DeepMem
Mem0-compatible persistent memory for AI agents: write facts once, recall them semantically.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"deepmem": {
"url": "https://deepmem.dev/mcp"
}
}
}Remote endpoints
https://deepmem.dev/mcpstreamable-httpWhat it can do
Tool inventory
Tools (2)
🟡deepmem_write(messages, user_id, infer, agent_id, run_id, ...)
Write conversation messages to DeepMemory for fact extraction and persistent storage. Messages are processed by an LLM to extract structured memories, which are then embedded and stored in a vector database for later semantic search. Set infer=True to enable LLM fact extraction (produces richer memories but costs one LLM call). Set infer=False to store raw messages without extraction. Returns a list of memory IDs for successfully stored facts.
Input Schema
{
"type": "object",
"properties": {
"messages": {
"items": {
"additionalProperties": true,
"type": "object"
},
"title": "Messages",
"type": "array"
},
"user_id": {
"default": "default",
"title": "User Id",
"type": "string"
},
"infer": {
"default": true,
"title": "Infer",
"type": "boolean"
},
"agent_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Agent Id"
},
"run_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Run Id"
},
"api_key": {
"default": "",
"title": "Api Key",
"type": "string"
}
},
"required": [
"messages"
],
"title": "deepmem_writeArguments"
}🟢deepmem_search(query, user_id, top_k, threshold, api_key)
Search memories stored in DeepMemory using semantic search. Returns the most relevant memories for the given query, ranked by hybrid scoring (vector similarity + BM25 keyword match + entity boost + time decay). Use this to retrieve context from past conversations before responding to the user. Memories are scoped to the user_id provided during write.
Input Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"user_id": {
"default": "default",
"title": "User Id",
"type": "string"
},
"top_k": {
"default": 10,
"title": "Top K",
"type": "integer"
},
"threshold": {
"default": 0.3,
"title": "Threshold",
"type": "number"
},
"api_key": {
"default": "",
"title": "Api Key",
"type": "string"
}
},
"required": [
"query"
],
"title": "deepmem_searchArguments"
}Community
Evidence