Mustafa

Learning machines and memory

Agents that get better every run: learning stores for users, sessions, entities and shared knowledge - for single agents and teams.

Learning
Memory
Agents
Teams
Learning docs
  1. Run
    • Agent
    • Team
  2. Learning stores
    • User profile
    • User memory
    • Session context
    • Entity memory
    • Learned knowledge
    • Decision log
  3. Next run
    • Recalled into context
    • Gets better each time
Learning modes: Always (extracts in the background), Agentic (the agent decides what to save), Propose (asks first).
Learning machine = agent + learning stores.

A learning machine is a set of stores an agent writes to while it works - user profile, user memory, session context, entity memory, learned knowledge and a decision log - and reads from next time. Pal is built on it.

  • Built the learning-machine primitives, including full support for teams: learning context, async learning tools and automatic history.
  • Kept system prompts and tool scaffolding out of what gets learned, so memories are about the user, not the plumbing.
  • Added per-run limits on memory updates across every learning store.
  • Kept user identity intact through memory updates, deletes and topic queries.
Back to Work