OpenMemory MCP: Giving AI Tools "Shared Memory"
Break the memory islands between AI tools and improve work efficiency and interactive experience.
Core content:
1. OpenMemory MCP solves the problem of memory fragmentation between AI tools
2. Private memory server realizes local data storage and protects privacy
3. Two-stage intelligent memory processing and graph structure memory understanding improve information processing efficiency
Due to special circumstances, I have to switch between Cursor and Windsurf recently. After explaining the project requirements in detail in Cursor, I have to introduce them again when I switch to Windsurf. This "memory split" not only wastes time, but also seriously affects our interactive experience with AI tools.
What if AI tools can share memory? The preferences and habits you set in one place will automatically take effect in another place; the project background you explained does not need to be repeated. This is the core problem that OpenMemory MCP aims to solve.
The secret weapon of memory sharing: OpenMemory MCP
OpenMemory MCP is a private memory server running on your computer that creates a shared, persistent memory layer for AI tools that support the MCP protocol. Unlike traditional solutions, it adopts a local-first strategy, where all data is stored on your device and not uploaded to the cloud, ensuring data privacy and full control.
Do you see this interface? This is your memory control center, where you can view and manage the memories shared by all AI tools, just as simple and intuitive as managing your notes.
Uncovering the secrets: How does OpenMemory MCP work?
Imagine that OpenMemory MCP is like a "shared brain" between AI tools. Its core is an MCP server built on FastAPI, which allows any tool that supports the MCP protocol to seamlessly access and share memory through standardized memory operation APIs.
Smart Memory Processing: A Two-Stage Process
The memory processing behind OpenMemory does not simply store all the conversation content, but adopts Mem0's two-stage intelligent processing flow:
Extraction phase : The system analyzes your conversations and extracts information from three sources - the latest exchanges, scrolling summaries, and recent message history. It does not mechanically record everything, but intelligently extracts key information worth remembering, just like the human brain.
Update phase : Each new piece of information is compared with the stored memory, and the system intelligently decides:
• Added new memories • Update existing information • Remove outdated or contradictory content • Leave unchanged (if new information is not significant enough)
This design keeps the memory bank streamlined, consistent, and efficient, just as our brains actively forget unimportant details and only retain key information.
Graph structure memory: not just remember, but understand
Even more powerful is that OpenMemory also provides a graph-enhanced version, Mem0g, which not only remembers scattered facts, but also understands the relationship between things:
This graph structure enables AI to understand complex networks of relationships, such as multi-level relationships such as "Project A depends on Component B, and Component B is managed by Team C", thereby providing more comprehensive and insightful responses when answering questions.
Technical insider: How is OpenMemory MCP implemented?
For the technical enthusiasts, let's take a look at the implementation details of OpenMemory MCP. Its core code is located inopenmemory/api/app/mcp_server.py, this file defines the main functions and API of the MCP server.
Robust server design
""" MCP Server for OpenMemory with resilient memory client handling.
This module implements an MCP (Model Context Protocol) server that provides
memory operations for OpenMemory. The memory client is initialized lazily
to prevent server crashes when external dependencies (like Ollama) are unavailable.
Key features:
-Lazy memory client initialization
- Graceful error handling for unavailable dependencies
- Fallback to database-only mode when vector store is unavailable
- Proper logging for debugging connection issues
- Environment variable parsing for API keys
"""
# Initialize MCP
mcp = FastMCP( "mem0-mcp-server" )
# Delay initialization of memory client to avoid startup failure
def get_memory_client_safe ():
"""Get memory client with error handling. Returns None if client cannot be initialized."""
try :
return get_memory_client()
except Exception as e:
logging.warning( f"Failed to get memory client: {e} " )
return None Note the lazy initialization strategy here, which ensures that the server can still start and provide basic functions even if some external dependencies (such as the vector database) are temporarily unavailable. This design allows OpenMemory MCP to run stably in various environments and will not completely crash due to the failure of a component.
Core memory operations
OpenMemory MCP provides four main memory operation APIs to allow AI tools to easily access shared memory:
1. Add memory : When you tell the AI assistant something about yourself, your preferences or any important information, it calls this API to store the information.
@mcp.tool( description= "Add a new memory. This method is called everytime the user informs anything about themselves..." )
async def add_memories ( text: str ) -> str :
# Securely obtain the memory client
memory_client = get_memory_client_safe()
if not memory_client:
return "Error: Memory system is currently unavailable. Please try again later."
# Add memory and handle responses
response = memory_client.add(text, user_id=uid, metadata={
"source_app" : "openmemory" ,
"mcp_client" : client_name,
})
# Update the database and record the change history
# ...2. Search memory : Whenever you ask the AI assistant a question, it will call this API to find relevant memories to provide more accurate and personalized answers.
@mcp.tool( description= "Search through stored memories. This method is called EVERYTIME the user asks anything." )
async def search_memory ( query: str ) -> str :
uid = user_id_var.get( None )
client_name = client_name_var.get( None )
if not uid:
return "Error: user_id not provided"
if not client_name:
return "Error: client_name not provided"
# Get memory client safely
memory_client = get_memory_client_safe()
if not memory_client:
return "Error: Memory system is currently unavailable. Please try again later."
# ...
# embeddings
embeddings = memory_client.embedding_model.embed(query, "search" )
# Vector database qdrant search
hits = memory_client.vector_store.client.query_points(
collection_name=memory_client.vector_store.collection_name,
query=embeddings,
query_filter=filters,
limit = 10 ,
)
# Process search results
memories = hits.points
memories = [
{
"id" : memory.id ,
"memory" : memory.payload[ "data" ],
"hash" : memory.payload.get( "hash" ),
"created_at" : memory.payload.get( "created_at" ),
"updated_at" : memory.payload.get( "updated_at" ),
"score" : memory.score,
}
for memory in memories
]
# ...3. List memories : allows you to view all stored memories for easy management and organization.
@mcp.tool( description= "List all memories in the user's memory" )
async def list_memories () -> str :
# ...
# Get or create user and app
user, app = get_user_and_app(db, user_id=uid, app_id=client_name)
# Get all memories
memories = memory_client.get_all(user_id=uid)
# ...4. Delete memory : Use when you need to clear certain memories to ensure that you have full control over your data.
Together, these APIs form the core functionality of OpenMemory MCP, enabling any MCP-compatible tool to seamlessly access and share memory.
Containerized deployment: simple and powerful
OpenMemory MCP uses Docker containerized deployment to make installation and maintenance extremely simple. The entire system consists of three main components:
services:
# Vector database: storing and retrieving memory
mem0_store:
image: qdrant/qdrant
ports:
- "6333:6333"
volumes:
- mem0_storage:/mem0/storage
# MCP server: Provides memory operation API
openmemory-mcp:
image: mem0/openmemory-mcp
build: api/
environment:
-USER
- API_KEY
ports:
- "8765:8765"
# User Interface: Managing and Viewing Memory
openmemory-ui:
image: mem0/openmemory-ui:latest
ports:
- "3000:3000" This containerized design not only simplifies deployment, but also ensures isolation and independent updates between components, improving the maintainability and stability of the entire system.
Real-world application: How OpenMemory MCP can change the way you work
OpenMemory MCP is not just a technical concept, it can significantly improve your work efficiency in many practical scenarios:
Cross-tool project development
Imagine a workflow like this: you plan project requirements with Claude Desktop in the morning, write code in Cursor at noon, and debug problems with Windsurf in the afternoon. With OpenMemory MCP, these tools can access the same project memory, and you no longer need to repeatedly explain project background, technology selection, or specific requirements.
AI assistants remember your previous decisions and preferences, providing consistent help no matter which tool you use. It’s like having a virtual assistant that understands your entire project, appearing in different forms in different scenarios, but with the core memory always remaining consistent.
Seamless transfer of personal preferences
Are you tired of repeatedly setting your coding style, documentation format or communication tone preferences in each new tool? With OpenMemory MCP, you only need to set it once in one tool, and other tools will automatically pick up these preferences.
For example, you tell Cursor that you like to use a specific code commenting style, and when you switch to Windsurf, it already knows this and automatically applies the same style without you having to tell it again.
Project Knowledge Base
For complex projects, background knowledge and contextual information are often scattered across various documents and conversations. OpenMemory MCP can serve as your project knowledge base, centrally storing key information and intelligently extracting it when needed.
No matter which AI tool you use, it can access this unified knowledge base to provide you with consistent project support. It's like equipping all AI tools with the same project encyclopedia, allowing them to serve you based on the same knowledge base.
Performance and efficiency: data speaks
The Mem0-based memory system excels in terms of performance. Compared to OpenAI’s memory solution, it achieves:
• 26% higher response accuracy : more accurately understand and apply historical context • 91% lower latency : almost real-time memory retrieval • 90% token usage savings : significantly reduce API call costs
These data mean that OpenMemory MCP not only provides a better user experience, but also significantly reduces usage costs, especially in large-scale application scenarios.
Getting started: three easy steps
Want to experience the power of OpenMemory MCP? Just three steps:
1. Run the installation command:
curl -sL https://raw.githubusercontent.com/mem0ai/mem0/main/openmemory/run.sh | bash
2. Set up your OpenAI API key:
export OPENAI_API_KEY=your_api_key
3. Access interface:
• OpenMemory MCP server: http://localhost:8765 • OpenMemory UI: http://localhost:3000
That's it! Now your AI tool has the ability to share memory.
Future Outlook: Memory is just the beginning
OpenMemory MCP represents a new direction for AI memory management, but this is just the beginning. As more tools and platforms support the MCP protocol, we can expect:
• Richer memory types and relational models • Smarter memory retrieval and integration algorithms • More granular privacy controls and access management • Memory sync and backup across devices
By standardizing memory operations and keeping data local, OpenMemory MCP not only improves performance but also paves the way for deeper collaboration between AI tools.
Conclusion: Your memory, your control
As AI tools become increasingly popular, OpenMemory MCP solves a key problem: how to enable AI to truly remember things that are important to you while keeping those memories private and controllable.
It keeps your AI memories private, portable, and completely under your control—just as they should be. Whether you are a developer, researcher, or everyday user of AI tools, OpenMemory MCP can provide you with a smarter, more consistent, and more personalized AI interaction experience.
