ashlr-workbench

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Local coding agent workbench: OpenHands + Goose + Aider + ashlrcode with ashlr-plugin MCP servers

2 stars ShellOthers Updated Jun 29, 2026

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Documentation

ashlr-workbench

Your local coding-agent HQ — four agents, one local LLM, ten MCP servers,

zero cloud dependencies.

What this is

A curated, fully-local toolbox that wires four open-source coding agents

(OpenHands, Goose, Aider, ashlrcode) to a single LM Studio model

(Qwen3-Coder-30B) and the same set of ten ashlr-plugin MCP servers. The

workbench supplies one CLI (`aw`), one healthcheck, and one update path so the

whole stack feels cohesive instead of four loose tools you have to babysit. No

data leaves the machine unless you opt into a cloud fallback.

Quick start

bash

# 1. Clone the supporting plugin (provides the 10 MCP servers)
git clone https://github.com/ashlrai/ashlr-plugin ~/Desktop/ashlr-plugin
cd ~/Desktop/ashlr-plugin && bun install

# 2. Clone the workbench
git clone  ~/Desktop/ashlr-workbench
cd ~/Desktop/ashlr-workbench
cp .env.example .env   # then edit

# 3. Start LM Studio, load qwen/qwen3-coder-30b, click "Start Server"

# 4. Verify
./bin/aw doctor

# 5. Use
./bin/aw start aider          # interactive session in cwd
./bin/aw start openhands      # autonomous Docker-based agent

To put `aw` on your PATH (review first):

bash

ln -sf ~/Desktop/ashlr-workbench/bin/aw /usr/local/bin/aw

The agent lineup

Agent	Form	Best for	Cost
OpenHands	Docker daemon + GUI	Autonomous multi-step PRs, browser-driven tasks	Heavy RAM
Goose	Native Rust CLI	Fast tool-using sessions, smart-approve loop	Light
Aider	Python CLI	Surgical file-by-file refactors with explicit diffs	Light
ashlrcode	npm/Bun CLI (Mason's)	Personal day-to-day work, hooks-aware, cloud fallback	Light

Pick the one that matches the *shape* of the work — see "Usage examples" below.

Architecture

code

┌─────────────────────────────────────────────┐
                │              ashlr-workbench                │
                │                                             │
                │   bin/aw  →  scripts/{start,health,update}  │
                └─────────────────────────────────────────────┘
                                    │
        ┌───────────────────┬───────┴────────┬──────────────────┐
        ▼                   ▼                ▼                  ▼
   ┌──────────┐        ┌────────┐       ┌────────┐         ┌──────────┐
   │ OpenHands│        │ Goose  │       │ Aider  │         │ashlrcode │
   │ (docker) │        │ (rust) │       │(python)│         │ (bun)    │
   └────┬─────┘        └───┬────┘       └───┬────┘         └────┬─────┘
        │                  │                │                   │
        └──────────────┬───┴────────────────┴───────────────────┘
                       │  same MCP surface
                       ▼
            ┌─────────────────────────┐         ┌────────────────┐
            │  ashlr-plugin (10 MCPs) │   ←──   │  LM Studio     │
            │  efficiency / sql /     │         │  Qwen3-Coder   │
            │  bash / tree / http /   │         │  -30B  :1234   │
            │  diff / logs / genome / │         └────────────────┘
            │  orient / github        │
            └─────────────────────────┘         ┌────────────────┐
                                                │ Ollama :11434  │
                                                │  (fallback)    │
                                                └────────────────┘

All four agents talk to the same LLM and the same MCP tools, so behavior is

consistent regardless of which one you launch.

Requirements

macOS 14+ (Apple Silicon recommended; Intel works but slower)
Docker Desktop — for OpenHands
LM Studio with `qwen/qwen3-coder-30b` loaded — primary LLM
Bun ≥ 1.1 — for ashlr-plugin MCP servers and ashlrcode
Python 3.12+ — for Aider
Node ≥ 20 / npm — for `npm install -g ashlrcode`
~32 GB free RAM (Qwen3-Coder-30B in 4-bit needs ~24 GB live)
~30 GB free disk (Docker images + model)
Optional: Ollama as fallback LLM
Optional: `gh` CLI for GitHub PAT (`GITHUB_TOKEN="$(gh auth token)"`)

Installation

bash

# Plugin (provides the MCP servers all 4 agents share)
git clone https://github.com/ashlrai/ashlr-plugin ~/Desktop/ashlr-plugin
cd ~/Desktop/ashlr-plugin && bun install

# Workbench
git clone  ~/Desktop/ashlr-workbench
cd ~/Desktop/ashlr-workbench
cp .env.example .env

# Per-agent installers (only run the ones you want)
./scripts/install-goose.sh           # Goose via Homebrew
pipx install aider-chat              # Aider (or: pip install --user aider-chat)
npm install -g ashlrcode             # ashlrcode
# OpenHands needs no install — `aw start openhands` pulls the image on first run

After install, verify:

bash

./bin/aw doctor    # actionable diagnosis
./bin/aw health    # full 13-point check

Usage examples

Scenario	Best agent	Command
"Refactor this one file's error handling"	Aider	`aw start aider .`
"Add tests, run them, fix until green — autonomous"	OpenHands	`aw start openhands` then GUI prompt
"Quick interactive session, mostly tool calls"	Goose	`aw start goose`
"My usual driver — hooks, recall, fast"	ashlrcode	`aw start ashlrcode`
"Open a PR for me end-to-end"	OpenHands	give it a GitHub URL in the GUI
"Explain what this codebase does"	Goose	`aw start goose` → "orient on cwd"

Configuration

Per-agent configs live under `agents/`:

code

agents/openhands/config.toml      # OpenHands runtime settings
agents/openhands/mcp.json         # MCP servers wired into OpenHands
agents/goose/config.yaml          # Goose source-of-truth (copied to runtime on launch)
agents/aider/aider.conf.yml       # Aider model + UX
agents/ashlrcode/settings.json    # ashlrcode overlay (XAI primary, LM Studio fallback)

Workbench-wide environment lives in `.env` (see `.env.example`).

Troubleshooting

Symptom	Fix
`aw start openhands` says Docker not running	Launch Docker Desktop, wait for whale icon, retry
LM Studio "endpoint not responding"	Open LM Studio → Developer → Start Server, load the model
MCP server fails to start in any agent	`cd ~/Desktop/ashlr-plugin && bun install`
OpenHands GUI loads but agent errors on first turn	Confirm `qwen/qwen3-coder-30b` is the loaded model
`aider` not found after install	`pipx ensurepath` or add `~/.local/bin` to PATH
`ashlrcode` complains about XAI_API_KEY	Set in `.env` or run with LM Studio fallback

For anything else, run `aw doctor` — it prints the exact fix for each problem

it detects.

Contributing / extending

To add a new agent:

1. Create `agents//` with the agent's config file(s).

2. Create `scripts/start-.sh` that launches it pointed at the workbench

config and the LM Studio endpoint.

3. (If it has a daemon) Create `scripts/stop-.sh`.

4. Add it to the `case` statements in `bin/aw` (`require_agent`, `cmd_start`,

`cmd_stop`, `cmd_status`).

5. Add validation lines for its config in `scripts/healthcheck.sh`.

6. Update this README's agent-lineup table and architecture diagram.

See `CLAUDE.md` for project-wide conventions.

License

MIT — see LICENSE.

Frequently asked questions

What is ashlr-workbench?

ashlr-workbench is Local coding agent workbench: OpenHands + Goose + Aider + ashlrcode with ashlr-plugin MCP servers

How do I install ashlr-workbench?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is ashlr-workbench open source?

Yes — it is hosted on GitHub at https://github.com/ashlrai/ashlr-workbench and has 2 stars.

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