multi-agent memory is harder than it looks

been building a team collaboration tool and realized most memory systems fall apart with multiple people. EverMemOS actually handles it.

the problem: when you have 5 people in a chat, you need to track:

• who said what
• which topics each person cares about
• relationship dynamics
• who knows what

most simple memory systems just dump everything into one vector DB. that doesn't work.

how EverMemOS does it:

uses a scene parameter - assistant for 1-on-1, group_chat for multi-person. different extraction strategies for each.

for group chat, it tracks:

• sender and sender_name
• group_id and group_name
• message timestamps
• user roles (from metadata)

so when you query "what does Alice think about the project timeline?", it can track who said what and build per-person profiles, not just return generic results.

why this matters:

in a team setting, context is personal. Alice's concerns about deadlines are different from Bob's technical constraints. a memory system that treats all messages the same is useless.

EverMemOS builds profiles per person, tracks relationships, maintains context. that's enterprise-grade.

implementation:

API accepts messages with full metadata. batch processing for group chats. supports their standardized GroupChatFormat.

github.com/EverMind-AI/EverMemOS has the code if you want to see how they handle it.

most memory systems are built for chatbots. this one actually thinks about teams.