DeepMem
Mem0-compatible persistent memory for AI agents: write facts once, recall them semantically.
¿Debería usar esto?
Calidad y seguridad
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"deepmem": {
"url": "https://deepmem.dev/mcp"
}
}
}Puntos de conexión remotos
https://deepmem.dev/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (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.
Esquema de entrada
{
"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.
Esquema de entrada
{
"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"
}Comunidad
Evidencia