Provides a persistent memory solution using a local knowledge graph, allowing applications to remember information across interactions.
Provides a persistent memory solution using a local knowledge graph, allowing applications to remember information across interactions. The optimized memory mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 14 defined tools rather than through you.
This is to test and demonstrate Claude AI's coding abilities, as well as good AI workflows and prompt design. This is a fork of a Python Memory MCP Server (I believe the official one is in Java) which uses SQLite for a backend.
Everything the assistant can do here goes through one of these:
create_entities — Create multiple new entities in the knowledge graphInput — entities (array of objects)create_relations — Create multiple new relations between entitiesadd_observations — Add new observations to existing entitiesdelete_entities — Remove entities and their relationsdelete_observations — Remove specific observations from entitiesdelete_relations — Remove specific relations from the graphread_graph — Read the entire knowledge graphsearch_nodes — Search for nodes based on queryopen_nodes — Retrieve specific nodes by nameReturns — - Requested entitiesEntities — Entities are the primary nodes in the knowledge graph. Each entity has: - A unique name (identifier) - An entity type (e.g., "person"Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Optimized Memory's toolset — create_entities, Input, create_relations and 11 more — is a fair guide to whether it matches your workflow. It is maintained by AgentWong; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Optimized Memory's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| create_entities | Create multiple new entities in the knowledge graph |
| Input | entities (array of objects) |
| create_relations | Create multiple new relations between entities |
| add_observations | Add new observations to existing entities |
| delete_entities | Remove entities and their relations |
| delete_observations | Remove specific observations from entities |
| delete_relations | Remove specific relations from the graph |
| read_graph | Read the entire knowledge graph |
| search_nodes | Search for nodes based on query |
| open_nodes | Retrieve specific nodes by name |
| Returns | - Requested entities |
| 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 |
| Relations | Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other. |
| Observations | Observations are discrete pieces of information about an entity. They are: |
{
"mcpServers": {
"memory": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/memory"]
}
}
}Configuration as documented by the project. Restart the client after saving.
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.