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mem0: AI Memory Layer

Get universal memory for AI agents with mem0, for founders building AI-powered apps, backed by 59k+ GitHub stars
intermediate⏱ 30 minutesπŸ’΅ Free (self-hosted)
62,469 stars7,283 forksPythonHealth Score 9/10Updated 8/4/2026100% free Β· open source
What it is

Builds a universal memory layer for AI agents, giving them context and recall.

What you can make with it

Automations like a personal assistant that remembers your schedule and tasks.

How it helps

With a universal memory layer, your AI agent can recall previously learned knowledge and apply it to new situations.

Real use case example

"A founder building a mental health chatbot uses mem0 to create a memory layer that recalls patient conversations, diagnoses, and treatment plans."

If you're new

Choose this when you're just starting to learn about AI agents and want a simple way to build memory and context.

If you're senior

Reach for this when you need to create complex AI agents with long-term memory and recall.

Common confusion cleared up

Don't confuse this with traditional AI model storage – mem0 creates an AI agent's memory and context.

Best inside these AI tools
Claude DesktopClaude CodeGeminiGemini Code Assist
Pairs with
Claude DesktopClaude CodeClaude APIGemini
Why we list it on WorkflowStacks: mem0 is a widely-used, open-source library that simplifies the development of advanced AI agents.
What it does

Mem0 provides a universal memory layer for AI agents, enabling them to store and retrieve information in a flexible and scalable way

Install / run
pip install mem0
When to use it
  • β€’When building AI agents that need to retain knowledge over time
  • β€’When integrating multiple AI models that require shared memory
  • β€’When developing conversational AI that requires context persistence
Quick start
  1. 1Import the mem0 library in your Python script with `import mem0`
  2. 2Create a new mem0 instance with `mem = mem0.Memory()`
  3. 3Store a value in memory using `mem.store(key='example_key', value='example_value')`
  4. 4Retrieve a value from memory using `mem.retrieve(key='example_key')`
  5. 5Configure mem0 using the `mem0.yaml` file for advanced settings
Ready-to-paste prompt
mem.store(key='user_name', value='John Doe'); mem.retrieve(key='user_name')
Heads up: Mem0 requires Python 3.8 or later to run, so ensure your environment meets this requirement before installation
Saves to your device

Topics

agents
ai
ai-agents
application
chatbots
chatgpt
genai
llm
long-term-memory
memory
memory-management
python
rag
state-management
How mem0: AI Memory Layer works
Codeflow
Free to inspect

Mem0: AI Memory Layer is a very large Python project (~106k lines across 712 code files, plus 281 test files). You install it into your AI tool with one command; there is nothing to run yourself. Last commit this month, Apache-2.0 license, has a test suite.

Size
Very large codebase
~106k lines Β· 712 code files Β· days to read β€” use, don't read
Setup
Install as a skill / plugin
Add it to Claude Code (or your AI tool) with one command β€” 42 skills inside. Nothing to run yourself.
Runs on
Inside your AI tool
Helper scripts use Python
Python 49%TypeScript 48%Shell 2%JavaScript 1%
Where to start reading
  1. 1
    README.md
    Start here β€” what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    server/main.py
    Where the program starts running
  4. 4
    mem0/__init__.py
    Inside mem0/ β€” the main logic begins here
  5. 5
    examples/graph-db-demo/alice-memories.png
    A worked example β€” copy this to get going
What's in each folder
mem0/Core code β€” the actual logic150 files
skills/Prompts, skills & agent definitions43 files
integrations/Sub-packages / plugins305 files
server/Backend / API184 files
cli/Command-line entry points93 files
docs/Documentation337 files
examples/Examples you can copy183 files
mem0-ts/Folder240 files
READMEHas testsDocumentedCI checksExamples includedApache-2.0 licenseUpdated this month
Quick Actions
Details
Creator
mem0ai
Language
Python
Category
support
Published
6/20/2023

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