nixpkgs/docs/packages/mem0.md

mem0

Long-term memory layer for AI agents with REST API support.

Description

Mem0 provides a sophisticated memory management system for AI applications and agents. It enables AI assistants to maintain persistent memory across conversations and sessions using vector storage.

Features

• 💾 Long-term Memory: Persistent memory across conversations
• 🔍 Semantic Search: Vector-based similarity search
• 🤖 Multiple LLM Support: OpenAI, Anthropic, Groq, Ollama, etc.
• 📊 Vector Stores: Qdrant, Chroma, Pinecone, pgvector, etc.
• 🌐 REST API: Easy integration via HTTP endpoints
• 🎯 Multi-modal Support: Text and image memories
• 🔧 Configurable: Flexible LLM and embedding models

Installation

Via Overlay

{pkgs, ...}: {
  environment.systemPackages = with pkgs; [
    mem0
  ];
}

Direct Reference

{pkgs, ...}: {
  environment.systemPackages = with pkgs; [
    inputs.m3ta-nixpkgs.packages.${pkgs.system}.mem0
  ];
}

Run Directly

nix run git+https://code.m3ta.dev/m3tam3re/nixpkgs#mem0

Usage

Command Line

# Start mem0 server
mem0-server

# With custom port
MEM0_PORT=8080 mem0-server

# With LLM provider
MEM0_LLM_PROVIDER=openai OPENAI_API_KEY=sk-xxx mem0-server

Python Library

from mem0 import Memory

# Initialize with OpenAI
memory = Memory(
    llm_provider="openai",
    llm_model="gpt-4o-mini",
    vector_store="qdrant",
    openai_api_key="sk-xxx"
)

# Add a memory
result = memory.add(
    "I prefer coffee over tea",
    metadata={"user_id": "123"}
)

# Search memories
memories = memory.search("What does the user prefer?")

REST API

# Start server
mem0-server

# Add memory
curl -X POST http://localhost:8000/v1/memories \
  -H "Content-Type: application/json" \
  -d '{"content": "User likes Python"}'

# Search memories
curl http://localhost:8000/v1/memories/search?q=Python

Configuration

Environment Variables

# LLM Configuration
export MEM0_LLM_PROVIDER=openai
export MEM0_LLM_MODEL=gpt-4o-mini
export MEM0_LLM_TEMPERATURE=0.7
export OPENAI_API_KEY=sk-xxx

# Vector Store
export MEM0_VECTOR_PROVIDER=qdrant
export QDRANT_HOST=localhost
export QDRANT_PORT=6333

# Server
export MEM0_HOST=0.0.0.0
export MEM0_PORT=8000
export MEM0_WORKERS=4
export MEM0_LOG_LEVEL=info

NixOS Module (Recommended)

Use the NixOS module for production:

{config, ...}: {
  imports = [m3ta-nixpkgs.nixosModules.mem0];

  m3ta.mem0 = {
    enable = true;
    port = 8000;
    llm = {
      provider = "openai";
      apiKeyFile = "/run/secrets/openai-api-key";
      model = "gpt-4o-mini";
    };
    vectorStore = {
      provider = "qdrant";
      config = {
        host = "localhost";
        port = 6333;
      };
    };
  };
}

See mem0 Module for full module documentation.

Supported LLM Providers

Provider	Model Examples	Notes
openai	gpt-4, gpt-3.5-turbo	Most tested
anthropic	claude-3-opus, claude-3-sonnet	Requires key
groq	mixtral-8x7b-32768	Fast inference
ollama	llama2, mistral	Local only
together	llama-2-70b	API access

Supported Vector Stores

Provider	Requirements	Notes
qdrant	qdrant server	Recommended
chroma	chroma server	Simple setup
pgvector	PostgreSQL + pgvector	SQL-based
pinecone	Pinecone API	Cloud only
redis	Redis stack	Fast
elasticsearch	ES cluster	Scalable

Use Cases

AI Chatbot with Memory

from mem0 import Memory

memory = Memory()

# During conversation
memory.add("User works at Google as a software engineer")

# Later conversations
memories = memory.search("Where does the user work?")
# Returns: ["User works at Google as a software engineer"]

Personal Assistant

memory = Memory()

# Store preferences
memory.add("User prefers dark mode", metadata={"category": "preferences"})
memory.add("User is vegetarian", metadata={"category": "diet"})

# Retrieve relevant info
memories = memory.search("What should I cook for the user?")

Code Assistant

memory = Memory()

# Store project context
memory.add("This is a NixOS project with custom packages")

# Later analysis
memories = memory.search("What kind of project is this?")

Requirements

Vector Store

You need a vector store running. Example with Qdrant:

services.qdrant = {
  enable = true;
  port = 6333;
};

LLM Provider

You need API keys or local models:

# OpenAI
services.mem0.llm.apiKeyFile = "/run/secrets/openai-api-key";

# Or use agenix
age.secrets.openai-api-key.file = ./secrets/openai.age;

Build Information

• Version: 1.0.0
• Language: Python
• License: Apache-2.0
• Source: GitHub

Python Dependencies

• litellm - Multi-LLM support
• qdrant-client - Qdrant client
• pydantic - Data validation
• openai - OpenAI client
• fastapi - REST API
• uvicorn - ASGI server
• sqlalchemy - Database ORM

Platform Support

• Linux (primary)
• macOS (may work)
• Windows (not tested)

Related

• mem0 Module - NixOS module documentation
• Adding Packages - How to add new packages
• Port Management - Managing service ports
• Quick Start - Getting started guide