Mem0 + Agno Interactive Agent Demo

Overview

This project showcases a minimal but powerful example of building AI agents that can store, recall, and reason using long-term memory and language models.

Notebook Link- https://colab.research.google.com/drive/14Ne2Bqlo4b_2-qmmHwFeqLw211qpwC4-?usp=sharing

Documentation- https://docs.google.com/document/d/1e27TTLQlV02jW8hPHslZvtftYegStSoyAnXsmTmWGSU/edit?usp=sharing

It combines three major components:

• Agno Agent Framework – to define agent behaviors like storing or retrieving.
• Mem0 Memory Client – to persist and search user memory.
• Hugging Face LLM – to enable intelligent, context-aware responses.

The entire system runs as a simple command-line interface, and demonstrates core ideas in AI agent workflows:

• Agent chaining (one agent calling another),
• Persistent memory integration,
• LLM-enhanced reasoning.

It is suitable for beginners learning agent development, as well as researchers prototyping memory-augmented agents.

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Objectives

• Understand how to use Mem0 to store and retrieve context.
• Build modular agents using Agno.
• Create smart reasoning agents with LLM + memory integration.
• Demonstrate how agents can be composed into chains for more complex logic.

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Installation

Use these commands in Google Colab or a Python 3.8+ environment:

!pip install agno
!pip install mem0ai
!pip install transformers