Designing a Memory System for Multi-Agent AI — Building 'Never-Forget' Agents with PostgreSQL + pgvector
As SaaS developers, we're constantly pushing the boundaries of what's possible with multi-agent AI in our applications. One of the biggest challenges we face is ensuring that our AI agents don't "forget" crucial information, making them less effective over time. In this article, we'll explore how to design robust memory systems for multi-agent AI in SaaS applications using PostgreSQL and Prisma.
Understanding the Problem
Multi-agent AI systems consist of multiple agents that interact with each other to achieve a common goal. However, when these agents forget important information, the system's performance suffers. This can be due to various factors, such as:
- Session-based forgetting**: When a session ends, the context is lost, and the agents forget the previous discussions or decisions.
- State-based forgetting**: When the system's state changes, the agents forget the previous state, leading to inconsistencies.
- Data-driven forgetting**: When data is updated or deleted, the agents forget the associated information.
Designing a Robust Memory System
To build a robust memory system for multi-agent AI, we need to consider the following key aspects:
- Data persistence**: Ensure that the data is stored persistently across sessions and state changes.
- Data consistency**: Maintain data consistency across all agents and ensure that they have a unified view of the data.
- Scalability**: Design the system to scale horizontally and handle a large number of agents and interactions.
Using PostgreSQL and Prisma
PostgreSQL is a powerful, open-source relational database that can be used to store and manage complex data structures. Prisma is a modern ORM (Object-Relational Mapping) tool that simplifies database interactions in Node.js applications.
Setting up PostgreSQL and Prisma
To get started, create a new PostgreSQL database and install the Prisma CLI using npm:
npm install -g @prisma/cli
Next, create a new Prisma schema file (`prisma/schema.prisma`) and define the data model:
model Agent {
id String @id @default(cuid())
name String
memory Memory[]
}
model Memory {
id String @id @default(cuid())
key String
value String
agentId String
agent Agent
}
Implementing Data Persistence
To implement data persistence, we can use Prisma's `prisma client` to store and retrieve data from the PostgreSQL database:
import { PrismaClient } from '@prisma/client';
const prisma = new PrismaClient();
// Store data
async function storeData(agentId, key, value) {
const memory = await prisma.memory.create({
data: {
key,
value,
agentId,
},
});
return memory;
}
// Retrieve data
async function retrieveData(agentId, key) {
const memory = await prisma.memory.findFirst({
where: {
agentId,
key,
},
});
return memory?.value;
}
Implementing Data Consistency
To implement data consistency, we can use Prisma's `findMany` method to retrieve all related data and update the agents accordingly:
async function updateAgents() {
const agents = await prisma.agent.findMany({
include: {
memory: true,
},
});
agents.forEach((agent) => {
agent.memory.forEach((memory) => {
// Update the agent's memory
// ...
});
});
}
Conclusion
Designing a robust memory system for multi-agent AI in SaaS applications requires careful consideration of data persistence, consistency, and scalability. By using PostgreSQL and Prisma, we can build a robust and scalable memory system that ensures our AI agents don't "forget" crucial information. If you're using DiggaByte's Next.js + Prisma stack, you can easily integrate this approach into your application.
Example Use Cases
Here are some example use cases for the memory system:
- Session-based forgetting**: Store session data in the memory system to prevent forgetting crucial information when a session ends.
- State-based forgetting**: Use the memory system to store the system's state and update it accordingly when the state changes.
- Data-driven forgetting**: Store data-driven information in the memory system to prevent forgetting associated information when data is updated or deleted.