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Introduction to the Group Chat

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Overview

The Group Chat feature enables Mem0 to process conversations involving multiple participants and automatically attribute memories to individual speakers. This allows for precise tracking of each participant’s preferences, characteristics, and contributions in collaborative discussions, team meetings, or multi-agent conversations. When you provide messages with participant names, Mem0 automatically:
  • Extracts memories from each participant’s messages separately
  • Attributes each memory to the correct speaker using their name as the user_id or agent_id
  • Maintains individual memory profiles for each participant

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How Group Chat Works

Mem0 automatically detects group chat scenarios when messages contain a name field: 
{
  "role": "user",
  "name": "Alice",
  "content": "Hey team, I think we should use React for the frontend"
}
When names are present, Mem0:
  • Formats messages as "Alice (user): content" for processing
  • Extracts memories with proper attribution to each speaker
  • Stores memories with the speaker’s name as the user_id (for users) or agent_id (for assistants/agents)

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Memory Attribution Rules

  • User Messages: The name field becomes the user_id in stored memories
  • Assistant/Agent Messages: The name field becomes the agent_id in stored memories
  • Messages without names: Fall back to standard processing using role as identifier

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Using Group Chat

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Basic Group Chat

Add memories from a multi-participant conversation: 
from mem0 import MemoryClient

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

# Group chat with multiple users
messages = [
    {"role": "user", "name": "Alice", "content": "Hey team, I think we should use React for the frontend"},
    {"role": "user", "name": "Bob", "content": "I disagree, Vue.js would be better for our use case"},
    {"role": "user", "name": "Charlie", "content": "What about considering Angular? It has great enterprise support"},
    {"role": "assistant", "content": "All three frameworks have their merits. Let me summarize the pros and cons of each."}
]

response = client.add(
    messages,
    run_id="group_chat_1",
    output_format="v1.1",
    infer=True
)
print(response)

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Retrieving Group Chat Memories

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Get All Memories for a Session

Retrieve all memories from a specific group chat session: 
# Get all memories for a specific run_id
filters = {
    "AND": [
        {"user_id": "*"},
        {"run_id": "group_chat_1"}
    ]
}

all_memories = client.get_all(version="v2", filters=filters, page=1)
print(all_memories)

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Get Memories for a Specific Participant

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

charlie_memories = client.get_all(version="v2", filters=filters, page=1)
print(charlie_memories)

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Search Within Group Chat Context

Search for specific information within a group chat session: 
# 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,
    version="v2"
)
print(search_response)

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Async Mode Support

Group chat also supports async processing for improved performance: 
# Group chat with async mode
response = client.add(
    messages,
    run_id="groupchat_async",
    output_format="v1.1",
    infer=True,
    async_mode=True
)
print(response)

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Message Format Requirements

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Required Fields

Each message in a group chat must include:
  • role: The participant’s role ("user", "assistant", "agent")
  • content: The message content
  • name: The participant’s name (required for group chat detection)

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Example Message Structure

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

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Supported Roles

  • user: Human participants (memories stored with user_id)
  • assistant: AI assistants (memories stored with agent_id)

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Best Practices

  1. Consistent Naming: Use consistent names for participants across sessions to maintain proper memory attribution.
  2. Clear Role Assignment: Ensure each participant has the correct role (user, assistant, or agent) for proper memory categorization.
  3. Session Management: Use meaningful run_id values to organize group chat sessions and enable easy retrieval.
  4. Memory Filtering: Use filters to retrieve memories from specific participants or sessions when needed.
  5. Async Processing: Use async_mode=True for large group conversations to improve performance.
  6. Search Context: Leverage the search functionality to find specific information within group chat contexts.

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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
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