Creating an AI Agent with Self-Managing Memory

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The challenge with AI agents is that they do not have a persistent memory. We have to manage their memory explicitly.

For example – you are chatting with ChatGPT and later you close that session. When you open a new session, ChatGPT does not have any memory of your previous interaction.

To solve this problem, people started sending the conversation history back to the model so that model would have the context.

This approach works for some initial conversations but then more input text means more money. Also, there is a limit on input size.

So how can we solve this problem?

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Creating an AI Agent with Self-Managing Memory

AI models process information based on the input they receive during a conversation. If a user mentions, “My friend’s name is Virat,” and later asks, “Write a poem about my friend,” the AI must recall that “Virat” is the friend’s name.

However, traditional AI models do not store this context persistently; every time a user interacts with the AI, previous conversations must be resent, leading to inefficiencies:

To solve these challenges, we can design an AI agent that maintains an editable memory section, allowing it to store and modify information as needed.

Let us see how can we do that in this tutorial

You can find the code notebook here – https://github.com/tayaln/AI-Agent-with-self-managing-memory

To use it, download and then upload it to your Google Drive and open it as a Google Colab file.

Pre-requisite

– An Open Mind to learn new things

– OpenAI account

– OpenAI API Key

Concepts we discussed in this video

• AI Memory Challenge
• Limitations of Context Management
• Memory Storage in AI Agents
• Implementing Memory Functions
• AI Agent Decision-Making
• Persistent Memory Implementation
• Function Calling for AI Memory Management

Implementing AI Memory Management

To solve this, we need a system that allows the AI to edit and manage memory efficiently. This is done by assigning a dedicated space for memory storage that the AI can modify when necessary.

Step 1: Setting Up Dependencies

First, we need to install the required dependencies and access the OpenAI API key. Assuming you are familiar with generating and using API keys, we proceed to set up the AI model:

We’ll be using GPT-4 and setting up a simple chatbot:

Since the AI has no memory, it will respond:

“I’m sorry, but I don’t know your name.”

Step 2: Introducing Memory Storage

To give our AI a memory, we define a memory storage section:

Now, we modify the system prompt to include memory:

When we ask the AI, “What is my name?”, it should now respond:

“Your name is Sachin.”

Step 3: Editing Memory Dynamically

To allow the AI to update its memory, we define a tool.

Step 4: Implementing an AI Agent Loop

To make the AI interact more effectively, we implement a loop that ensures memory updates:

Now, the AI agent will remember and update user information dynamically.

Conclusion

With this approach, we have successfully implemented a self-managing memory system for an AI agent.

This enables the AI to retain user-specific information, update it as needed, and provide a more seamless conversational experience.

We hope this guide helps you build smarter AI agents. Stay tuned for more AI tutorials, and happy coding!

Hope you found this helpful! Stay tuned for more AI tutorials.

If you have any queries, or suggestions, share them with me on LinkedIn – https://www.linkedin.com/in/nikhileshtayal/

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