# Mem0 — Memory Layer for AI Applications

> Add persistent, personalized memory to AI agents and assistants. Mem0 stores user preferences, past interactions, and learned context across sessions.

## Install

Copy the content below into your project:

## Quick Use

```bash
pip install mem0ai
```

```python
from mem0 import Memory

m = Memory()

# Add memories
m.add("I prefer Python over JavaScript", user_id="alice")
m.add("My project uses PostgreSQL and Redis", user_id="alice")

# Search memories
results = m.search("what database does alice use?", user_id="alice")
print(results)
# [{'memory': 'Uses PostgreSQL and Redis', 'score': 0.95}]
```

## What is Mem0?

Mem0 (pronounced "memo") is a memory layer for AI applications. It automatically extracts, stores, and retrieves relevant memories from conversations — giving AI agents persistent context across sessions without manual prompt engineering.

**Answer-Ready**: Mem0 is a memory infrastructure for AI apps that stores user preferences, facts, and context across sessions, enabling personalized AI experiences with simple add/search APIs.

## Core Features

### 1. Automatic Memory Extraction
Mem0 identifies and stores key facts from conversations:

```python
m.add("I'm allergic to peanuts and I live in SF", user_id="bob")
# Stores: ["Allergic to peanuts", "Lives in San Francisco"]
```

### 2. Multi-Level Memory

```python
# User-level: personal preferences
m.add("Dark mode preferred", user_id="alice")

# Agent-level: learned behaviors
m.add("Always check tests before committing", agent_id="dev-bot")

# Session-level: conversation context
m.add("Working on the auth refactor", user_id="alice", run_id="session-42")
```

### 3. Memory Management

```python
# Get all memories for a user
all_memories = m.get_all(user_id="alice")

# Update a specific memory
m.update(memory_id="mem-123", data="Prefers dark mode in all apps")

# Delete
m.delete(memory_id="mem-123")

# Memory history
history = m.history(memory_id="mem-123")
```

### 4. Graph Memory (Beta)
Store relationships between entities:

```python
from mem0 import Memory

config = {"graph_store": {"provider": "neo4j", "config": {...}}}
m = Memory.from_config(config)

m.add("Alice manages the payments team", user_id="org")
# Creates: Alice --manages--> Payments Team
```

### 5. Platform Integrations

```python
# With OpenAI
from mem0 import MemoryClient
client = MemoryClient(api_key="mem0-key")

# With LangChain
from langchain.memory import Mem0Memory
memory = Mem0Memory(mem0_api_key="...")
```

## FAQ

**Q: Where are memories stored?**
A: Local mode uses Qdrant (embedded). Cloud mode uses Mem0 managed infra.

**Q: How is it different from RAG?**
A: RAG retrieves from static documents. Mem0 learns and updates from conversations dynamically.

**Q: Is it free?**
A: Open-source for self-hosting. Cloud platform has a free tier.

## Source & Thanks

- GitHub: [mem0ai/mem0](https://github.com/mem0ai/mem0) (25k+ stars)
- Docs: [docs.mem0.ai](https://docs.mem0.ai)

<!-- ZH -->

## 快速使用

```bash
pip install mem0ai
```

三行代码为 AI 应用添加持久记忆层。

## 什么是 Mem0？

Mem0 是 AI 应用的记忆基础设施。自动从对话中提取、存储和检索相关记忆，让 AI 代理跨会话保持上下文。

**一句话总结**：Mem0 为 AI 应用存储用户偏好、事实和上下文，通过简单的 add/search API 实现个性化体验。

## 核心功能

### 1. 自动记忆提取
从对话中识别关键事实并存储。

### 2. 多层记忆
用户级、代理级、会话级三层记忆体系。

### 3. 图记忆（Beta）
存储实体间关系，构建知识图谱。

## 常见问题

**Q: 和 RAG 有什么区别？**
A: RAG 从静态文档检索，Mem0 从对话中动态学习和更新。

**Q: 免费吗？**
A: 自托管开源免费，云平台有免费层。

## 来源与致谢

- GitHub: [mem0ai/mem0](https://github.com/mem0ai/mem0) (25k+ stars)

---
Source: https://tokrepo.com/en/workflows/96da1f40-1823-4d87-a84f-7d8269edeb24
Author: Agent Toolkit