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Bank-Grade Durable WorkflowsPowered by your own Database

Highway is a production-grade durable workflow orchestration system that combines PostgreSQL's ACID guarantees with a declarative DSL to provide atomic, fault-tolerant workflow execution at scale.

Get started Why Highway

Live Workflow Visualization

Watch Workflows Execute in Real-Time

See how Highway handles complex scenarios: parallel execution, automatic retries, compensation tasks, and stateful loops - all with bank-grade durability.

Disaster Recovery Simulation

Phase 0/8

What you're seeing

Real-time visualization showing parallel branches, automatic retries, on_failure callbacks, compensation tasks, ForEach iterations, Switch cases, and While loops - all with bank-grade durability.

A better way to build reliable systems

Highway provides the reliability of a transactional database with the flexibility of a modern workflow engine.

Unmatched Reliability

Build robust, fault-tolerant systems with features designed for production-grade applications.

Bank-Grade Atomicity: All workflow operations are atomic using PostgreSQL transactions, ensuring your data and workflow state are always consistent.
Durable Execution: Workflows survive crashes, restarts, and failures. Execution resumes automatically from the last checkpoint.
Circuit Breakers: Automatic failure detection and recovery for external services prevents cascading failures and improves system resilience.

Developer-Friendly Experience

A powerful and intuitive DSL, along with real-time monitoring tools, makes building and debugging workflows a breeze.

Declarative DSL: Define complex workflows with a simple, readable, and powerful Python DSL. No more boilerplate code.
Event-Driven Architecture: Support for human-in-the-loop, webhooks, and fan-out/join patterns allows you to build complex, event-driven systems.
Real-Time Monitoring: WebSocket log streaming and a detailed UI provide real-time visibility into workflow execution and system health.

How Highway Compares

A critical comparison of Highway Workflow Engine against industry standards.

Core Architecture

Highway

Database-Centric: Postgres is the queue, state store, and lock manager.

Temporal

Event Sourcing: History of events replayed to rebuild state.

Airflow

DAG Scheduler: Python files parsed periodically, tasks scheduled in DB.

Atomicity

Highway

Strongest: Application DB and Workflow State in same transaction (if Postgres).

Temporal

Strong: Guaranteed via Sagas/Activities, but App DB writes are separate.

Airflow

Weak: Task state and App DB are separate. No built-in atomicity.

Parallelism & Joins

Highway

Native Fork-Join: Provides a native ParallelOperator for forking and a JoinOperator with multiple modes (All Of, Any Of, All Success) for powerful, declarative synchronization.

Temporal

Native: async/await pattern handles fork/join naturally.

Airflow

Native: DAG structure defines parallel/join behavior implicitly.

Latency

Highway

Low (<100ms): LISTEN/NOTIFY pushes tasks to workers immediately.

Temporal

Low: Long-polling on gRPC.

Airflow

High: Polling loop (Scheduler -> Executor -> Worker) often adds seconds.

Use Case

Highway

Transactional Business Logic & Data Pipelines: Excels at payment processing, approvals, and complex ETL/data workflows where ACID guarantees with application data are critical.

Temporal

Microservice Orchestration: Connecting many distributed services reliably.

Airflow

Data Pipelines: Primarily focused on batch jobs and ETL scheduling.

Feature	Highway Workflow Engine	Temporal	Apache Airflow
Core Architecture	Database-Centric: Postgres is the queue, state store, and lock manager.	Event Sourcing: History of events replayed to rebuild state.	DAG Scheduler: Python files parsed periodically, tasks scheduled in DB.
Atomicity	Strongest: Application DB and Workflow State in same transaction (if Postgres).	Strong: Guaranteed via Sagas/Activities, but App DB writes are separate.	Weak: Task state and App DB are separate. No built-in atomicity.
Parallelism & Joins	Native Fork-Join: Provides a native `ParallelOperator` for forking and a `JoinOperator` with multiple modes (All Of, Any Of, All Success) for powerful, declarative synchronization.	Native: `async/await` pattern handles fork/join naturally.	Native: DAG structure defines parallel/join behavior implicitly.
Latency	Low (<100ms): `LISTEN/NOTIFY` pushes tasks to workers immediately.	Low: Long-polling on gRPC.	High: Polling loop (Scheduler -> Executor -> Worker) often adds seconds.
Use Case	Transactional Business Logic & Data Pipelines: Excels at payment processing, approvals, and complex ETL/data workflows where ACID guarantees with application data are critical.	Microservice Orchestration: Connecting many distributed services reliably.	Data Pipelines: Primarily focused on batch jobs and ETL scheduling.