Block 1 - Simple Memory
- Type / Role
- @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
- Config choices
- Version 1.3
This workflow is provided as-is. Please review and test before using in production.
Using the knowledge graphs instead of RAG vector stores This workflow creates an AI chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge ba...
@n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.chattrigger, n8n-nodes-base.stickynote, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.httprequesttool
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by InfraNodus.
Original n8n.io sourceThis workflow creates an AI chatbot agent that has access to several knowledge bases at the same time (used as "experts").
These knowledge bases are provided using the InfraNodus GraphRAG using the knowledge graphs and providing high-quality responses without the need to set up complex RAG vector store workflows.
The advantages of using GraphRAG instead of the standard vector stores for knowledge are:
This template uses the n8n AI agent node as an orchestrating agent that decides which tool (knowledge graph) to use based on the user's prompt.
Here's a description step by step:
You need an InfraNodus GraphRAG API account and key to use this workflow.
body name field.You can use this same workflow with a Telegram bot, so you can interact with it using Telegram. There are many more customizations available.
Check out the complete guide at https://support.noduslabs.com/hc/en-us/articles/20174217658396-Using-InfraNodus-Knowledge-Graphs-as-Experts-for-AI-Chatbot-Agents-in-n8n
Also check out the video tutorial with a demo:
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.
| Workflow | AI Chatbot Agent with a Panel of Experts using InfraNodus GraphRAG Knowledge |
|---|---|
| Complexity | intermediate |
| Nodes | 14 |
| Categories | Internal Wiki, AI RAG |
| Author | InfraNodus |
| Published | 26 May 2025 |
Use the JSON export at /data/workflows/4402/4402.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.
Using the knowledge graphs instead of RAG vector stores This workflow creates an AI chatbot agent that has access to several knowledge bases at the same time (used as "experts"). These knowledge ba...
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 Internal Wiki, AI RAG use case.