markov-protocols 0.5.0

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

markov-protocols

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

LLMs are stochastic. When a workflow must happen reliably — collect these fields, validate them, call that action, handle a correction — prompting alone drifts and hallucinates. markov-protocols is the deterministic referee: you model the workflow as a state machine, the agent reads the current step for guidance and reports what it learned, and the machine decides — deterministically — what happens next. It never calls an LLM, runs a tool, or performs I/O; your host does that.

• Deterministic: same definition + same collected data → same decision, every time.
• Correction-aware: if the customer changes their mind, the machine rewinds and re-runs only what's affected (and tells you if a side effect must be compensated).
• Typed & serializable: author in Python or as JSON/YAML; validated, mypy-clean, py.typed.

Install

pip install markov-protocols
pip install "markov-protocols[yaml]"   # optional: YAML import/export

Requires Python 3.13+.

Quickstart

from markov_protocols import (
    Workflow, DataCollectionState, ActionExecuteState,
    Requirement, ValueType, Ref, Transition, Session,
)

# 1. Define the workflow (plain data).
workflow = Workflow.compile(
    name="intake",
    initial="Collect email",
    states=[
        DataCollectionState(
            title="Collect email",
            requires=[Requirement(field="email", description="the customer's email")],
        ),
        ActionExecuteState(
            title="Send confirmation",
            payload={"to": Ref(field="email")},   # a Ref, filled from collected data
            requires=[Requirement(field="confirmation_sent", type=ValueType.BOOLEAN)],  # its result
        ),
    ],
    transitions=[
        Transition(source_id="collect-email", target_id="send-confirmation"),
    ],
).value  # compile() returns a Result; .value is the validated Workflow

# 2. Run it. Feed in whatever your agent extracted from the conversation.
session = Session.start(workflow)

outcome = session.update({"email": "[e-mail]"}).value
print(outcome.current_state.id)      # 'send-confirmation'  (advanced automatically)
print(outcome.directive.payload)     # {'to': '[e-mail]'}  (resolved, ready to run)

# 3. Your host runs the action and reports the result back.
session.update({"confirmation_sent": True})
print(session.is_finished)           # True

update() is the single input verb: it records the data, then fast-forwards through every step the data already satisfies — so one call can advance several steps, or hold position when it can't.

Your agent loop

The machine is the deterministic referee; your host owns everything stochastic or side-effectful:

session = Session.start(workflow)
while not session.is_finished:
    values  = my_llm.extract(conversation, session.current_state)   # your LLM (stochastic)
    outcome = session.update(values).value                          # the machine (deterministic)

    directive = outcome.directive
    if directive and directive.ready:                               # a side effect to run
        result = my_host.run(directive.payload)                     # your webhook / function
        session.update({outcome.awaiting[0]: result})              # report it to the awaited field

Guidance & validation

Constrain values with options (an enum with per-value descriptions) or any Condition. When a step can't advance, blockers tells you why — and the convenience views split it apart:

from markov_protocols import DataCollectionState, Requirement, Option, Workflow, Session

triage = Workflow.compile(
    name="triage",
    initial="Detect intent",
    states=[DataCollectionState(
        title="Detect intent",
        requires=[Requirement(
            field="intent",
            description="what the customer wants",
            options=[Option(value="buy", description="wants to purchase"),
                     Option(value="support", description="needs help")],
        )],
    )],
).value

outcome = Session.start(triage).update({"intent": "rent"}).value
print(outcome.missing_fields)          # []           — the field IS present...
print(outcome.invalid_fields)          # ['intent']   — ...but 'rent' isn't allowed
print(outcome.to_llm_extended())       # factual, prompt-ready text (no business prompts, facts only):
# Step: Detect intent
# - intent: what the customer wants (one of: buy (wants to purchase), support (needs help))
# Blocked by:
# - 'intent' is invalid: 'rent' is not allowed; choose one of: buy (wants to purchase), support (needs help)

Branch on a value with a transition guard: Transition(source_id=..., target_id=..., guard=Eq(field="intent", value="buy")).

Save & load (JSON / YAML)

from markov_protocols import to_yaml, from_yaml, export_to_file, import_from_file

text   = to_yaml(workflow)            # -> YAML string
result = from_yaml(text)              # -> Result[Workflow]  (validated; a bad doc fails, never crashes)

export_to_file(workflow, "flow.json") # format from the extension (.json/.yaml/.yml)
loaded = import_from_file("flow.yaml")

A workflow document is easy to hand-author — type + title + the state's own fields:

name: intake
initial: Collect email
states:
  - type: DATA_COLLECTION
    title: Collect email
    requires:
      - field: email
transitions: []

Concepts

Term	What it is
Workflow	the authored graph of states + transitions (Workflow.compile() validates it)
State	a step — every state declares requires: list[Requirement]; types differ by who fills them
Requirement	a field a state expects: field, required (default true), type, options, condition
Transition	a directed link, optionally guarded by a Condition (branching)
Session	one conversation's live run; drive it with session.update(values)
Blackboard	the shared, deterministic record of collected values
Directive	a fully-resolved instruction for your host to execute (never run by the machine)
Blocker	why the current step hasn't advanced: MISSING / INVALID / AWAITING_*

Documentation

• Usage manual
• Design & architecture
• Runnable example

Development

uv sync            # environment + dev tools
uv run pytest      # tests
uv run ruff check  # lint
uv run mypy        # type-check

License

MIT