DEV Community

Taniya Butola
Taniya Butola

Posted on

Customer Support Memory Agent

#agents #ai #llm #showdev

I Built a Customer Support AI That Remembers Users Across Sessions (Hindsight + Groq)

Most AI support systems today are fast, fluent, and helpful.

But they all share one major flaw.

They forget.

A user explains their issue, comes back later, and the system treats them like a new customer. Same questions. Same context. Same frustration.

That’s the problem I wanted to solve.

So I built a Customer Support Memory Agent — an AI system that remembers past interactions and uses them to improve future responses.

You can try the live project here:
https://customer-support-memory-agent.streamlit.app/

The Core Problem: Stateless Support Feels Broken

Most support bots are stateless.

That means:

  • No memory of previous conversations
  • No awareness of user history
  • No continuity across sessions

Here’s what that looks like:

Day 1*
User: My payment failed
Agent: Please provide your transaction ID

Day 2*
User: Any update?
Agent: Can you describe your issue?

Even with a powerful model, the experience feels disconnected.

And in real-world support, this is unacceptable.

The Idea: Memory as a First-Class Feature

Instead of improving just the response quality, I focused on context retention.

The goal was simple:

Build a system where every interaction improves the next one.

To achieve this, I used:

  • Hindsight for long-term memory (recall + retain)
  • Groq for fast LLM responses

This combination allows the system to:

  • Recall past issues using a user_id
  • Use that context while generating responses
  • Store new interactions for future use

How the System Works

The architecture is intentionally simple and effective:

User → Memory Check → Recall → LLM → Response → Store Memory

Here’s the flow in detail:

  1. User sends a message
  2. System checks if memory is enabled
  3. If enabled, Hindsight retrieves relevant past data
  4. This memory is injected into the prompt
  5. Groq generates a response
  6. The interaction is stored back into memory

Each user is mapped to a unique memory bank:

cs-{user_id}

This ensures that even if the user starts a new chat session, the system can still recall previous interactions.

Core Pipeline

mermaid
flowchart LR
A[User message] --> B{Memory on?}
B -->|Yes| C[Hindsight recall]
B -->|No| D[No history]
C --> E[Groq response]
D --> E
E --> F[Reply]
F --> G{Memory on?}
G -->|Yes| H[Hindsight retain]
G -->|No| I[Done]

Tech Stack

  • LLM: Groq (llama-3.1-8b-instant)
  • Memory Layer: Hindsight
  • Backend: FastAPI
  • Frontend: React (Vite)
  • Cloud Demo: Streamlit

This setup allows both:

  • Quick demo via Streamlit
  • Full production-style setup using React + FastAPI

Screenshots

Streamlit UI + Chat Flow

Backend Logic (Memory + LLM Integration)

Key Feature: Memory Toggle

One important feature is the memory toggle.

  • Memory OFF
    Acts like a normal chatbot (no recall, no storage)

  • Memory ON
    Enables full memory pipeline (recall + retain)

This makes it easy to demonstrate the real impact of memory.

Demo: What Actually Changes

Try this in the live app:

  1. Turn Memory ON

  2. Use a customer ID

  3. Send:
    “My payment failed yesterday when I tried to renew my plan.”

  4. Start a new chat

  5. Ask:
    “Any update?”

With Memory

The system will respond with context:
“Yesterday your payment failed during renewal…”

Without Memory

The system responds generically:
“Can you describe your issue?”

Challenges I Faced

1. Storing Too Much Data

Initially, I stored full conversations.

This caused:

  • Slower responses
  • Irrelevant context
  • Harder debugging

Fix: Store only meaningful interactions.

2. Passing Entire Chat History

I assumed more context = better results.

It didn’t.

The model became:

  • Slower
  • Less accurate

Fix: Use Hindsight to retrieve only relevant memory snippets.

3. Weak Demo Clarity

At first, people didn’t notice the improvement.

Fix:
I redesigned the demo:

  • Same user
  • Same issue
  • Clear before vs after

That made the value obvious.

What I Learned

Memory > Model Size

You don’t need a bigger model.

You need better context.

Simplicity Wins

The entire system is just:

Retrieve → Generate → Store

And that’s enough to build something impactful.

User Experience is Everything

A small feature like memory can completely change how users perceive intelligence.

Real-World Applications

This system can be extended to:

  • Customer support platforms
  • CRM tools
  • Healthcare assistants
  • Learning systems

Anywhere users return, memory becomes critical.

Future Improvements

  • Memory summarization
  • Priority-based recall
  • User behavior profiling
  • Feedback learning loop

Final Thoughts

Most AI systems today are built for single interactions.

But real users don’t behave that way.

They return. They expect continuity. They expect systems to remember them.

This project is a step toward that direction.

Not by making AI smarter, but by making it more aware.

Try It Yourself

Live Demo:
https://customer-support-memory-agent.streamlit.app/

Repository:
https://github.com/vaibhav-srivastava-1/Customer-Support-Memory-Agent

Top comments (0)