More Features

Event Memory

Automatically extract structured events from conversations, including time, location, participants, and other key elements.

1. What is Event Memory?

Event Memory extracts structured events from conversations and keeps key elements such as time, location, and participants, so the AI can accurately recall and reference past experiences with better continuity.

Typical scenarios
  • Schedule tracking: remember which project was finished last week, or which exam is coming next month;
  • Customer service history: trace complaints, inquiries, returns, and other key events;
  • Game / role-play story memory: keep critical plot nodes the character has experienced.
Event Memory complements Profile — Profile maintains a stable picture of who someone is, while Event Memory records what they went through.

2. Key Concepts

  • Event（event）: A structured event extracted from conversation; the basic unit of Event Memory.
  • Title（title）: A short description of the event, such as "camping by West Lake with friends".
  • Content（content）: A detailed summary of the event, including key information.
    • Event time: When the event happened; prefers chat_time from the message when available.
    • Participants: People involved in the event, identified from conversation context.
  • Custom extraction config: Supports a custom Event Memory extraction prompt to control how events are extracted and what to focus on.

3. Workflow

sequenceDiagram
    autonumber
    participant User as End user
    participant App as AI product
    participant Mem as MemOS
    participant LLM as LLM used by the product

    User->>App: Send message / chat
    App->>Mem: searchMemory
    Mem-->>App: Recall event memory + other memories
    App->>LLM: Build context and generate reply
    LLM-->>App: Return reply
    App-->>User: Show reply
    App->>Mem: addMessage
    Mem->>Mem: Extract structured events from the conversation

Flow summary:

  1. Retrieve: During chat, recall relevant Event Memory and other memory categories, then inject them into the LLM context;
  2. Generate: The LLM generates a reply with the recalled events;
  3. Extract: After messages are added, MemOS automatically extracts structured events and stores them.

4. Examples

Add messages

Include "event" in allow_memory_view when adding messages to trigger Event Memory extraction.

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://memos.memtensor.cn/api/openmem/v1"

data = {
    "user_id": "memos_user_123",
    "conversation_id": "conv_0624",
    "allow_memory_view": ["event", "detail_factual", "preference"],
    "messages": [
        {"role": "user", "content": "Last Tuesday afternoon I did a proposal review with Li Si. The customer liked the second version and wants delivery moved up to the end of July."},
        {"role": "assistant", "content": "Got it. Want me to summarize the key conclusions from the review?"}
    ]
}

res = requests.post(
    f"{BASE_URL}/add/message",
    headers={"Authorization": f"Token {API_KEY}"},
    json=data
)
print(res.json())

Search Event Memory

When calling searchMemory, pass "event" in include_memory_view to recall Event Memory (not included by default). Related events are returned in event_detail_list.

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://memos.memtensor.cn/api/openmem/v1"

data = {
    "user_id": "memos_user_123",
    "query": "Any recent reviews or meetings?",
    "include_memory_view": ["event"]
}

res = requests.post(
    f"{BASE_URL}/search/memory",
    headers={"Authorization": f"Token {API_KEY}"},
    json=data
)
print(res.json())

Example response:

{
  "code": 0,
  "message": "ok",
  "data": {
    "event_detail_list": [
      {
        "id": "d7785b39-1374-4738-b449-3e86ab1f8b2f",
        "event_key": "方案评审与交付调整",
        "event_value": "用户与李四于上周二下午完成方案评审，客户对第二版方案表示满意，并希望将交付时间提前至七月底。",
        "event_type": "EventMemory",
        "create_time": 1783929751765,
        "conversation_id": "conv_event_verify_001",
        "status": "activated",
        "update_time": 1783929757755,
        "relativity": 0.6364,
        "event_time": ["上周二下午", "七月底"],
        "event_location": [],
        "event_roles": ["用户", "李四", "客户"]
      }
    ]
  }
}

Get event details

Use get/memory/{memory_id} to fetch a single Event Memory.

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://memos.memtensor.cn/api/openmem/v1"
memory_id = "d7785b39-1374-4738-b449-3e86ab1f8b2f"

res = requests.get(
    f"{BASE_URL}/get/memory/{memory_id}",
    headers={"Authorization": f"Token {API_KEY}"}
)
print(res.json())

Unlike Profile Memory, Event Memory can be retrieved with GET /get/memory/{id}.


Custom extraction config

Event Memory supports a custom extraction prompt in the dashboard when you need finer control over extraction granularity or focus:

  1. Sign in to the MemOS Dashboard, open your project, and go to Custom Extraction;
  2. Edit the Event Memory prompt — manually or with AI assistance;
  3. Use Quick Debug to validate: enter sample dialogue, click Run Extraction, and check whether the extracted events look right;
  4. Save when ready.


5. Related features

Profile

Maintain a structured profile for a user or AI Agent; complements Event Memory

Memory Categories

How Event Memory differs from other memory types

Profile

Self-Evolving