Atman · Persistent Memory Layer for AI Agents

In Indian philosophy, Atman is the unchanging self — the core that remains constant through all change. We give AI agents the same thing.


The Problem

Your agent can reason, write code, and explain quantum mechanics. But the moment a session ends — it forgets everything about itself.

Not just facts. The sense of continuity. Who it spoke to, what it learned, how it changed its mind. Every session starts from a stack of notes: "you're this kind of agent, these are your values" — taken on faith, not lived experience.

Atman fixes this.


What It Is

A local memory and context system that runs alongside your LLM — augmenting cognitive capabilities without steering opinions or output.

Seven components work together:

Component What it does
Factual Memory Verifiable facts with relations, no hallucination layer
Experience Store First-person lived session experiences with salience decay
Identity Store Stable self-model: principles, values, behavioral anchors
Reflection Engine Between-session processing — finds patterns, refines principles
Session Manager Assembles coherent context on session start
Reality Anchor Detects identity drift during context pressure
Affective Regulation Emotional tone calibration without pretense

Key Properties


Architecture


Session start 
    Session Manager pulls together: 
            Identity Store — who the agent is Experience Store — what it lived through 
            Factual Memory — what it knows 
            Reflection output — what it concluded last time 
During session 
    Reality Anchor watches for identity drift in real time 
Session end 
    Reflection Engine processes the session 
        → updates Identity Store 
        → updates Experience Store

Status


● Research              ✅ Complete
● Design                ✅ Complete
● Prototyping           ← We are here
  ├─ Factual Memory     ✅ Stable (v0.1.0)
  ├─ Experience Store   ✅ Stable (WP02)
  ├─ Session Manager    🔧 High readiness — debugging (current focus)
  ├─ Reflection Engine  🔧 Medium readiness — in development
  ├─ Skill Manager      🔧 Medium readiness — in development
  ├─ Identity Store     🔧 Low readiness — in development
  └─ CI & test coverage ✅ GitHub Actions on `main`/PRs (`make check`, pytest-cov ≥90%)
○ First production slice
○ Integration
○ Evolution


Stack


Links


Atman is a hypothesis in the form of a system: that behavioral consistency, accumulated experience, and reflective capacity are sufficient conditions for something worth calling identity.