Choose an Ollama model dynamically for each chat request
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Workflow Description
A template that creates an intelligent agent receiving messages via chat interface, processing them using a local language model, while maintaining an organized conversation history to ensure context continuity and response quality.
How it works
- 1.Trigger the agent when receiving a message from the user
- 2.Process the message using Ollama local language model
- 3.Maintain conversation memory from previous exchanges
- 4.Send the processed response back to the user via chat
Use cases
- Smart customer support that understands context and conversation history
- Personal assistant that learns from interactions and improves responses
- Specialized local consulting without relying on external servers
Requirements
- Local language model compatible with Ollama
- Chat interface or application supporting Chat Trigger
- Sufficient memory to store conversation history
Service Value
Ready-made workflow template for automation delivery and service execution.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (16)
When chat message received
Chat Trigger
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Ollama Dynamic LLM
Lm Chat Ollama
LLM Router
Agent
AI Agent with Dynamic LLM
Agent
Ollama phi4
Lm Chat Ollama
Router Chat Memory
Memory Buffer Window
Agent Chat Memory
Memory Buffer Window
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