Block 1 - Model Selector
- Type / Role
- @n8n/n8n-nodes-langchain.modelSelector - modelSelector
- Config choices
- Version 1
This workflow is provided as-is. Please review and test before using in production.
Smart Telegram AI Assistant with Memory Summarization & Dynamic Model Selection Optimize your AI workflows, cut costs, and get faster, more accurate answers. Description Tired of expen...
@n8n/n8n-nodes-langchain.modelselector, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatgooglegemini, n8n-nodes-base.postgres, n8n-nodes-base.code, n8n-nodes-base.telegram, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.googlegemini
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by John Alejandro SIlva.
Original n8n.io source> Optimize your AI workflows, cut costs, and get faster, more accurate answers.
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:
This template is for anyone who needs an AI chatbot on Telegram that balances cost, performance, and intelligence.
Whether you’re scaling a business or just want a smarter assistant, this workflow adapts to your needs and budget.
chat_memory) from the Gray section SQL. telegram ai-assistant chatbot postgresqlsummarization memory gemini dynamic-routingworkflow-optimization cost-saving voice-to-text
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 served as a foundational guide for this architecture.
Want to customize this workflow for your business or project? Let’s connect:
📧 Email: [email protected]
🔗 LinkedIn: John Alejandro Silva Rodríguez
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 36 workflow blocks. Download the JSON for the full node graph.
| Workflow | Cheaper, faster, accurate answers with memory summarization & dynamic routing! |
|---|---|
| Complexity | advanced |
| Nodes | 36 |
| Categories | AI Chatbot, Multimodal AI |
| Author | John Alejandro SIlva |
| Published | 25 Aug 2025 |
Use the JSON export at /data/workflows/7851/7851.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Smart Telegram AI Assistant with Memory Summarization & Dynamic Model Selection Optimize your AI workflows, cut costs, and get faster, more accurate answers. Description Tired of expen...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your AI Chatbot, Multimodal AI use case.