@kernel.chat/memory-tiers

@kernel.chat/memory-tiers

Three-tier generative memory for AI agents: observations → reflections → identity. Based on Stanford's Generative Agents paper. Zero LLM calls.

Part of the kernel.chat open-source stack. Used by @kernel.chat/kbot to give specialist agents a persistent sense of what they've seen, what it means, and who they're becoming over time.

Why three tiers

Single-tier memory (a flat log of everything) doesn't scale: every query re-scans the whole history. Two-tier memory (raw + summary) loses the why behind the what.

This package implements three tiers, each derived from the one below:

• Observations — raw events the agent encountered (user asked X, tool returned Y, error Z occurred). Append-only.
• Reflections — synthesized patterns across observations ("the user often asks about Polymarket on Fridays"). Generated periodically by deterministic summarization rules.
• Identity — durable traits that emerge from reflections over time ("treats every regulated-industry question as audit-grade by default"). The slowest-changing layer; the agent's working self-model.

The synthesis between tiers is rule-based and deterministic — no LLM call. This makes it cheap to run continuously and replayable for audit.

Install

npm install @kernel.chat/memory-tiers

Usage

import { MemorySystem } from '@kernel.chat/memory-tiers'

const memory = new MemorySystem({
  // Optional config — thresholds for synthesis cadence, retention, etc.
})

// Tier 1 — feed observations as they happen.
memory.observe('User asked about Polymarket Trump 2028 odds', 'query', {
  threadId: 'abc',
})
memory.observe('Tool call: polymarket_query succeeded in 312ms', 'tool')

// Tier 2 — periodically synthesize reflections.
const reflections = memory.synthesize()

// Tier 3 — let reflections accumulate into identity.
const identity = memory.evolve()

// Read each tier back.
const observations = memory.getObservations('query')
const allReflections = memory.getReflections()
const traits = memory.getIdentity()

// Persist across runs.
memory.save('./memory.json')
memory.load('./memory.json')

// Inspect.
console.log(memory.summary())
console.log(memory.getStats())

Public API

Export	Shape
MemorySystem	Main class — observe, getObservations, synthesize, getReflections, evolve, getIdentity, toJSON/fromJSON, save/load, getStats, summary
Observation	Tier 1 entry
ObservationCategory	Union of categories (query, tool, error, etc.)
Reflection	Tier 2 entry
IdentityTrait	Tier 3 entry
MemoryState	Serialized state shape
MemoryConfig	Constructor config

Status

v1.0.x — production use inside @kernel.chat/kbot; light external test coverage. The three-tier model has been running in kbot since early 2026. The public API is stable. External test suite expanded in v1.1.

File issues at github.com/isaacsight/kernel if you hit edge cases outside the kernel.chat usage path.

Related packages

Package	Discipline
@kernel.chat/kbot	The agent itself
@kernel.chat/prompt-evolver	Prompt self-optimization from traces
@kernel.chat/skill-router	Bayesian routing across specialists
@kernel.chat/tool-forge	Runtime tool creation

See docs/agentic-engineering.md for the field map this package sits inside.

License

MIT.