AI Agents
Build an Intelligent Agent with Memory and Continuous Conversation
15 nodes
279
142
Automatic trigger
Workflow Description
Automation that builds a smart agent capable of interacting with users through conversation, maintaining dialogue context in memory, and processing requests using multiple language models (OpenAI and Ollama) to generate contextual responses.
How it works
- 1.Capture user messages via the chat trigger channel
- 2.Process messages through the intelligent agent using LLM chains
- 3.Store conversation history in a sliding window memory buffer
- 4.Generate responses using OpenAI and Ollama models in parallel
- 5.Execute HTTP requests based on agent decisions
- 6.Return responses to the user
Use cases
- Create a customer support chatbot that remembers conversation history
- Build a virtual assistant combining multiple AI models for complex queries
- Develop an internal help agent that learns from previous interactions
Requirements
- OpenAI API key for model access
- Local or cloud-hosted Ollama server running
- Permissions to send HTTP requests to external services
- Understanding of n8n memory buffer configuration
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Chat Trigger
AI Agent
OpenAI
Memory
LLM Chain
Lm Chat Ollama
Details
Trigger
Automatic trigger
Nodes
15
Apps
6
Views
279
Downloads
142
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 (15)
/
When chat message received
Chat Trigger
AI Agent
Agent
DeepSeek
OpenAI
Window Buffer Memory
Memory Buffer Window
Basic LLM Chain2
LLM Chain
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Ollama DeepSeek
Lm Chat Ollama
Sticky Note2
Sticky Note
Sticky Note3
Sticky Note
DeepSeek JSON Body
HTTP Request
DeepSeek Raw Body
HTTP Request
Sticky Note4
Sticky Note
Sticky Note5
Sticky Note
Sticky Note6
Sticky Note