Ai agent with memory layer

Built a memory-powered AI customer support agent using Hindsight

I recently built a small AI customer support agent for Hack With Hyderabad 3.0 using Hindsight as the long-term memory layer.

The problem I wanted to address was simple: normal chatbots often treat each request independently, even when previous interactions contain useful context.

My agent follows this flow:

User Query → Hindsight Recall → LLM Reasoning → Response → Hindsight Retain

For example:

First interaction: "My payment failed three times today. I am using an HDFC debit card."

Later: "My payment failed again. What should I do?"

Hindsight can recall the relevant previous interaction and provide that context to the LLM.

Tech stack:

• Python
• Streamlit
• Hindsight
• Groq

The main idea is not retraining the model. Instead, the agent uses persistent memory to bring relevant previous experiences into future interactions.

Would love to hear feedback on the approach and ideas for improving the memory/agent architecture.