Analysis updated 2026-05-18
Run a plain-text task through an AI agent that can read files and run shell commands
Switch between around 20 AI providers with a configuration change instead of code changes
Run several agents in parallel, each in its own isolated repository branch
Drive the engine headlessly from another application over a JSON-Lines protocol
| ferroxlabs/wayland-core | akitaonrails/ratatui-bubbletea | deepdiy/pdf2md | |
|---|---|---|---|
| Stars | 20 | 20 | 20 |
| Language | Rust | Rust | Rust |
| Setup difficulty | easy | easy | easy |
| Complexity | 3/5 | 2/5 | 2/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Available as a prebuilt binary via npm or GitHub Releases, refuses to run shell commands without a working sandbox.
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.
A Rust command-line engine that runs AI agents against local files and shell commands, supporting around 20 model providers.
Mainly Rust. The stack also includes Rust, CLI, Bubblewrap.
Use freely, including commercially, as long as you include the license and note any changes you make.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.