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Deterministic Boundary Layers core substrate on KL Kernel Logic

Project description

DBL Core

CI / Tests PyPI version Python >=3.11 Typing: Typed

DBL Core is a deterministic event substrate for the Deterministic Boundary Layer (DBL). It records intent, decisions, and executions as a single ordered stream.

Why DBL Core exists

DBL Core exists to provide a deterministic, audit-stable event substrate for systems that need to separate:

  • intent from decision
  • decision from execution
  • normative history from observational artifacts

It is designed for systems where replayability, auditability, and governance correctness matter more than convenience or performance.

Mental Model

DBL Core maintains a single append-only event stream V:

INTENT → DECISION → (optional) EXECUTION → (optional) PROOF

Only DECISION events are normative. All other data is treated as observational and excluded from digests.

Scope

  • Single-stream event model with deterministic t_index.
  • Canonical serialization and digest for events and behavior logs.
  • Gate decision events (ALLOW or DENY) as explicit Deltas.
  • Embeds kernel traces as observational artifacts with canonical integrity digests.

Non-Goals

  • No policy engine or templates.
  • No execution of user tasks.
  • No orchestration, UX flows, or intelligence.
  • No time, randomness, or I/O side effects.

What DBL Core is not

DBL Core is intentionally minimal. It is not:

  • a workflow engine
  • a policy engine
  • a domain framework
  • an execution orchestrator
  • an LLM wrapper

If you need domain semantics, validation, or verdict logic, implement a domainrunner on top of DBL Core.

Contract-first design

DBL Core behavior is defined by a stable, normative contract.

  • Code must conform to the contract.
  • Tests enforce contract invariants.
  • Domain-specific semantics are explicitly out of scope.

See:

Contract

Install

pip install dbl-core

Requires kl-kernel-logic>=0.5.0 and Python 3.11+.

Public API

  • DblEvent, DblEventKind
  • BehaviorV
  • GateDecision
  • normalize_trace is a canonicalization adapter only

Ordering

Ordering is derived from t_index (position in V). Timestamps and runtime fields are observational only.

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