Lms

An LM Studio provider plugin for OpenCode.

What it does

• Discovers the chat models your LM Studio server is hosting and exposes them in OpenCode. Embedding models are filtered out by default (OpenCode has no slot that consumes them); list one in provider.lmstudio.models to opt it back in.
• Loads an unloaded LLM on first reference; load progress is logged to the OpenCode server log. Loads at a capped context window (contextLength, default 32k — agent sessions open well past 8k) to keep VRAM in check, and tags the model with an idle ttl (default 1h) so it auto-evicts when unused. A resident instance loaded with a smaller window than the policy is evicted and reloaded at it; concurrent sessions referencing the same cold model share a single load.
• Self-heals when LM Studio starts after OpenCode: discovery is retried on demand (throttled to 30s) from the first request that needs it, so auto-load and model listing recover without an OpenCode restart.
• Forwards an Authorization: Bearer … header to LM Studio when apiKey is set.
• Demotes reasoning_effort: "max" to "xhigh" before requests leave OpenCode, since LM Studio rejects max.
• Sets each model's OpenCode capability flags from what LM Studio reports — reasoning, attachment (vision models), temperature, family, etc. — and marks reasoning-capable models with interleaved: { field: "reasoning_content" } so OpenCode renders the streaming reasoning trace live in the TUI. Tool calling is always advertised as available (LM Studio's trained_for_tool_use flag is unreliable as a gate, so it's used only for a discovery-log diagnostic, not to disable tools).
• Suppresses OpenCode's auto-generated reasoning-effort picker (low/medium/high) for models that only support binary on/off reasoning, since every choice would route to "on" inside LM Studio anyway. Graduated reasoning models keep the picker.

Compatibility

Requires OpenCode ≥ 1.16.2 (validated on 1.17.15). The @opencode-ai/plugin dependency pin governs the plugin API types only, not the OpenCode runtime version you run against.

Set up

Add the plugin and provider to ~/.config/opencode/opencode.jsonc:

{
  "provider": {
    "lmstudio": {
      "name": "LM Studio",
      "options": {
        "baseURL": "http://127.0.0.1:1234",
        "apiKey": "sk-lm-..."
      }
    }
  },
  "model": "lmstudio/google/gemma-4-26b-a4b",
  "plugin": ["@hellogravel/opencode-lms"]
}

Start LM Studio's server (lms server start) and restart OpenCode.

• This plugin extends OpenCode's built-in lmstudio provider in place — it supplies the live model list via OpenCode's provider.models hook. Do not add lmstudio to disabled_providers, or the plugin's models get disabled too.
• apiKey is only needed when LM Studio has API token auth enabled.
• baseURL should not include a /v1 suffix — the plugin appends it.

Upgrading from a pre-0.2 build? The provider id changed from lms to lmstudio. Rename the provider.lms config key to provider.lmstudio, update any "model": "lms/…" reference to "lmstudio/…", and drop the old "disabled_providers": ["lmstudio"] line.

Options

Under provider.lmstudio.options:

Option	Type	Default	Description
baseURL	string	"http://127.0.0.1:1234"	LM Studio server URL
apiKey	string	—	Bearer token sent in the Authorization header
autoDetect	boolean	true	When baseURL is not set, probe localhost ports 1234, 8080, 11434
disableAutoLoad	boolean	false	Skip the auto-load step on first model reference
autoDownload	boolean	false	Download a missing model on first reference (off by default — a typo could trigger a multi-GB download)
loadTimeout	number	600000	Load/unload timeout in ms
downloadTimeout	number	1800000	Download timeout in ms
timeout	number	600000	Overall chat-completion request timeout in ms
chunkTimeout	number	120000	Inter-chunk (time-to-next-token) timeout in ms — raise for SWA models (see note)
contextLength	number	32768	Global cap on the context window a model is loaded with (the VRAM knob). A model whose max is below this loads at its max; adjust per-model with models[<id>].contextLength. A resident instance with a smaller window is unloaded and reloaded at this value on first use, and the advertised limit.context follows it, so OpenCode budgets against the real window
ttl	number	3600	Idle seconds before a loaded model auto-evicts (frees VRAM), sent on each chat completion. The countdown resets on every request, so active models stay resident. 0 = resident (never auto-evict). Reach is limited by LM Studio's API — see the TTL note below

TTL vs auto-load (LM Studio ≤0.4.19): LM Studio applies a request's ttl only when that request JIT-loads the model; it ignores ttl on already-loaded instances, and the REST load endpoint rejects a ttl key outright (HTTP 400). Since this plugin auto-loads cold models via REST (to apply contextLength), plugin-loaded models evict on LM Studio's server-default idle TTL, not options.ttl. The configured ttl fully applies only when disableAutoLoad is set (JIT loads — which also bypass the context cap). Verified live on 0.4.19.

SWA models (e.g. Gemma) and chunkTimeout: llama.cpp can't reuse the prompt cache for sliding-window-attention models, so every turn reprocesses the entire prompt from scratch. No streamed chunks are emitted during that prompt-processing phase, so a large prompt can exceed chunkTimeout before the first token — the request aborts and retries, reprocessing from 0% again, looping indefinitely. If you see prompt processing restart from 0% repeatedly, raise chunkTimeout (e.g. to match timeout) and/or shrink the prompt by disabling unused tools/MCP servers.

Model overrides

Override per-model metadata under provider.lmstudio.models[<id>]:

"models": {
  "google/gemma-4-e4b": {
    "name": "Gemma 4 E4B",
    "reasoning": true,
    "contextLength": 32768,
    "limit": { "context": 131072, "output": 131072 }
  }
}

Field	Type	Description
id	string	LM Studio model identifier (e.g. google/gemma-4-e4b)
name	string	Display name
reasoning	boolean	Mark the model as reasoning-capable
tool_call	boolean	Mark the model as supporting tool calls
modalities	object	e.g. { input: ["text","image"], output: ["text"] }
limit	object	{ context: <ctx>, output: <out> } — UI metadata, not the load-time window
contextLength	number	Load-time context window for this model (the VRAM knob); overrides the global contextLength, still clamped to the model's max

Overrides merge on top of discovered models.

Development

git clone https://github.com/hellogravel/opencode-lms
cd opencode-lms
npm install
npm run build       # Compile TypeScript → dist/
npm run typecheck
npm run test:run

For a containerized OpenCode runtime with this plugin loaded, see docker/.

docker/smoke.sh boots that harness (latest OpenCode, plugin baked in) and asserts GET /config/providers returns 200 and lists lmstudio — the loader-level regression check the unit suite can't cover. Run it before a release and after any OpenCode version bump.

test-live.mjs exercises the plugin against a live LM Studio server:

npm run build
LMS_BASE_URL=http://192.168.1.10:1234 \
LMS_API_KEY=sk-lm-... \
  node test-live.mjs

License

MIT