Custom Tags

Use tags according to your business needs when adding messages.
Note

You need to pass the tag list when calling addMessage (Click here for detailed API documentation)
Only then can you use tags for filtering when calling searchMemory (Click here for detailed API documentation)

This article focuses on functional description. For detailed API fields and limits, please click the text links above.

MemOS automatically generates tags for each memory, but these tags may not be completely consistent with the tags used in your business. You can pass a list of custom tags when adding messages, and MemOS will automatically apply relevant tags to the memory content based on the meaning of the tags you provide.

When to use custom tags?
You want MemOS to use the product team's existing tag system to label memory content.You need to apply these tags to generate structured content.

1. Tag Mechanism

  • Automatic Tag Generation: MemOS analyzes semantics when processing memories and automatically generates relevant tags for subsequent retrieval and filtering.
  • Custom Tags: When adding messages, you can pass a set of custom tags through the tags field as a candidate tag set.
  • Semantic Matching: MemOS will judge the semantic similarity between the memory content and the tag list provided by the developer, select matching tags from them, and write them into the tags field of the memory along with the system-generated tags.

2. Usage Example

Tip
  • Tag content should be concise while clearly distinguishing the meanings of different categories to facilitate identification and matching.
  • Use a unified list under the same project dimension and do not replace it easily to ensure consistency in retrieval and filtering.

3. Add Message

import os
import json
import requests

# Replace with your MemOS API Key
os.environ["MEMOS_API_KEY"] = "YOUR_API_KEY"
os.environ["MEMOS_BASE_URL"] = "https://memos.memtensor.cn/api/openmem/v1"

data = {
    "user_id": "memos_user_123",
    "conversation_id": "1210",
    "messages": [
        {"role": "user","content": "How is the weather today?"},
        {"role": "assistant","content": "Shanghai, December 10th, cloudy, temperature 8-12 degrees."}
    ],
    "tags":["Weather","Cloudy"],
    "async_mode":False
}

headers = {
  "Content-Type": "application/json",
  "Authorization": f"Token {os.environ['MEMOS_API_KEY']}"
}
url = f"{os.environ['MEMOS_BASE_URL']}/add/message"

res = requests.post(url=url, headers=headers, data=json.dumps(data))

print(f"result: {res.json()}")

Search Memory

import os
import json
import requests

# Replace with your MemOS API Key
os.environ["MEMOS_API_KEY"] = "YOUR_API_KEY"
os.environ["MEMOS_BASE_URL"] = "https://memos.memtensor.cn/api/openmem/v1"

data = {
    "user_id": "memos_user_123",
    "query": "Shanghai Weather"
}
headers = {
  "Content-Type": "application/json",
  "Authorization": f"Token {os.environ['MEMOS_API_KEY']}"
}
url = f"{os.environ['MEMOS_BASE_URL']}/search/memory"

res = requests.post(url=url, headers=headers, data=json.dumps(data))

print(f"result: {res.json()}")

Output Result

"memory_detail_list": [
  {
    "id": "9bc102cb-76d8-4a59-86d7-8fd1c4542407",
    "memory_key": "Weather Conditions",
    "memory_value": "On December 10, 2025, the weather in Shanghai was cloudy with temperatures between 8 and 12 degrees.",
    "memory_type": "WorkingMemory",
    "create_time": 1765376340736,
    "conversation_id": "1210",
    "status": "activated",
    "confidence": 0.99,
    "tags": [
      "Weather",
      "Cloudy",
      "Temperature"
    ],
    "update_time": 1765376340737,
    "relativity": 0.82587826
  }
]

Multimodal Messages

Integrate images and documents into interactions with MemOS when adding messages.

Knowledge Base

Create a knowledge base associated with the project, and combine user memory with knowledge base to supplement knowledge when retrieving memories.