Welcome to Ragtime OS on Ragtime OS

Installation

Mon, 01 Jan 0001 00:00:00 +0000

Installation#

To get started with Ragtime OS, follow these steps:

Prerequisites#

Git: Ensure you have Git installed.
Go: Required for building Ragtime OS components.
FUSE: Required for filesystem integration (on macOS, install macfuse).

Clone & Build#

git clone https://github.com/byronellis/ragtime.git
cd ragtime
go build -o ragtime-os cmd/ragtime-os/main.go

Setup#

Initialize the OS:
```
./ragtime-os init
```
Mount the Filesystem:
```
./ragtime-os mount /mnt/ragtime
```
Connect Your Agent: Point your agent harness (like Claude Code) to the mounted directory.

Next Steps#

Learn about The Score.
Explore Ragtime Bridge.
Conduct an Agent Ensemble.

The Score

Mon, 01 Jan 0001 00:00:00 +0000

Coding harness memory systems, any number of orchestration systems, and even tools like OpenClaw are all built around a single thing: a collection of files that any agent uses to coordinate its activities. Even skills and basic instructions take the form of the humble Markdown file. These are quick and easy to implement and rely on instructions that are fairly context-efficient — no need to introduce the infrastructural overhead of MCP servers or even a new CLI.

The Bridge

Mon, 01 Jan 0001 00:00:00 +0000

Every time your agent uses a tool, submits a prompt, or requests a permission, it fires a hook event. Ragtime’s Bridge functionality provides a simple shell integration rt hook and rt statusline that is used to intercept those commands and provide responses if desired. This allows us to implement advanced telemetry, especially with Claude Code which also provides statusline functionality we can intercept, as well as integrate with the same Starlark rule system used in The Score virtual filesystem to customize the agent environment on the fly.

The Ensemble

Mon, 01 Jan 0001 00:00:00 +0000

Terminal multiplexers have recently experienced something of a renaissance as a key tool in driving multi-agent workflows. Steve Yegge even mentions learning tmux as a key skill in agentic coding and the ability to capture agent output and inject “typing” programmatically is indeed extremely useful when trying to drive agent coding harnesses. This is particularly the case as the various harness providers start to limit certain plans to only using their designated harness. Even if they weren’t these providers are tuning their LLMs to work best with their agent harness so there are distinct advantages to staying withihn their respective ecosystems.