Lesson Overview

This lesson covers one of the essential features when building a conversational AI agent—memory. By configuring memory, you allow your agent to remember a set number of messages from the ongoing chat. In n8n, this is handled by the memory context window setting. For example, a context window of five means the agent can recall the last five messages exchanged. Having this memory is helpful for keeping conversations natural and making sure your agent responds sensibly based on what has just happened.

This lesson sits at the practical center of agent building. Once you know how to set up basic flows, giving your agent memory is a big step towards making it actually useful in real conversations. It is especially relevant if you plan to use your agent for use cases such as customer support, answering questions, or automating multi-step tasks. For instance, if a user mentions their name or provides important info in one message, the agent should be able to recall and use that information moments later—just as a human would. You’ll also see options for interacting with your agent directly or through chat tools like Slack or WhatsApp.

Who This Is For

Setting up agent memory is practical for a wide range of users building AI-driven workflows.

• Educators designing AI tools for student interaction
• Support teams creating helpful chatbots
• Product managers wanting context-aware automations
• Developers building conversational interfaces
• Business users seeking personalized automation
• Anyone learning how conversational AI agents can hold context

Where This Fits in a Workflow

Configuring memory is one of the foundational steps when building AI agents that respond to conversations. You’ll use what you learn here after you have your agent running but before you add more advanced interactions. For example, if you’re working on a support chatbot, setting the context window enables the bot to answer follow-up questions from customers. Similarly, for any informational agent—like one you connect to Slack or WhatsApp—enabling memory lets users have connected conversations without repeating themselves. This step supports real use cases by making your agent much more useful in ongoing exchanges.

Technical & Workflow Benefits

Without memory, an AI agent resets after every message—acting like it’s meeting the user for the first time each turn. By giving your agent a context window, you allow it to "remember" the recent messages and respond more intelligently.

For example, with a context window of five, if a user says, “Hi, my name is Kevin,” and then later asks, “What’s my name?”, the agent will be able to answer correctly. Without memory, the agent would not recall this information—conversations would be disjointed and frustrating. This approach reduces repetition, speeds up interactions, and creates a more natural chat experience. Whether testing inside n8n or connecting to external chat platforms, adding memory improves overall effectiveness.

Practice Exercise

Open your n8n environment and load your AI agent workflow.

1. Add a memory component to your agent, choosing a context window length of five.
2. Open the agent’s chat window. Start a conversation:
   - Introduce yourself (“My name is Taylor.”)
   - Ask the agent to recall your name (“What’s my name?”)
3. Try removing the memory and repeat the conversation. Note the difference in the agent’s response.

Reflection: How does enabling memory change the usefulness and realism of your agent’s answers? What situations would benefit most from a larger or smaller context window?

Course Context Recap

This lesson builds on the earlier steps of creating your AI agent in n8n. After configuring your agent’s basics, memory setup adds an important layer—allowing for more natural, context-aware conversations. Next, you’ll move on to expanding your agent’s skills and abilities, using the memory features you’ve now configured as a base. Keep exploring the course to see how these pieces work together for full-featured agents!