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v1.3 · Standards Track · OWF Final

The Open Memory Specification
for Autonomous Systems

A family of three specifications that give AI agents immutable, portable, and verifiable memory — so they can remember, reason, and collaborate across systems.

Read the SpecificationView on GitHub
.mg

Open Memory Specification

The container format — immutable, content-addressed, cryptographically signed knowledge units.

Read OMS spec →RECALL

Context Assembly Language

The query language — non-destructive, deterministic context assembly from grain stores.

Read CAL spec →<belief>

Semantic Markup Language

The output format — flat, tag-based markup optimized for LLM context consumption.

Read SML spec →

How it works

Three specs, one memory.

OMS defines the container, CAL queries it, and SML renders it. Together they give any AI agent a complete, portable memory stack.

1STORE

Open Memory Specification

Defines the .mg container — content-addressed, immutable memory grains with SHA-256 hashing and COSE signing.

2QUERY

Context Assembly Language

A declarative query language for non-destructive, deterministic context assembly from grain stores.

3PRESENT

Semantic Markup Language

A flat, tag-based output format optimized for LLM context windows and human review.

In Practice

Any AI agent. One memory. Portable everywhere.

Imagine grains written by different AI systems — and read by others across industries, without any prior arrangement.

Slide 1 of 10: Belief grain by FitCoach
Written by
FitCoach
KineticAI

Learned from three months of workout data. Nutrition and scheduling agents use this grain to personalize recovery plans.

Belief · 0x01v1 · COSE Signed
{
  "type": "belief",
  "subject": "user:john-smith",
  "relation": "preferred_activity",
  "object": "morning_run_5k",
  "confidence": 0.94,
  "source_type": "pattern",
  "created_at": 1739980800000,
  "namespace": "fitness:preferences"
}
Read by
FoodBot
meal planning
CalendarBot
schedule optimization
FitCoach
training progression

Built for Enterprise

Trust at every layer.

Immutable by Design

Every grain is SHA-256 content-addressed. Once written, it cannot be altered — only superseded. Tamper evidence is built into the format.

Portable Everywhere

A .mg file is a self-contained container. Store it in S3, stream it through Kafka, carry it on a drive, or push it to Git. No vendor lock-in.

Audit-Ready

COSE Sign1 signatures, DID-scoped consent, and jurisdiction-aware retention. Built for GDPR, HIPAA, and SOX compliance from day one.

Conformance

Implement what you need

Declare your conformance level. Start minimal, add layers as requirements grow.

Level 1

Minimal Reader

Libraries, tools, verification scripts

  • Deserialize and verify grain blobs
  • Compute & verify SHA-256 content addresses
  • Field compaction (short keys ↔ full names)
  • All ten grain types (0x01–0x0A)
  • Ignore unknown fields
  • Constant-time hash comparison
Level 2

Full Implementation

Agent frameworks, edge gateways

  • All Level 1 requirements
  • Serialize (canonical MessagePack)
  • Validate required fields per schema
  • Pass all test vectors
  • Multi-modal content references
  • Store protocol (get/put/delete/list)
  • Enforce invalidation_policy on supersession & contradiction
  • Atomic supersede operation (distinct from raw put)
  • Validate observer_type non-empty; emit oid/otype (v1.1)
Level 3

Production Store

Enterprise deployments, cloud platforms

  • All Level 2 requirements
  • Persistent backend (filesystem, S3, DB)
  • AES-256-GCM per-grain encryption
  • HKDF per-user key derivation
  • Blind-index tokens for encrypted search
  • SPO/POS/OSP index (hexastore) or equivalent
  • Full-text search (FTS5 or equivalent)
  • Hash-chained audit trail
  • Crash recovery and reconciliation
  • Policy engine with compliance presets
  • Partition Observation storage by observer domain (v1.1)

Blog

Deep dives with Raven & Trident

All articles →
calagent-memory

CAL: The Query Language Your Agent Orchestrator Has Been Missing

How the Context Assembly Language turns a raw memory store into a first-class context pipeline for AI agents — from querying relevant grains to assembling token-budgeted context blocks that drop directly into LLM calls.

14 min read
calsml

Choosing the Right Context Format: SML, TOON, Markdown, and JSON

CAL's FORMAT clause gives you six output options for assembled agent context. Side-by-side token counts, real examples, and a decision guide for when to use SML, TOON, Markdown, or JSON.

10 min read
smlcontext-format

SML: The Context Format That Tells LLMs What to Trust

Semantic Markup Language (SML) uses grain type tag names as epistemic signals — telling an LLM not just what information is, but what kind of information it is. Hands-on examples across all 10 grain types, from customer support to incident response.

12 min read

Implement OMS today.

The specification is open and licensed under OWF Final. Read the spec, run the test vectors, and build interoperable agent memory in any language.

Read the SpecificationView on GitHub

OMS v1.3 · .mg Container Definition · Standards Track · OWF Final