explaingit

ferroxlabs/wayland-core

Analysis updated 2026-05-18

20RustAudience · developerComplexity · 3/5LicenseSetup · easy

TLDR

A Rust command-line engine that runs AI agents against local files and shell commands, supporting around 20 model providers.

Mindmap

mindmap
  root((wayland-core))
    What it does
      Runs AI agent tasks
      Reads files and runs shell commands
      Fans out to parallel agents
    Tech stack
      Rust
      CLI
    Use cases
      Multi provider agent tasks
      Parallel worktree swarms
      Sandboxed automation
    Audience
      Developers

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Run a plain-text task through an AI agent that can read files and run shell commands

USE CASE 2

Switch between around 20 AI providers with a configuration change instead of code changes

USE CASE 3

Run several agents in parallel, each in its own isolated repository branch

USE CASE 4

Drive the engine headlessly from another application over a JSON-Lines protocol

What is it built with?

RustCLIBubblewrapAppContainer

How does it compare?

ferroxlabs/wayland-coreakitaonrails/ratatui-bubbleteadeepdiy/pdf2md
Stars202020
LanguageRustRustRust
Setup difficultyeasyeasyeasy
Complexity3/52/52/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Available as a prebuilt binary via npm or GitHub Releases, refuses to run shell commands without a working sandbox.

Use freely, including commercially, as long as you include the license and note any changes you make.

In plain English

Wayland Core is an AI agent engine written in Rust that runs from the command line. You give it a task in plain text, and it connects to a language model, uses local tools like file reading and shell commands, and works through the task step by step. It is designed to be a standalone engine, not tied to any single AI provider or any particular editor. The tool supports around 20 AI providers out of the box, including Anthropic, OpenAI, Google Gemini, AWS, Azure, Groq, DeepSeek, Mistral, and others, plus a catalog of over a hundred additional model endpoints. Switching between providers is a configuration change rather than a code change. When a provider call fails, the engine automatically retries, reconnects if the connection drops mid-stream, rotates through backup API keys, and falls over to a different provider if one is set as a fallback. Beyond running a single agent, Wayland Core can fan work out to multiple agents running in parallel. A worktree swarm runs several workers simultaneously, each in its own isolated copy of the repository on its own branch, so a failure in one worker does not affect the others. Declarative workflows let you define a sequence of stages that are validated against a schema, and a stage that produces invalid output is automatically retried with the error included. Tool execution happens inside a sandbox by default. On Linux it uses bubblewrap, on macOS it uses sandbox-exec, and on Windows it uses AppContainer. If no working sandbox is available and the user has not explicitly opted out, the engine refuses to run model-driven shell commands rather than running them without protection. Network egress is controlled through a single point, and a classifier blocks requests that look like they might be sending data to unexpected destinations. The engine runs three ways from one binary: as a one-shot command that answers a question and exits, as a full-screen interactive terminal interface, or as a headless process that another application drives over a JSON-Lines protocol. It is available through npm as a prebuilt binary, from GitHub Releases, or compiled from source. The project is licensed under Apache-2.0.

Copy-paste prompts

Prompt 1
Explain how Wayland Core's worktree swarm runs multiple agents in isolated branches at once
Prompt 2
Help me configure Wayland Core to fall back to a backup AI provider when one fails
Prompt 3
Show me how the sandboxing works on Linux, macOS, and Windows for shell command safety
Prompt 4
Walk me through defining a declarative workflow with stages validated against a schema

Frequently asked questions

What is wayland-core?

A Rust command-line engine that runs AI agents against local files and shell commands, supporting around 20 model providers.

What language is wayland-core written in?

Mainly Rust. The stack also includes Rust, CLI, Bubblewrap.

What license does wayland-core use?

Use freely, including commercially, as long as you include the license and note any changes you make.

How hard is wayland-core to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is wayland-core for?

Mainly developer.

Open on GitHub → Explain another repo

This repo across BitVibe Labs

Verify against the repo before relying on details.