Group Chat

Overview

The Group Chat feature helps you use Mem0 with conversations involving multiple participants, such as team meetings or multi-agent conversations. You control which speaker a memory belongs to by scoping each add() call with user_id, agent_id, and run_id; Mem0 does not infer that scope automatically from the conversation.

When you scope conversations correctly, Mem0:

Extracts memories from each participant's messages
Keeps each participant's memories in a separate profile, addressed by the user_id or agent_id you assigned them
Lets you retrieve any participant's memories independently using filters

How Group Chat Works

Mem0 does not automatically split a multi-participant conversation into separate memories per speaker. A name field on a message is stored as context for extraction, but it does not change which user_id or agent_id the resulting memories are scoped to: that scope is always whatever user_id, agent_id, or run_id you pass to add().

To keep separate memory profiles per participant, scope each participant's messages explicitly: call add() once per participant with their own user_id, or use run_id to group the conversation and filter by participant in your own message metadata.

Memory Attribution Rules

Memories are always scoped to the user_id, agent_id, and run_id you pass to add(), not to the name field on individual messages.
If you need per-participant memories, call add() separately for each participant's messages with that participant's user_id.

Using Group Chat

Basic Group Chat

Scope each participant's messages with their own user_id and a shared run_id for the session. Call add() once per participant:

python

from mem0 import MemoryClient

client = MemoryClient(api_key="your-api-key")

# Each participant gets their own user_id; run_id ties them to one session
client.add(
    [{"role": "user", "content": "Hey team, I think we should use React for the frontend"}],
    user_id="alice", run_id="group_chat_1",
)
client.add(
    [{"role": "user", "content": "I'd prefer Vue.js for our use case"}],
    user_id="bob", run_id="group_chat_1",
)
response = client.add(
    [{"role": "user", "content": "Consider Angular, it has great enterprise support"}],
    user_id="charlie", run_id="group_chat_1",
)
print(response)

json

{
  "event_id": "4d82478a-8d50-47e6-9324-1f65efff5829",
  "status": "PENDING"
}

</CodeGroup>

add() is asynchronous: it queues extraction and returns immediately. Poll get_all (see below) once processing completes to see the extracted memories. Each participant's memory is scoped to the user_id you passed, so filtering by run_id returns all three, and filtering by a single user_id returns just that participant.

The `name` field does not change scope

The name field is stored as extraction context only. Attribution follows the user_id/agent_id/run_id you pass to add(), never the name. Passing two different names in one add() call does not split the memories across two profiles:

python

# BOTH messages are scoped to user_id="team_session", NOT to "alice"/"bob"
client.add(
    [
        {"role": "user", "name": "Alice", "content": "I strongly prefer React"},
        {"role": "user", "name": "Bob", "content": "I strongly prefer Vue"},
    ],
    user_id="team_session", run_id="group_chat_2",
)

# Every extracted memory lands under user_id="team_session"
client.get_all(filters={"AND": [{"user_id": "team_session"}]})

# Nothing is stored under user_id="alice" or user_id="bob"
client.get_all(filters={"AND": [{"user_id": "alice"}]})  # -> no results from this call

</CodeGroup>

To keep Alice's and Bob's memories in separate profiles, call add() once per participant with their own user_id, as shown in Basic Group Chat above.

Retrieving Group Chat Memories

Get All Memories for a Session

Retrieve all memories from a specific group chat session:

python

# Get all memories for a specific run_id
# Use wildcard "*" for user_id to match all participants
filters = {
    "AND": [
        {"user_id": "*"},
        {"run_id": "group_chat_1"}
    ]
}

all_memories = client.get_all(filters=filters, page=1)
print(all_memories)

json

{
    "count": 3,
    "next": null,
    "previous": null,
    "results": [
        {
            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
            "memory": "suggests considering Angular because it has great enterprise support",
            "user_id": "charlie",
            "run_id": "group_chat_1",
            "created_at": "2025-06-21T05:51:11.007223-07:00",
            "updated_at": "2025-06-21T05:51:11.626562-07:00"
        },
        {
            "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
            "memory": "prefers Vue.js for our use case",
            "user_id": "bob",
            "run_id": "group_chat_1",
            "created_at": "2025-06-21T05:51:08.675301-07:00",
            "updated_at": "2025-06-21T05:51:09.319269-07:00"
        },
        {
            "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
            "memory": "prefers using React for the frontend",
            "user_id": "alice",
            "run_id": "group_chat_1",
            "created_at": "2025-06-21T05:51:05.943223-07:00",
            "updated_at": "2025-06-21T05:51:06.982539-07:00"
        }
    ]
}

</CodeGroup>

Get Memories for a Specific Participant

Retrieve memories from a specific participant in a group chat:

python

# Get memories for a specific participant
filters = {
    "AND": [
        {"user_id": "charlie"},
        {"run_id": "group_chat_1"}
    ]
}

charlie_memories = client.get_all(filters=filters, page=1)
print(charlie_memories)

json

{
    "count": 1,
    "next": null,
    "previous": null,
    "results": [
        {
            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
            "memory": "suggests considering Angular because it has great enterprise support",
            "user_id": "charlie",
            "run_id": "group_chat_1",
            "created_at": "2025-06-21T05:51:11.007223-07:00",
            "updated_at": "2025-06-21T05:51:11.626562-07:00"
        }
    ]
}

</CodeGroup>

Search Within Group Chat Context

Search for specific information within a group chat session:

python

# Search within group chat context
filters = {
    "AND": [
        {"user_id": "charlie"},
        {"run_id": "group_chat_1"}
    ]
}

search_response = client.search(
    query="What are the tasks?",
    filters=filters
)
print(search_response)

json

{
    "results": [
        {
            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
            "memory": "suggests considering Angular because it has great enterprise support",
            "user_id": "charlie",
            "run_id": "group_chat_1",
            "created_at": "2025-06-21T05:51:11.007223-07:00",
            "updated_at": "2025-06-21T05:51:11.626562-07:00"
        }
    ]
}

</CodeGroup>

Message Format Requirements

Required Fields

Each message must include:

role: The participant's role ("user", "assistant", "agent")
content: The message content
name (optional): The participant's name, stored as context for extraction. It does not change which user_id or agent_id a memory is scoped to.

Example Message Structure

json

{
  "role": "user",
  "name": "Alice",
  "content": "I think we should use React for the frontend"
}

Roles

user: Human participants
assistant: AI assistants

Best Practices

Consistent Scoping: Use a consistent user_id (or agent_id) per participant across sessions so their memories stay in one profile.
Clear Role Assignment: Ensure each participant has the correct role (user, assistant, or agent) for proper memory categorization.
Session Management: Use meaningful run_id values to organize group chat sessions and enable easy retrieval.
Memory Filtering: Use filters to retrieve memories from specific participants or sessions when needed.
Async Processing: Memory additions are processed asynchronously by default, which is ideal for large group conversations.
Search Context: Leverage the search functionality to find specific information within group chat contexts.

Use Cases

Team Meetings: Track individual team member preferences and contributions
Customer Support: Maintain separate memory profiles for different customers
Multi-Agent Systems: Manage conversations with multiple AI assistants
Collaborative Projects: Track individual preferences and expertise areas
Group Discussions: Maintain context for each participant's viewpoints

If you have any questions, please feel free to reach out to us using one of the following methods:

Overview

How Group Chat Works

Memory Attribution Rules

Using Group Chat

Basic Group Chat

The name field does not change scope

Retrieving Group Chat Memories

Get All Memories for a Session

Get Memories for a Specific Participant

Search Within Group Chat Context

Message Format Requirements

Required Fields

Example Message Structure

Roles

Best Practices

Use Cases

The `name` field does not change scope