Building an Agentic RAG Workflow in n8n (Step-by-Step Guide)

The AI world is shifting fast from simple chatbot prompts to agentic workflows where Large Language Models (LLMs) can reason, retrieve, and act autonomously.

But what happens when you combine RAG with agentic reasoning inside an automation platform like n8n?

In this guide, we’ll build a step-by-step Agentic RAG workflow in n8n, a setup that lets your AI agents retrieve knowledge, reason about it, and then take the next best action, all without human intervention.

What is an Agentic RAG Workflow?

Before we dive into n8n, let’s break down the concept:

• RAG:
  A technique where your LLM (like ChatGPT, Gemini, or Llama 3) retrieves relevant documents from a knowledge base before generating a response.

• Agentic Workflow:
  A workflow where the AI isn’t just answering questions it can decide what to do next, trigger new actions, and call APIs without you telling it step-by-step.

When combined, Agentic RAG means:

1. The AI retrieves relevant information.
2. It interprets that information in context.
3. It decides the next best action like sending an email, creating a report, or updating a database.

Why Use n8n for Agentic RAG?
Platforms like LangChain or LlamaIndex focus on RAG pipelines, but they often require heavy coding.

n8n offers:

• No-code/low-code workflow building with advanced logic control.
• Native integrations with APIs, databases, and cloud storage.
• Easy chaining of AI reasoning with real-world actions.

Read more: Automate Your Workflow with n8n

Step-by-Step: Building an Agentic RAG Workflow in n8n

Step 1: Define the Agent’s Role
Defining the role ensures the AI:

• Knows its boundaries.
• Understands its goals.
• Decides actions aligned with the workflow.

*Example: *“You are a medical research assistant. Retrieve the most relevant research papers about a given topic, summarize key findings, and email a daily digest to the research team.”

Step 2: Capture the User Input

• Use a Webhook Trigger or Form Submission Node to receive a query (e.g., “Summarize latest studies on AI in radiology”).
• Store the query in a variable.

Step 3: Retrieve Relevant Context (The R in RAG)

1. HTTP Request Node → Call Pinecone/Weaviate API.
2. Send the user’s query as an embedding search
3. Retrieve top N results (e.g., 5 most relevant documents).

Tip: You can preprocess data in n8n with a Function Node to clean or filter irrelevant hits.

Step 4: Send Context to the LLM (The G in RAG)

• Use n8n’s OpenAI, Anthropic, or Google AI node.
• Pass both the original query and the retrieved documents into the LLM’s prompt.

Example system prompt:
You are an AI assistant with access to a research database.
Use the retrieved documents to answer accurately.
If the answer is not found, clearly say "Not enough data"

Step 5: Add Agentic Reasoning

• Use a Function Node to interpret the LLM’s output.
• Detect “next action” instructions embedded in the AI’s reply.

If the LLM says: “Summarized findings are ready. Email the report to the research team.”

Step 6: Execute Actions
Depending on the agent’s decision:
Email Node → **Send report.
**Slack Node → Post updates.
Database Node → Log summaries.

Because n8n supports conditional logic, you can build branching flows:
If high priority → send SMS alert.
If normal priority → store in database only.

Step 7: Add a Feedback Loop
Agentic workflows improve over time if they learn:

• Store past queries and actions in a database.
• Feed performance metrics back into the LLM prompt (e.g., “last time you missed relevant paper X”).

Step 8: Monitor and Optimize

• Use n8n’s Execution Log to see where time is spent.
• Add error handling (e.g., retry failed API calls).
• Periodically re-index your knowledge base

Example Use Case

Healthcare Research Assistant: Doctors ask for the latest treatment guidelines → AI retrieves, summarizes, and sends updates.

Customer Support Knowledge Agent: Support staff ask a question → AI retrieves product manuals → sends step-by-step resolution guide.

Sales Intelligence Bot: Sales team requests competitor news → AI fetches articles, scores relevance, and updates CRM.

Furthermore
By combining RAG’s factual grounding with agentic reasoning inside n8n, you create a system that can not only answer questions but also act on them. If you want to build such intelligent, automated workflows for your business, you can hire n8n experts to design, implement, and optimize them for maximum impact.