DeepMem
Mem0-compatible persistent memory for AI agents: write facts once, recall them semantically.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"deepmem": {
"url": "https://deepmem.dev/mcp"
}
}
}Remote-Endpunkte
https://deepmem.dev/mcpstreamable-httpWas es kann
Tool-Inventar
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.
Eingabe-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.
Eingabe-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
Nachweis