Declarative YAML workflows on Temporal. No SDK boilerplate.

Zigflow lets you define and run Temporal workflows using YAML, built on the CNCF Serverless Workflow specification. You write a workflow definition; Zigflow compiles it into a fully-featured Temporal workflow with retries, state management and deterministic execution. No Go, Java or TypeScript workflow code required.

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Run your first workflow in a few minutes and see the result.

1. Install Zigflow

   brew tap zigflow/tap
   brew install --cask zigflow

   For other options, see installation docs.

2. Start a Temporal server

   Requires the Temporal CLI

   temporal server start-dev

   The Temporal UI will be available at http://localhost:8233.

3. Create a workflow

   Save this as workflow.yaml:

   document:
     dsl: 1.0.0
     taskQueue: zigflow
     workflowType: hello-world
     version: 1.0.0
   do:
     - greet:
         output:
           as:
             data: ${ . }
         set:
           message: Hello from Ziggy

4. Run it

   zigflow run -f workflow.yaml

5. Trigger the workflow

   temporal workflow start \
     --type hello-world \
     --task-queue zigflow \
     --workflow-id my-first-workflow
   
   temporal workflow result \
     --workflow-id my-first-workflow

   You should see the workflow output in the CLI.

• Documentation
• Examples
• CLI reference

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Writing Temporal workflows in code is powerful, but it comes with overhead: learning the SDK, wiring up workers and keeping workflow code deterministic. Zigflow provides an opinionated declarative layer on top of Temporal, built around proven workflow patterns to simplify common orchestration use cases while retaining Temporal’s reliability and execution model.

Zigflow is an opinionated, declarative layer on top of Temporal. It trades some SDK-level flexibility for faster development and consistent workflow structure.

Capability		Custom DSL
Workflow definition model	Imperative code	Declarative	Declarative
Control and flexibility	Maximum	Depends on implementation	Opinionated and constrained
Retries and durability	✅ Native Temporal	⚠️ You build it	✅ Native Temporal
Continue-as-new	✅ Supported (you implement and tune it)	⚠️ You build it	✅ Automatic (Zigflow continues as new for you)
CNCF spec alignment	❌	❌ Usually bespoke	✅ Serverless Workflow v1.0+
Validation before execution	Manual or custom	Depends on implementation	✅ Built-in validation
Boilerplate and worker setup	Required	Depends on implementation	Minimal
Kubernetes deployment	Manual	Manual	✅ Helm chart included
Multi-language activities	✅	Depends on implementation	✅ Any Temporal SDK

Use the SDK when you need maximum flexibility. Use Zigflow when you want consistent, declarative orchestration with less boilerplate.

Zigflow is designed for teams who value speed, consistency and declarative orchestration over maximum SDK flexibility.

• Teams who want to ship workflows faster: Zigflow removes SDK boilerplate and worker wiring, so you can define, validate and run workflows in minutes.
• Platform teams building an internal orchestration layer: centralise workflow execution without requiring every team to learn or embed a Temporal SDK.
• Teams who prefer opinionated, declarative guardrails: Zigflow constrains workflow structure in useful ways, reducing accidental complexity.
• Workflow-as-configuration adopters: store, version and review workflow definitions as data rather than application code.
• Organisations already using Temporal who want a simpler path for straightforward orchestration without sacrificing reliability.
• Tool builders and automation platforms that need a spec-compliant execution engine behind a UI or higher-level abstraction.

• You need advanced Temporal workflow APIs and fine tuning: if you rely on nuanced child workflow behaviour, rich per-call options, or patterns that are easier to express directly in code, the Temporal SDK gives you the most flexibility.
• You are building a deeply customised Temporal platform layer: if your architecture depends heavily on SDK-level interceptors, custom middleware, or tight framework integrations, an SDK-first approach provides the broadest control surface.
• Your workflows are highly dynamic at runtime: if the structure of the workflow is generated as it runs, or you are effectively building an execution engine for arbitrary user logic, a code-first workflow is often a better fit.
• Your team prefers workflow development as application code: if stepping through workflow logic in a debugger, refactoring with compiler guarantees, and unit testing workflow functions directly are central to your development process, the SDK model may feel more natural.
• Your workflow contains substantial domain logic rather than orchestration: Zigflow excels at the declarative orchestration of activities and services. If most of your business logic lives inside the workflow itself, with complex in-memory decision making or algorithmic behaviour, writing workflows directly in an SDK may be clearer.

In those cases, use Temporal directly. Zigflow is an opinionated layer on top of Temporal, not a replacement for its full SDK capabilities.

1. You define a workflow using YAML based on the Serverless Workflow spec.
2. Zigflow validates the definition before execution.
3. Zigflow compiles the definition into a Temporal workflow implementation.
4. A worker is started for the configured namespace and task queue.
5. Activities are executed by your existing Temporal workers.

Zigflow handles orchestration while your services execute the work.

Sends an email every 30 days for 12 months using an HTTP endpoint.

document:
  dsl: 1.0.0
  taskQueue: zigflow
  workflowType: send-email
  version: 0.0.1
do:
  - for:
      for:
        in: ${ 12 } # repeat 12 times (once per month)
      do:
        - sendEmail:
            call: http
            with:
              method: POST
              endpoint: https://example.com/send-email
              headers:
                Content-Type: application/json
              body:
                to: user@example.com
                subject: Hello
                message: Hello from Zigflow
        - wait30Days:
            wait:
              days: 30

zigflow run -f ./workflow.yaml

Zigflow starts the worker. You can then trigger the workflow from any Temporal SDK, the Temporal UI or the Temporal CLI.

• Task Queue: zigflow
• Workflow Type: send-email

document:
  dsl: 1.0.0
  taskQueue: zigflow
  workflowType: activity-call
  version: 0.0.1
  metadata:
    activityOptions:
      startToCloseTimeout:
        minutes: 1
input:
  schema:
    format: json
    document:
      type: object
      required:
        - userId
      properties:
        userId:
          type: string
do:
  - captureInput:
      set:
        requestedUserId: ${ $input.userId }
        requestId: ${ uuid }
  - fetchProfile:
      call: activity
      with:
        name: activitycall.FetchProfile
        arguments:
          - ${ $data.requestedUserId }
          - ${ $data.requestId }
        taskQueue: activity-call-worker
  - generateWelcome:
      call: activity
      with:
        name: activitycall.GenerateWelcome
        arguments:
          - ${ $data.fetchProfile }
        taskQueue: activity-call-worker
  - finalize:
      set:
        workflowId: ${ $data.requestId }
        profile: ${ $data.fetchProfile }
        message: ${ $data.generateWelcome.message }

More examples (child workflows, fork/fan-out, signals, queries, try-catch, schedules and more) are in examples/.

• Temporal DSL: declarative YAML definitions that compile to Temporal workflows
• CNCF standard: aligned with Serverless Workflow v1.0+
• Validation first: definitions are validated before execution; invalid or unsupported constructs are rejected with actionable errors
• Multi-language activities: activity workers can use any Temporal SDK
• Low-code and visual-ready: suitable for UI workflow builders and orchestration tools
• Kubernetes-native: Helm chart included for cluster deployments
• Open source: Apache 2.0 licence, contributions welcome

Zigflow is named after Ziggy, Temporal's official mascot.

Ziggy is a tardigrade, a microscopic animal that is basically indestructible. Sound familiar?

Also, the colours are based on the Ziggy Stardust lightning bolt.

Telemetry helps the maintainers understand whether Zigflow is being used in real production environments. No personal or identifiable user data is collected.

When a worker starts, Zigflow sends:

• an anonymous installation ID (generated locally on first run, or derived from the container hostname)
• the Zigflow version
• basic runtime information (OS, architecture, container detection)
• approximate server country (2-letter code, derived once at startup)

The country value is derived once at startup and is not tied to any identity. No IP addresses are stored.

When workflows are executed, Zigflow sends a periodic heartbeat (once per minute) containing:

• the total number of workflow runs since the worker started
• the worker uptime in seconds

Heartbeats are only sent when the run count changes. Idle workers do not emit repeated telemetry.

Zigflow does not collect:

• workflow definitions
• workflow inputs or outputs
• execution IDs
• task names
• hostnames
• environment variable values
• organisation identifiers
• IP addresses or precise location data

Telemetry exists solely to understand real-world adoption and usage.

Opting out is straightforward:

# Environment variable
DISABLE_TELEMETRY=true

# CLI flag
--disable-telemetry

Zigflow is under active development and evolving steadily.

• The core execution engine and validation pipeline are stable.
• The DSL schema follows semantic versioning.
• Kubernetes deployment via Helm is supported.
• Backwards compatibility is maintained within major versions.
• Zigflow is actively used in internal tooling and early adopter environments.

Zigflow is designed for long-term use as the declarative orchestration layer on top of Temporal.

Bug reports, feedback and contributions are welcome.

• Temporal
• CNCF Serverless Workflow
• Helm chart

Contributions are welcome. See CONTRIBUTING.md for guidelines on naming conventions, dev setup and commit style.

Made with contrib.rocks.

Use https://zigflow.dev/llms.txt for structured information about Zigflow for AI assistants and tooling.

Distributed under the Apache-2.0 licence.

© 2025 - 2026 Zigflow authors