stabilize 0.9.1

Highway Workflow Engine - Stabilize execution layer

Stabilize

Highway Workflow Engine - Stabilize execution layer.

A lightweight 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[rag]       # RAG-powered pipeline generation
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
• RAG-powered pipeline generation from natural language

Quick Start

from stabilize import Workflow, StageExecution, TaskExecution, WorkflowStatus
from stabilize.persistence.sqlite import SqliteWorkflowStore
from stabilize.queue.sqlite_queue import SqliteQueue
from stabilize.queue.processor import QueueProcessor
from stabilize.orchestrator import Orchestrator
from stabilize.tasks.interface import Task
from stabilize.tasks.result import TaskResult
from stabilize.tasks.registry import 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(":memory:")
queue = SqliteQueue(":memory:")

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

# Create processor and orchestrator
processor = QueueProcessor.create(queue, store, 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!'}

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"}, ...),
    ],
)

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

RAG-Powered Pipeline Generation

Stabilize includes an AI-powered assistant that generates pipeline code from natural language descriptions using RAG (Retrieval-Augmented Generation).

Requirements

pip install stabilize[rag]  # Installs ragit dependency

You also need:

• Local Ollama for embeddings: ollama pull nomic-embed-text
• Ollama API key for LLM generation (uses ollama.com cloud)

Setup

Create a .env file with your API key:

OLLAMA_API_KEY=your_api_key_here

Initialize the embedding cache:

# Initialize with default context (Stabilize docs + examples)
stabilize rag init

# Include your own code as additional training context
stabilize rag init --additional-context /path/to/your/code/

# Force regenerate embeddings
stabilize rag init --force

Generate Pipelines

# Generate a pipeline from natural language
stabilize rag generate "create a pipeline that processes CSV files in parallel"

# Save to file
stabilize rag generate "build a CI/CD pipeline with test and deploy stages" > my_pipeline.py

Clear Cache

stabilize rag clear

Configuration

Environment variables:

Variable	Default	Description
OLLAMA_API_KEY	(required)	API key for ollama.com
OLLAMA_BASE_URL	https://ollama.com	LLM endpoint URL
OLLAMA_EMBEDDING_URL	http://localhost:11434	Local Ollama for embeddings

Example

$ stabilize rag generate "create a hello world pipeline"

from stabilize import Workflow, StageExecution, TaskExecution
from stabilize.tasks.interface import Task
from stabilize.tasks.result import TaskResult
...

CLI Reference

stabilize mg-up [--db-url URL]      Apply pending PostgreSQL migrations
stabilize mg-status [--db-url URL]  Show migration status
stabilize rag init [--force] [--additional-context PATH]  Initialize RAG embeddings
stabilize rag generate "prompt"     Generate pipeline from natural language
stabilize rag clear                 Clear embedding cache

Naming Alignment with highway_dsl

highway_dsl	stabilize
Workflow	Workflow
TaskOperator	Task interface
RetryPolicy	RetryableTask
TimeoutPolicy	OverridableTimeoutRetryableTask

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