# ollie

What happens if agent primitives are exposed as an ordinary filesystem? How much can be subtracted from AI agent implementations without reducing their usefulness? These are some of the motivating questions behind `ollie`.

- The [core system](https://github.com/lneely/ollie-core) provides a public library of AI agent primitives.
- The [9p filesystem server (Go)](https://github.com/lneely/ollie-9p) creates a virtual filesystem exposing the important state and behaviors as ordinary files and operations.
- A reference [TUI](https://github.com/lneely/ollie-tui) implementation proves that it is possible to build a functioning and user-friendly terminal front-end using only the Go standard library.
- And, just because I like my Emacs friends, a reference [Emacs interface](https://github.com/lneely/ollie-el) front-end implementation.

## Project Structure

- `core/`:
- `tui/`:
- `9p/`:
- `el/`:

## Getting Started

Clone with submodules:

```bash
git clone --recurse-submodules https://github.com/lneely/ollie.git
```

Or after cloning:

```bash
git submodule update --init --recursive
```

## Building

Build everything from the monorepo root:

```bash
mk
```

Or build specific components:

```bash
mk core    # Build core library
mk tui     # Build terminal UI
mk 9p      # Build 9p filesystem server
```

For Emacs Lisp, copy `el/ollie.el` into your own Emacs configuration directory, and:

```lisp
(require 'ollie)
```

## Development

Update all submodules to latest:

```bash
git submodule update --remote
```

## Observed Benefits

**Crash resiliency** — `reasoning_plan` persists plans to the `ollie/pl` filesystem by default. If a session crashes or is killed mid-task, recovery is trivial: point a fresh session at the plan that was being executed. The agent's prompting already tells it how to discover plans in `pl/`, so it can resume cold — reading the plan, seeing which steps are done and which aren't, and picking up where the previous session left off. No context replay, no manual re-prompting, no lost work.

## Credits

Many sources of inspiration:

- [Plan 9 from Bell Labs](https://9fans.net) — for an interesting system
- [@9fans](https://github.com/9fans) — for the Plan 9 port
- [Suckless](https://suckless.org) — for articulating good software development principles
- [@simonfxr](https://github.com/simonfxr) — for a solid agent baseline to "borrow" from, and other nifty ideas
- [@aws](https://github.com/aws/amazon-q-developer-cli) — for a solid open-source agent implementation

## License

GPLv3