Open Source AWS Step Functions Alternatives

Kestra is an event-driven orchestration platform that unifies scheduled and real-time automation behind one declarative interface. Workflows are YAML, and the definition is rewritten whenever someone changes a flow from the UI, an API call or Terraform, so the orchestration logic stays managed as code no matter which door the change came through.

A flow lives in a namespace and holds tasks, while triggers decide when it runs, on a schedule or on an event such as a file arriving or a message landing in Kafka, Redis, Pulsar, MQTT, AWS SQS or Google Pub/Sub. Inputs and variables carry data through, and outputs generate artifacts visible in the interface.

The platform is language-agnostic, and the plugin ecosystem is where that shows.

• Run anywhere: Tasks execute locally, on remote servers over SSH, in Docker containers or as Kubernetes jobs.
• Any language: Python, Node.js, R, Go and Shell scripts run as tasks, alongside SQL queries and HTTP calls.
• Resilience: Retries, timeouts, error handling, conditional branching, backfills, subflows and dynamic tasks.
• Editor: Syntax highlighting, auto-completion, live validation and a topology view of the flow graph.
• Version control: Push flows from the built-in editor to a Git branch and keep CI/CD in the loop.

A single Docker command starts a local server on port 8080. Installation guides cover Docker Compose, Podman, Kubernetes, AWS, Google Cloud and Azure, with CloudFormation and Terraform templates for cloud deployments.

Prefect is a workflow orchestration framework for building data pipelines in Python, aimed at the gap between a script that runs on someone's laptop and a pipeline the whole team depends on.

You add flow and task decorators to functions you have already written, and the framework takes over scheduling, retries, caching and observation. Pipelines can react to events, branch on conditions and recover from unexpected changes without being rewritten around a new abstraction.

A handful of pieces carry most of the work.

• Flows and tasks: two decorators turn plain Python functions into an orchestrated, tracked workflow.
• Deployments: calling serve with a cron expression and parameters puts a workflow on a schedule.
• Retries and caching: failures are retried and repeated work is cached without extra plumbing.
• Event automations: deployments can be triggered by events rather than only by the clock.
• Server and UI: one command runs a local Prefect server with a dashboard on port 4200.

Installation is pip or uv, and Prefect needs Python 3.10 or newer. Workflow activity can be monitored on a self-hosted Prefect server or on the managed Prefect Cloud dashboard, and runs can be started from the interface or the command line as well as from your own code.

Retry loops, recovery code and hand rolled state machines eat the time meant for the application. Temporal is a durable execution platform that takes that work over, without giving up scale or reliability.

The server executes units of application logic called Workflows in a resilient manner, handling intermittent failures and retrying failed operations on its own. It is a mature technology that began as a fork of Uber's Cadence, and it is developed by Temporal Technologies, a startup founded by the people who created Cadence.

Getting from install to a running workflow is a short path.

• Local server: one Homebrew install and one start command bring up a prebuilt image with its dependencies.
• Samples: Go and Java sample repositories run against the local server, from hello world upwards.
• CLI: the Temporal CLI lists namespaces and workflows and drives the running server.
• Web UI: a browser interface on port 8233 shows your sample workflows executing.
• Language support: Workflows, Activities and Workers are written in one of the supported SDK languages.

This repository holds the source of the server itself rather than the SDKs, and the server architecture is documented alongside the code. Proposals for new features have a repository of their own, and a community site handles questions from people getting started.

Trigger.dev is a platform for writing background tasks in TypeScript and running them without a timeout. Serverless functions cut work off after a few minutes, which is the wrong shape for an agent that browses, transcodes or waits on a human decision.

Tasks live in your own codebase, version controlled and reviewed like everything else, and reach the platform through the SDK. Durability comes from checkpointing, so a run survives interruption, and atomic versioning means a new deployment leaves runs already in flight untouched.

The runtime gives you rather more than an executor.

• Queues and concurrency: concurrency rules, batch triggering, idempotency and automatic retries after an uncaught error.
• Waits and approvals: pause a run for a set duration, or until a person approves, rejects or comments.
• Realtime: subscribe to runs from your frontend with React hooks, including streaming model responses.
• Build extensions: hook into the build to add system packages, browsers, Python scripts or FFmpeg.
• Observability: full traces and logs per run, plus tags, run metadata, bulk actions and failure alerts.

Development, staging, preview and production environments are supported, and preview branches integrate with Git workflows and Vercel. Machines are configured by vCPU and memory per task. The managed cloud removes infrastructure work entirely, and self-hosting is documented for Docker Compose and for Kubernetes through an official Helm chart.

Hatchet orchestrates background tasks, AI agents and durable workflows at scale. It removes the custom admin tooling teams end up writing around traditional queues, where a task vanishes from the broker once it finishes and leaves nothing to inspect, replay or debug.

Postgres is the durability layer for both the task runtime and the observability system, which is what makes the platform straightforward to self-host. SDKs cover Python, TypeScript, Go and Ruby, and a CLI brings up a local server on macOS, Linux or WSL with Docker installed.

The feature set spans queuing, orchestration and the operations around them.

• Durable tasks: recover from failure mid-run, with pause and resume conditions built from durable sleep and event waits.
• DAGs: express data pipelines and multi-step workflows as directed acyclic graphs.
• Retries and scheduling: cover configurable retry policies with exponential backoff, cron jobs and scheduled runs.
• Fair scheduling: applies concurrency policies, priorities, rate limits and worker slots so one workload cannot starve the rest.
• Task routing: assigns work by worker labels or weighted affinity rules.
• Observability: ships a real-time web UI with alerting and logging, plus OpenTelemetry and Prometheus metrics.

Hatchet is MIT licensed and has been load tested to 10,000 tasks per second, with the maintainers noting that durability costs more resources than a Redis or RabbitMQ queue reaching higher raw throughput. Instances are multi-tenant by default with users and roles, so one deployment can serve several teams.

Inngest replaces the queue, the state store and the scheduler that usually sit behind background work. Developers write durable step functions in their own application code, and the infrastructure that normally has to be assembled and watched over disappears from the job.

A function has three parts, triggers from events, cron schedules or webhooks, flow control that governs how runs are enqueued and executed, and steps. Wrapping code in a step makes it retry on failure independently, which lets one workflow run for months and recover from errors as it goes.

The self-hosted server is worth understanding before you run it.

• Event API: receives events over HTTP, authenticates them with event keys and publishes to an internal stream.
• Runner: schedules runs, resumes paused functions on matching events and cancels others by expression.
• Queue: multi-tenant and multi-tier, handling concurrency, throttling, prioritization, debouncing, rate limiting and batching.
• Executor: executes steps, writes incremental run state and retries after failures.
• Dashboard and APIs: GraphQL and REST access plus a UI for apps, functions and run history.

SDKs cover TypeScript and JavaScript, Python, Go and Kotlin or Java, all under Apache 2.0, while the server and CLI carry the Server Side Public License with delayed open source publication. Local development runs the Dev Server from one npx command with production parity, then functions deploy to your own infrastructure and sync with the hosted platform or a self-hosted server, which invokes them over HTTPS when triggering events arrive.

Orchestration is rarely the job people signed up for, and Dagu is built around that fact. It ships as a single binary with a built-in web UI, needs no external database and no message broker, and runs on Linux, macOS and Windows.

Workflows are directed acyclic graphs written in declarative YAML that sits next to your scripts. Structure stays configuration rather than code, so nothing imports the engine: delete the YAML and the scripts run exactly as they did before.

• Step types: shell commands, Docker containers, Kubernetes Jobs and remote commands over SSH run without modification.
• Scheduling: cron syntax with timezones, overlap policies and catch-up windows.
• Composition: reusable sub-DAGs and concurrency controls for work that runs in parallel.
• Operations: logs, run history, retries, notifications, human tasks and webhook triggers in one place.
• MCP server: assistants can inspect workflows and runs, maintain wiki pages, apply changes and control runs.

State lives in local files, so a single machine can carry thousands of workflow runs per day, queues and resource limits decide how many execute at once, and workers spread execution across machines when one node stops being enough.

Teams point it at ETL pipelines, interdependent legacy scripts, media conversion, server automation over SSH, container and Kubernetes work, and self-service runbooks that non-engineering colleagues can trigger themselves. Installers cover a shell script, Homebrew, npm, Docker and a Helm chart.