# SuperMemory — Universal AI Memory Layer for Agents and Apps

> SuperMemory is an open-source memory infrastructure that lets AI agents and applications remember, retrieve, and reason over past interactions and user data using vector storage and semantic search.

## Install

Save as a script file and run:

# SuperMemory — Universal AI Memory Layer for Agents and Apps

## Quick Use
```bash
git clone https://github.com/supermemoryai/supermemory.git
cd supermemory
cp .env.example .env
# Fill in your API keys
pnpm install
pnpm dev
```

## Introduction
SuperMemory provides a memory layer that any AI agent or application can plug into for persistent, searchable recall across sessions. It solves the fundamental problem of LLM statelessness by storing interactions in a vector database and retrieving relevant context on demand.

## What SuperMemory Does
- Stores and indexes conversational history, documents, and user data for semantic retrieval
- Provides an API that AI agents call to save and recall memories across sessions
- Supports multiple memory spaces for organizing context by project, user, or topic
- Offers a Chrome extension for saving web content directly into the memory store
- Integrates with popular AI frameworks and chat interfaces via REST endpoints

## Architecture Overview
SuperMemory is built as a Next.js monorepo with a Cloudflare Workers backend. Incoming data is chunked and embedded using configurable embedding models, then stored in a vector database (Vectorize or compatible). At query time, semantic search retrieves the most relevant memory chunks, which are injected into the LLM prompt as context. A background worker handles indexing, deduplication, and memory consolidation to keep the store efficient.

## Self-Hosting & Configuration
- Clone the repository and configure environment variables for your embedding provider and vector store
- Supports Cloudflare Workers, Vectorize, and D1 as the default infrastructure stack
- Set API keys for OpenAI or compatible embedding models in .env
- Run locally with pnpm dev for development, or deploy to Cloudflare for production
- Configure memory spaces and access controls through the admin dashboard

## Key Features
- Semantic search across all stored memories with configurable relevance thresholds
- Multi-space memory organization for isolating contexts between projects or users
- Chrome extension for one-click web page capture into the memory layer
- REST API compatible with any AI agent framework or custom application
- Automatic memory consolidation and deduplication to prevent context bloat

## Comparison with Similar Tools
- **Mem0** — focuses on user-level memory for personalization; SuperMemory provides broader document and conversation storage
- **Zep** — offers session-based memory with knowledge graph extraction; SuperMemory emphasizes simplicity and self-hosting
- **LangChain Memory** — tightly coupled to LangChain; SuperMemory is framework-agnostic with a standalone API
- **Khoj** — serves as a personal AI assistant; SuperMemory focuses purely on the memory infrastructure layer

## FAQ
**Q: What embedding models does SuperMemory support?**
A: It supports OpenAI embeddings by default and can be configured to use any compatible embedding API.

**Q: Can I use SuperMemory with my existing AI agent?**
A: Yes. SuperMemory exposes a REST API that any agent framework can call to store and retrieve memories.

**Q: How does it handle large amounts of data?**
A: Data is chunked, embedded, and stored in a vector database with automatic deduplication and consolidation.

**Q: Is it production-ready?**
A: SuperMemory is actively developed with a growing community. Self-hosted deployments run on Cloudflare infrastructure for scalability.

## Sources
- https://github.com/supermemoryai/supermemory
- https://supermemory.ai

---
Source: https://tokrepo.com/en/workflows/asset-87129798
Author: Script Depot