stabilize 0.17.0

Stabilize Workflow Orchestration - Agentic Parallel Workflows

Stabilize

A lightweight full featured Python workflow execution engine with DAG-based stage orchestration.

Requirements

• Python 3.11+
• SQLite (included) or PostgreSQL 12+

Installation

pip install stabilize            # SQLite support only
pip install stabilize[postgres]  # PostgreSQL support
pip install stabilize[all]       # All features

Features

• Message-driven DAG execution engine
• Parallel and sequential stage execution
• Synthetic stages (before/after/onFailure)
• PostgreSQL and SQLite persistence
• Pluggable task system
• Retry and timeout support
• Event sourcing with full audit trail, replay, and projections
• All 43 Workflow Control-Flow Patterns (van der Aalst et al.)

Advanced Workflow Patterns

Stabilize implements all 43 Workflow Control-Flow Patterns (WCP) from the academic literature:

Branching & Synchronization

from stabilize.models.stage import JoinType, SplitType

# OR-Split (WCP-6): Conditional multi-branch activation
StageExecution(
    ref_id="triage",
    split_type=SplitType.OR,
    split_conditions={
        "police": "emergency_type == 'crime'",
        "ambulance": "injury_severity > 0",
    },
    ...
)

# Discriminator Join (WCP-9): Fire on first upstream completion
StageExecution(
    ref_id="triage",
    join_type=JoinType.DISCRIMINATOR,
    requisite_stage_ref_ids={"check_breathing", "check_pulse"},
    ...
)

# N-of-M Join (WCP-30): Fire when 3 of 5 complete
StageExecution(
    ref_id="proceed",
    join_type=JoinType.N_OF_M,
    join_threshold=3,
    requisite_stage_ref_ids={"r1", "r2", "r3", "r4", "r5"},
    ...
)

Signals & Suspend/Resume

from stabilize import Task, TaskResult

class ApprovalTask(Task):
    def execute(self, stage):
        if stage.context.get("_signal_name") == "approved":
            return TaskResult.success()
        return TaskResult.suspend()  # Wait for external signal

# Send signal to resume
from stabilize.queue.messages import SignalStage
queue.push(SignalStage(
    execution_id=workflow.id, stage_id=stage.id,
    signal_name="approved", signal_data={"user": "alice"},
    persistent=True,  # Buffered if stage not yet suspended
))

Mutual Exclusion, Milestones, Deferred Choice

# Mutex (WCP-39): Only one stage with same key runs at a time
StageExecution(ref_id="update_db", mutex_key="shared_db", ...)

# Milestone (WCP-18): Only enabled while milestone stage is RUNNING
StageExecution(ref_id="change_route", milestone_ref_id="issue_ticket", milestone_status="RUNNING", ...)

# Deferred Choice (WCP-16): First branch to start wins
StageExecution(ref_id="agent_contact", deferred_choice_group="response", ...)
StageExecution(ref_id="escalate", deferred_choice_group="response", ...)

See Flow Control Guide for complete documentation of all 43 patterns.

Comparison to Industry Standards

┌───────────────┬────────────────────┬──────────────────────┬───────────────────┐
│ Feature       │ stabilize          │ Spinnaker (Orca)     │ Airflow           │
├───────────────┼────────────────────┼──────────────────────┼───────────────────┤
│ State Storage │ Atomic (DB+Queue)  │ Atomic (Redis/SQL)   │ Atomic (SQL)      │
│ Concurrency   │ Optimistic Locking │ Distributed Lock     │ Database Row Lock │
│ Resilience    │ Queue-based (DLQ)  │ Queue-based (DLQ)    │ Scheduler Loop    │
│ Flow Control  │ 43 WCP Patterns    │ Rigid DAG            │ Rigid DAG         │
│ Complexity    │ Low (Library)      │ High (Microservices) │ High (Platform)   │
└───────────────┴────────────────────┴──────────────────────┴───────────────────┘

• If you are looking for a strictly atomic and highly distributed system, please take a look into Highway.

Quick Start

from stabilize import (
    Workflow, StageExecution, TaskExecution,
    SqliteWorkflowStore, SqliteQueue, QueueProcessor, Orchestrator,
    Task, TaskResult, TaskRegistry,
)

# Define a custom task
class HelloTask(Task):
    def execute(self, stage: StageExecution) -> TaskResult:
        name = stage.context.get("name", "World")
        return TaskResult.success(outputs={"greeting": f"Hello, {name}!"})

# Create a workflow
workflow = Workflow.create(
    application="my-app",
    name="Hello Workflow",
    stages=[
        StageExecution(
            ref_id="1",
            type="hello",
            name="Say Hello",
            tasks=[
                TaskExecution.create(
                    name="Hello Task",
                    implementing_class="hello",
                    stage_start=True,
                    stage_end=True,
                ),
            ],
            context={"name": "Stabilize"},
        ),
    ],
)

# Setup persistence and queue
store = SqliteWorkflowStore("sqlite:///:memory:", create_tables=True)
queue = SqliteQueue("sqlite:///:memory:")
queue._create_table()

# Register tasks
registry = TaskRegistry()
registry.register("hello", HelloTask)

# Create processor (auto-registers all default handlers)
processor = QueueProcessor(queue, store=store, task_registry=registry)

orchestrator = Orchestrator(queue)

# Run workflow
store.store(workflow)
orchestrator.start(workflow)
processor.process_all(timeout=10.0)

# Check result
result = store.retrieve(workflow.id)
print(f"Status: {result.status}")  # WorkflowStatus.SUCCEEDED
print(f"Output: {result.stages[0].outputs}")  # {'greeting': 'Hello, Stabilize!'}

Built-in Tasks

Stabilize includes ready-to-use tasks for common operations:

ShellTask - Execute Shell Commands

from stabilize import ShellTask

registry.register("shell", ShellTask)

# Use in stage context
context = {
    "command": "npm install && npm test",
    "cwd": "/app",
    "timeout": 300,
    "env": {"NODE_ENV": "test"},
}

HTTPTask - HTTP/API Requests

from stabilize import HTTPTask

registry.register("http", HTTPTask)

# GET with JSON parsing
context = {"url": "https://api.example.com/data", "parse_json": True}

# POST with JSON body
context = {"url": "https://api.example.com/users", "method": "POST", "json": {"name": "John"}}

# With authentication
context = {"url": "https://api.example.com/private", "bearer_token": "token"}

# File upload
context = {"url": "https://api.example.com/upload", "method": "POST", "upload_file": "/path/to/file.pdf"}

See examples/ directory for complete examples.

Parallel Stages

Stages with shared dependencies run in parallel:

#     Setup
#    /     \
#  Test   Lint
#    \     /
#    Deploy

workflow = Workflow.create(
    application="my-app",
    name="CI/CD Pipeline",
    stages=[
        StageExecution(ref_id="setup", type="setup", name="Setup", ...),
        StageExecution(ref_id="test", type="test", name="Test",
                      requisite_stage_ref_ids={"setup"}, ...),
        StageExecution(ref_id="lint", type="lint", name="Lint",
                      requisite_stage_ref_ids={"setup"}, ...),
        StageExecution(ref_id="deploy", type="deploy", name="Deploy",
                      requisite_stage_ref_ids={"test", "lint"}, ...),
    ],
)

Dynamic Routing

Stabilize supports dynamic flow control with TaskResult.jump_to() for conditional branching and retry loops:

from stabilize import Task, TaskResult, TransientError

class RouterTask(Task):
    """Route to different stages based on conditions."""
    def execute(self, stage: StageExecution) -> TaskResult:
        if stage.context.get("tests_passed"):
            return TaskResult.success()
        else:
            # Jump to another stage with context
            return TaskResult.jump_to(
                "retry_stage",
                context={"retry_reason": "tests failed"}
            )

Stateful Retries

Preserve progress across transient error retries with context_update:

class ProgressTask(Task):
    def execute(self, stage: StageExecution) -> TaskResult:
        processed = stage.context.get("processed_items", 0)
        try:
            # Process next batch
            new_processed = process_batch(processed)
            return TaskResult.success(outputs={"total": new_processed})
        except RateLimitError:
            # Preserve progress for next retry
            raise TransientError(
                "Rate limited",
                retry_after=30,
                context_update={"processed_items": processed + 10}
            )

The context_update is merged into the stage context before the retry, allowing tasks to resume from where they left off.

Event Sourcing

Enable full audit trail and event replay with one line:

from stabilize.events import configure_event_sourcing, SqliteEventStore

# Enable event sourcing — handlers automatically record all state transitions
event_store = SqliteEventStore("sqlite:///events.db", create_tables=True)
configure_event_sourcing(event_store)

# Run workflows as usual — events are recorded automatically

# Subscribe to events for monitoring
from stabilize.events import get_event_bus, EventType
bus = get_event_bus()
bus.subscribe("monitor", lambda e: print(e.event_type.value))

# Replay events to reconstruct state at any point
from stabilize.events import EventReplayer
replayer = EventReplayer(event_store)
state = replayer.rebuild_workflow_state(workflow.id)

# Build analytics with projections
from stabilize.events import StageMetricsProjection
metrics = StageMetricsProjection()
bus.subscribe("metrics", metrics.apply)

See examples/event-sourcing-example.py for a complete walkthrough.

Database Setup

SQLite

No setup required. Schema is created automatically.

PostgreSQL

Apply migrations using the CLI:

# Using mg.yaml in current directory
stabilize mg-up

# Using database URL
stabilize mg-up --db-url postgres://user:pass@host:5432/dbname

# Using environment variable
MG_DATABASE_URL=postgres://user:pass@host:5432/dbname stabilize mg-up

# Check migration status
stabilize mg-status

Example mg.yaml:

database:
  host: localhost
  port: 5432
  user: postgres
  password: postgres
  dbname: stabilize

CLI Reference

stabilize mg-up [--db-url URL]      Apply pending PostgreSQL migrations
stabilize mg-status [--db-url URL]  Show migration status
stabilize monitor [--db-url URL]    Real-time workflow monitoring dashboard
stabilize prompt                    Output documentation for pipeline code generation

Running Tests

# All tests (requires Docker for PostgreSQL)
pytest tests/ -v

# SQLite tests only (no Docker)
pytest tests/ -v -k sqlite

License

Apache 2.0