Sacred review

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Sacred is an open-source Python framework for researchers and developers organizing computational experiments. It is aimed at people who want to define experiments in Python and record enough context to reproduce runs, rather than use a hosted tracking dashboard. Configurations can be defined in code, dictionaries, or files, then changed from Python or the command line. Sacred captures run results, errors, dependencies, machine information, and local source files with repository information. It runs on Linux, Windows, and macOS.

Its central strength is recording context alongside outcomes. Configured values can be injected into captured functions, and users can launch experiments and variants from Python or the command line. Sacred captures standard output and error streams, reports run heartbeats, and supports automatic seeding for reproducible runs. Files created during a run can be stored as artifacts when the selected observer supports them. This makes it a fit for Python workflows where experiment records and reproducibility matter; it is less suited to teams seeking a ready-made dashboard or verified run-comparison workflow.

Sacred leaves storage choices to the user. Observers can write run data to MongoDB, local files, TinyDB, SQL databases, Amazon S3, or Google Cloud Storage, so deployment and maintenance depend on the chosen destination and the user's environment. Slack and Telegram are also listed as integrations. The framework is self-hosted and free, with documentation and community support channels. Choose Sacred if you are comfortable working in Python and configuring storage yourself; consider another product if you need a hosted service or a built-in tracking interface.

Sacred pros and cons

• Where it wins
  • Captures configurations, results, errors, dependencies, and machine details
  • Stores run data through observers, from local files to cloud storage
  • Supports command-line variants and automatic seeding for reproducibility
• Where it doesn't
  • Requires users to run it in their own Python environment
  • Run storage depends on configuring an observer
  • No verified run-comparison feature or hosted tracking dashboard

Sacred fact sheet, pricing and score →

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Last updated 7 Oct 2026 · How we research and update