Loc Knowledge Graph Memory Server
A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
Core Concepts
Entities
Entities are the primary nodes in the knowledge graph. Each entity has:
- A unique name (identifier)
- An entity type (e.g., "person", "organization", "event")
- A list of observations
Example:
{
"name": "John_Smith",
"entityType": "person",
"observations": ["Speaks fluent Spanish"]
}
Relations
Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other.
Example:
{
"from": "John_Smith",
"to": "Anthropic",
"relationType": "works_at"
}
Observations
Observations are discrete pieces of information about an entity. They are:
- Stored as strings
- Attached to specific entities
- Can be added or removed independently
- Should be atomic (one fact per observation)
Example:
{
"entityName": "John_Smith",
"observations": [
"Speaks fluent Spanish",
"Graduated in 2019",
"Prefers morning meetings"
]
}
API
Tools
-
create_entities
- Create multiple new entities in the knowledge graph
- Input:
entities(array of objects)- Each object contains:
name(string): Entity identifierentityType(string): Type classificationobservations(string[]): Associated observations
- Each object contains:
- Ignores entities with existing names
-
create_relations
- Create multiple new relations between entities
- Input:
relations(array of objects)- Each object contains:
from(string): Source entity nameto(string): Target entity namerelationType(string): Relationship type in active voice
- Each object contains:
- Skips duplicate relations
-
add_observations
- Add new observations to existing entities
- Input:
observations(array of objects)- Each object contains:
entityName(string): Target entitycontents(string[]): New observations to add
- Each object contains:
- Returns added observations per entity
- Fails if entity doesn't exist
-
delete_entities
- Remove entities and their relations
- Input:
entityNames(string[]) - Cascading deletion of associated relations
- Silent operation if entity doesn't exist
-
delete_observations
- Remove specific observations from entities
- Input:
deletions(array of objects)- Each object contains:
entityName(string): Target entityobservations(string[]): Observations to remove
- Each object contains:
- Silent operation if observation doesn't exist
-
delete_relations
- Remove specific relations from the graph
- Input:
relations(array of objects)- Each object contains:
from(string): Source entity nameto(string): Target entity namerelationType(string): Relationship type
- Each object contains:
- Silent operation if relation doesn't exist
-
read_graph
- Read the entire knowledge graph
- No input required
- Returns complete graph structure with all entities and relations
-
search_nodes
- Search for nodes based on query
- Input:
query(string) - Searches across:
- Entity names
- Entity types
- Observation content
- Returns matching entities and their relations
-
open_nodes
- Retrieve specific nodes by name
- Input:
names(string[]) - Returns:
- Requested entities
- Relations between requested entities
- Silently skips non-existent nodes
-
extract_locations
- Extract and add location entities from text with geographic relationships
- Input:
text(string): Text to extract locations fromsourceEntity(string, optional): Entity that mentions these locations
- Automatically extracts:
- Cities with states/countries: "New York, NY", "Paris, France"
- Street addresses: "123 Main Street", "456 Oak Avenue"
- Landmarks: "Central Park", "Golden Gate Bridge"
- Geographic features: "Mount Rushmore", "Lake Michigan"
- Administrative regions: US states, countries
- Creates:
- Location entities with
entityType: "location" - Geographic metadata stored as observations
- Hierarchical "located_in" relationships (city → state → country)
- "mentions_location" relations if sourceEntity provided
- Location entities with
- Returns created entities and relations
Usage with Claude Desktop
Setup
Add this to your claude_desktop_config.json:
Docker
{
"mcpServers": {
"memory": {
"command": "docker",
"args": [
"run",
"-i",
"-v",
"claude-memory:/app/dist",
"--rm",
"mcp/memory"
]
}
}
}
NPX
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"]
}
}
}
NPX with custom setting
The server can be configured using the following environment variables:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"],
"env": {
"MEMORY_FILE_PATH": "/path/to/custom/memory.json"
}
}
}
}
MEMORY_FILE_PATH: Path to the memory storage JSON file (default:memory.jsonin the server directory)
VS Code Installation Instructions
For quick installation, use one of the one-click installation buttons below:
For manual installation, you can configure the MCP server using one of these methods:
Method 1: User Configuration (Recommended)
Add the configuration to your user-level MCP configuration file. Open the Command Palette (Ctrl + Shift + P) and run MCP: Open User Configuration. This will open your user mcp.json file where you can add the server configuration.
Method 2: Workspace Configuration
Alternatively, you can add the configuration to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.
For more details about MCP configuration in VS Code, see the official VS Code MCP documentation.
NPX
{
"servers": {
"memory": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-memory"]
}
}
}
Docker
{
"servers": {
"memory": {
"command": "docker",
"args": [
"run",
"-i",
"-v",
"claude-memory:/app/dist",
"--rm",
"mcp/memory"
]
}
}
}
System Prompt
The prompt for utilizing memory depends on the use case. Changing the prompt will help the model determine the frequency and types of memories created.
Here is an example prompt for chat personalization. You could use this prompt in the "Custom Instructions" field of a Claude.ai Project.
Follow these steps for each interaction:
1. User Identification:
- You should assume that you are interacting with default_user
- If you have not identified default_user, proactively try to do so.
2. Memory Retrieval:
- Always begin your chat by saying only "Remembering..." and retrieve all relevant information from your knowledge graph
- Always refer to your knowledge graph as your "memory"
3. Memory
- While conversing with the user, be attentive to any new information that falls into these categories:
a) Basic Identity (age, gender, location, job title, education level, etc.)
b) Behaviors (interests, habits, etc.)
c) Preferences (communication style, preferred language, etc.)
d) Goals (goals, targets, aspirations, etc.)
e) Relationships (personal and professional relationships up to 3 degrees of separation)
4. Memory Update:
- If any new information was gathered during the interaction, update your memory as follows:
a) Create entities for recurring organizations, people, and significant events
b) Connect them to the current entities using relations
c) Store facts about them as observations
Building
Docker:
docker build -t mcp/memory -f src/memory/Dockerfile .
For Awareness: a prior mcp/memory volume contains an index.js file that could be overwritten by the new container. If you are using a docker volume for storage, delete the old docker volume's index.js file before starting the new container.
License
This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.