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

# 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 <this-repo-url> ~/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):

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

                ┌─────────────────────────────────────────────┐
                │              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

# 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 <this-repo-url> ~/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:

./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/:

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/<name>/ with the agent's config file(s).
2. Create scripts/start-<name>.sh that launches it pointed at the workbench config and the LM Studio endpoint.
3. (If it has a daemon) Create scripts/stop-<name>.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.