engrammic-primitives 0.1.3

EAG schema primitives for epistemic context management

Engrammic Primitives

The schema library for Engrammic, a structured memory system for AI agents.

Most AI agents treat context like a scratchpad. Engrammic treats it like cognition: observations become claims, claims become facts, facts become beliefs. This library defines the types and rules that make that work.

Library for integrators. If you just want to use Engrammic memory in your agent, see engrammic-mcp (hosted) or engrammic-engine (local).

Install

pip install engrammic-primitives

What's Inside

Schema types for four cognitive layers:

Layer	What it holds	Example
Memory	Raw observations	"User mentioned they prefer dark mode"
Knowledge	Claims with evidence	"User prefers dark mode" (based on 3 mentions)
Wisdom	Synthesized beliefs	"Optimize for low-light viewing in this user's sessions"
Intelligence	Reasoning chains	Step-by-step derivation of a conclusion

Scoring functions for promotion decisions:

from primitives.eag import combined_confidence, should_promote_r1

# When should a claim become a fact?
decision = should_promote_r1(confidence=0.8, corroboration_count=3)

Transition predicates for enforcing layer rules (e.g., Knowledge requires evidence).

Protocols for storage backends (implement these to build your own store).

When to Use This

You're building something that stores and retrieves structured agent context, and you want compatibility with the Engrammic ecosystem.

If you just want to use Engrammic:

• engrammic-mcp for hosted or self-hosted service
• engrammic-engine for local-only (SQLite)

Modules

Module	Purpose
primitives.schema	Node and edge type definitions
primitives.eag	Confidence, promotion, decay logic
primitives.eag.transitions	Layer transition predicates and constraints
primitives.protocols	Storage and lifecycle interfaces
primitives.scoring	Freshness and relevance formulas

Learn More

• EAG Paradigm for the full cognitive architecture spec

License

Apache 2.0