Knowledge Graph Memory Server provides persistent memory for Claude through a local knowledge graph, allowing the AI to remember information across conversations. Developed by itseasy21 as a fork of the original Memory Server, it stores data as entities with observations and relations between them, enabling structured information retrieval and complex knowledge representation. The implementation supports customizable memory paths, cross-platform compatibility, and offers a comprehensive API for creating, updating, and querying graph elements, making it ideal for personalized chat experiences where context retention is essential.
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Create multiple new entities in the knowledge graph. Input: `entities` (array of objects) where each object contains `name` (string), `entityType` (string), and `observations` (string[]). Ignores entities with existing names.
Create multiple new relations between entities. Input: `relations` (array of objects) where each object contains `from` (string), `to` (string), and `relationType` (string). Skips duplicate relations.
Add new observations to existing entities. Input: `observations` (array of objects) where each object contains `entityName` (string) and `contents` (string[]). Returns added observations per entity and fails if the entity doesn't exist.
Remove entities and their relations. Input: `entityNames` (string[]). Performs cascading deletion of associated relations and is silent if the entity doesn't exist.
Remove specific observations from entities. Input: `deletions` (array of objects) where each object contains `entityName` (string) and `observations` (string[]). Silent operation if the observation doesn't exist.
Remove specific relations from the graph. Input: `relations` (array of objects) where each object contains `from` (string), `to` (string), and `relationType` (string). Silent operation if the relation doesn't exist.
Read the entire knowledge graph. No input required. Returns complete graph structure with all entities and relations.
Search for nodes based on query. Input: `query` (string). Searches across entity names, entity types, and observation content. Returns matching entities and their relations.
Retrieve specific nodes by name. Input: `names` (string[]). Returns requested entities and relations between requested entities, silently skipping non-existent nodes.
