diff --git a/docs/components/vectordbs/dbs/upstash-vector.mdx b/docs/components/vectordbs/dbs/upstash-vector.mdx
index 6b20544b8..1858516f8 100644
--- a/docs/components/vectordbs/dbs/upstash-vector.mdx
+++ b/docs/components/vectordbs/dbs/upstash-vector.mdx
@@ -8,6 +8,10 @@ description: "Use Upstash Vector as a serverless vector database in Mem0 with op
 
 You can enable the built-in embedding models by setting `enable_embeddings` to `True`. This allows you to use Upstash's embedding models for vectorization.
 
+<Note>
+    Server-side Upstash embeddings (`enable_embeddings`) are available in the Python SDK only. The TypeScript SDK always embeds text with your configured embedder before writing to Upstash, so use the external embedding provider setup below.
+</Note>
+
 ```python
 import os
 from mem0 import Memory
@@ -34,7 +38,8 @@ m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category"
 
 ### Usage with external embedding providers
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -58,6 +63,36 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+// Set OPENAI_API_KEY, UPSTASH_VECTOR_REST_URL, and UPSTASH_VECTOR_REST_TOKEN in your environment.
+const config = {
+  embedder: {
+    provider: "openai",
+    config: {
+      apiKey: process.env.OPENAI_API_KEY,
+      model: "text-embedding-3-large",
+    },
+  },
+  vectorStore: {
+    provider: "upstash_vector",
+    config: {
+      collectionName: "memories",
+      url: process.env.UPSTASH_VECTOR_REST_URL,
+      token: process.env.UPSTASH_VECTOR_REST_TOKEN,
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Likes to play cricket on weekends", {
+  userId: "alice",
+  metadata: { category: "hobbies" },
+});
+```
+</CodeGroup>
+
 ### Config
 
 Here are the parameters available for configuring Upstash Vector:
@@ -74,3 +109,7 @@ Here are the parameters available for configuring Upstash Vector:
   When `url` and `token` are not provided, the `UPSTASH_VECTOR_REST_URL` and
   `UPSTASH_VECTOR_REST_TOKEN` environment variables are used.
 </Note>
+
+<Note>
+  The TypeScript SDK uses camelCase config keys (`collectionName`, `url`, `token`), where `collectionName` is required. Pass `url` and `token` (or a preconfigured `client`) explicitly, since the TypeScript SDK does not read them from environment variables. `enable_embeddings` is not supported in TypeScript.
+</Note>
diff --git a/mem0-ts/package.json b/mem0-ts/package.json
index 04a0bcaf4..54c4d5822 100644
--- a/mem0-ts/package.json
+++ b/mem0-ts/package.json
@@ -122,6 +122,7 @@
     "@turbopuffer/turbopuffer": "^2.0.0",
     "@types/jest": "29.5.14",
     "@types/pg": "8.11.0",
+    "@upstash/vector": "^1.2.3",
     "better-sqlite3": "^12.6.2",
     "cassandra-driver": "4.8.0",
     "cloudflare": "^4.2.0",
diff --git a/mem0-ts/pnpm-lock.yaml b/mem0-ts/pnpm-lock.yaml
index 5fe30d89c..bafa6ba9f 100644
--- a/mem0-ts/pnpm-lock.yaml
+++ b/mem0-ts/pnpm-lock.yaml
@@ -74,6 +74,9 @@ importers:
       '@types/pg':
         specifier: 8.11.0
         version: 8.11.0
+      '@upstash/vector':
+        specifier: ^1.2.3
+        version: 1.2.3
       axios:
         specifier: ^1.16.0
         version: 1.17.0
@@ -1366,6 +1369,9 @@ packages:
     resolution: {integrity: sha512-jIXhD0eWQ1JA6ln/5Dltyx22UxWNrw0hZmhy2rlv6m6KgF7kplHx3g0fzi09lNmTJQRR91OlemYp3xFnvDK9og==}
     engines: {node: '>=20.0.0'}
 
+  '@upstash/vector@1.2.3':
+    resolution: {integrity: sha512-yXsWKeuHNYyH72BcSZd3bV5ZD5MybAoTvKxkMaeV2UzuGfNzbHBVh5eO+ysTWTFAf8I9XcOueF4tZfAGjCa4Iw==}
+
   abort-controller@3.0.0:
     resolution: {integrity: sha512-h8lQ8tacZYnR3vNQTgibj+tODHI5/+l06Au2Pcriv/Gmet0eaj4TwWH41sO9wnHDiQsEj19q0drzdWdeAHtweg==}
     engines: {node: '>=6.5'}
@@ -5234,6 +5240,8 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
+  '@upstash/vector@1.2.3': {}
+
   abort-controller@3.0.0:
     dependencies:
       event-target-shim: 5.0.1
diff --git a/mem0-ts/src/oss/package.json b/mem0-ts/src/oss/package.json
index 3ea60e431..7d97a894e 100644
--- a/mem0-ts/src/oss/package.json
+++ b/mem0-ts/src/oss/package.json
@@ -16,6 +16,7 @@
     "@anthropic-ai/sdk": "^0.18.0",
     "@google/genai": "^0.7.0",
     "@qdrant/js-client-rest": "^1.13.0",
+    "@upstash/vector": "^1.2.3",
     "@types/node": "^20.11.19",
     "@types/pg": "^8.11.0",
     "@types/redis": "^4.0.10",
diff --git a/mem0-ts/src/oss/src/index.ts b/mem0-ts/src/oss/src/index.ts
index e3e15ab40..e7f7b2134 100644
--- a/mem0-ts/src/oss/src/index.ts
+++ b/mem0-ts/src/oss/src/index.ts
@@ -32,8 +32,11 @@ export * from "./vector_stores/langchain";
 export * from "./vector_stores/vectorize";
 export * from "./vector_stores/azure_ai_search";
 export * from "./vector_stores/pgvector";
+export * from "./vector_stores/upstash_vector";
 export * from "./vector_stores/azure_mysql";
 export * from "./vector_stores/cassandra";
 export * from "./vector_stores/s3_vectors";
 export * from "./vector_stores/vertex_ai_vector_search";
+export * from "./vector_stores/pinecone";
+export * from "./vector_stores/turbopuffer";
 export * from "./utils/factory";
diff --git a/mem0-ts/src/oss/src/utils/factory.ts b/mem0-ts/src/oss/src/utils/factory.ts
index 79197c0d7..1a9526f74 100644
--- a/mem0-ts/src/oss/src/utils/factory.ts
+++ b/mem0-ts/src/oss/src/utils/factory.ts
@@ -42,6 +42,7 @@ import { LangchainEmbedder } from "../embeddings/langchain";
 import { LangchainVectorStore } from "../vector_stores/langchain";
 import { AzureAISearch } from "../vector_stores/azure_ai_search";
 import { PGVector } from "../vector_stores/pgvector";
+import { UpstashVector } from "../vector_stores/upstash_vector";
 import { AzureMySQLDB } from "../vector_stores/azure_mysql";
 import { VertexAIVectorSearch } from "../vector_stores/vertex_ai_vector_search";
 import { CassandraDB } from "../vector_stores/cassandra";
@@ -136,6 +137,8 @@ export class VectorStoreFactory {
         return new VertexAIVectorSearch(config as any);
       case "pgvector":
         return new PGVector(config as any);
+      case "upstash_vector":
+        return new UpstashVector(config as any);
       case "azure_mysql":
         return new AzureMySQLDB(config as any);
       case "cassandra":
diff --git a/mem0-ts/src/oss/src/vector_stores/upstash_vector.ts b/mem0-ts/src/oss/src/vector_stores/upstash_vector.ts
new file mode 100644
index 000000000..4de38e2b2
--- /dev/null
+++ b/mem0-ts/src/oss/src/vector_stores/upstash_vector.ts
@@ -0,0 +1,240 @@
+import { Index, QueryResult, Vector } from "@upstash/vector";
+import { VectorStore } from "./base";
+import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
+
+interface UpstashVectorConfig extends VectorStoreConfig {
+  collectionName: string;
+  url?: string;
+  token?: string;
+  client?: Index<Record<string, unknown>>;
+}
+
+type UpstashMetadata = Record<string, unknown>;
+
+export class UpstashVector implements VectorStore {
+  private readonly client: Index<UpstashMetadata>;
+  private readonly collectionName: string;
+
+  constructor(config: UpstashVectorConfig) {
+    if (!config.collectionName) {
+      throw new Error("collectionName is required for Upstash Vector.");
+    }
+
+    if (config.client) {
+      this.client = config.client;
+    } else if (config.url && config.token) {
+      this.client = new Index({
+        url: config.url,
+        token: config.token,
+      });
+    } else {
+      throw new Error("Either a client or url and token must be provided.");
+    }
+
+    this.collectionName = config.collectionName;
+  }
+
+  async initialize(): Promise<void> {
+    return;
+  }
+
+  async insert(
+    vectors: number[][],
+    ids: string[],
+    payloads: Record<string, any>[],
+  ): Promise<void> {
+    const upsertData = vectors.map((vector, idx) => {
+      return {
+        id: ids[idx],
+        vector,
+        metadata: payloads[idx] ?? {},
+      };
+    });
+
+    await this.client.upsert(upsertData, { namespace: this.collectionName });
+  }
+
+  async search(
+    query: number[],
+    topK: number = 5,
+    filters?: SearchFilters,
+  ): Promise<VectorStoreResult[]> {
+    const response = await this.client.query<UpstashMetadata>(
+      {
+        vector: query,
+        topK,
+        filter: this.convertFilters(filters),
+        includeMetadata: true,
+      },
+      { namespace: this.collectionName },
+    );
+
+    return response.map((result) => this.parseResult(result));
+  }
+
+  async keywordSearch(
+    query: string,
+    topK: number = 5,
+    filters?: SearchFilters,
+  ): Promise<VectorStoreResult[] | null> {
+    try {
+      const response = await this.client.query<UpstashMetadata>(
+        {
+          data: query,
+          topK,
+          filter: this.convertFilters(filters),
+          includeMetadata: true,
+        },
+        { namespace: this.collectionName },
+      );
+
+      return response.map((result) => this.parseResult(result));
+    } catch (error) {
+      console.error(`Error during keyword search for query '${query}':`, error);
+      return null;
+    }
+  }
+
+  async get(vectorId: string): Promise<VectorStoreResult | null> {
+    const response = await this.client.fetch<UpstashMetadata>([vectorId], {
+      includeMetadata: true,
+      namespace: this.collectionName,
+    });
+    const vector = response[0];
+
+    if (!vector) {
+      return null;
+    }
+
+    return {
+      id: String(vector.id),
+      payload: (vector.metadata ?? {}) as Record<string, any>,
+    };
+  }
+
+  async update(
+    vectorId: string,
+    vector: number[],
+    payload: Record<string, any>,
+  ): Promise<void> {
+    // Upstash's `update` can't set the vector and metadata in one call (its
+    // payload is a discriminated union of vector | data | metadata), so a
+    // single `upsert` replaces both atomically, the same way insert() writes.
+    await this.client.upsert(
+      {
+        id: vectorId,
+        vector,
+        metadata: payload,
+      },
+      { namespace: this.collectionName },
+    );
+  }
+
+  async delete(vectorId: string): Promise<void> {
+    await this.client.delete(vectorId, { namespace: this.collectionName });
+  }
+
+  async deleteCol(): Promise<void> {
+    await this.client.reset({ namespace: this.collectionName });
+  }
+
+  async list(
+    filters?: SearchFilters,
+    topK: number = 100,
+  ): Promise<[VectorStoreResult[], number]> {
+    const results: VectorStoreResult[] = [];
+    let cursor = "0";
+
+    do {
+      const response = await this.client.range<UpstashMetadata>(
+        {
+          cursor,
+          limit: Math.min(100, topK - results.length),
+          includeMetadata: true,
+        },
+        { namespace: this.collectionName },
+      );
+
+      for (const vector of response.vectors) {
+        if (this.matchesFilters(vector, filters)) {
+          results.push({
+            id: String(vector.id),
+            payload: (vector.metadata ?? {}) as Record<string, any>,
+          });
+        }
+
+        if (results.length >= topK) {
+          break;
+        }
+      }
+
+      cursor = response.nextCursor;
+      // Upstash returns an empty-string cursor once the scan is exhausted (it
+      // never comes back as "0"), so "" is the termination sentinel. Checking
+      // for "0" here would re-scan from the start and return duplicates.
+    } while (cursor !== "" && results.length < topK);
+
+    return [results, results.length];
+  }
+
+  async getUserId(): Promise<string> {
+    return "anonymous-upstash-vector";
+  }
+
+  async setUserId(): Promise<void> {
+    return;
+  }
+
+  async reset(): Promise<void> {
+    await this.deleteCol();
+  }
+
+  private parseResult(result: QueryResult<UpstashMetadata>): VectorStoreResult {
+    return {
+      id: String(result.id),
+      payload: (result.metadata ?? {}) as Record<string, any>,
+      score: result.score,
+    };
+  }
+
+  private stringifyFilterValue(value: unknown): string {
+    if (typeof value === "string") {
+      return JSON.stringify(value);
+    }
+
+    if (typeof value === "boolean") {
+      return value ? "true" : "false";
+    }
+
+    return String(value);
+  }
+
+  private convertFilters(filters?: SearchFilters): string | undefined {
+    if (!filters) {
+      return undefined;
+    }
+
+    const expressions = Object.entries(filters)
+      .filter(([, value]) => value !== undefined && value !== null)
+      .map(([key, value]) => `${key} = ${this.stringifyFilterValue(value)}`);
+
+    return expressions.length > 0 ? expressions.join(" AND ") : undefined;
+  }
+
+  private matchesFilters(
+    vector: Vector<UpstashMetadata>,
+    filters?: SearchFilters,
+  ): boolean {
+    if (!filters) {
+      return true;
+    }
+
+    return Object.entries(filters).every(([key, value]) => {
+      if (value === undefined || value === null) {
+        return true;
+      }
+
+      return vector.metadata?.[key] === value;
+    });
+  }
+}
diff --git a/mem0-ts/src/oss/tests/factory.unit.test.ts b/mem0-ts/src/oss/tests/factory.unit.test.ts
index 72b18c4ee..0b4cdeeb1 100644
--- a/mem0-ts/src/oss/tests/factory.unit.test.ts
+++ b/mem0-ts/src/oss/tests/factory.unit.test.ts
@@ -158,6 +158,11 @@ jest.mock("../src/vector_stores/pgvector", () => ({
     .fn()
     .mockImplementation((config) => ({ type: "pgvector", config })),
 }));
+jest.mock("../src/vector_stores/upstash_vector", () => ({
+  UpstashVector: jest
+    .fn()
+    .mockImplementation((config) => ({ type: "upstash-vector", config })),
+}));
 jest.mock("../src/vector_stores/azure_mysql", () => ({
   AzureMySQLDB: jest
     .fn()
@@ -299,6 +304,7 @@ describe("VectorStoreFactory", () => {
     ["vectorize"],
     ["azure-ai-search"],
     ["pgvector"],
+    ["upstash_vector"],
     ["azure_mysql"],
     ["cassandra"],
     ["s3-vectors"],
diff --git a/mem0-ts/src/oss/tests/upstash_vector.unit.test.ts b/mem0-ts/src/oss/tests/upstash_vector.unit.test.ts
new file mode 100644
index 000000000..b740ab724
--- /dev/null
+++ b/mem0-ts/src/oss/tests/upstash_vector.unit.test.ts
@@ -0,0 +1,166 @@
+import { UpstashVector } from "../src/vector_stores/upstash_vector";
+
+describe("UpstashVector", () => {
+  const namespace = "memories";
+
+  function createClient(overrides: Record<string, jest.Mock> = {}) {
+    return {
+      upsert: jest.fn().mockResolvedValue("Success"),
+      query: jest.fn().mockResolvedValue([]),
+      fetch: jest.fn().mockResolvedValue([]),
+      update: jest.fn().mockResolvedValue({ updated: 1 }),
+      delete: jest.fn().mockResolvedValue({ deleted: 1 }),
+      reset: jest.fn().mockResolvedValue("Success"),
+      range: jest.fn().mockResolvedValue({ vectors: [], nextCursor: "" }),
+      ...overrides,
+    };
+  }
+
+  it("upserts vectors into the collection namespace", async () => {
+    const client = createClient();
+    const store = new UpstashVector({
+      collectionName: namespace,
+      client: client as any,
+    });
+
+    await store.insert(
+      [[0.1, 0.2]],
+      ["memory-1"],
+      [{ data: "hello", user_id: "user-1" }],
+    );
+
+    expect(client.upsert).toHaveBeenCalledWith(
+      [
+        {
+          id: "memory-1",
+          vector: [0.1, 0.2],
+          metadata: { data: "hello", user_id: "user-1" },
+        },
+      ],
+      { namespace },
+    );
+  });
+
+  it("queries vectors with converted filters", async () => {
+    const client = createClient({
+      query: jest.fn().mockResolvedValue([
+        {
+          id: "memory-1",
+          score: 0.9,
+          metadata: { data: "hello", user_id: "user-1" },
+        },
+      ]),
+    });
+    const store = new UpstashVector({
+      collectionName: namespace,
+      client: client as any,
+    });
+
+    const results = await store.search([0.1, 0.2], 3, {
+      user_id: "user-1",
+      active: true,
+    });
+
+    expect(client.query).toHaveBeenCalledWith(
+      {
+        vector: [0.1, 0.2],
+        topK: 3,
+        filter: 'user_id = "user-1" AND active = true',
+        includeMetadata: true,
+      },
+      { namespace },
+    );
+    expect(results).toEqual([
+      {
+        id: "memory-1",
+        payload: { data: "hello", user_id: "user-1" },
+        score: 0.9,
+      },
+    ]);
+  });
+
+  it("fetches, updates, deletes, resets, and lists vectors in the namespace", async () => {
+    const client = createClient({
+      fetch: jest.fn().mockResolvedValue([
+        {
+          id: "memory-1",
+          metadata: { data: "hello" },
+        },
+      ]),
+      range: jest
+        .fn()
+        .mockResolvedValueOnce({
+          vectors: [
+            { id: "memory-1", metadata: { user_id: "user-1" } },
+            { id: "memory-2", metadata: { user_id: "user-2" } },
+          ],
+          nextCursor: "2",
+        })
+        .mockResolvedValueOnce({
+          vectors: [{ id: "memory-3", metadata: { user_id: "user-1" } }],
+          nextCursor: "",
+        }),
+    });
+    const store = new UpstashVector({
+      collectionName: namespace,
+      client: client as any,
+    });
+
+    await expect(store.get("memory-1")).resolves.toEqual({
+      id: "memory-1",
+      payload: { data: "hello" },
+    });
+    await store.update("memory-1", [0.3], { data: "updated" });
+    await store.delete("memory-1");
+    await store.deleteCol();
+    await store.reset();
+    await expect(store.list({ user_id: "user-1" }, 2)).resolves.toEqual([
+      [
+        { id: "memory-1", payload: { user_id: "user-1" } },
+        { id: "memory-3", payload: { user_id: "user-1" } },
+      ],
+      2,
+    ]);
+
+    expect(client.fetch).toHaveBeenCalledWith(["memory-1"], {
+      includeMetadata: true,
+      namespace,
+    });
+    expect(client.upsert).toHaveBeenCalledWith(
+      { id: "memory-1", vector: [0.3], metadata: { data: "updated" } },
+      { namespace },
+    );
+    expect(client.delete).toHaveBeenCalledWith("memory-1", { namespace });
+    expect(client.reset).toHaveBeenCalledTimes(2);
+    expect(client.reset).toHaveBeenCalledWith({ namespace });
+  });
+
+  it("stops paging when the cursor is exhausted instead of re-scanning", async () => {
+    // Upstash returns nextCursor "" at the end of a scan. If list() treated ""
+    // as "keep going" (e.g. by checking for "0"), it would re-fetch from the
+    // start and pile up duplicates until it hit topK. With fewer vectors than
+    // topK, a single page must end the scan: one range call, no duplicates.
+    const client = createClient({
+      range: jest.fn().mockResolvedValue({
+        vectors: [
+          { id: "memory-1", metadata: { user_id: "user-1" } },
+          { id: "memory-2", metadata: { user_id: "user-1" } },
+        ],
+        nextCursor: "",
+      }),
+    });
+    const store = new UpstashVector({
+      collectionName: namespace,
+      client: client as any,
+    });
+
+    const [rows, count] = await store.list({ user_id: "user-1" }, 100);
+
+    expect(client.range).toHaveBeenCalledTimes(1);
+    expect(rows).toEqual([
+      { id: "memory-1", payload: { user_id: "user-1" } },
+      { id: "memory-2", payload: { user_id: "user-1" } },
+    ]);
+    expect(count).toBe(2);
+  });
+});
diff --git a/mem0-ts/tsup.config.ts b/mem0-ts/tsup.config.ts
index ae8d290cc..e9189b7cf 100644
--- a/mem0-ts/tsup.config.ts
+++ b/mem0-ts/tsup.config.ts
@@ -20,6 +20,7 @@ const external = [
   "@google-cloud/aiplatform",
   "@mistralai/mistralai",
   "@supabase/supabase-js",
+  "@upstash/vector",
   "@azure/search-documents",
   "@azure/identity",
   "cloudflare",
diff --git a/mem0/configs/vector_stores/upstash_vector.py b/mem0/configs/vector_stores/upstash_vector.py
index d4c3c7c3b..a382012f3 100644
--- a/mem0/configs/vector_stores/upstash_vector.py
+++ b/mem0/configs/vector_stores/upstash_vector.py
@@ -29,6 +29,13 @@ class UpstashVectorConfig(BaseModel):
 
         if not client and not (url and token):
             raise ValueError("Either a client or URL and token must be provided.")
+
+        # Persist the env-resolved credentials so the provider constructor receives
+        # them; the validator used to check the env vars but drop them, so an
+        # env-var-only config passed validation and then raised on build.
+        if not client:
+            values["url"] = url
+            values["token"] = token
         return values
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
diff --git a/tests/vector_stores/test_upstash_vector.py b/tests/vector_stores/test_upstash_vector.py
index 070e78bd1..5628028c7 100644
--- a/tests/vector_stores/test_upstash_vector.py
+++ b/tests/vector_stores/test_upstash_vector.py
@@ -4,6 +4,7 @@ from unittest.mock import MagicMock, call, patch
 
 import pytest
 
+from mem0.configs.vector_stores.upstash_vector import UpstashVectorConfig
 from mem0.vector_stores.upstash_vector import UpstashVector
 
 
@@ -393,3 +394,27 @@ def test_search_vectors_multi_query_namespace_at_top_level(upstash_instance):
     assert [r.id for r in results] == ["id1", "id2", "id3"]
     assert results[0].score == 0.9
     assert results[1].payload == {"name": "vector2"}
+
+
+def test_env_var_only_config_builds_provider(monkeypatch):
+    """Regression: an env-var-only config (no url/token/client) must build.
+
+    VectorStoreFactory does ``UpstashVector(**config.model_dump())``. The config
+    validator read ``UPSTASH_VECTOR_REST_URL``/``UPSTASH_VECTOR_REST_TOKEN`` only
+    to pass its presence check, then returned the config unchanged, so
+    ``model_dump()`` still carried ``url=token=None`` and construction raised
+    "Either a client or URL and token must be provided." — even though the docs
+    advertise env-var setup. The resolved credentials must reach the ctor.
+    """
+    monkeypatch.setenv("UPSTASH_VECTOR_REST_URL", "https://example.upstash.io")
+    monkeypatch.setenv("UPSTASH_VECTOR_REST_TOKEN", "tok_123")
+
+    dumped = UpstashVectorConfig(collection_name="mem0").model_dump()
+    assert dumped["url"] == "https://example.upstash.io"
+    assert dumped["token"] == "tok_123"
+
+    # Mirrors VectorStoreFactory.create: instance(**config.model_dump()).
+    with patch("mem0.vector_stores.upstash_vector.Index") as mock_index:
+        UpstashVector(**dumped)
+
+    mock_index.assert_called_once_with("https://example.upstash.io", "tok_123")