Rbbt started as the Ruby Bioinformatics Toolkit.

Over time, many parts of “Rbbt core” were refactored into smaller, reusable libraries under the Scout umbrella. This makes it easier to:

  • reuse the same core primitives in different projects
  • keep configuration and prompts reproducible
  • expose workflows as tools for agents
  • support non-Ruby task implementations (notably Python)

A useful mental model:

  • Rbbt is the user-facing framework + bioinformatics workflows ecosystem.
  • Scout is the modern core library split that powers Rbbt.

Package map (where things live)

Area What you get Scout package(s)
Workflow engine tasks, steps, dependencies, job directories, provenance/persistence scout-gear
TSV / data tables streaming TSV, semantics, indexing, traversal scout-gear
Resources bootstrapping data/software resources scout-essentials
CMD / processes running external tools, logging, streaming IO scout-essentials
AI / chat files endpoints, chat compilation, tool calling, agents scout-ai
Python bridge call Python from Ruby; pandas/TSV helpers; Python workflow client scout-rig

Why the split matters for documentation

This documentation site keeps Rbbt as the front-door (the stable name many users search for), but increasingly documents features in “Scout terms”:

  • because the newest capabilities land in Scout packages first
  • because non-bioinformatics users may consume Scout directly

When a page says “Scout”, you can usually read it as “the modern core of Rbbt”.

How to proceed (practical guidance)

If you are a workflow user

Start with:

If you are a workflow developer

Start with:

If you want the AI-native interface

Start with:

Notes on naming

You may encounter both rbbt and scout command names in different environments.

  • Many deployments keep rbbt as the canonical CLI.
  • Some Scout-only deployments use scout for framework-only usage.

This documentation tries to:

  • use rbbt in Rbbt user-facing examples
  • mention scout-ai explicitly for the AI tooling

See also:


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