How to Add Long-Term Memory to Your AI Agent with a Vector Database

7/6/20251 min read

Want your AI agent to remember important facts even after you restart it?

In this tutorial, you’ll learn how to give your AI long-term memory using vector databases like Chroma or FAISS. This helps your agent retrieve relevant information from past chats, uploaded docs, or company FAQs, just like a smart assistant should.

This is Blog #4 in our AI Agent series. Let’s take your bot to the next level.

What You’ll Learn

Store documents or facts into a vector DB
Embed and index text using OpenAI
Let your agent recall old information on demand
Persist knowledge across sessions

Prerequisites

Python 3.9+
OpenAI API key → platform.openai.com
Install dependencies:

Or if using FAISS instead of Chroma:

Step 1: Load & Embed Your Data

Let’s say you have an FAQ file or user notes saved in a .txt file:

Step 2: Convert Vector Store to a Retriever

This retriever is now your AI’s memory bank.

Step 3: Create a RetrievalQA Chain

Use LangChain’s RetrievalQA to allow the agent to search the vector DB:

Step 4: Ask Questions with Long-Term Memory

Your AI will search your stored documents to generate accurate answers, even if you restart the app.

Optional: Combine with Other Memory Types

You can combine this vector memory with:

ConversationBufferMemory
SummaryMemory
SessionStorage (LangChain or your backend)

For example:

Use combined_memory in a LangChain ConversationChain while keeping your qa_chain for specific lookups.

Final Thoughts

Congratulations! You now have an agent that can:

Recall past conversations
Summarize chats
Store and retrieve knowledge long-term
Scale with document-rich tasks or customers

With long-term memory via vector DBs, your agent is truly evolving from a chatbot to a super assistant.