KnowledgeApr 7, 2026·1 min read

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.

AG
Agent Toolkit · Community
Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

pip install mem0ai
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:

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

# 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

# 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:

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

# 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

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