flowr 1.0.0

non-deterministic state machine specification to knead workflows

A declarative, validatable YAML format for non-deterministic state machine workflows.

Read the specification →

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The specification

flowr defines what a workflow is — its states, transitions, guard conditions, subflows — not what it does. No execution engine. No side effects. No opinions about retries, timeouts, or error handling. A YAML file declares structure. A validator checks integrity. Tools query, track, and visualise. The format is the foundation.

What the specification covers:

• States with unique ids, per-state attributes, and transition mappings
• Transitions that resolve to state ids or declared exit names
• Guard conditions gated by evidence-based expressions using 6 operators (==, !=, >=, <=, >, <)
• Named condition groups reusable across transitions on the same state
• Subflows with call-stack semantics — push on entry, pop on exit
• Within-flow cycles for iterative workflows
• Validation rules — structural integrity checks independent of any runtime

What the specification does not cover:

• Execution engines, side-effect hooks, retry logic, timeout handling
• Parallel (fork-join) states
• Orchestration, scheduling, or event dispatch

→ Full specification with examples and visual diagrams

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The reference implementation

This repository contains a Python reference implementation — a CLI and library that validates, queries, and tracks flow definitions conforming to the specification. The specification is the contract; this code is one tool that honours it.

flowr validate deploy.yaml          →  valid: True
flowr states deploy.yaml            →  prepare, execute, review
flowr next deploy.yaml review       →  approve → deployed [blocked]  need: score=>=80
                                      →  reject → failed
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. Auto-enters initial subflow on session init. 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
  approve → deployed [blocked]  need: score=>=80
  reject → failed

$ 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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Who is this for?

Specification adopters

You build editors, visualizers, CI systems, or orchestration layers. You need a structural format for workflow state machines — not an execution engine. The flowr specification gives you states, transitions, guard conditions, and subflows in a declarative YAML format with a conforming validator. Any tool can parse it. Read the spec.

Agent operators

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

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.

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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 all transitions with trigger→target and condition status
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 (auto-enters initial subflow)
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 (works with validate, states, check, next, transition)

<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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Documentation

• Specification — the flowr format with examples and visual diagrams
• 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.