent
telegramTrigger
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.