{"id":50449286,"url":"https://github.com/jmsnll/flowfunc","last_synced_at":"2026-05-31T23:05:34.160Z","repository":{"id":295950346,"uuid":"989122330","full_name":"jmsnll/flowfunc","owner":"jmsnll","description":"The Dead Simple, Local-First Workflow Runner for Python.","archived":false,"fork":false,"pushed_at":"2025-07-11T23:27:38.000Z","size":452,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-12T02:08:39.028Z","etag":null,"topics":["python","workflow","workflows","yaml"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jmsnll.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-23T15:33:59.000Z","updated_at":"2025-06-28T06:44:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"17ff4f76-cf59-4f81-a39d-71dc278307b1","html_url":"https://github.com/jmsnll/flowfunc","commit_stats":null,"previous_names":["jmsnll/flowfunc"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jmsnll/flowfunc","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jmsnll%2Fflowfunc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jmsnll%2Fflowfunc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jmsnll%2Fflowfunc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jmsnll%2Fflowfunc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jmsnll","download_url":"https://codeload.github.com/jmsnll/flowfunc/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jmsnll%2Fflowfunc/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33752309,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-05-31T02:00:06.040Z","response_time":95,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["python","workflow","workflows","yaml"],"created_at":"2026-05-31T23:05:33.228Z","updated_at":"2026-05-31T23:05:34.148Z","avatar_url":"https://github.com/jmsnll.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ⚠️ Alpha Notice\n\n**This project is in early alpha. APIs, features, and instructions are subject to change. Documentation may be outdated. Use with caution.**\n\n---\n\n# flowfunc\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n**flowfunc: The Dead Simple, Local-First Workflow Runner for Python.**\n\n---\n\n`flowfunc` is for the developer, researcher, or tinkerer who needs more than a shell script but less than a production-grade orchestrator like Airflow or Dagster. It helps you define and run multi-step, reproducible workflows on your local machine with zero infrastructure overhead.\n\nIt's built on a few core principles:\n* **Local-First:** Everything runs on your machine. No servers, no schedulers, no databases.\n* **Zero Infrastructure:** If you have Python and `pip`, you have everything you need.\n* **Python-Native:** Your workflow steps are just Python functions.\n* **Simplicity Over Complexity:** `flowfunc` is designed to be obvious, not clever.\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/jmsnll/flowfunc.git\n\n# Navigate into the project directory\ncd flowfunc\n\n# Install the package\npip install .\n```\n\n### Quickstart: Your First Workflow\n\nLet's build a simple workflow that greets a user and saves the message to a file.\n\n**1. Initialize a new project:**\n\nThis command creates a simple project structure for you.\n\n```bash\nflowfunc init my_first_workflow\ncd my_first_workflow\n```\n\nThis will generate two files: `main.py` for your Python logic and `workflow.yaml` to define the workflow.\n\n**2. Write your Python functions (`main.py`):**\n\nYour workflow steps are just plain Python functions.\n\n```python\n# main.py\n\ndef create_greeting(name: str) -\u003e str:\n    \"\"\"Creates a greeting message.\"\"\"\n    print(f\"Creating greeting for {name}...\")\n    return f\"Hello, {name}!\"\n\ndef save_message(message: str, output_path: str) -\u003e str:\n    \"\"\"Saves the message to a text file.\"\"\"\n    print(f\"Saving message to {output_path}...\")\n    with open(output_path, \"w\") as f:\n        f.write(message)\n    return output_path\n```\n\n**3. Define your workflow (`workflow.yaml`):**\n\nThis YAML file connects your Python functions into a directed acyclic graph (DAG).\n\n```yaml\n# workflow.yaml\n\napiVersion: flowfunc.dev/v1alpha1\nkind: Workflow\nmetadata:\n  name: hello-world-workflow\n  description: \"A simple workflow to greet a user and save the message.\"\n\nspec:\n  # Python module where your functions are defined\n  module: main\n\n  # Inputs for the entire workflow\n  inputs:\n    - name: user_name\n      description: \"The name of the person to greet.\"\n      default: \"World\"\n    - name: output_file\n      default: \"greeting.txt\"\n\n  # The steps of your workflow\n  steps:\n    - name: make_greeting\n      # Calls the create_greeting function\n      function: create_greeting\n      # Maps workflow inputs to function arguments\n      args:\n        name: \"{{ inputs.user_name }}\"\n\n    - name: write_to_file\n      # Calls the save_message function\n      function: save_message\n      # The `message` argument comes from the output of the `make_greeting` step\n      args:\n        message: \"{{ steps.make_greeting.outputs.return_value }}\"\n        output_path: \"{{ inputs.output_file }}\"\n```\n\n**4. Run the workflow!**\n\n```bash\nflowfunc run workflow.yaml\n```\n\nYou can also override inputs from the command line:\n\n```bash\nflowfunc run workflow.yaml --input user_name=Alice --input output_file=alice.txt\n```\n\nA new file, `alice.txt`, will be created with the content \"Hello, Alice!\". A `.flowfunc` directory will also appear, containing metadata and a summary of your run.\n\n### Key Commands\n\n* `flowfunc run \u003cfile\u003e`: Execute a workflow.\n* `flowfunc init \u003cdir\u003e`: Create a new boilerplate project.\n* `flowfunc new \u003cfile\u003e`: Create a new boilerplate workflow file.\n* `flowfunc graph \u003cfile\u003e`: Display the workflow's dependency graph in the terminal.\n* `flowfunc docs \u003cfile\u003e`: View the workflow's documentation (inputs, outputs, descriptions) in the terminal.\n\n### Why `flowfunc`?\n\nChoose `flowfunc` when:\n* Your project has grown beyond a single script.\n* You need to reliably chain together multiple steps (e.g., download data -\u003e process it -\u003e generate a plot).\n* You want to track inputs and outputs for reproducibility without a complex setup.\n* You want a simple CLI to run your pipelines.\n\nAvoid `flowfunc` when you need:\n* Distributed execution across multiple machines.\n* A centralized scheduler for time-based runs.\n* A UI dashboard and enterprise features.\n\nFor those cases, consider mature tools like [Dagster](https://dagster.io/), [Prefect](https://www.prefect.io/), or [Argo Workflows](https://argoproj.github.io/argo-workflows/).\n\n### Under the Hood\n\n`flowfunc` provides the workflow management layer (schema, CLI, persistence, etc.) and uses the excellent [pipefunc](https://github.com/ML-Dev-Ops/pipefunc) library for the core DAG resolution and execution.\n\n### Contributing\n\nContributions are welcome! Please read the `CONTRIBUTING.md` file for details on how to set up your development environment and submit a pull request.\n\n### License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjmsnll%2Fflowfunc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjmsnll%2Fflowfunc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjmsnll%2Fflowfunc/lists"}