Stabilize — Embedded Workflow Orchestration for AI Agents

	Stabilize	Airflow	Temporal	Prefect
Setup	pip install stabilize	Webserver + scheduler + DB + Celery workers	Server cluster + worker processes + DB	Cloud account + agent deployment
Infrastructure	Zero — it's a library	High — separate cluster	High — separate cluster	Medium — cloud dependency
State Storage	Atomic DB+Queue transactions	Database row locks	Event-sourced (server-side)	Cloud-managed
Flow Control	43 WCP Patterns	Rigid DAG	Workflows + activities	Rigid DAG
Event Sourcing	Built-in with replay	None	Core architecture	None
AI Integration	MCP server + prompt CLI	None	None	None

AI-Native

Built for AI Agents

Stabilize is designed from the ground up for programmatic workflow creation. Every workflow is defined in Python code — no YAML, no UI, no drag-and-drop. AI coding agents generate correct Stabilize code on the first try.

• ✓ MCP documentation server with 26 tools for AI-assisted development
• ✓ stabilize prompt CLI outputs LLM-optimized docs for context windows
• ✓ Dynamic routing lets AI agents make decisions during execution
• ✓ Event sourcing gives agents full observability into execution history
• ✓ Sub-workflows enable recursive, agent-orchestrated pipeline composition

Connect MCP Server

SSE mcp.stabilize.rodmena.ai/sse

TOOL get_started

TOOL workflow_patterns

TOOL event_sourcing

TOOL error_handling

TOOL task_implementation

TOOL search_docs

TOOL ... and 20 more

$ stabilize prompt > context.txt

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

class RouteTask(Task):
    """AI agent decides the next step based on analysis."""
    def execute(self, stage):
        if stage.context.get("sentiment") == "negative":
            return TaskResult.jump_to("escalate")  # Dynamic routing
        return TaskResult.success()

# One-line event sourcing setup
db = "./workflows.db"
store = SqliteWorkflowStore(f"sqlite:///{db}", create_tables=True)
queue = SqliteQueue(f"sqlite:///{db}", table_name="queue")
configure_event_sourcing(SqliteEventStore(f"sqlite:///{db}", create_tables=True))

workflow = Workflow.create(
    application="ai-agent",
    name="Intelligent Routing",
    stages=[
        StageExecution(
            ref_id="analyze", type="python", name="Analyze Input",
            context={"script": "RESULT = analyze(INPUT['data'])"},
            tasks=[TaskExecution.create("Run", "python",
                   stage_start=True, stage_end=True)],
        ),
        StageExecution(
            ref_id="route", type="route", name="AI Decision",
            requisite_stage_ref_ids={"analyze"},
            context={},
            tasks=[TaskExecution.create("Route", "route",
                   stage_start=True, stage_end=True)],
        ),
        StageExecution(
            ref_id="respond", type="http", name="Send Response",
            requisite_stage_ref_ids={"route"},
            context={"url": "https://api.example.com/respond",
                     "method": "POST"},
            tasks=[TaskExecution.create("Send", "http",
                   stage_start=True, stage_end=True)],
        ),
        StageExecution(
            ref_id="escalate", type="http", name="Escalate Issue",
            context={"url": "https://api.example.com/escalate",
                     "method": "POST"},
            tasks=[TaskExecution.create("Alert", "http",
                   stage_start=True, stage_end=True)],
        ),
    ],
)