feat(embeddings): add OpenRouter provider

.env.example

@@ -68,7 +68,7 @@ OM_WEAVIATE_CLASS=OpenMemory
 # --------------------------------------------
 # Embeddings Configuration
 # --------------------------------------------
-# Available providers: openai, gemini, aws, ollama, local, synthetic, orcarouter
+# Available providers: openai, openrouter, gemini, aws, ollama, local, synthetic, orcarouter
 # Embedding models per sector can be configured in models.yaml
 #
 # NOTE: Your selected TIER (fast/smart/deep) affects how embeddings work:
@@ -105,6 +105,11 @@ OM_EMBED_DELAY_MS=200
 # Model override for all sector embeddings (leave empty to use defaults)
 # OM_OPENAI_MODEL=text-embedding-qwen3-embedding-4b
 
+# OpenRouter Embeddings (OpenAI-compatible gateway)
+# OM_OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
+# Model override for all sector embeddings (leave empty to use defaults)
+# OM_OPENROUTER_EMBEDDING_MODEL=openai/text-embedding-3-small
+
 # Alibaba Cloud Model Studio (Bailian / DashScope) uses the same
 # OpenAI-compatible provider. Set OPENAI_API_KEY and OM_OPENAI_API_KEY empty
 # when using DASHSCOPE_API_KEY so the explicit OpenAI keys keep precedence.
@@ -127,6 +132,9 @@ OM_MAX_PAYLOAD_SIZE=1000000
 # OpenAI Embeddings
 OPENAI_API_KEY=your-openai-api-key-here
 
+# OpenRouter Embeddings
+OPENROUTER_API_KEY=sk-or-v1-your-key-here
+
 # Google Gemini Embeddings
 GEMINI_API_KEY=your-gemini-api-key-here
 

README.md

@@ -308,7 +308,7 @@ results = mem.search("allergies", user_id="user123")
   “Waypoint” traces that show exactly which nodes were used in context.
 
 - **Embeddings**  
-  OpenAI, Gemini, Ollama, AWS, [OrcaRouter](https://www.orcarouter.ai), synthetic fallback.
+  OpenAI, [OpenRouter](https://openrouter.ai), Gemini, Ollama, AWS, [OrcaRouter](https://www.orcarouter.ai), synthetic fallback.
 
 - **Integrations**  
   LangChain, CrewAI, AutoGen, Streamlit, MCP, VS Code, IDEs.

dashboard/app/api/settings/route.ts

@@ -47,6 +47,8 @@ export async function GET() {
 
         const masked = { ...settings }
         if (masked.OPENAI_API_KEY) masked.OPENAI_API_KEY = '***'
+        if (masked.OPENROUTER_API_KEY) masked.OPENROUTER_API_KEY = '***'
+        if (masked.OM_OPENROUTER_API_KEY) masked.OM_OPENROUTER_API_KEY = '***'
         if (masked.DASHSCOPE_API_KEY) masked.DASHSCOPE_API_KEY = '***'
         if (masked.GEMINI_API_KEY) masked.GEMINI_API_KEY = '***'
         if (masked.AWS_SECRET_ACCESS_KEY) masked.AWS_SECRET_ACCESS_KEY = "***"

dashboard/app/settings/page.tsx

@@ -188,7 +188,7 @@ const SETTING_METADATA: Record<string, SettingInfo> = {
         label: 'Embedding Provider',
         description: 'AI provider for generating embeddings (used in SMART/DEEP tiers)',
         type: 'select',
-        options: ['openai', 'gemini', 'aws', 'ollama', 'local', 'synthetic', 'orcarouter']
+        options: ['openai', 'openrouter', 'gemini', 'aws', 'ollama', 'local', 'synthetic', 'orcarouter']
     },
     OM_VEC_DIM: {
         category: 'Embeddings',
@@ -231,6 +231,20 @@ const SETTING_METADATA: Record<string, SettingInfo> = {
         type: 'text',
         placeholder: 'text-embedding-3-small'
     },
+    OM_OPENROUTER_BASE_URL: {
+        category: 'Embeddings',
+        label: 'OpenRouter Base URL',
+        description: 'OpenRouter OpenAI-compatible API endpoint',
+        type: 'text',
+        placeholder: 'https://openrouter.ai/api/v1'
+    },
+    OM_OPENROUTER_EMBEDDING_MODEL: {
+        category: 'Embeddings',
+        label: 'OpenRouter Model Override',
+        description: 'Override the default OpenRouter embedding model',
+        type: 'text',
+        placeholder: 'openai/text-embedding-3-small'
+    },
     OM_ORCAROUTER_BASE_URL: {
         category: 'Embeddings',
         label: 'OrcaRouter Base URL',
@@ -259,6 +273,13 @@ const SETTING_METADATA: Record<string, SettingInfo> = {
         type: 'password',
         placeholder: 'sk-...'
     },
+    OPENROUTER_API_KEY: {
+        category: 'API Keys',
+        label: 'OpenRouter API Key',
+        description: 'API key for OpenRouter embeddings',
+        type: 'password',
+        placeholder: 'sk-or-v1-...'
+    },
     DASHSCOPE_API_KEY: {
         category: 'API Keys',
         label: 'DashScope API Key',

docker-compose.yml

@@ -39,6 +39,11 @@ services:
       - DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY:-}
       - OM_OPENAI_BASE_URL=${OM_OPENAI_BASE_URL:-https://api.openai.com/v1}
       - OM_OPENAI_MODEL=${OM_OPENAI_MODEL:-}
+      - OPENROUTER_API_KEY=${OPENROUTER_API_KEY:-}
+      - OM_OPENROUTER_API_KEY=${OM_OPENROUTER_API_KEY:-}
+      - OM_OPENROUTER_BASE_URL=${OM_OPENROUTER_BASE_URL:-https://openrouter.ai/api/v1}
+      - OM_OPENROUTER_MODEL=${OM_OPENROUTER_MODEL:-}
+      - OM_OPENROUTER_EMBEDDING_MODEL=${OM_OPENROUTER_EMBEDDING_MODEL:-}
 
       # Gemini Provider
       - GEMINI_API_KEY=${GEMINI_API_KEY:-}

docs/mcp.md

@@ -38,7 +38,7 @@ Configure your MCP client (`.mcp.json` for Cursor/Windsurf, or Claude settings):
 #### Environment Variables
 
 - `OM_METADATA_BACKEND`: Database type (`sqlite` or `postgres`)
-- `OM_EMBEDDINGS`: Embedding provider (`openai`, `aws`, `ollama`, `synthetic`, `orcarouter`)
+- `OM_EMBEDDINGS`: Embedding provider (`openai`, `openrouter`, `aws`, `ollama`, `synthetic`, `orcarouter`)
 - Database credentials (if using Postgres): `OM_PG_HOST`, `OM_PG_DB`, `OM_PG_USER`, `OM_PG_PASSWORD`
 
 ## Memory Systems
@@ -612,7 +612,7 @@ If `type="factual"` returns no results:
 
 Ensure environment variables are set:
 - Postgres: `OM_PG_HOST`, `OM_PG_DB`, `OM_PG_USER`, `OM_PG_PASSWORD`
-- Embeddings: `OPENAI_API_KEY` (or a compatible provider key such as `DASHSCOPE_API_KEY` or `ORCAROUTER_API_KEY`)
+- Embeddings: `OPENAI_API_KEY` (or a compatible provider key such as `OPENROUTER_API_KEY`, `DASHSCOPE_API_KEY`, or `ORCAROUTER_API_KEY`)
 
 ## Performance
 

models.yml

@@ -8,6 +8,7 @@
 episodic:
   ollama: nomic-embed-text
   openai: text-embedding-3-small
+  openrouter: openai/text-embedding-3-small
   gemini: models/gemini-embedding-001
   aws: amazon.titan-embed-text-v2:0
   local: all-MiniLM-L6-v2
@@ -16,6 +17,7 @@ episodic:
 semantic:
   ollama: nomic-embed-text
   openai: text-embedding-3-small
+  openrouter: openai/text-embedding-3-small
   gemini: models/gemini-embedding-001
   aws: amazon.titan-embed-text-v2:0
   local: all-MiniLM-L6-v2
@@ -24,6 +26,7 @@ semantic:
 procedural:
   ollama: nomic-embed-text
   openai: text-embedding-3-small
+  openrouter: openai/text-embedding-3-small
   gemini: models/gemini-embedding-001
   aws: amazon.titan-embed-text-v2:0
   local: all-MiniLM-L6-v2
@@ -32,6 +35,7 @@ procedural:
 emotional:
   ollama: nomic-embed-text
   openai: text-embedding-3-small
+  openrouter: openai/text-embedding-3-small
   gemini: models/gemini-embedding-001
   aws: amazon.titan-embed-text-v2:0
   local: all-MiniLM-L6-v2
@@ -40,6 +44,7 @@ emotional:
 reflective:
   ollama: nomic-embed-text
   openai: text-embedding-3-large
+  openrouter: openai/text-embedding-3-small
   gemini: models/gemini-embedding-001
   aws: amazon.titan-embed-text-v2:0
   local: all-mpnet-base-v2
@@ -61,4 +66,3 @@ reflective:
 
 #AWS models:
 # - amazon.titan-embed-text-v2:0 (1024d, 512d, 256)
-

packages/openmemory-js/README.md

@@ -93,7 +93,7 @@ OM_DB_PATH=./data/om.db              # sqlite file path (default: ./data/openmem
 OM_DB_URL=sqlite://:memory:          # or use in-memory db
 
 # embeddings
-OM_EMBEDDINGS=ollama                 # synthetic | openai | gemini | ollama
+OM_EMBEDDINGS=ollama                 # synthetic | openai | openrouter | gemini | ollama
 OM_OLLAMA_URL=http://localhost:11434
 OM_OLLAMA_MODEL=embeddinggemma       # or nomic-embed-text, mxbai-embed-large
 
@@ -102,6 +102,11 @@ OM_OLLAMA_MODEL=embeddinggemma       # or nomic-embed-text, mxbai-embed-large
 # OM_OPENAI_BASE_URL=https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1
 # OM_OPENAI_MODEL=text-embedding-v4
 
+# OpenRouter through its OpenAI-compatible endpoint
+# OPENROUTER_API_KEY=sk-or-v1-...
+# OM_OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
+# OM_OPENROUTER_EMBEDDING_MODEL=openai/text-embedding-3-small
+
 # openai
 OPENAI_API_KEY=sk-...
 OM_OPENAI_MODEL=text-embedding-3-small

packages/openmemory-js/src/core/cfg.ts

@@ -52,6 +52,16 @@ export const env = {
         "https://api.openai.com/v1",
     ),
     openai_model: process.env.OM_OPENAI_MODEL,
+    openrouter_key:
+        process.env.OPENROUTER_API_KEY ||
+        process.env.OM_OPENROUTER_API_KEY ||
+        "",
+    openrouter_base_url: str(
+        process.env.OM_OPENROUTER_BASE_URL,
+        "https://openrouter.ai/api/v1",
+    ),
+    openrouter_model: process.env.OM_OPENROUTER_MODEL,
+    openrouter_embedding_model: process.env.OM_OPENROUTER_EMBEDDING_MODEL,
     gemini_key:
         process.env.GEMINI_API_KEY || process.env.OM_GEMINI_API_KEY || "",
     AWS_REGION: process.env.AWS_REGION || "",

packages/openmemory-js/src/core/models.ts

@@ -51,6 +51,7 @@ const get_defaults = (): model_cfg => ({
     episodic: {
         ollama: "nomic-embed-text",
         openai: "text-embedding-3-small",
+        openrouter: "openai/text-embedding-3-small",
         gemini: "models/gemini-embedding-001",
         aws: "amazon.titan-embed-text-v2:0",
         siray: "text-embedding-3-small",
@@ -60,6 +61,7 @@ const get_defaults = (): model_cfg => ({
     semantic: {
         ollama: "nomic-embed-text",
         openai: "text-embedding-3-small",
+        openrouter: "openai/text-embedding-3-small",
         gemini: "models/gemini-embedding-001",
         aws: "amazon.titan-embed-text-v2:0",
         siray: "text-embedding-3-small",
@@ -71,6 +73,7 @@ const get_defaults = (): model_cfg => ({
         openai: "text-embedding-3-small",
         gemini: "models/gemini-embedding-001",
         aws: "amazon.titan-embed-text-v2:0",
+        openrouter: "openai/text-embedding-3-small",
         orcarouter: "orcarouter/auto",
         local: "all-MiniLM-L6-v2",
     },
@@ -79,6 +82,7 @@ const get_defaults = (): model_cfg => ({
         openai: "text-embedding-3-small",
         gemini: "models/gemini-embedding-001",
         aws: "amazon.titan-embed-text-v2:0",
+        openrouter: "openai/text-embedding-3-small",
         orcarouter: "orcarouter/auto",
         local: "all-MiniLM-L6-v2",
     },
@@ -87,6 +91,7 @@ const get_defaults = (): model_cfg => ({
         openai: "text-embedding-3-large",
         gemini: "models/gemini-embedding-001",
         aws: "amazon.titan-embed-text-v2:0",
+        openrouter: "openai/text-embedding-3-small",
         orcarouter: "orcarouter/auto",
         local: "all-mpnet-base-v2",
     },
@@ -99,6 +104,12 @@ export const get_model = (sector: string, provider: string): string => {
     if (provider === "openai" && process.env.OM_OPENAI_MODEL) {
         return process.env.OM_OPENAI_MODEL;
     }
+    if (
+        provider === "openrouter" &&
+        process.env.OM_OPENROUTER_EMBEDDING_MODEL
+    ) {
+        return process.env.OM_OPENROUTER_EMBEDDING_MODEL;
+    }
     if (provider === "orcarouter" && process.env.OM_ORCAROUTER_EMBEDDING_MODEL) {
         return process.env.OM_ORCAROUTER_EMBEDDING_MODEL;
     }

packages/openmemory-js/src/memory/embed.ts

@@ -109,14 +109,20 @@ export async function embedQueryForAllSectors(
         return result;
     }
 
-    if (env.emb_kind === "gemini" && env.gemini_key) {
+    if (
+        env.embed_mode === "simple" &&
+        (env.emb_kind === "gemini" ||
+            env.emb_kind === "openai" ||
+            env.emb_kind === "openrouter" ||
+            env.emb_kind === "orcarouter")
+    ) {
         try {
             const txts: Record<string, string> = {};
             for (const s of sectors) txts[s] = query;
-            return await emb_gemini(txts);
+            return await emb_batch_with_fallback(txts);
         } catch (e) {
             console.error(
-                `[EMBED] Gemini batch failed, falling back to sequential: ${e}`,
+                `[EMBED] Batch query embedding failed, falling back to sequential: ${e}`,
             );
         }
     }
@@ -134,6 +140,8 @@ async function embed_with_provider(
     switch (provider) {
         case "openai":
             return await emb_openai(t, s);
+        case "openrouter":
+            return await emb_openrouter(t, s);
         case "gemini":
             return (await emb_gemini({ [s]: t }))[s];
         case "ollama":
@@ -205,6 +213,9 @@ async function emb_batch_with_fallback(
                 case "openai":
                     result = await emb_batch_openai(txts);
                     break;
+                case "openrouter":
+                    result = await emb_batch_openrouter(txts);
+                    break;
                 case "orcarouter":
                     result = await emb_batch_orcarouter(txts);
                     break;
@@ -291,6 +302,58 @@ async function emb_batch_openai(
     return out;
 }
 
+async function emb_openrouter(t: string, s: string): Promise<number[]> {
+    if (!env.openrouter_key) throw new Error("OpenRouter key missing");
+    const m = get_model(s, "openrouter");
+    const r = await fetchWithTimeout(
+        `${env.openrouter_base_url.replace(/\/$/, "")}/embeddings`,
+        {
+            method: "POST",
+            headers: {
+                "content-type": "application/json",
+                authorization: `Bearer ${env.openrouter_key}`,
+            },
+            body: JSON.stringify({
+                input: t,
+                model: env.openrouter_embedding_model || m,
+                ...(env.vec_dim ? { dimensions: env.vec_dim } : {}),
+            }),
+        },
+    );
+    if (!r.ok) throw new Error(`OpenRouter: ${r.status}`);
+    return ((await r.json()) as any).data[0].embedding;
+}
+
+async function emb_batch_openrouter(
+    txts: Record<string, string>,
+): Promise<Record<string, number[]>> {
+    if (!env.openrouter_key) throw new Error("OpenRouter key missing");
+    const secs = Object.keys(txts),
+        m = get_model("semantic", "openrouter");
+    const r = await fetchWithTimeout(
+        `${env.openrouter_base_url.replace(/\/$/, "")}/embeddings`,
+        {
+            method: "POST",
+            headers: {
+                "content-type": "application/json",
+                authorization: `Bearer ${env.openrouter_key}`,
+            },
+            body: JSON.stringify({
+                input: Object.values(txts),
+                model: env.openrouter_embedding_model || m,
+                ...(env.vec_dim ? { dimensions: env.vec_dim } : {}),
+            }),
+        },
+    );
+    if (!r.ok) throw new Error(`OpenRouter batch: ${r.status}`);
+    const d = (await r.json()) as any,
+        out: Record<string, number[]> = {};
+    secs.forEach((sector, index) => {
+        out[sector] = d.data[index].embedding;
+    });
+    return out;
+}
+
 const task_map: Record<string, string> = {
     episodic: "RETRIEVAL_DOCUMENT",
     semantic: "SEMANTIC_SIMILARITY",
@@ -631,6 +694,7 @@ export async function embedMultiSector(
                 simp &&
                 (env.emb_kind === "gemini" ||
                     env.emb_kind === "openai" ||
+                    env.emb_kind === "openrouter" ||
                     env.emb_kind === "orcarouter")
             ) {
                 console.error(
@@ -738,6 +802,7 @@ export const getEmbeddingInfo = () => {
             env.embed_mode === "simple" &&
             (env.emb_kind === "gemini" ||
                 env.emb_kind === "openai" ||
+                env.emb_kind === "openrouter" ||
                 env.emb_kind === "orcarouter"),
         advanced_parallel: env.adv_embed_parallel,
         embed_delay_ms: env.embed_delay_ms,
@@ -754,6 +819,18 @@ export const getEmbeddingInfo = () => {
             emotional: get_model("emotional", "openai"),
             reflective: get_model("reflective", "openai"),
         };
+    } else if (env.emb_kind === "openrouter") {
+        i.configured = !!env.openrouter_key;
+        i.base_url = env.openrouter_base_url;
+        i.model_override = env.openrouter_embedding_model || null;
+        i.batch_api = env.embed_mode === "simple";
+        i.models = {
+            episodic: get_model("episodic", "openrouter"),
+            semantic: get_model("semantic", "openrouter"),
+            procedural: get_model("procedural", "openrouter"),
+            emotional: get_model("emotional", "openrouter"),
+            reflective: get_model("reflective", "openrouter"),
+        };
     } else if (env.emb_kind === "gemini") {
         i.configured = !!env.gemini_key;
         i.batch_api = env.embed_mode === "simple";