Decouple Channels from Partners for Long-term Learning Scenarios

[tianpan]

First of all, thank you for building such an amazing project. I've been exploring DeepTutor as the foundation for a long-term AI learning companion for my child, and I really appreciate the direction of Partners, Memory, and the unified runtime.

While experimenting with Partners, I noticed a potential architectural limitation for long-term educational use.

Currently, communication channels (Feishu, WeChat, Telegram, etc.) are attached directly to a Partner.

This means the relationship is essentially:

Channel
→ Partner
→ Conversation
→ Memory

For a tutoring scenario, however, I believe the primary entity should be the learner, not the teacher.

For example:

• A student may spend several years talking with a "Feynman" Partner.
• Later, I may want to upgrade the teaching style, replace the Partner with a better version, or switch to another tutor.
• Since the conversation history, partner memory, and channel are all tied to that Partner, replacing or deleting the Partner becomes difficult without losing continuity.

In real education, teachers change, but a student's learning history remains.

From that perspective, I wonder whether it would make sense to introduce a learner-centric architecture, something like:

Channel
→ Learner
→ Learning Workspace
→ Conversations
→ Memory
→ Knowledge
→ Current Partner

Partners would then become replaceable teaching personas, while the learner's context remains persistent.

This could also make it possible to:

• upgrade a Partner without migrating conversations,
• allow multiple Partners to teach the same learner,
• preserve a student's learning history independently of any particular teacher,
• support long-term personalized tutoring more naturally.

I'm curious whether you've considered this direction, or whether there are architectural reasons for keeping Channels bound directly to Partners.

I'd love to hear your thoughts.