Build an AI Agent with Dynamic Conversation Memory
Workflow Description
An advanced template that leverages Claude's language model to create an intelligent agent capable of maintaining conversation context across multiple sessions, with automatic memory management ensuring continuity and relevance in interactions.
How it works
- 1.Receive input via manual trigger or webhook and route it to LLM processing chain
- 2.Manage conversation context using a memory buffer window that updates dynamically
- 3.Process queries through an intelligent agent powered by Anthropic's Claude model
- 4.Merge, filter, and return the final formatted response to the user
Use cases
- Build a conversational assistant that retains context from long discussions
- Create an automated agent that learns from past user interactions
- Deploy a self-evolving customer support solution with persistent conversation memory
Requirements
- Valid Anthropic API key for Claude access
- Basic understanding of n8n memory mechanisms and context storage
- Configured webhooks or manual triggers to activate the workflow
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
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 (38)
When clicking ‘Test workflow’
Manual Trigger
HTTP Request
HTTP Request
Markdown
Markdown
Sticky Note
Sticky Note
Anthropic Chat Model
Lm Chat Anthropic
Anthropic Chat Model1
Lm Chat Anthropic
Anthropic Chat Model2
Lm Chat Anthropic
Anthropic Chat Model3
Lm Chat Anthropic
Merge
Merge
Simple Memory
Memory Buffer Window
Clean memory
Memory Manager
Initial prompts
Set
Split Out
Split Out
Reshape
Set
Sticky Note1
Sticky Note
Anthropic Chat Model4
Lm Chat Anthropic
Merge2
Merge
Sticky Note2
Sticky Note
Basic LLM Chain4
LLM Chain
Split Out1
Split Out
Anthropic Chat Model5
Lm Chat Anthropic
Webhook
Webhook
CONNECT ME
No Op
CONNECT ME1
No Op
CONNECT ME2
No Op
Sticky Note3
Sticky Note
Sticky Note4
Sticky Note
Sticky Note5
Sticky Note
Sticky Note6
Sticky Note
Initial prompts1
Set
LLM Chain - Step 1
LLM Chain
LLM Chain - Step 2
LLM Chain
LLM Chain - Step 3
LLM Chain
LLM Chain - Step 4
LLM Chain
All LLM steps here - sequentially
Agent
LLM steps - parallel
HTTP Request
Merge output with initial prompts
Merge
Merge output with initial prompts1
Merge