PyRunner is a self-hosted Python automation platform for running, scheduling, and managing Python scripts. It is built for developers who want control over their automation workflows and is presented as open source and free forever.
The platform groups its functions into six building blocks. Script management includes a built-in code editor for writing, editing, and organizing Python scripts, along with tagging and categorization for faster access. Scheduling supports runs at intervals, daily times, weekly times, and full cron expressions. Each script runs in its own virtual environment with custom pip packages, which is described as avoiding dependency conflicts between jobs. PyRunner also supports secrets management, storing API keys and credentials in encrypted form and injecting them as environment variables at runtime. Webhooks can trigger scripts via HTTP, including events from GitHub, Slack, Stripe, or any other service that sends events. For monitoring, it captures full stdout and stderr output for every run.
The page shows a quick start based on a single Docker container and gives a docker run command for deployment. It also refers to a dashboard and describes starting in three steps: pull the container, upload a script, then schedule it and watch the logs. The site says there are no accounts to create and no SaaS to configure, and that users keep their own server and data. It also links to GitHub and documentation. In class terms, PyRunner is a Python script automation and orchestration tool.
PyRunner is a Workflow automation project. It simplifies running, scheduling, and managing Python scripts via a web interface for automation tasks. PyRunner is an open-source project aimed at developers and automation engineers. The project is open source (MIT). PyRunner is available on the web and the command line, and it can be self-hosted.
Behind PyRunner is hassancs91, and it first shipped in 2026. The project is developed in the open on GitHub with 137 stars and 34 commits in the last 90 days. Key capabilities include script scheduling, web interface, and docker deployment.
Summary written by a language model from the project’s public pages.
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