feat: support modelTypes whitelist for discovered LM Studio models

README.md

@@ -30,6 +30,8 @@ Add the plugin to your `opencode.json`:
 {
   "$schema": "https://opencode.ai/config.json",
   "plugin": [
+    // For dev testing, you can replace this with file:$YOUR_LOCAL_PLUGIN_REPO_PATH
+    // Example: file:/Users/you/workspace/opencode-lmstudio
     "opencode-lmstudio@latest"
   ],
   "provider": {
@@ -77,6 +79,40 @@ You can also manually configure the provider with specific models:
 
 The plugin will automatically discover and add any additional models available in LM Studio that aren't already configured.
 
+### Filter Models by `modelTypes`
+
+Configure `modelTypes` under `provider.lmstudio.options.modelTypes`:
+
+```json
+{
+  "$schema": "https://opencode.ai/config.json",
+  "plugin": [
+    "opencode-lmstudio@latest"
+  ],
+  "provider": {
+    "lmstudio": {
+      "npm": "@ai-sdk/openai-compatible",
+      "name": "LM Studio (local)",
+      "options": {
+        "baseURL": "http://127.0.0.1:1234/v1",
+        "modelTypes": ["unknown"]
+      }
+    }
+  }
+}
+```
+
+Allowed values:
+
+- `chat`
+- `embedding`
+- `unknown`
+
+Current behavior:
+
+- `modelTypes` is derived from the LM Studio model `id`, not a dedicated API type field.
+- `unknown` means the model `id` did not match the plugin's current `chat` or `embedding` rules.
+
 ## How It Works
 
 1. On OpenCode startup, the plugin's `config` hook is called
@@ -99,4 +135,3 @@ MIT
 ## Contributing
 
 Contributions are welcome! Please feel free to submit a Pull Request.
-

src/index.ts

@@ -1 +1 @@
-export { LMStudioPlugin } from './plugin'
+export { LMStudioPlugin } from './plugin'

src/plugin/enhance-config.ts

@@ -3,9 +3,25 @@ import { ToastNotifier } from '../ui/toast-notifier'
 import { categorizeModel, formatModelName, extractModelOwner } from '../utils'
 import { normalizeBaseURL, checkLMStudioHealth, discoverLMStudioModels, autoDetectLMStudio } from '../utils/lmstudio-api'
 import type { PluginInput } from '@opencode-ai/plugin'
-import type { LMStudioModel } from '../types'
+import type { LMStudioModel, ModelType } from '../types'
 
 const modelStatusCache = new ModelStatusCache()
+const allowedModelTypes = ['chat', 'embedding', 'unknown'] as const
+
+function getConfiguredModelTypes(lmstudioProvider: any): Set<ModelType> | null {
+  const modelTypes = lmstudioProvider?.options?.modelTypes ?? lmstudioProvider?.options?.model_types
+
+  if (!Array.isArray(modelTypes) || modelTypes.length === 0) {
+    return null
+  }
+
+  return new Set(
+    modelTypes.filter((modelType: unknown): modelType is ModelType =>
+      typeof modelType === 'string' &&
+      (allowedModelTypes as readonly string[]).includes(modelType)
+    )
+  )
+}
 
 export async function enhanceConfig(
   config: any,
@@ -64,6 +80,7 @@ export async function enhanceConfig(
       // Merge discovered models with configured models
       const existingModels = lmstudioProvider.models || {}
       const discoveredModels: Record<string, any> = {}
+      const configuredModelTypes = getConfiguredModelTypes(lmstudioProvider)
       let chatModelsCount = 0
       let embeddingModelsCount = 0
 
@@ -77,6 +94,11 @@ export async function enhanceConfig(
         // Only add if not already configured
         if (!existingModels[modelKey] && !existingModels[model.id]) {
           const modelType = categorizeModel(model.id)
+
+          if (configuredModelTypes && !configuredModelTypes.has(modelType)) {
+            continue
+          }
+
           const owner = extractModelOwner(model.id)
           const modelConfig: any = {
             id: model.id,
@@ -153,4 +175,3 @@ export async function enhanceConfig(
     toastNotifier.warning("Plugin configuration failed", "Configuration Error").catch(() => {})
   }
 }
-

src/utils/validation/validate-config.ts

@@ -1,5 +1,31 @@
 import type { ValidationResult } from './validation-result'
 
+const allowedModelTypes = new Set(['chat', 'embedding', 'unknown'])
+const allowedModelTypesList = Array.from(allowedModelTypes).join(', ')
+
+function formatInvalidModelTypeError(modelType: unknown): string {
+  const value = String(modelType)
+  const suggestion = value === 'embedded' ? ' Did you mean "embedding"?' : ''
+  return `LM Studio provider options.modelTypes contains invalid value: ${value}. Allowed values: ${allowedModelTypesList}.${suggestion}`
+}
+
+function validateModelTypesOption(
+  errors: string[],
+  modelTypes: unknown,
+  optionPath: string
+): void {
+  if (!Array.isArray(modelTypes)) {
+    errors.push(`LM Studio provider ${optionPath} must be an array`)
+    return
+  }
+
+  for (const modelType of modelTypes) {
+    if (typeof modelType !== 'string' || !allowedModelTypes.has(modelType)) {
+      errors.push(formatInvalidModelTypeError(modelType))
+    }
+  }
+}
+
 export function validateConfig(config: any): ValidationResult {
   const errors: string[] = []
   const warnings: string[] = []
@@ -34,6 +60,12 @@ export function validateConfig(config: any): ValidationResult {
         } else if (!isValidURL(lmstudio.options.baseURL)) {
           warnings.push('LM Studio provider baseURL may be invalid')
         }
+
+        if (lmstudio.options.modelTypes !== undefined) {
+          validateModelTypesOption(errors, lmstudio.options.modelTypes, 'options.modelTypes')
+        } else if (lmstudio.options.model_types !== undefined) {
+          validateModelTypesOption(errors, lmstudio.options.model_types, 'options.model_types')
+        }
       }
 
       // Validate models configuration
@@ -58,4 +90,3 @@ function isValidURL(url: string): boolean {
     return false
   }
 }
-