flowr 0.4.0

non-deterministic state machine specification to knead workflows

Define workflow state machines in YAML. Validate, query, and track them from the terminal.

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⚠️ Beta — do not install. This project is under active development. The API, package structure, and configuration may change without notice until the first stable release.

You write a flow definition in YAML. flowr checks that it is structurally valid, tells you what states exist and which transitions are available, and keeps track of where you are — across invocations, across subflows. One specification format. One CLI. No runtime engine, no side effects, no opinions about what your workflow should do — only what it is and whether it holds together.

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Who is this for?

Agent Operators — Persist workflow state across CLI invocations

You run flowr check, then flowr transition, then flowr check again — each time passing the flow name and current state by hand. Or you let sessions track it: flowr session init deploy-flow, flowr --session transition approve, flowr --session check. The session file remembers where you are. Push into a subflow; pop back out. No state to reconstruct, no context to pass.

Developers — Validate and query workflow definitions from code or terminal

You write a flow YAML. You need to know: is it valid? Which states can I reach from here? Does this transition have guard conditions? flowr validate, flowr states, flowr next, flowr check answer these questions — from the terminal for humans, from the Python library for tools.

Tool Authors — Build on a specification, not a runtime

flowr defines a YAML format for non-deterministic state machines with per-state attributes, guard conditions, and subflows. The validator enforces structural constraints. The library parses flows into dataclasses. No execution engine, no side-effect hooks — a clean foundation for editors, visualizers, or orchestration layers.

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What it does

flowr validate deploy.yaml          →  valid: True
flowr states deploy.yaml            →  prepare, execute, review
flowr next deploy.yaml review       →  approve (guarded), reject
flowr transition deploy.yaml review approve --evidence score=85
                                    →  from: review, to: deployed
flowr session init deploy-flow       →  session created at state: prepare
flowr --session transition approve  →  from: prepare, to: review
flowr mermaid deploy.yaml           →  stateDiagram-v2 ...

Validation. Structural constraints — missing fields, ambiguous targets, cross-flow cycles, subflow exit contracts — checked against the specification.

Query. States, transitions, conditions, attributes — ask any question the flow can answer.

Sessions. Init, show, set-state, transition, list. Subflow push/pop for nested workflows. One --session flag turns any command session-aware.

Config. flowr config shows where every value comes from — default, pyproject.toml, or CLI override.

Mermaid export. Generate state diagrams from any flow definition.

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Quick start

Install:

pip install flowr

Requires Python 3.13+.

Define a flow:

flow: deploy
version: 1.0.0
exits: [deployed, failed]

states:
  - id: prepare
    next:
      ready: execute

  - id: execute
    next:
      success: deployed
      error: failed

  - id: review
    next:
      approve:
        to: deployed
        when: { score: ">=80" }
      reject: failed

Use it:

$ flowr validate deploy.yaml
valid: True

$ flowr states deploy.yaml
prepare
execute
review

$ flowr next deploy.yaml review
state: review
next: approve (guarded)
next: reject

$ flowr transition deploy.yaml review approve --evidence score=85
from: review
trigger: approve
to: deployed

$ flowr session init deploy-flow
flow: deploy-flow
state: prepare
name: default

$ flowr --session transition approve
from: prepare
trigger: approve
to: review

$ flowr session show
flow: deploy-flow
state: review
name: default
stack: (none)

$ flowr config
project_root = /my/project  (default)
flows_dir = .flowr/flows  (default)
sessions_dir = .flowr/sessions  (default)
default_flow = main-flow  (default)
default_session = default  (default)

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CLI Reference

Command	Description
flowr validate <flow>	Validate a flow definition
flowr states <flow>	List all state ids
flowr check <flow> <state> [<target>]	Show state details or transition conditions
flowr next <flow> <state> [--evidence K=V]	Show valid next transitions
flowr transition <flow> <state> <trigger> [--evidence K=V]	Compute next state
flowr mermaid <flow>	Export as Mermaid state diagram
flowr session init <flow> [--name NAME]	Create a new session at the flow's initial state
flowr session show [--name NAME] [--format FORMAT]	Display current session state
flowr session set-state <state> [--name NAME]	Update the session's current state
flowr session list [--format FORMAT]	List all sessions
flowr config [--json]	Show resolved configuration with sources
flowr --session <command>	Run a command using session state

<flow> accepts a file path or a short flow name (resolved from .flowr/flows/). Use --flows-dir to override the configured flows directory. All commands accept --json for machine-readable output. Evidence: --evidence key=value (repeatable) or --evidence-json '{"key": "value"}'.

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Architecture

flowr/
├── domain/           # Core domain — Flow, State, Transition, Session, conditions, validation
│   ├── flow_definition.py
│   ├── loader.py
│   ├── session.py
│   ├── condition.py
│   ├── validation.py
│   └── mermaid.py
├── infrastructure/   # Adapters — config resolution, session persistence
│   ├── config.py
│   └── session_store.py
├── cli/              # Primary adapter — CLI commands, resolution, output formatting
│   ├── resolution.py
│   ├── session_cmd.py
│   └── output.py
└── __main__.py       # CLI entrypoint — argparse dispatch

Hexagonal architecture. Domain has no infrastructure dependencies. CLI is the primary adapter. Session store is a secondary adapter behind a Protocol port.

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Why does this exist

No existing YAML standard covers non-deterministic state machine workflows with per-state agent assignment and filesystem-as-source-of-truth. Existing solutions (XState, SCXML, Serverless Workflow, BPMN) target execution engines or deterministic workflows. flowr fills this gap: a declarative, validatable, toolable format for workflows that branch on evidence rather than control flow.

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Documentation

• flowr docs — hosted documentation
• Flow Definition Specification — authoritative YAML format reference
• System Overview — architecture, domain model, module structure
• Product Definition — product boundaries, users, and scope

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Development

uv sync --all-extras       # install with dev dependencies
uv run task test            # run tests
uv run task test-fast       # fast tests only
uv run task test-build      # full suite with coverage
uv run task lint            # lint and format
uv run task static-check    # type checking

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License

MIT — see LICENSE.