MemoryDesk: Building an AI Customer Support Agent with Presistent Memory

MemoryDesk: Building an AI Customer Support Agent with Persistent Memory

The Problem

AI customer-support agents are useful, but a common limitation is that conversations can feel disconnected. When a customer starts a new conversation, the agent may not have access to useful context from previous interactions.

For Hack With Hyderabad 3.0, we wanted to explore a different approach: an AI support agent that can maintain useful customer memory across separate conversations.

Our Solution

We built MemoryDesk, an AI customer-support application that combines an AI agent with persistent memory.

The demo follows a customer named Rahul.

In the first conversation, Rahul reports that his Pro-plan payment failed. The agent investigates the simulated support issue and retains useful information.

We then start a completely new conversation.

Rahul says:

"Hey, my payment isn't working again."

Instead of treating this as an entirely unrelated interaction, the system can retrieve relevant information from the customer's previous interaction through the persistent memory layer.

Architecture

MemoryDesk uses:

• Next.js and React for the interface
• TypeScript for the application
• OpenClaw for the AI agent
• Hindsight for persistent memory
• A server-side API layer for agent and memory orchestration

The browser does not receive credentials. The application server communicates with OpenClaw and Hindsight, while the interface displays the relevant results returned by the memory layer.

The Demo

The demo has three main steps:

1. Run Conversation 1.
2. Start a genuinely new conversation.
3. Run Conversation 2 and retrieve relevant information from persistent memory.

The important part is that the previous chat transcript is not simply copied into the new conversation.

This allows us to demonstrate the difference between ordinary conversation history and persistent AI memory.

Why Persistent Memory Matters

Persistent memory can allow support agents to maintain useful customer context across separate interactions.

For example, instead of asking a returning customer to repeatedly explain the same issue, an agent can retrieve relevant information from previous interactions and use it when appropriate.

What We Learned

The biggest challenge was making sure that a "new conversation" was actually a new session while still allowing the memory system to retain information independently.

This project also gave us practical experience with AI coding agents, OpenClaw, persistent memory systems, full-stack development, debugging, testing, and Git/GitHub workflows.

Technology Stack

• Next.js
• React
• TypeScript
• Node.js
• OpenClaw
• Hindsight
• GitHub

Hackathon

Built for Hack With Hyderabad 3.0.

Team: Spyder Coders

Conclusion

MemoryDesk explores how persistent memory can make AI customer-support interactions more continuous across conversations.

The project is a prototype, but the core idea is simple:

A new conversation doesn't necessarily have to mean starting from zero.