How to Build an AI Agent with Multi-Session Memory (Step-by-Step Guide)
7/5/20251 min read
Why Multi-Session Memory Matters in AI
If you’ve ever chatted with a chatbot that forgot what you said two minutes ago, you understand why memory is crucial. In today’s agentic AI systems, memory transforms basic bots into persistent, intelligent assistants that can:
Retain long-term task context
Recall facts or preferences from previous sessions
Summarize and manage large conversations
In this tutorial, you’ll build an AI agent with multi-session memory using LangChain and OpenAI, including:
Conversation Buffer Memory
Conversation Summary Memory
Vector-based Retrieval Memory
No prior experience with LangChain or memory modules is needed!
Prerequisites
You’ll need:
Python 3.9+
A .txt file with some content (e.g., startup_faq.txt)
Install dependencies:
Step 1: Set Up OpenAI and LangChain
This sets up the foundational model that powers your agent.
Step 2: Add Buffer Memory
Buffer memory helps your agent remember the recent conversation.
Step 3: Add Summary Memory
Summary memory compresses previous conversations into a short summary to save token space.
Step 4: Add Vector Memory for Knowledge Recall
This lets your agent recall facts or documents from embedded knowledge.
Step 5: Combine Memory Types into One Agent
Combine buffer and summary memory together:
Step 6: Test Your Agent
Run some simple prompts to test your memory:
Full Working Code
Here is the complete code to copy and run:
Final Thoughts
Multi-session memory is essential for building realistic, helpful AI agents. With LangChain, you can easily add memory layers that improve personalization, reduce friction, and supercharge usability.
Next Step: Try integrating long-term memory with a persistent vector DB or a backend database.