diff --git a/extensions/memory-lancedb/clawdbot.plugin.json b/extensions/memory-lancedb/clawdbot.plugin.json
index 94cd679ca..9174341ec 100644
--- a/extensions/memory-lancedb/clawdbot.plugin.json
+++ b/extensions/memory-lancedb/clawdbot.plugin.json
@@ -6,12 +6,24 @@
       "label": "OpenAI API Key",
       "sensitive": true,
       "placeholder": "sk-proj-...",
-      "help": "API key for OpenAI embeddings (or use ${OPENAI_API_KEY})"
+      "help": "API key for embeddings (use 'not-needed' for local servers)"
     },
     "embedding.model": {
       "label": "Embedding Model",
       "placeholder": "text-embedding-3-small",
-      "help": "OpenAI embedding model to use"
+      "help": "Embedding model name"
+    },
+    "embedding.baseUrl": {
+      "label": "Custom Endpoint URL",
+      "placeholder": "http://localhost:8080/v1",
+      "help": "Custom OpenAI-compatible embedding endpoint (for local/self-hosted servers)",
+      "advanced": true
+    },
+    "embedding.dimensions": {
+      "label": "Vector Dimensions",
+      "placeholder": "1536",
+      "help": "Override vector dimensions (required for custom models not in built-in list)",
+      "advanced": true
     },
     "dbPath": {
       "label": "Database Path",
@@ -39,11 +51,15 @@
             "type": "string"
           },
           "model": {
+            "type": "string"
+          },
+          "baseUrl": {
             "type": "string",
-            "enum": [
-              "text-embedding-3-small",
-              "text-embedding-3-large"
-            ]
+            "description": "Custom OpenAI-compatible endpoint URL"
+          },
+          "dimensions": {
+            "type": "number",
+            "description": "Override vector dimensions for custom models"
           }
         },
         "required": [
diff --git a/extensions/memory-lancedb/config.ts b/extensions/memory-lancedb/config.ts
index c0382392f..9fbe4f9ba 100644
--- a/extensions/memory-lancedb/config.ts
+++ b/extensions/memory-lancedb/config.ts
@@ -7,6 +7,8 @@ export type MemoryConfig = {
     provider: "openai";
     model?: string;
     apiKey: string;
+    baseUrl?: string;      // Custom endpoint URL (for local/self-hosted embeddings)
+    dimensions?: number;   // Override vector dimensions
   };
   dbPath?: string;
   autoCapture?: boolean;
@@ -34,10 +36,14 @@ function assertAllowedKeys(
   throw new Error(`${label} has unknown keys: ${unknown.join(", ")}`);
 }
 
-export function vectorDimsForModel(model: string): number {
+export function vectorDimsForModel(model: string, customDims?: number): number {
+  // Custom dimensions override built-in model lookup
+  if (customDims) return customDims;
   const dims = EMBEDDING_DIMENSIONS[model];
   if (!dims) {
-    throw new Error(`Unsupported embedding model: ${model}`);
+    throw new Error(
+      `Unsupported embedding model: ${model}. Specify dimensions manually via embedding.dimensions.`
+    );
   }
   return dims;
 }
@@ -54,7 +60,10 @@ function resolveEnvVars(value: string): string {
 
 function resolveEmbeddingModel(embedding: Record<string, unknown>): string {
   const model = typeof embedding.model === "string" ? embedding.model : DEFAULT_MODEL;
-  vectorDimsForModel(model);
+  // Skip dimension validation if custom dimensions provided
+  if (typeof embedding.dimensions !== "number") {
+    vectorDimsForModel(model);
+  }
   return model;
 }
 
@@ -70,15 +79,19 @@ export const memoryConfigSchema = {
     if (!embedding || typeof embedding.apiKey !== "string") {
       throw new Error("embedding.apiKey is required");
     }
-    assertAllowedKeys(embedding, ["apiKey", "model"], "embedding config");
+    assertAllowedKeys(embedding, ["apiKey", "model", "baseUrl", "dimensions"], "embedding config");
 
     const model = resolveEmbeddingModel(embedding);
+    const baseUrl = typeof embedding.baseUrl === "string" ? embedding.baseUrl : undefined;
+    const dimensions = typeof embedding.dimensions === "number" ? embedding.dimensions : undefined;
 
     return {
       embedding: {
         provider: "openai",
         model,
         apiKey: resolveEnvVars(embedding.apiKey),
+        baseUrl,
+        dimensions,
       },
       dbPath: typeof cfg.dbPath === "string" ? cfg.dbPath : DEFAULT_DB_PATH,
       autoCapture: cfg.autoCapture !== false,
@@ -90,12 +103,24 @@ export const memoryConfigSchema = {
       label: "OpenAI API Key",
       sensitive: true,
       placeholder: "sk-proj-...",
-      help: "API key for OpenAI embeddings (or use ${OPENAI_API_KEY})",
+      help: "API key for embeddings (use 'not-needed' for local servers)",
     },
     "embedding.model": {
       label: "Embedding Model",
       placeholder: DEFAULT_MODEL,
-      help: "OpenAI embedding model to use",
+      help: "Embedding model name",
+    },
+    "embedding.baseUrl": {
+      label: "Custom Endpoint URL",
+      placeholder: "http://localhost:8080/v1",
+      help: "Custom OpenAI-compatible embedding endpoint (for local/self-hosted servers)",
+      advanced: true,
+    },
+    "embedding.dimensions": {
+      label: "Vector Dimensions",
+      placeholder: "1536",
+      help: "Override vector dimensions (required for custom models not in built-in list)",
+      advanced: true,
     },
     dbPath: {
       label: "Database Path",
diff --git a/extensions/memory-lancedb/index.ts b/extensions/memory-lancedb/index.ts
index e7daab6f5..9add4fa50 100644
--- a/extensions/memory-lancedb/index.ts
+++ b/extensions/memory-lancedb/index.ts
@@ -156,8 +156,9 @@ class Embeddings {
   constructor(
     apiKey: string,
     private model: string,
+    baseUrl?: string,
   ) {
-    this.client = new OpenAI({ apiKey });
+    this.client = new OpenAI({ apiKey, baseURL: baseUrl });
   }
 
   async embed(text: string): Promise<number[]> {
@@ -223,9 +224,16 @@ const memoryPlugin = {
   register(api: MoltbotPluginApi) {
     const cfg = memoryConfigSchema.parse(api.pluginConfig);
     const resolvedDbPath = api.resolvePath(cfg.dbPath!);
-    const vectorDim = vectorDimsForModel(cfg.embedding.model ?? "text-embedding-3-small");
+    const vectorDim = vectorDimsForModel(
+      cfg.embedding.model ?? "text-embedding-3-small",
+      cfg.embedding.dimensions,
+    );
     const db = new MemoryDB(resolvedDbPath, vectorDim);
-    const embeddings = new Embeddings(cfg.embedding.apiKey, cfg.embedding.model!);
+    const embeddings = new Embeddings(
+      cfg.embedding.apiKey,
+      cfg.embedding.model!,
+      cfg.embedding.baseUrl,
+    );
 
     api.logger.info(
       `memory-lancedb: plugin registered (db: ${resolvedDbPath}, lazy init)`,