Quick Start: Get Up and Running with MemOS

What You'll Learn

Welcome! This guide will help you install, initialize, and run your first memory-augmented LLM app in just a few minutes.

In just a few minutes, you'll learn how to set up a minimal, working MemOS pipeline that connects your LLM with persistent, searchable memory. By the end, you'll be able to store, retrieve, and update simple memories for a user or session — the foundation for building memory-augmented chatbots and agents.

Install MemOS

pip install MemoryOS

Optional
If you want to use local transformer models, make sure you have PyTorch installed.

Requirement for Neo4j Desktop
If you plan to use Neo4j for graph memory, install Neo4j Desktop (community edition support coming soon!)

Create a Minimal Config

For this Quick Start, we'll use the built-in GeneralTextMemory — no external vector DB or graph DB needed.

from memos.configs.mem_os import MOSConfig

# init MOSConfig
mos_config = MOSConfig.from_json_file("examples/data/config/simple_memos_config.json")

Create a User & Register a MemCube

import uuid
from memos.mem_os.main import MOS

mos = MOS(mos_config)

# Generate a unique user ID
user_id = str(uuid.uuid4())

# Create the user
mos.create_user(user_id=user_id)

# Register a simple memory cube for this user
mos.register_mem_cube("examples/data/mem_cube_2", user_id=user_id)

Add Your First Memory

# Add some conversational history
mos.add(
    messages=[
        {"role": "user", "content": "I love playing football."},
        {"role": "assistant", "content": "That's awesome! "}
    ],
    user_id=user_id
)

Retrieve & Search Memory

# Search for memories related to your query
result = mos.search(
  query="What does the user love?",
  user_id=user_id
)

print("Memories found:", result["text_mem"])

Save & Load Memory

# Save your memory cube
mos.dump("tmp/my_mem_cube")

# Later, you can load it back
mos.load("tmp/my_mem_cube")

Next Steps

Congratulations! You've just run a minimal memory-augmented pipeline with MemOS.

Ready to level up?

Structured Memory: Try TreeTextMemory for graph-based, hierarchical knowledge.
Activation Memory: Speed up multi-turn chat with KVCacheMemory.
Parametric Memory: Use adapters/LoRA for on-the-fly skill injection.
Graph & Vector Backends: Connect Neo4j or Qdrant for production-scale vector/graph search.

Need Help?

Check out:

Architecture

MemOS is made up of core modules that work together to turn your LLM into a truly memory-augmented system — from orchestration to storage to retrieval.

Your First Memory

Let’s build your first plaintext memory in MemOS! GeneralTextMemory is the easiest way to get hands-on with extracting, embedding, and searching simple text memories.