YAML Workflow

Expose declarative YAML-defined workflows as MCP tools over stdio
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About

Expose declarative YAML-defined workflows as MCP tools over stdio
Details

Kind: MCP servers
Topic: No topic detected
Publisher: orieg
Origin: official
Category: ferramentas
Transport: local
Version: 0.9.6
Stars: 3
Open pull requests: 1
Last push: 2026-08-31T06:31:21Z
Repository state: ativo
Language: Python
License: MIT
Added: 2026-08-29 04:00:58
Updated: 2026-08-29 04:00:58
Origin id: io.github.orieg/yaml-workflow
README

# YAML Workflow

<!-- mcp-name: io.github.orieg/yaml-workflow -->

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A lightweight workflow engine for CI/CD pipelines, data processing, and DevOps automation. Define reproducible, version-controlled workflows in YAML — run them locally, in CI, or on any machine with Python installed.

## Why yaml-workflow?

Most workflow tools require servers, databases, and complex infrastructure. **yaml-workflow** takes a GitOps approach — workflows are plain YAML files, version-controlled alongside your code:

| | yaml-workflow | Airflow / Prefect / Dagster |
|---|---|---|
| **Setup** | `pip install yaml-workflow` | Server, database, scheduler, workers |
| **Configuration** | Plain YAML files | Python DAGs + infrastructure config |
| **Dependencies** | 2 (PyYAML, Jinja2) | 50+ packages, Docker, PostgreSQL |
| **Use case** | Local automation, scripts, CI/CD, data pipelines | Enterprise orchestration at scale |
| **Learning curve** | Minutes | Hours to days |
| **State** | File-based, resumable | Database-backed |

**Choose yaml-workflow when you need:**
- Simple task automation without infrastructure overhead
- Reproducible pipelines defined in version-controlled YAML
- Batch processing with parallel execution
- State persistence and workflow resume after failures
- A lightweight alternative to shell scripts with better error handling
- GitOps-friendly pipelines that live in your repo alongside the code
- A single tool that runs the same pipeline locally and in CI

## Features

- YAML-driven workflow definition with Jinja2 templating
- Multiple task types: shell, Python, file, template, HTTP, batch
- Workflow composition via `imports` — reuse steps across workflows
- Plugin system via entry points — `pip install yaml-workflow-myplugin`
- Watch mode — `--watch` to re-run on file changes
- Dry-run mode to preview without executing
- Workflow visualization (ASCII branching DAG and Mermaid)
- Parallel execution with configurable worker pools
- State persistence and resume capability
- Retry mechanisms with configurable strategies
- Namespaced variables (`args`, `env`, `steps`, `batch`)
- Flow control with custom step sequences and conditions
- Extensible task system via `@register_task` decorator
- Parallel step execution via `depends_on` — run independent steps concurrently
- Secrets validation — fail fast if required environment variables are missing
- Structured output (`--format json`) for CI integration and scripting
- MCP server — expose workflows as AI agent tools (`pip install yaml-workflow[mcp]`)
- Web dashboard — monitor runs and trigger workflows (`pip install yaml-workflow[serve]`)
- GitHub Action — run workflows in CI with `uses: orieg/yaml-workflow@v0.9.3`

## Use Cases

- **CI/CD pipelines** — multi-step build, test, deploy workflows in YAML
- **Data processing** — batch ETL pipelines with retry and resume on failure
- **DevOps automation** — infrastructure tasks with secrets management and notifications
- **AI/LLM pipelines** — orchestrate API calls with auth, retry, and batch processing
- **Local automation** — replace shell scripts with reproducible, parameterized workflows

## Quick Start

```bash
# Install (isolated CLI — recommended)
pipx install yaml-workflow            # Core CLI
pipx install 'yaml-workflow[all]'     # + web dashboard + MCP server

# Or with pip
pip install yaml-workflow

# Initialize example workflows
yaml-workflow init

# Run a workflow with parameters
yaml-workflow run workflows/hello_world.yaml name=Alice
```

**Example workflow** (`hello_world.yaml`):

```yaml
name: Hello World
description: A simple greeting workflow

params:
  name:
    type: string
    default: World

steps:
  - name: create_greeting
    task: template
    inputs:
      template: "Hello, {{ args.name }}!"
      output_file: greeting.txt

  - name: show_greeting
    task: shell
    inputs:
      command: cat greeting.txt
```

### Visualize workflows

```bash
yaml-workflow visualize workflows/data_pipeline.yaml
```

```
  Workflow: Data Pipeline

  ┌─────────────────┐
  │  detect_format  │
  │   python_code   │
  └─────────────────┘
           │
           ▼
  ┌────────────────┐  ┌────────────────┐  ┌────────────────┐  ┌────────────────┐
  │  process_json  │  │  process_csv   │  │  process_xml   │  │ handle_unknown │
  │     shell      │  │     shell      │  │     shell      │  │     shell      │
  └────────────────┘  └────────────────┘  └────────────────┘  └────────────────┘
           │
           ▼
  ┌─────────────────┐
  │ generate_report │
  │   python_code   │
  └─────────────────┘
```

Adjacent conditional steps are automatically grouped as branches. Use `--format mermaid` to export for docs or GitHub rendering.

### Dry-run mode

Preview what a workflow would do without executing anything:

```bash
yaml-workflow run workflows/hello_world.yaml name=Alice --dry-run
```

```
[DRY-RUN] Workflow: Hello World
[DRY-RUN] Steps: 2 to execute

  [DRY-RUN] Step 'create_greeting' — task: template — WOULD EXECUTE
    template: Hello, Alice!
    output_file: greeting.txt
  [DRY-RUN] Step 'show_greeting' — task: shell — WOULD EXECUTE
    command: cat greeting.txt

[DRY-RUN] Complete. 2 step(s) would execute, 0 would be skipped.
[DRY-RUN] No files were written. No tasks were executed.
```

### Workflow composition

Reuse steps across workflows with `imports`:

```yaml
# main.yaml
imports:
  - ./shared/logging_steps.yaml
  - ./shared/common_params.yaml

steps:
  - name: my_step
    task: shell
    inputs:
      command: echo "runs after imported steps"
```

Imported steps are prepended. Imported params provide defaults that the main workflow can override. Supports transitive imports with circular detection.

### Parallel Steps

Run independent steps concurrently with `depends_on`:

```yaml
steps:
  - name: fetch_api
    task: http.request
    inputs: {url: "https://api.example.com/data"}

  - name: fetch_db
    task: python_code
    inputs:
      code: "result = query_database()"

  - name: merge
    task: python_code
    depends_on: [fetch_api, fetch_db]
    inputs:
      code: |
        api_data = steps["fetch_api"]["result"]
        db_data = steps["fetch_db"]["result"]
        result = {"merged": True}
```

### Watch mode

Automatically re-run on file changes during development:

```bash
yaml-workflow run workflows/hello_world.yaml name=Alice --watch
```

Monitors the workflow file and all imported files. Press `Ctrl+C` to stop.

### GitHub Actions

Run workflows in CI with the [yaml-workflow action](https://github.com/marketplace/actions/yaml-workflow):

```yaml
- name: Run pipeline
  uses: orieg/yaml-workflow@v0.9.3
  id: pipeline
  with:
    workflow: workflows/deploy.yaml
    params: |
      env=production
      version=1.2.0
    format: json

- name: Use results
  run: echo '${{ steps.pipeline.outputs.result }}'
```

### Docker & Kubernetes

Run anywhere without installing Python:

```bash
# Run a workflow in Docker
docker run --rm -v $(pwd)/workflows:/app/workflows \
  ghcr.io/orieg/yaml-workflow run /app/workflows/pipeline.yaml

# Start the web dashboard
docker run -p 8080:8080 -v $(pwd)/workflows:/app/workflows \
  ghcr.io/orieg/yaml-workflow
```

Deploy on Kubernetes with the Helm chart:

```bash
helm install my-workflows ./helm/yaml-workflow \
  --set-file workflows.files.pipeline\\.yaml=workflows/pipeline.yaml
```

Compatible with ArgoCD (GitOps) and Argo Workflows. See the [Kubernetes guide](https://orieg.github.io/yaml-workflow/guide/kubernetes/).

### More commands

```bash
# List available workflows
yaml-workflow list

# Validate a workflow (with JSON output for CI)
yaml-workflow validate workflows/hello_world.yaml --format json

# Resume a failed workflow
yaml-workflow run workflows/hello_world.yaml --resume

# Structured output for scripting
yaml-workflow run workflows/pipeline.yaml --format json --output results.json
```

## Documentation

Full documentation is available at **[orieg.github.io/yaml-workflow](https://orieg.github.io/yaml-workflow/)**.

- [Getting Started](https://orieg.github.io/yaml-workflow/guide/getting-started/) - Installation and first workflow
- [Task Types](https://orieg.github.io/yaml-workflow/guide/tasks/basic-tasks/) - Shell, Python, file, template, and batch tasks
- [Workflow Structure](https://orieg.github.io/yaml-workflow/workflow-structure/) - YAML configuration reference
- [Templating](https://orieg.github.io/yaml-workflow/guide/templating/) - Jinja2 variable substitution
- [State Management](https://orieg.github.io/yaml-workflow/state/) - Persistence and resume
- [Task Development](https://orieg.github.io/yaml-workflow/guide/task-development/) - Creating custom tasks
- [API Reference](https://orieg.github.io/yaml-workflow/reference/) - Full API documentation

## Contributing

Contributions are welcome! See the [Contributing Guide](https://orieg.github.io/yaml-workflow/contributing/development/) for development setup and guidelines.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.