markov-protocols 0.1.0

A deterministic finite state machine for modeling workflows that guide AI agents.

markov-protocols

A Finite State Machine capable of modeling workflows to provide guidance to an AI agent with determinism.

A standalone Python module providing the state-machine mechanism, meant to be reused across other Python projects.

Status

Slices 1–3 complete — the engine is functionally whole:

• Definition — conditions, references, metadata, the State interface with three concrete state types, transitions, the registry, and Workflow.compile().
• Runtime — Blackboard, the Event history, Directive materialization, and Session.start/update with the write + fast-forward passes.
• Integrity — a correction (ValueChanged) reopens the states that depend on it, strict-rewinds to the earliest one, clears reopened actions' outputs so they re-run, and flags had_executed for host compensation. All derived from history — no parallel bookkeeping.

See docs/DESIGN.md for the full architecture and build plan.

Development

Managed with uv.

uv sync            # create the environment and install dev tools
uv run pytest      # run the test suite
uv run ruff check  # lint
uv run mypy        # type-check

Layout

src/markov_protocols/
    __init__.py        # curated public API
    result.py          # Result[T, E] / ErrorType — the error-handling pattern
    base.py            # StrictModel / StrictOpenModel
    slug.py            # deterministic title -> id
    conditions/        # the typed boolean vocabulary + pure evaluator
    references.py      # Ref + resolve + referenced_fields
    definition/        # metadata, State interface + concrete states, Transition,
                       #   registry, Workflow.compile()
    runtime/           # Blackboard, Event history, Session + update() pipeline
tests/                 # one property-focused module per unit

Conventions

• Operations return a Result[T, E] instead of raising exceptions.
• Target Python 3.13+.