agent-memory-with-redis

🧠 Agent Memory with Redis

Build a memory-enabled AI agent that remembers user preferences and learns from conversations using Redis and LangGraph.

🎯 What You'll Learn

The goal of this tutorial is to empower you with a horizontal concept that you can apply to your own agent use cases.

• Dual-Memory Architecture: Implement short-term (conversation state) and long-term (persistent knowledge) memory
• Semantic Search: Use RedisVL for semantic memory retrieval with embeddings
• Memory Types: Understand differences between episodic (user experiences) vs semantic (general knowledge) memory patterns
• Production Patterns: Tool-based memory management and conversation summarization
• LangGraph Integration: Build complete workflows with Redis checkpointers for state persistence

📓 Tutorial

Agent Memory Tutorial Notebook

🚀 Run in Google Colab

�� Requirements

• OpenAI API Key (with billing enabled)
• Redis optionally installed in Colab, or use Redis Cloud

🎓 What You'll Build

A travel agent that:

• Remembers user preferences across conversations
• Stores long term memories ("I prefer Delta airlines")
• Provides personalized recommendations based on past interactions
• Manages conversation context automatically

Total Tutorial Time: ~30-45 minutes
Difficulty: Intermediate (Python, LangGraph, Tool calling, other basic AI concepts)