Fix broken link to expiration-date page in delete.mdx

Generated-By: mintlify-agent

docs/components/vectordbs/dbs/valkey.mdx

@@ -50,4 +50,25 @@ Here are the parameters available for configuring Valkey:
 | `hnsw_m` | Number of bi-directional links for HNSW | `16` |
 | `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
 | `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
+| `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
 | `distance_metric` | Distance metric for vector similarity | `cosine` |
+
+## Cluster Mode
+
+To use Valkey with cluster mode enabled (CME), set `cluster_mode` to `true`:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "valkey",
+        "config": {
+            "collection_name": "memories",
+            "valkey_url": "valkey://cluster-endpoint:6379",
+            "embedding_model_dims": 1536,
+            "cluster_mode": True
+        }
+    }
+}
+```
+
+When cluster mode is enabled, the connector uses `ValkeyCluster` instead of the standalone client, which handles `MOVED`/`ASK` redirections automatically. Search queries are coordinated across all shards by the valkey-search module's built-in coordinator. See the [valkey-search documentation](https://github.com/valkey-io/valkey-search) for details on cluster mode behavior.

docs/cookbooks/frameworks/chrome-extension.mdx

@@ -1,70 +0,0 @@
----
-title: Browser Extension Memory
-description: "Add Mem0's universal memory layer to Chrome chat surfaces."
----
-
-
-Enhance your AI interactions with Mem0, a Chrome extension that introduces a universal memory layer across platforms like ChatGPT, Claude, and Perplexity. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.
-
-<Note>
-  We now support Grok! The Mem0 Chrome Extension has been updated to work with Grok, bringing the same powerful memory capabilities to your Grok conversations.
-</Note>
-
-
-## Features
-
-- **Universal Memory Layer**: Share context seamlessly across ChatGPT, Claude, Perplexity, and Grok.
-- **Smart Context Detection**: Automatically captures relevant information from your conversations.
-- **Intelligent Memory Retrieval**: Surfaces pertinent memories at the right time.
-- **One-Click Sync**: Easily synchronize with existing ChatGPT memories.
-- **Memory Dashboard**: Manage all your memories in one centralized location.
-
-## Installation
-
-You can install the Mem0 Chrome Extension using one of the following methods:
-
-### Method 1: Chrome Web Store Installation
-
-1. **Download the Extension**: Open Google Chrome and navigate to the [Mem0 Chrome Extension page](https://chromewebstore.google.com/detail/mem0/onihkkbipkfeijkadecaafbgagkhglop?hl=en).
-2. **Add to Chrome**: Click on the "Add to Chrome" button.
-3. **Confirm Installation**: In the pop-up dialog, click "Add extension" to confirm. The Mem0 icon should now appear in your Chrome toolbar.
-
-### Method 2: Manual Installation
-
-1. **Download the Extension**: Clone or download the extension files from the [Mem0 Chrome Extension GitHub repository](https://github.com/mem0ai/mem0-chrome-extension).
-2. **Access Chrome Extensions**: Open Google Chrome and navigate to `chrome://extensions`.
-3. **Enable Developer Mode**: Toggle the "Developer mode" switch in the top right corner.
-4. **Load Unpacked Extension**: Click "Load unpacked" and select the directory containing the extension files.
-5. **Confirm Installation**: The Mem0 Chrome Extension should now appear in your Chrome toolbar.
-
-## Usage
-
-1. **Locate the Mem0 Icon**: After installation, find the Mem0 icon in your Chrome toolbar.
-2. **Sign In**: Click the icon and sign in with your Google account.
-3. **Interact with AI Assistants**:
-   - **ChatGPT and Perplexity**: Continue your conversations as usual; Mem0 operates seamlessly in the background.
-   - **Claude**: Click the Mem0 button or use the shortcut `Ctrl + M` to activate memory functions.
-
-## Configuration
-
-- **API Key**: Obtain your API key from the Mem0 Dashboard to connect the extension to the Mem0 API.
-- **User ID**: This is your unique identifier in the Mem0 system. If not provided, it defaults to `chrome-extension-user`.
-
-## Demo Video
-
-<iframe width="700" height="400" src="https://www.youtube.com/embed/dqenCMMlfwQ?si=zhGVrkq6IS_0Jwyj" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
-
-## Privacy and Data Security
-
-Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.
-
----
-
-<CardGroup cols={2}>
-  <Card title="Build a Mem0 Companion" icon="users" href="/cookbooks/essentials/building-ai-companion">
-    Learn the foundations of memory-powered assistants that work across platforms.
-  </Card>
-  <Card title="Multimodal Support" icon="image" href="/platform/features/multimodal-support">
-    Extend your browser interactions with vision and audio memory.
-  </Card>
-</CardGroup>

docs/cookbooks/overview.mdx

@@ -201,10 +201,7 @@ Here are some examples of how Mem0 can be integrated into various applications:
   >
     Persistent personality for Eliza agents.
   </Card>
-  <Card title="Browser Extension Memory" icon="globe" href="/cookbooks/frameworks/chrome-extension">
+  </CardGroup>
-    Universal memory layer for Chrome.
-  </Card>
-</CardGroup>
 
 ---
 

docs/core-concepts/memory-operations/delete.mdx

@@ -216,7 +216,7 @@ memory.delete_all(user_id="alice")
 ## Put it into practice
 
 - Review the <Link href="/api-reference/memory/delete-memory">Delete Memory API reference</Link>, plus <Link href="/api-reference/memory/batch-delete">Batch Delete</Link> and <Link href="/api-reference/memory/delete-memories">Filtered Delete</Link>.
-- Pair deletes with <Link href="/platform/features/expiration-date">Expiration Policies</Link> to automate retention.
+- Pair deletes with <Link href="/cookbooks/essentials/memory-expiration-short-and-long-term">Expiration Policies</Link> to automate retention.
 
 ## See it live
 
@@ -236,6 +236,6 @@ memory.delete_all(user_id="alice")
     title="Enable Expiration Policies"
     description="Automate retention with the platform’s expiration feature."
     icon="clock"
-    href="/platform/features/expiration-date"
+    href="/cookbooks/essentials/memory-expiration-short-and-long-term"
   />
 </CardGroup>

docs/docs.json

@@ -373,7 +373,6 @@
                       "cookbooks/frameworks/llamaindex-multiagent",
                       "cookbooks/frameworks/multimodal-retrieval",
                       "cookbooks/frameworks/eliza-os-character",
-                      "cookbooks/frameworks/chrome-extension",
                       "cookbooks/frameworks/gemini-3-with-mem0-mcp",
                       "cookbooks/frameworks/mirofish-swarm-memory"
                     ]
@@ -798,7 +797,11 @@
     },
     {
       "source": "/examples/chrome-extension",
-      "destination": "/cookbooks/frameworks/chrome-extension"
+      "destination": "/cookbooks/overview"
+    },
+    {
+      "source": "/cookbooks/frameworks/chrome-extension",
+      "destination": "/cookbooks/overview"
     },
     {
       "source": "/examples",
@@ -838,7 +841,7 @@
     },
     {
       "source": "/v0x/examples/chrome-extension",
-      "destination": "/cookbooks/frameworks/chrome-extension"
+      "destination": "/cookbooks/overview"
     },
     {
       "source": "/v0x/examples/youtube-assistant",

docs/llms.txt

@@ -245,7 +245,6 @@ Key differentiators:
 - [LlamaIndex Multiagent](https://docs.mem0.ai/cookbooks/frameworks/llamaindex-multiagent): Multi-agent systems with shared memory
 - [Multimodal Retrieval](https://docs.mem0.ai/cookbooks/frameworks/multimodal-retrieval): Memory systems handling text, images, and documents
 - [Eliza OS Character](https://docs.mem0.ai/cookbooks/frameworks/eliza-os-character): Character-based AI with persistent personality
-- [Chrome Extension](https://docs.mem0.ai/cookbooks/frameworks/chrome-extension): Browser extensions that remember user interactions
 - [Gemini with Mem0 MCP](https://docs.mem0.ai/cookbooks/frameworks/gemini-3-with-mem0-mcp): Google Gemini integration using MCP server
 - [Mirofish Swarm Memory](https://docs.mem0.ai/cookbooks/frameworks/mirofish-swarm-memory): Swarm-based multi-agent memory patterns
 

docs/openapi.json

@@ -1048,8 +1048,8 @@
 											},
 											"expiration_date": {
 												"type": "string",
-												"format": "date-time",
+												"format": "date",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"description": "The date when the memory will expire. Format: YYYY-MM-DD.",
 												"title": "Expiration date",
 												"nullable": true,
 												"default": null
@@ -1233,7 +1233,7 @@
 					"memories"
 				],
 				"description": "Delete memories by filter. At least one filter is required \u2014 previously omitting all filters silently deleted everything; now it returns a validation error.",
-				"operationId": "memories_delete",
+				"operationId": "memories_delete_all",
 				"parameters": [
 					{
 						"name": "user_id",
@@ -1393,8 +1393,8 @@
 											},
 											"expiration_date": {
 												"type": "string",
-												"format": "date-time",
+												"format": "date",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"description": "The date when the memory will expire. Format: YYYY-MM-DD.",
 												"title": "Expiration date",
 												"nullable": true,
 												"default": null
@@ -1540,8 +1540,8 @@
 											},
 											"expiration_date": {
 												"type": "string",
-												"format": "date-time",
+												"format": "date",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"description": "The date when the memory will expire. Format: YYYY-MM-DD.",
 												"title": "Expiration date",
 												"nullable": true,
 												"default": null
@@ -1675,8 +1675,8 @@
 											},
 											"expiration_date": {
 												"type": "string",
-												"format": "date-time",
+												"format": "date",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"description": "The date when the memory will expire. Format: YYYY-MM-DD.",
 												"title": "Expiration date",
 												"nullable": true,
 												"default": null
@@ -1739,7 +1739,7 @@
 				"tags": [
 					"memories"
 				],
-				"operationId": "memories_read",
+				"operationId": "memories_entity_read",
 				"responses": {
 					"200": {
 						"description": "Successfully retrieved memories.",
@@ -5176,9 +5176,10 @@
 						"nullable": true
 					},
 					"expiration_date": {
-						"description": "The date and time when the memory will expire. Format: YYYY-MM-DD",
+						"description": "The date when the memory will expire. Format: YYYY-MM-DD",
 						"title": "Expiration date",
 						"type": "string",
+						"format": "date",
 						"nullable": true
 					},
 					"org_id": {

mem0-plugin/.claude-plugin/plugin.json

@@ -8,6 +8,6 @@
   },
   "homepage": "https://mem0.ai",
   "repository": "https://github.com/mem0ai/mem0",
-  "logo": "logo.svg",
+  "license": "Apache-2.0",
-  "license": "Apache-2.0"
+  "keywords": ["memory", "personalization", "mcp", "semantic-search"]
 }

mem0-plugin/hooks/hooks.json

@@ -1,5 +1,4 @@
 {
-  "description": "Mem0 memory capture hooks — automatic memory extraction at key lifecycle points",
   "hooks": {
     "SessionStart": [
       {

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "2.4.6",
+  "version": "3.0.0-beta.1",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",

mem0-ts/src/client/mem0.ts

@@ -1,6 +1,7 @@
 import axios from "axios";
 import {
   AllUsers,
+  PaginatedMemories,
   ProjectOptions,
   Memory,
   MemoryHistory,
@@ -221,7 +222,7 @@ export default class MemoryClient {
     this._captureEvent("add", [payloadKeys]);
 
     const response = await this._fetchWithErrorHandling(
-      `${this.host}/v3/memories/`,
+      `${this.host}/v3/memories/add/`,
       {
         method: "POST",
         headers: this.headers,
@@ -284,7 +285,7 @@ export default class MemoryClient {
     );
   }
 
-  async getAll(options?: GetAllMemoryOptions): Promise<Array<Memory>> {
+  async getAll(options?: GetAllMemoryOptions): Promise<PaginatedMemories> {
     // Reject top-level entity params - must use filters instead
     rejectTopLevelEntityParams(options as Record<string, any>, "getAll");
 
@@ -293,12 +294,11 @@ export default class MemoryClient {
     this._captureEvent("get_all", [payloadKeys]);
     const { page, pageSize, filters, ...rest } = options ?? {};
     const body: Record<string, any> = {
-      output_format: "v1.1",
       ...camelToSnakeKeys(rest),
       ...(filters && { filters }),
     };
 
-    let url = `${this.host}/v2/memories/`;
+    let url = `${this.host}/v3/memories/`;
     if (page && pageSize) {
       url += `?page=${page}&page_size=${pageSize}`;
     }
@@ -308,7 +308,7 @@ export default class MemoryClient {
       headers: this.headers,
       body: JSON.stringify(body),
     });
-    return Array.isArray(response) ? response : (response?.results ?? response);
+    return response;
   }
 
   async search(

mem0-ts/src/client/mem0.types.ts

@@ -142,6 +142,13 @@ export interface AllUsers {
   previous: any;
 }
 
+export interface PaginatedMemories {
+  count: number;
+  next: string | null;
+  previous: string | null;
+  results: Array<Memory>;
+}
+
 export interface ProjectResponse {
   customInstructions?: string;
   customCategories?: string[];

mem0-ts/src/client/tests/integration/batch.test.ts

@@ -26,7 +26,7 @@ describeIntegration("MemoryClient Integration — Batch Operations", () => {
 
   beforeAll(async () => {
     cleanup = suppressTelemetryNoise();
-    client = createTestClient();
+    client = await createTestClient();
     memoryIds = await seedTestMemories(client, TEST_USER_ID);
   });
 

mem0-ts/src/client/tests/integration/crud.test.ts

@@ -26,9 +26,9 @@ describeIntegration("MemoryClient Integration — CRUD", () => {
   let cleanup: () => void;
   let memoryIds: string[] = [];
 
-  beforeAll(() => {
+  beforeAll(async () => {
     cleanup = suppressTelemetryNoise();
-    client = createTestClient();
+    client = await createTestClient();
   });
 
   afterAll(async () => {
@@ -120,14 +120,20 @@ describeIntegration("MemoryClient Integration — CRUD", () => {
   // ─── Get all ──────────────────────────────────────────────
   describe("get all memories", () => {
     test("returns all memories for test user", async () => {
-      const memories = await client.getAll({
+      const response = await client.getAll({
         filters: { user_id: TEST_USER_ID },
       });
 
-      expect(Array.isArray(memories)).toBe(true);
+      // Paginated shape: { count, next, previous, results: [...] }
-      expect(memories.length).toBeGreaterThanOrEqual(memoryIds.length);
+      expect(response).toHaveProperty("count");
+      expect(response).toHaveProperty("next");
+      expect(response).toHaveProperty("previous");
+      expect(response).toHaveProperty("results");
+      expect(typeof response.count).toBe("number");
+      expect(Array.isArray(response.results)).toBe(true);
+      expect(response.results.length).toBeGreaterThanOrEqual(memoryIds.length);
 
-      for (const mem of memories) {
+      for (const mem of response.results) {
         expect(typeof mem.id).toBe("string");
         expect(typeof mem.memory).toBe("string");
       }
@@ -140,8 +146,12 @@ describeIntegration("MemoryClient Integration — CRUD", () => {
         pageSize: 1,
       });
 
-      // Paginated response is an object with results array
       expect(page1).toBeDefined();
+      expect(page1).toHaveProperty("count");
+      expect(page1).toHaveProperty("next");
+      expect(page1).toHaveProperty("previous");
+      expect(Array.isArray(page1.results)).toBe(true);
+      expect(page1.results.length).toBeLessThanOrEqual(1);
     });
   });
 
@@ -197,13 +207,15 @@ describeIntegration("MemoryClient Integration — CRUD", () => {
       expect(result).toHaveProperty("eventId");
     });
 
-    test("getAll for non-existent user returns empty array", async () => {
+    test("getAll for non-existent user returns empty results", async () => {
-      const memories = await client.getAll({
+      const response = await client.getAll({
         filters: { user_id: `nonexistent-user-${randomUUID()}` },
       });
 
-      expect(Array.isArray(memories)).toBe(true);
+      expect(response).toHaveProperty("results");
-      expect(memories.length).toBe(0);
+      expect(Array.isArray(response.results)).toBe(true);
+      expect(response.results.length).toBe(0);
+      expect(response.count).toBe(0);
     });
 
     test("deleteAll for non-existent user does not throw", async () => {

mem0-ts/src/client/tests/integration/helpers.ts

@@ -18,9 +18,13 @@ export const describeIntegration = API_KEY ? describe : describe.skip;
  * Create a MemoryClient with the real API key.
  * Call this inside beforeAll — not at module scope — so it only
  * runs when the suite is not skipped.
+ *
+ * Returns an initialized client ready for immediate use.
  */
-export function createTestClient(): MemoryClient {
+export async function createTestClient(): Promise<MemoryClient> {
-  return new MemoryClient({ apiKey: API_KEY! });
+  const client = new MemoryClient({ apiKey: API_KEY! });
+  await client.ping();
+  return client;
 }
 
 /**
@@ -63,10 +67,11 @@ export async function waitForMemories(
   maxRetries = 4,
 ): Promise<Memory[]> {
   for (let attempt = 1; attempt <= maxRetries; attempt++) {
-    const memories = await withRetry(() =>
+    const response = await withRetry(() =>
       client.getAll({ filters: { user_id: userId } }),
     );
-    if (Array.isArray(memories) && memories.length >= minCount) {
+    const memories = response.results ?? [];
+    if (memories.length >= minCount) {
       return memories;
     }
     if (attempt < maxRetries) {

mem0-ts/src/client/tests/integration/initialization.test.ts

@@ -24,9 +24,9 @@ describeIntegration("MemoryClient Integration — Initialization", () => {
   let client: MemoryClient;
   let cleanup: () => void;
 
-  beforeAll(() => {
+  beforeAll(async () => {
     cleanup = suppressTelemetryNoise();
-    client = createTestClient();
+    client = await createTestClient();
   });
 
   afterAll(() => cleanup());

mem0-ts/src/client/tests/integration/management.test.ts

@@ -27,7 +27,7 @@ describeIntegration("MemoryClient Integration — Users & Project", () => {
 
   beforeAll(async () => {
     cleanup = suppressTelemetryNoise();
-    client = createTestClient();
+    client = await createTestClient();
     await seedTestMemories(client, TEST_USER_ID);
   });
 

mem0-ts/src/client/tests/integration/search.test.ts

@@ -27,7 +27,7 @@ describeIntegration("MemoryClient Integration — Search & History", () => {
 
   beforeAll(async () => {
     cleanup = suppressTelemetryNoise();
-    client = createTestClient();
+    client = await createTestClient();
     memoryIds = await seedTestMemories(client, TEST_USER_ID);
   });
 

mem0-ts/src/client/tests/memoryClient.crud.test.ts

@@ -21,33 +21,33 @@ installConsoleSuppression();
 // ─── add() ───────────────────────────────────────────────
 
 describe("MemoryClient - add()", () => {
-  test("sends POST to /v3/memories/", async () => {
+  test("sends POST to /v3/memories/add/", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
-    extra.set("/v3/memories/", { status: 200, body: [createMockMemory()] });
+    extra.set("/v3/memories/add/", { status: 200, body: [createMockMemory()] });
     const mock = setupMockFetch(extra);
 
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await client.add([{ role: "user", content: "Hello" }], { userId: "u1" });
 
-    expect(findFetchCall(mock, "/v3/memories/", "POST")).toBeDefined();
+    expect(findFetchCall(mock, "/v3/memories/add/", "POST")).toBeDefined();
   });
 
   test("includes messages in request body", async () => {
     const messages = [{ role: "user" as const, content: "Hello, I am Alex" }];
     const extra = new Map<string, { status: number; body: unknown }>();
-    extra.set("/v3/memories/", { status: 200, body: [createMockMemory()] });
+    extra.set("/v3/memories/add/", { status: 200, body: [createMockMemory()] });
     const mock = setupMockFetch(extra);
 
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await client.add(messages, { userId: "u1" });
 
-    const call = findFetchCall(mock, "/v3/memories/", "POST");
+    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
     expect(getFetchBody(call!).messages).toEqual(messages);
   });
 
   test("includes user_id in request body", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
-    extra.set("/v3/memories/", { status: 200, body: [createMockMemory()] });
+    extra.set("/v3/memories/add/", { status: 200, body: [createMockMemory()] });
     const mock = setupMockFetch(extra);
 
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
@@ -55,19 +55,19 @@ describe("MemoryClient - add()", () => {
       user_id: "user_1",
     });
 
-    const call = findFetchCall(mock, "/v3/memories/", "POST");
+    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
     expect(getFetchBody(call!).user_id).toBe("user_1");
   });
 
   test("sends empty messages array without crashing", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
-    extra.set("/v3/memories/", { status: 200, body: [] });
+    extra.set("/v3/memories/add/", { status: 200, body: [] });
     const mock = setupMockFetch(extra);
 
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await client.add([], { userId: "u1" });
 
-    const call = findFetchCall(mock, "/v3/memories/", "POST");
+    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
     expect(getFetchBody(call!).messages).toEqual([]);
   });
 });

mem0-ts/src/client/tests/memoryClient.search.test.ts

@@ -254,7 +254,7 @@ describe("MemoryClient - getAll() entity param rejection", () => {
 
   test("accepts filters with user_id", async () => {
     const extra = new Map<string, { status: number; body: unknown }>();
-    extra.set("/v2/memories/", {
+    extra.set("/v3/memories/", {
       status: 200,
       body: { results: [] },
     });
@@ -262,6 +262,6 @@ describe("MemoryClient - getAll() entity param rejection", () => {
 
     const client = new MemoryClient({ apiKey: TEST_API_KEY });
     await client.getAll({ filters: { user_id: "u1" } });
-    expect(findFetchCall(mock, "/v2/memories/", "POST")).toBeDefined();
+    expect(findFetchCall(mock, "/v3/memories/", "POST")).toBeDefined();
   });
 });

mem0-ts/src/oss/src/config/defaults.ts

@@ -22,7 +22,7 @@ export const DEFAULT_MEMORY_CONFIG: MemoryConfig = {
     config: {
       baseURL: "https://api.openai.com/v1",
       apiKey: process.env.OPENAI_API_KEY || "",
-      model: "gpt-4.1-nano-2025-04-14",
+      model: "gpt-5-mini",
       modelProperties: undefined,
     },
   },

mem0-ts/src/oss/src/config/manager.ts

@@ -110,6 +110,7 @@ export class ConfigManager {
             ((userConf as Record<string, unknown>)?.lmstudio_base_url as
               | string
               | undefined) ??
+            userConf?.url ??
             defaultConf.baseURL;
 
           return {

mem0-ts/src/oss/src/llms/azure.ts

@@ -18,7 +18,7 @@ export class AzureOpenAILLM implements LLM {
       endpoint: endpoint as string,
       ...rest,
     });
-    this.model = config.model || "gpt-4";
+    this.model = config.model || "gpt-5-mini";
   }
 
   async generateResponse(

mem0-ts/src/oss/src/llms/openai.ts

@@ -11,7 +11,7 @@ export class OpenAILLM implements LLM {
       apiKey: config.apiKey,
       baseURL: config.baseURL,
     });
-    this.model = config.model || "gpt-4.1-nano-2025-04-14";
+    this.model = config.model || "gpt-5-mini";
   }
 
   async generateResponse(

mem0-ts/src/oss/src/llms/openai_structured.ts

@@ -8,7 +8,7 @@ export class OpenAIStructuredLLM implements LLM {
 
   constructor(config: LLMConfig) {
     this.openai = new OpenAI({ apiKey: config.apiKey });
-    this.model = config.model || "gpt-4-turbo-preview";
+    this.model = config.model || "gpt-5-mini";
   }
 
   async generateResponse(

mem0-ts/src/oss/src/memory/index.ts

@@ -52,6 +52,7 @@ import {
   ENTITY_BOOST_WEIGHT,
   ScoredResult,
 } from "../utils/scoring";
+import { getDefaultVectorStoreDbPath } from "../utils/sqlite";
 
 // Entity params that must be passed via filters - check both snake_case and camelCase
 const ENTITY_PARAMS = [
@@ -82,6 +83,58 @@ function rejectTopLevelEntityParams(
   }
 }
 
+/**
+ * Validates and normalizes an entity ID.
+ * - Trims leading/trailing whitespace
+ * - Rejects empty or whitespace-only strings
+ * - Rejects strings containing internal whitespace
+ * @returns The trimmed entity ID, or undefined if input is undefined
+ * @throws Error if entity ID is invalid
+ */
+function validateAndTrimEntityId(
+  value: string | undefined,
+  name: string,
+): string | undefined {
+  if (value === undefined) return undefined;
+  const trimmed = value.trim();
+  if (trimmed === "") {
+    throw new Error(
+      `Invalid ${name}: cannot be empty or whitespace-only. Provide a valid identifier.`,
+    );
+  }
+  if (/\s/.test(trimmed)) {
+    throw new Error(
+      `Invalid ${name}: cannot contain whitespace. Provide a valid identifier without spaces.`,
+    );
+  }
+  return trimmed;
+}
+
+/**
+ * Validates search parameters.
+ * @throws Error if threshold or topK are invalid
+ */
+function validateSearchParams(threshold?: number, topK?: number): void {
+  if (threshold !== undefined) {
+    if (typeof threshold !== "number" || isNaN(threshold)) {
+      throw new Error("threshold must be a valid number");
+    }
+    if (threshold < 0 || threshold > 1) {
+      throw new Error(
+        `Invalid threshold: ${threshold}. Must be between 0 and 1 (inclusive).`,
+      );
+    }
+  }
+  if (topK !== undefined) {
+    if (typeof topK !== "number" || isNaN(topK) || !Number.isInteger(topK)) {
+      throw new Error("topK must be a valid integer");
+    }
+    if (topK < 0) {
+      throw new Error(`Invalid topK: ${topK}. Must be a non-negative integer.`);
+    }
+  }
+}
+
 export class Memory {
   private config: MemoryConfig;
   private customInstructions: string | undefined;
@@ -195,15 +248,10 @@ export class Memory {
         ...this.config.vectorStore.config,
         collectionName: entityCollectionName,
       };
-      // For file-based stores (memory/SQLite), use a separate DB path for entities.
+      // For file-based stores (memory/SQLite), always use a separate DB for entities
-      // If dbPath was set explicitly, derive the entity path from it. If it's unset,
+      if (this.config.vectorStore.provider === "memory") {
-      // leave it unset — the vector store's default-path logic now scopes by
+        const basePath = entityConfig.dbPath || getDefaultVectorStoreDbPath();
-      // collectionName, so the entity store will land in its own file automatically.
+        entityConfig.dbPath = basePath.replace(/\.db$/, "_entities.db");
-      if (entityConfig.dbPath) {
-        entityConfig.dbPath = entityConfig.dbPath.replace(
-          /\.db$/,
-          "_entities.db",
-        );
       }
       this._entityStore = VectorStoreFactory.create(
         this.config.vectorStore.provider,
@@ -279,6 +327,13 @@ export class Memory {
     messages: string | Message[],
     config: AddMemoryOptions,
   ): Promise<SearchResult> {
+    // Validate messages input
+    if (messages === undefined || messages === null) {
+      throw new Error(
+        "messages is required and cannot be undefined or null. Provide a string or array of messages.",
+      );
+    }
+
     await this._ensureInitialized();
     await this._captureEvent("add", {
       message_count: Array.isArray(messages) ? messages.length : 1,
@@ -286,14 +341,12 @@ export class Memory {
       has_filters: !!config.filters,
       infer: config.infer,
     });
-    const {
+    const { metadata = {}, filters = {}, infer = true } = config;
-      userId,
+
-      agentId,
+    // Validate and trim entity IDs
-      runId,
+    const userId = validateAndTrimEntityId(config.userId, "userId");
-      metadata = {},
+    const agentId = validateAndTrimEntityId(config.agentId, "agentId");
-      filters = {},
+    const runId = validateAndTrimEntityId(config.runId, "runId");
-      infer = true,
-    } = config;
 
     // Convert camelCase entity params to snake_case for storage (matches API and search/getAll filters)
     if (userId) filters.user_id = metadata.user_id = userId;
@@ -807,14 +860,32 @@ export class Memory {
     // Reject top-level entity params - must use filters instead
     rejectTopLevelEntityParams(config as Record<string, any>, "search");
 
+    // Validate search parameters (before applying defaults)
+    validateSearchParams(config.threshold, config.topK);
+
+    // Validate and trim entity IDs in filters
+    const normalizedFilters = config.filters
+      ? {
+          ...config.filters,
+          user_id: validateAndTrimEntityId(config.filters.user_id, "user_id"),
+          agent_id: validateAndTrimEntityId(
+            config.filters.agent_id,
+            "agent_id",
+          ),
+          run_id: validateAndTrimEntityId(config.filters.run_id, "run_id"),
+        }
+      : {};
+
     await this._ensureInitialized();
+    const { topK = 20, threshold = 0.1 } = config;
+
     await this._captureEvent("search", {
       query_length: query.length,
-      topK: config.topK,
+      topK,
       has_filters: !!config.filters,
     });
-    const { topK = 100, threshold = 0.1 } = config;
+
-    let effectiveFilters: Record<string, any> = { ...(config.filters || {}) };
+    let effectiveFilters: Record<string, any> = { ...normalizedFilters };
 
     // Apply enhanced metadata filtering if advanced operators are detected
     if (this._hasAdvancedOperators(effectiveFilters)) {
@@ -996,12 +1067,9 @@ export class Memory {
           createdAt: payload.createdAt,
           updatedAt: payload.updatedAt,
           score: scored.score,
-          metadata: {
+          metadata: Object.entries(payload)
-            ...Object.entries(payload)
+            .filter(([key]) => !excludedKeys.has(key))
-              .filter(([key]) => !excludedKeys.has(key))
+            .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),
-              .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),
-            scoreBreakdown: scored.scoreBreakdown,
-          },
           ...(payload.user_id && { user_id: payload.user_id }),
           ...(payload.agent_id && { agent_id: payload.agent_id }),
           ...(payload.run_id && { run_id: payload.run_id }),
@@ -1119,11 +1187,23 @@ export class Memory {
     // Reject top-level entity params - must use filters instead
     rejectTopLevelEntityParams(config as Record<string, any>, "getAll");
 
+    // Validate topK if provided (before applying defaults)
+    validateSearchParams(undefined, config.topK);
+
     await this._ensureInitialized();
-    const { topK = 100, filters = {} } = config;
+
+    const { topK = 20 } = config;
+
+    // Validate and trim entity IDs in filters
+    const filters = {
+      ...(config.filters || {}),
+      user_id: validateAndTrimEntityId(config.filters?.user_id, "user_id"),
+      agent_id: validateAndTrimEntityId(config.filters?.agent_id, "agent_id"),
+      run_id: validateAndTrimEntityId(config.filters?.run_id, "run_id"),
+    };
 
     await this._captureEvent("get_all", {
-      topK: topK,
+      topK,
       has_user_id: !!filters.user_id,
       has_agent_id: !!filters.agent_id,
       has_run_id: !!filters.run_id,

mem0-ts/src/oss/src/tests/better-sqlite3-migration.test.ts

@@ -197,7 +197,7 @@ describe("MemoryVectorStore (better-sqlite3)", () => {
     expect(result).not.toBeNull();
     expect(result!.id).toBe("id-1");
     expect(result!.payload.data).toBe("hello");
-    expect(result!.payload.userId).toBe("u1");
+    expect(result!.payload.user_id).toBe("u1");
   });
 
   it("get returns null for non-existent id", async () => {
@@ -253,7 +253,7 @@ describe("MemoryVectorStore (better-sqlite3)", () => {
       ],
     );
 
-    const results = await store.search(v, 10, { userId: "alice" });
+    const results = await store.search(v, 10, { user_id: "alice" });
     expect(results).toHaveLength(1);
     expect(results[0].id).toBe("id-1");
   });
@@ -320,7 +320,7 @@ describe("MemoryVectorStore (better-sqlite3)", () => {
     expect(all).toHaveLength(3);
     expect(totalAll).toBe(3);
 
-    const [filtered, totalFiltered] = await store.list({ userId: "alice" });
+    const [filtered, totalFiltered] = await store.list({ user_id: "alice" });
     expect(filtered).toHaveLength(2);
     expect(totalFiltered).toBe(2);
   });

mem0-ts/src/oss/src/tests/sqlite-backward-compat.test.ts

@@ -308,7 +308,7 @@ describe("backward compat: MemoryVectorStore", () => {
       );
 
       const results = await store.search(normalize([1, 0, 0]), 10, {
-        userId: "user2",
+        user_id: "user2",
       });
       expect(results).toHaveLength(1);
       expect(results[0].id).toBe("b");

mem0-ts/src/oss/src/utils/scoring.ts

@@ -59,11 +59,6 @@ export function normalizeBm25(
 export interface ScoredResult {
   id: string;
   score: number;
-  scoreBreakdown: {
-    semantic: number;
-    bm25: number;
-    entityBoost: number;
-  };
   payload: Record<string, any>;
 }
 
@@ -134,11 +129,6 @@ export function scoreAndRank(
     scored.push({
       id: memIdStr,
       score: combined,
-      scoreBreakdown: {
-        semantic: semanticScore,
-        bm25: bm25Score,
-        entityBoost: entityBoost,
-      },
       payload: result.payload,
     });
   }

mem0-ts/src/oss/src/utils/sqlite.ts

@@ -2,16 +2,8 @@ import fs from "fs";
 import os from "os";
 import path from "path";
 
-export function getDefaultVectorStoreDbPath(collectionName?: string): string {
+export function getDefaultVectorStoreDbPath(): string {
-  // Scope the default DB file by collection name so that parallel stores
+  return path.join(os.homedir(), ".mem0", "vector_store.db");
-  // (e.g. "memories" vs "memories_entities") don't collide in the same
-  // SQLite table. Without this, both collections write to the same
-  // `vectors` table and search results leak across them.
-  const filename =
-    collectionName && collectionName.length > 0
-      ? `vector_store_${collectionName.replace(/[^a-zA-Z0-9_-]/g, "_")}.db`
-      : "vector_store.db";
-  return path.join(os.homedir(), ".mem0", filename);
 }
 
 export function ensureSQLiteDirectory(dbPath: string): void {

mem0-ts/src/oss/src/vector_stores/memory.ts

@@ -19,12 +19,27 @@ export class MemoryVectorStore implements VectorStore {
   private dimension: number;
   private dbPath: string;
 
+  private static readonly CAMEL_TO_SNAKE: Record<string, string> = {
+    userId: "user_id",
+    agentId: "agent_id",
+    runId: "run_id",
+  };
+
+  private normalizePayload(payload: Record<string, any>): Record<string, any> {
+    for (const [camel, snake] of Object.entries(
+      MemoryVectorStore.CAMEL_TO_SNAKE,
+    )) {
+      if (camel in payload && !(snake in payload)) {
+        payload[snake] = payload[camel];
+        delete payload[camel];
+      }
+    }
+    return payload;
+  }
+
   constructor(config: VectorStoreConfig) {
     this.dimension = config.dimension || 1536; // Default OpenAI dimension
-    // Scope the default path by collectionName so that parallel stores
+    this.dbPath = config.dbPath || getDefaultVectorStoreDbPath();
-    // (e.g. memory vs entity) don't share the same `vectors` table.
-    this.dbPath =
-      config.dbPath || getDefaultVectorStoreDbPath(config.collectionName);
 
     if (!config.dbPath) {
       const oldDefault = path.join(process.cwd(), "vector_store.db");
@@ -249,7 +264,7 @@ export class MemoryVectorStore implements VectorStore {
       }[] = [];
 
       for (const row of rows) {
-        const payload = JSON.parse(row.payload);
+        const payload = this.normalizePayload(JSON.parse(row.payload));
         const memoryVector: MemoryVector = {
           id: row.id,
           vector: Array.from(
@@ -263,7 +278,7 @@ export class MemoryVectorStore implements VectorStore {
         };
 
         if (this.filterVector(memoryVector, filters)) {
-          const text = payload.text_lemmatized || payload.data || "";
+          const text = payload.textLemmatized || payload.data || "";
           candidates.push({ id: row.id, payload, tokens: this.tokenize(text) });
         }
       }
@@ -354,7 +369,7 @@ export class MemoryVectorStore implements VectorStore {
         row.vector.byteOffset,
         row.vector.byteLength / 4,
       );
-      const payload = JSON.parse(row.payload);
+      const payload = this.normalizePayload(JSON.parse(row.payload));
       const memoryVector: MemoryVector = {
         id: row.id,
         vector: Array.from(vector),
@@ -381,7 +396,7 @@ export class MemoryVectorStore implements VectorStore {
       .get(vectorId) as any;
     if (!row) return null;
 
-    const payload = JSON.parse(row.payload);
+    const payload = this.normalizePayload(JSON.parse(row.payload));
     return {
       id: row.id,
       payload,
@@ -421,7 +436,7 @@ export class MemoryVectorStore implements VectorStore {
     const results: VectorStoreResult[] = [];
 
     for (const row of rows) {
-      const payload = JSON.parse(row.payload);
+      const payload = this.normalizePayload(JSON.parse(row.payload));
       const memoryVector: MemoryVector = {
         id: row.id,
         vector: Array.from(

mem0-ts/src/oss/src/vector_stores/pgvector.ts

@@ -22,6 +22,7 @@ export class PGVector implements VectorStore {
   private useHnsw: boolean;
   private readonly dbName: string;
   private config: PGVectorConfig;
+  private _initPromise?: Promise<void>;
 
   constructor(config: PGVectorConfig) {
     this.collectionName = config.collectionName || "memories";
@@ -41,6 +42,13 @@ export class PGVector implements VectorStore {
   }
 
   async initialize(): Promise<void> {
+    if (!this._initPromise) {
+      this._initPromise = this._doInitialize();
+    }
+    return this._initPromise;
+  }
+
+  private async _doInitialize(): Promise<void> {
     try {
       await this.client.connect();
 
@@ -186,9 +194,9 @@ export class PGVector implements VectorStore {
           : "";
 
       const searchQuery = `
-        SELECT id, ts_rank_cd(to_tsvector('simple', payload->>'text_lemmatized'), plainto_tsquery('simple', $1)) AS score, payload
+        SELECT id, ts_rank_cd(to_tsvector('simple', payload->>'textLemmatized'), plainto_tsquery('simple', $1)) AS score, payload
         FROM ${this.collectionName}
-        WHERE to_tsvector('simple', payload->>'text_lemmatized') @@ plainto_tsquery('simple', $1)
+        WHERE to_tsvector('simple', payload->>'textLemmatized') @@ plainto_tsquery('simple', $1)
         ${filterClause}
         ORDER BY score DESC
         LIMIT $2

mem0-ts/src/oss/src/vector_stores/redis.ts

@@ -256,17 +256,25 @@ export class RedisDB implements VectorStore {
       const modulesResponse =
         (await this.client.moduleList()) as unknown as any[];
 
-      // Parse module list to find search module
+      const hasSearch = modulesResponse.some((mod: any) => {
-      const hasSearch = modulesResponse.some((module: any[]) => {
+        // node-redis v4+ returns objects: { name: "search", ver: ..., ... }
-        const moduleMap = new Map();
+        if (typeof mod === "object" && !Array.isArray(mod) && mod.name) {
-        for (let i = 0; i < module.length; i += 2) {
+          const name = String(mod.name).toLowerCase();
-          moduleMap.set(module[i], module[i + 1]);
+          return name === "search" || name === "searchlight";
         }
-        const moduleName = moduleMap.get("name");
+        // Fallback: legacy flat array format [key, value, key, value, ...]
-        return (
+        if (Array.isArray(mod)) {
-          moduleName?.toLowerCase() === "search" ||
+          const moduleMap = new Map();
-          moduleName?.toLowerCase() === "searchlight"
+          for (let i = 0; i < mod.length; i += 2) {
-        );
+            moduleMap.set(mod[i], mod[i + 1]);
+          }
+          const name = moduleMap.get("name");
+          return (
+            name?.toLowerCase() === "search" ||
+            name?.toLowerCase() === "searchlight"
+          );
+        }
+        return false;
       });
 
       if (!hasSearch) {

mem0-ts/src/oss/tests/config-manager.test.ts

@@ -127,6 +127,20 @@ describe("ConfigManager", () => {
       expect(config.llm.config.url).toBe("http://fallback:11434");
     });
 
+    it("should use url as baseURL fallback when no baseURL provided (issue #4715)", () => {
+      const config = ConfigManager.mergeConfig({
+        embedder: baseEmbedder,
+        vectorStore: baseVectorStore,
+        llm: {
+          provider: "ollama",
+          config: { model: "llama3.1:8b", url: "http://my-ollama-host:11434" },
+        },
+      });
+
+      expect(config.llm.config.baseURL).toBe("http://my-ollama-host:11434");
+      expect(config.llm.config.url).toBe("http://my-ollama-host:11434");
+    });
+
     it("should use default baseURL when no url or baseURL provided", () => {
       const config = ConfigManager.mergeConfig({
         embedder: baseEmbedder,

mem0-ts/src/oss/tests/dimension-autodetect.test.ts

@@ -469,7 +469,7 @@ describe("Memory – auto-initialization", () => {
       },
       llm: {
         provider: "openai",
-        config: { apiKey: "sk-fake", model: "gpt-4-turbo-preview" },
+        config: { apiKey: "sk-fake", model: "gpt-5-mini" },
       },
       historyDbPath: ":memory:",
       disableHistory: true,

mem0-ts/src/oss/tests/memory.add.test.ts

@@ -75,7 +75,7 @@ function createMemory(overrides: Partial<MemoryConfig> = {}): Memory {
     },
     llm: {
       provider: "openai",
-      config: { apiKey: "test-key", model: "gpt-4-turbo-preview" },
+      config: { apiKey: "test-key", model: "gpt-5-mini" },
     },
     historyDbPath: ":memory:",
     ...overrides,

mem0-ts/src/oss/tests/memory.crud.test.ts

@@ -75,7 +75,7 @@ function createMemory(): Memory {
     },
     llm: {
       provider: "openai",
-      config: { apiKey: "test-key", model: "gpt-4-turbo-preview" },
+      config: { apiKey: "test-key", model: "gpt-5-mini" },
     },
     historyDbPath: ":memory:",
   });

mem0-ts/src/oss/tests/memory.init.test.ts

@@ -69,7 +69,7 @@ function createMemory(overrides: Partial<MemoryConfig> = {}): Memory {
     },
     llm: {
       provider: "openai",
-      config: { apiKey: "test-key", model: "gpt-4-turbo-preview" },
+      config: { apiKey: "test-key", model: "gpt-5-mini" },
     },
     historyDbPath: ":memory:",
     ...overrides,
@@ -94,7 +94,7 @@ describe("Memory - Initialization", () => {
       },
       llm: {
         provider: "openai",
-        config: { apiKey: "test-key", model: "gpt-4" },
+        config: { apiKey: "test-key", model: "gpt-5-mini" },
       },
     };
     const mem = Memory.fromConfig(config);

mem0-ts/src/oss/tests/memory.validation.test.ts

@@ -0,0 +1,299 @@
+/**
+ * Unit tests for OSS SDK input validation.
+ *
+ * Validates fixes for:
+ * - Undefined/null message handling in add()
+ * - Threshold bounds validation (must be 0-1) in search()
+ * - TopK validation (must be non-negative) in search() and getAll()
+ * - Whitespace-only entity ID rejection in add(), search(), getAll()
+ */
+/// <reference types="jest" />
+import { Memory } from "../src/memory";
+
+jest.setTimeout(15000);
+
+// Mock Google modules to prevent @google/genai crash in CI
+jest.mock("../src/embeddings/google", () => ({
+  GoogleEmbedder: jest.fn(),
+}));
+jest.mock("../src/llms/google", () => ({
+  GoogleLLM: jest.fn(),
+}));
+
+jest.mock("../src/llms/openai", () => ({
+  OpenAILLM: jest.fn().mockImplementation(() => ({
+    generateResponse: jest.fn().mockResolvedValue(
+      JSON.stringify({
+        memory: [{ id: "0", text: "test memory", attributed_to: "user" }],
+      }),
+    ),
+  })),
+}));
+
+const mockEmbedding = new Array(1536).fill(0.1);
+jest.mock("../src/embeddings/openai", () => ({
+  OpenAIEmbedder: jest.fn().mockImplementation(() => ({
+    embed: jest.fn().mockResolvedValue(mockEmbedding),
+    embedBatch: jest.fn().mockResolvedValue([mockEmbedding]),
+  })),
+}));
+
+describe("Memory Input Validation", () => {
+  let memory: Memory;
+  const testUserId = "test-user-validation";
+
+  beforeAll(async () => {
+    memory = new Memory({
+      version: "v1.1",
+      embedder: {
+        provider: "openai",
+        config: { apiKey: "test-key", model: "text-embedding-3-small" },
+      },
+      llm: {
+        provider: "openai",
+        config: { apiKey: "test-key", model: "gpt-5-mini" },
+      },
+      vectorStore: {
+        provider: "memory",
+        config: { collectionName: "validation-test" },
+      },
+    });
+    // Wait for initialization
+    await new Promise((resolve) => setTimeout(resolve, 2000));
+  });
+
+  afterAll(async () => {
+    try {
+      await memory.reset();
+    } catch (e) {
+      // ignore cleanup errors
+    }
+  });
+
+  describe("add() message validation", () => {
+    it("should throw error when messages is undefined", async () => {
+      await expect(
+        // @ts-ignore - intentionally passing undefined
+        memory.add(undefined, { userId: testUserId }),
+      ).rejects.toThrow("messages is required");
+    });
+
+    it("should throw error when messages is null", async () => {
+      await expect(
+        // @ts-ignore - intentionally passing null
+        memory.add(null, { userId: testUserId }),
+      ).rejects.toThrow("messages is required");
+    });
+  });
+
+  describe("search() threshold validation", () => {
+    it("should throw error when threshold > 1.0", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          threshold: 1.5,
+        }),
+      ).rejects.toThrow("Invalid threshold");
+    });
+
+    it("should throw error when threshold = 1.1", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          threshold: 1.1,
+        }),
+      ).rejects.toThrow("Invalid threshold");
+    });
+
+    it("should throw error when threshold is negative", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          threshold: -0.5,
+        }),
+      ).rejects.toThrow("Invalid threshold");
+    });
+
+    it("should throw error when threshold = -0.1", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          threshold: -0.1,
+        }),
+      ).rejects.toThrow("Invalid threshold");
+    });
+
+    it("should accept threshold = 0 (edge case)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: testUserId },
+        threshold: 0,
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+
+    it("should accept threshold = 1.0 (edge case)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: testUserId },
+        threshold: 1.0,
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+
+    it("should accept threshold = 0.5 (normal valid value)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: testUserId },
+        threshold: 0.5,
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+  });
+
+  describe("search() topK validation", () => {
+    it("should throw error when topK is negative", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          topK: -5,
+        }),
+      ).rejects.toThrow("Invalid topK");
+    });
+
+    it("should throw error when topK = -1", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: testUserId },
+          topK: -1,
+        }),
+      ).rejects.toThrow("Invalid topK");
+    });
+
+    it("should accept topK = 0 (returns empty)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: testUserId },
+        topK: 0,
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+
+    it("should accept topK = 20 (normal value)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: testUserId },
+        topK: 20,
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+  });
+
+  describe("add() entity ID validation", () => {
+    it("should throw error when userId is whitespace-only", async () => {
+      await expect(
+        memory.add("test message", { userId: "   " }),
+      ).rejects.toThrow("Invalid userId");
+    });
+
+    it("should throw error when userId is tabs and newlines", async () => {
+      await expect(
+        memory.add("test message", { userId: "\t\n\t" }),
+      ).rejects.toThrow("Invalid userId");
+    });
+
+    it("should throw error when agentId is whitespace-only", async () => {
+      await expect(
+        memory.add("test message", { agentId: "   " }),
+      ).rejects.toThrow("Invalid agentId");
+    });
+
+    it("should throw error when runId is whitespace-only", async () => {
+      await expect(
+        memory.add("test message", { runId: "   " }),
+      ).rejects.toThrow("Invalid runId");
+    });
+
+    it("should throw error when userId contains internal whitespace", async () => {
+      await expect(
+        memory.add("test message", { userId: "user 123" }),
+      ).rejects.toThrow("Invalid userId: cannot contain whitespace");
+    });
+
+    it("should throw error when userId contains tab character", async () => {
+      await expect(
+        memory.add("test message", { userId: "user\t123" }),
+      ).rejects.toThrow("Invalid userId: cannot contain whitespace");
+    });
+
+    it("should accept userId with leading/trailing whitespace (trimmed)", async () => {
+      // Should not throw - leading/trailing whitespace is trimmed
+      const result = await memory.add("test message", {
+        userId: "  valid-user  ",
+      });
+      expect(result).toBeDefined();
+    });
+  });
+
+  describe("search() filter entity ID validation", () => {
+    it("should throw error when user_id in filters is whitespace-only", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: "   " },
+        }),
+      ).rejects.toThrow("Invalid user_id");
+    });
+
+    it("should throw error when agent_id in filters is whitespace-only", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { agent_id: "   " },
+        }),
+      ).rejects.toThrow("Invalid agent_id");
+    });
+
+    it("should throw error when user_id contains internal whitespace", async () => {
+      await expect(
+        memory.search("test query", {
+          filters: { user_id: "user 123" },
+        }),
+      ).rejects.toThrow("Invalid user_id: cannot contain whitespace");
+    });
+
+    it("should accept user_id with leading/trailing whitespace (trimmed)", async () => {
+      const result = await memory.search("test query", {
+        filters: { user_id: "  valid-user  " },
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+  });
+
+  describe("getAll() validation", () => {
+    it("should throw error when user_id is whitespace-only", async () => {
+      await expect(
+        memory.getAll({ filters: { user_id: "   " } }),
+      ).rejects.toThrow("Invalid user_id");
+    });
+
+    it("should throw error when topK is negative", async () => {
+      await expect(
+        memory.getAll({ filters: { user_id: testUserId }, topK: -1 }),
+      ).rejects.toThrow("Invalid topK");
+    });
+
+    it("should throw error when user_id contains internal whitespace", async () => {
+      await expect(
+        memory.getAll({ filters: { user_id: "user 123" } }),
+      ).rejects.toThrow("Invalid user_id: cannot contain whitespace");
+    });
+
+    it("should accept user_id with leading/trailing whitespace (trimmed)", async () => {
+      const result = await memory.getAll({
+        filters: { user_id: "  valid-user  " },
+      });
+      expect(result).toBeDefined();
+      expect(result.results).toBeDefined();
+    });
+  });
+});

mem0-ts/src/oss/tests/vector-store.unit.test.ts

@@ -85,14 +85,16 @@ describe("MemoryVectorStore - search", () => {
     expect(results).toHaveLength(1);
   });
 
-  test("filters by userId", async () => {
+  test("filters by user_id", async () => {
-    const results = await store.search(vec([1, 0, 0, 0]), 10, { userId: "u2" });
+    const results = await store.search(vec([1, 0, 0, 0]), 10, {
-    expect(results.every((r) => r.payload.userId === "u2")).toBe(true);
+      user_id: "u2",
+    });
+    expect(results.every((r) => r.payload.user_id === "u2")).toBe(true);
   });
 
   test("returns empty when filter matches nothing", async () => {
     const results = await store.search(vec([1, 0, 0, 0]), 10, {
-      userId: "nobody",
+      user_id: "nobody",
     });
     expect(results).toHaveLength(0);
   });
@@ -168,10 +170,10 @@ describe("MemoryVectorStore - list", () => {
     expect(results).toHaveLength(3);
   });
 
-  test("filters by userId", async () => {
+  test("filters by user_id", async () => {
-    const [results, count] = await store.list({ userId: "u1" });
+    const [results, count] = await store.list({ user_id: "u1" });
     expect(count).toBe(2);
-    expect(results.every((r) => r.payload.userId === "u1")).toBe(true);
+    expect(results.every((r) => r.payload.user_id === "u1")).toBe(true);
   });
 
   test("respects limit", async () => {

mem0-ts/src/oss/tests/vector-stores-compat.test.ts

@@ -95,19 +95,19 @@ describe("MemoryVectorStore – full backward compat", () => {
     expect(results.length).toBe(2);
     expect(results[0].id).toBe("id-1");
 
-    // Search with filters
+    // Search with filters (camelCase in payload is normalized to snake_case)
-    const filtered = await store.search(vec1, 2, { userId: "u1" });
+    const filtered = await store.search(vec1, 2, { user_id: "u1" });
     expect(filtered.length).toBe(2);
 
     // Update
     const vec3 = new Array(1536).fill(0);
     vec3[2] = 1.0;
-    await store.update("id-1", vec3, { data: "updated", userId: "u1" });
+    await store.update("id-1", vec3, { data: "updated", user_id: "u1" });
     const updated = await store.get("id-1");
     expect(updated!.payload.data).toBe("updated");
 
     // List
-    const [listed, count] = await store.list({ userId: "u1" });
+    const [listed, count] = await store.list({ user_id: "u1" });
     expect(count).toBe(2);
 
     // List with limit

mem0/client/main.py

@@ -167,7 +167,7 @@ class MemoryClient:
 
         kwargs = self._prepare_params(kwargs)
         payload = self._prepare_payload(messages, kwargs)
-        response = self.client.post("/v3/memories/", json=payload)
+        response = self.client.post("/v3/memories/add/", json=payload)
         response.raise_for_status()
         if "metadata" in kwargs:
             del kwargs["metadata"]
@@ -207,7 +207,7 @@ class MemoryClient:
             **kwargs: Optional parameters for filtering (filters, page, page_size).
 
         Returns:
-            A dictionary containing memories in v1.1 format: {"results": [...]}
+            A paginated dict: {"count": int, "next": str | None, "previous": str | None, "results": [...]}
 
         Raises:
             ValidationError: If the input data is invalid.
@@ -233,9 +233,9 @@ class MemoryClient:
                 "page": params.pop("page"),
                 "page_size": params.pop("page_size"),
             }
-            response = self.client.post("/v2/memories/", json=params, params=query_params)
+            response = self.client.post("/v3/memories/", json=params, params=query_params)
         else:
-            response = self.client.post("/v2/memories/", json=params)
+            response = self.client.post("/v3/memories/", json=params)
         response.raise_for_status()
         if "metadata" in kwargs:
             del kwargs["metadata"]
@@ -247,12 +247,7 @@ class MemoryClient:
                 "sync_type": "sync",
             },
         )
-        result = response.json()
+        return response.json()
-
-        # Ensure v1.1 format (wrap raw list if needed)
-        if isinstance(result, list):
-            return {"results": result}
-        return result
 
     @api_error_handler
     def search(self, query: str, options: Optional[SearchMemoryOptions] = None, **kwargs) -> Dict[str, Any]:
@@ -883,7 +878,7 @@ class MemoryClient:
 
         response = self.client.post("/v1/feedback/", json=data)
         response.raise_for_status()
-        capture_client_event("client.feedback", self, data, {"sync_type": "sync"})
+        capture_client_event("client.feedback", self, {**data, "sync_type": "sync"})
         return response.json()
 
     def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]:
@@ -1095,7 +1090,7 @@ class AsyncMemoryClient:
 
         kwargs = self._prepare_params(kwargs)
         payload = self._prepare_payload(messages, kwargs)
-        response = await self.async_client.post("/v3/memories/", json=payload)
+        response = await self.async_client.post("/v3/memories/add/", json=payload)
         response.raise_for_status()
         if "metadata" in kwargs:
             del kwargs["metadata"]
@@ -1119,7 +1114,7 @@ class AsyncMemoryClient:
             **kwargs: Optional parameters for filtering (filters, page, page_size).
 
         Returns:
-            A dictionary containing memories in v1.1 format: {"results": [...]}
+            A paginated dict: {"count": int, "next": str | None, "previous": str | None, "results": [...]}
 
         Raises:
             ValidationError: If the input data is invalid.
@@ -1145,9 +1140,9 @@ class AsyncMemoryClient:
                 "page": params.pop("page"),
                 "page_size": params.pop("page_size"),
             }
-            response = await self.async_client.post("/v2/memories/", json=params, params=query_params)
+            response = await self.async_client.post("/v3/memories/", json=params, params=query_params)
         else:
-            response = await self.async_client.post("/v2/memories/", json=params)
+            response = await self.async_client.post("/v3/memories/", json=params)
         response.raise_for_status()
         if "metadata" in kwargs:
             del kwargs["metadata"]
@@ -1159,12 +1154,7 @@ class AsyncMemoryClient:
                 "sync_type": "async",
             },
         )
-        result = response.json()
+        return response.json()
-
-        # Ensure v1.1 format (wrap raw list if needed)
-        if isinstance(result, list):
-            return {"results": result}
-        return result
 
     @api_error_handler
     async def search(self, query: str, options: Optional[SearchMemoryOptions] = None, **kwargs) -> Dict[str, Any]:
@@ -1761,5 +1751,5 @@ class AsyncMemoryClient:
 
         response = await self.async_client.post("/v1/feedback/", json=data)
         response.raise_for_status()
-        capture_client_event("client.feedback", self, data, {"sync_type": "async"})
+        capture_client_event("client.feedback", self, {**data, "sync_type": "async"})
         return response.json()

mem0/configs/llms/openai.py

@@ -29,7 +29,7 @@ class OpenAIConfig(BaseLlmConfig):
         openrouter_base_url: Optional[str] = None,
         site_url: Optional[str] = None,
         app_name: Optional[str] = None,
-        store: bool = False,
+        store: Optional[bool] = None,
         # Response monitoring callback
         response_callback: Optional[Callable[[Any, dict, dict], None]] = None,
     ):
@@ -53,6 +53,11 @@ class OpenAIConfig(BaseLlmConfig):
             openrouter_base_url: OpenRouter base URL, defaults to None
             site_url: Site URL for OpenRouter, defaults to None
             app_name: Application name for OpenRouter, defaults to None
+            store: Whether to store the conversation on OpenAI's server. Opt-in;
+                defaults to None (not sent). Set to True or False only if you
+                want the value forwarded to the OpenAI API. Leaving it None
+                avoids leaking the field into OpenAI-compatible backends that
+                reject unknown fields (Gemini, Groq, vLLM, etc.).
             response_callback: Optional callback for monitoring LLM responses.
         """
         # Initialize base parameters

mem0/configs/vector_stores/valkey.py

@@ -14,6 +14,7 @@ class ValkeyConfig(BaseModel):
     hnsw_m: int = Field(16, description="HNSW: number of connections per layer")
     hnsw_ef_construction: int = Field(200, description="HNSW: search width during index construction")
     hnsw_ef_runtime: int = Field(10, description="HNSW: search width during queries")
+    cluster_mode: bool = Field(False, description="Enable cluster mode for Valkey cluster (CME) deployments")
 
     @model_validator(mode="before")
     @classmethod

mem0/llms/azure_openai.py

@@ -39,7 +39,7 @@ class AzureOpenAILLM(LLMBase):
 
         # Model name should match the custom deployment name chosen for it.
         if not self.config.model:
-            self.config.model = "gpt-4.1-nano-2025-04-14"
+            self.config.model = "gpt-5-mini"
 
         api_key = self.config.azure_kwargs.api_key or os.getenv("LLM_AZURE_OPENAI_API_KEY")
         azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("LLM_AZURE_DEPLOYMENT")

mem0/llms/azure_openai_structured.py

@@ -18,7 +18,7 @@ class AzureOpenAIStructuredLLM(LLMBase):
 
         # Model name should match the custom deployment name chosen for it.
         if not self.config.model:
-            self.config.model = "gpt-4.1-nano-2025-04-14"
+            self.config.model = "gpt-5-mini"
 
         api_key = self.config.azure_kwargs.api_key or os.getenv("LLM_AZURE_OPENAI_API_KEY")
         azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("LLM_AZURE_DEPLOYMENT")

mem0/llms/litellm.py

@@ -16,7 +16,7 @@ class LiteLLM(LLMBase):
         super().__init__(config)
 
         if not self.config.model:
-            self.config.model = "gpt-4.1-nano-2025-04-14"
+            self.config.model = "gpt-5-mini"
 
     def _parse_response(self, response, tools):
         """

mem0/llms/openai.py

@@ -36,7 +36,7 @@ class OpenAILLM(LLMBase):
         super().__init__(config)
 
         if not self.config.model:
-            self.config.model = "gpt-4.1-nano-2025-04-14"
+            self.config.model = "gpt-5-mini"
 
         if os.environ.get("OPENROUTER_API_KEY"):  # Use OpenRouter
             self.client = OpenAI(
@@ -126,10 +126,11 @@ class OpenAILLM(LLMBase):
             params.update(**openrouter_params)
         
         else:
-            openai_specific_generation_params = ["store"]
+            # Only send OpenAI-specific parameters when the user has explicitly
-            for param in openai_specific_generation_params:
+            # configured them. OpenAI-compatible backends (Gemini, Groq, vLLM, etc.)
-                if hasattr(self.config, param):
+            # reject unknown fields, so `store` must be opt-in, not opt-out.
-                    params[param] = getattr(self.config, param)
+            if self.config.store is not None:
+                params["store"] = self.config.store
 
         if response_format:
             params["response_format"] = response_format

mem0/llms/openai_structured.py

@@ -12,7 +12,7 @@ class OpenAIStructuredLLM(LLMBase):
         super().__init__(config)
 
         if not self.config.model:
-            self.config.model = "gpt-4o-2024-08-06"
+            self.config.model = "gpt-5-mini"
 
         api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
         base_url = self.config.openai_base_url or os.getenv("OPENAI_API_BASE") or "https://api.openai.com/v1"

mem0/memory/main.py

@@ -110,6 +110,64 @@ def _reject_top_level_entity_params(kwargs: Dict[str, Any], method_name: str) ->
         )
 
 
+def _validate_and_trim_entity_id(value: Optional[str], name: str) -> Optional[str]:
+    """
+    Validates and normalizes an entity ID.
+    - Trims leading/trailing whitespace
+    - Rejects empty or whitespace-only strings
+    - Rejects strings containing internal whitespace
+
+    Args:
+        value: The entity ID value to validate
+        name: The parameter name (for error messages)
+
+    Returns:
+        The trimmed entity ID, or None if input is None
+
+    Raises:
+        ValueError: If entity ID is invalid
+    """
+    if value is None:
+        return None
+    trimmed = value.strip()
+    if trimmed == "":
+        raise ValueError(
+            f"Invalid {name}: cannot be empty or whitespace-only. Provide a valid identifier."
+        )
+    if any(c.isspace() for c in trimmed):
+        raise ValueError(
+            f"Invalid {name}: cannot contain whitespace. Provide a valid identifier without spaces."
+        )
+    return trimmed
+
+
+def _validate_search_params(threshold: Optional[float] = None, top_k: Optional[int] = None) -> None:
+    """
+    Validates search parameters.
+
+    Args:
+        threshold: Similarity threshold (must be between 0 and 1)
+        top_k: Number of results to return (must be non-negative integer)
+
+    Raises:
+        ValueError: If threshold or top_k are invalid
+    """
+    if threshold is not None:
+        if not isinstance(threshold, (int, float)):
+            raise ValueError("threshold must be a valid number")
+        if threshold < 0 or threshold > 1:
+            raise ValueError(
+                f"Invalid threshold: {threshold}. Must be between 0 and 1 (inclusive)."
+            )
+    if top_k is not None:
+        if not isinstance(top_k, int) or isinstance(top_k, bool):
+            raise ValueError("top_k must be a valid integer")
+        if top_k < 0:
+            raise ValueError(
+                f"Invalid top_k: {top_k}. Must be a non-negative integer."
+            )
+
+
 def _is_sensitive_field(field_name: str) -> bool:
     """Check if a field should be redacted for telemetry safety.
 
@@ -217,9 +275,14 @@ def _build_filters_and_metadata(
     base_metadata_template = deepcopy(input_metadata) if input_metadata else {}
     effective_query_filters = deepcopy(input_filters) if input_filters else {}
 
-    # ---------- add all provided session ids ----------
+    # ---------- validate and add all provided session ids ----------
     session_ids_provided = []
 
+    # Validate and trim entity IDs
+    user_id = _validate_and_trim_entity_id(user_id, "user_id")
+    agent_id = _validate_and_trim_entity_id(agent_id, "agent_id")
+    run_id = _validate_and_trim_entity_id(run_id, "run_id")
+
     if user_id:
         base_metadata_template["user_id"] = user_id
         effective_query_filters["user_id"] = user_id
@@ -334,6 +397,14 @@ class Memory(MemoryBase):
                 entity_config.collection_name = entity_collection
             elif isinstance(entity_config, dict):
                 entity_config['collection_name'] = entity_collection
+            # For Qdrant, share the existing client to avoid RocksDB lock contention
+            # when using embedded mode (path=...). QdrantConfig.client takes precedence
+            # over host/port/path.
+            if self.config.vector_store.provider == "qdrant" and hasattr(self.vector_store, "client"):
+                if hasattr(entity_config, "client"):
+                    entity_config.client = self.vector_store.client
+                elif isinstance(entity_config, dict):
+                    entity_config["client"] = self.vector_store.client
             self._entity_store = VectorStoreFactory.create(
                 self.config.vector_store.provider, entity_config
             )
@@ -868,7 +939,7 @@ class Memory(MemoryBase):
         self,
         *,
         filters: Optional[Dict[str, Any]] = None,
-        top_k: int = 100,
+        top_k: int = 20,
         **kwargs,
     ):
         """
@@ -878,20 +949,38 @@ class Memory(MemoryBase):
             filters (dict): Filter dict containing entity IDs and optional metadata filters.
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
-            top_k (int, optional): The maximum number of memories to return. Defaults to 100.
+            top_k (int, optional): The maximum number of memories to return. Defaults to 20.
 
         Returns:
             dict: A dictionary containing a list of memories under the "results" key.
                   Example for v1.1+: `{"results": [{"id": "...", "memory": "...", ...}]}`
 
         Raises:
-            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id.
+            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
+                or if top_k is invalid.
         """
         # Reject top-level entity params - must use filters instead
         _reject_top_level_entity_params(kwargs, "get_all")
 
+        # Validate top_k
+        _validate_search_params(top_k=top_k)
+
+        # Validate and trim entity IDs in filters
+        effective_filters = dict(filters) if filters else {}
+        if "user_id" in effective_filters:
+            effective_filters["user_id"] = _validate_and_trim_entity_id(
+                effective_filters["user_id"], "user_id"
+            )
+        if "agent_id" in effective_filters:
+            effective_filters["agent_id"] = _validate_and_trim_entity_id(
+                effective_filters["agent_id"], "agent_id"
+            )
+        if "run_id" in effective_filters:
+            effective_filters["run_id"] = _validate_and_trim_entity_id(
+                effective_filters["run_id"], "run_id"
+            )
+
         # Validate filters contains at least one entity ID
-        effective_filters = filters or {}
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError(
                 "filters must contain at least one of: user_id, agent_id, run_id. "
@@ -960,7 +1049,7 @@ class Memory(MemoryBase):
         self,
         query: str,
         *,
-        top_k: int = 100,
+        top_k: int = 20,
         filters: Optional[Dict[str, Any]] = None,
         threshold: float = 0.1,
         rerank: bool = False,
@@ -971,7 +1060,7 @@ class Memory(MemoryBase):
 
         Args:
             query (str): Query to search for.
-            top_k (int, optional): Maximum number of results to return. Defaults to 100.
+            top_k (int, optional): Maximum number of results to return. Defaults to 20.
             filters (dict): Filter dict containing entity IDs and optional metadata filters.
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
@@ -1000,13 +1089,29 @@ class Memory(MemoryBase):
                   Example for v1.1+: `{"results": [{"id": "...", "memory": "...", "score": 0.8, ...}]}`
 
         Raises:
-            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id.
+            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
+                or if threshold/top_k values are invalid.
         """
         # Reject top-level entity params - must use filters instead
         _reject_top_level_entity_params(kwargs, "search")
 
-        # Validate filters contains at least one entity ID
+        # Validate search parameters (before applying defaults)
+        _validate_search_params(threshold=threshold, top_k=top_k)
+
+        # Validate and trim entity IDs in filters
         effective_filters = filters.copy() if filters else {}
+        if "user_id" in effective_filters:
+            effective_filters["user_id"] = _validate_and_trim_entity_id(
+                effective_filters["user_id"], "user_id"
+            )
+        if "agent_id" in effective_filters:
+            effective_filters["agent_id"] = _validate_and_trim_entity_id(
+                effective_filters["agent_id"], "agent_id"
+            )
+        if "run_id" in effective_filters:
+            effective_filters["run_id"] = _validate_and_trim_entity_id(
+                effective_filters["run_id"], "run_id"
+            )
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError(
                 "filters must contain at least one of: user_id, agent_id, run_id. "
@@ -1188,8 +1293,6 @@ class Memory(MemoryBase):
             entity_boosts = self._compute_entity_boosts(query_entities, filters)
 
         # Step 7: Build candidate set from semantic results
-        # BM25 acts as a boost signal only (not recall-expanding) -- candidates must
-        # pass the semantic threshold gate, so only semantic results are candidates.
         candidates = []
         for mem in semantic_results:
             mem_id = str(mem.id)
@@ -1234,9 +1337,6 @@ class Memory(MemoryBase):
                 score=scored["score"],
             ).model_dump()
 
-            # Add score breakdown to metadata
-            memory_item_dict["score_breakdown"] = scored.get("score_breakdown", {})
-
             for key in promoted_payload_keys:
                 if key in payload:
                     memory_item_dict[key] = payload[key]
@@ -1640,6 +1740,14 @@ class AsyncMemory(MemoryBase):
                 entity_config.collection_name = entity_collection
             elif isinstance(entity_config, dict):
                 entity_config['collection_name'] = entity_collection
+            # For Qdrant, share the existing client to avoid RocksDB lock contention
+            # when using embedded mode (path=...). QdrantConfig.client takes precedence
+            # over host/port/path.
+            if self.config.vector_store.provider == "qdrant" and hasattr(self.vector_store, "client"):
+                if hasattr(entity_config, "client"):
+                    entity_config.client = self.vector_store.client
+                elif isinstance(entity_config, dict):
+                    entity_config["client"] = self.vector_store.client
             self._entity_store = VectorStoreFactory.create(
                 self.config.vector_store.provider, entity_config
             )
@@ -2115,7 +2223,7 @@ class AsyncMemory(MemoryBase):
         self,
         *,
         filters: Optional[Dict[str, Any]] = None,
-        top_k: int = 100,
+        top_k: int = 20,
         **kwargs,
     ):
         """
@@ -2125,20 +2233,38 @@ class AsyncMemory(MemoryBase):
             filters (dict): Filter dict containing entity IDs and optional metadata filters.
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
-            top_k (int, optional): The maximum number of memories to return. Defaults to 100.
+            top_k (int, optional): The maximum number of memories to return. Defaults to 20.
 
         Returns:
             dict: A dictionary containing a list of memories under the "results" key.
                   Example for v1.1+: `{"results": [{"id": "...", "memory": "...", ...}]}`
 
         Raises:
-            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id.
+            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
+                or if top_k is invalid.
         """
         # Reject top-level entity params - must use filters instead
         _reject_top_level_entity_params(kwargs, "get_all")
 
+        # Validate top_k
+        _validate_search_params(top_k=top_k)
+
+        # Validate and trim entity IDs in filters
+        effective_filters = dict(filters) if filters else {}
+        if "user_id" in effective_filters:
+            effective_filters["user_id"] = _validate_and_trim_entity_id(
+                effective_filters["user_id"], "user_id"
+            )
+        if "agent_id" in effective_filters:
+            effective_filters["agent_id"] = _validate_and_trim_entity_id(
+                effective_filters["agent_id"], "agent_id"
+            )
+        if "run_id" in effective_filters:
+            effective_filters["run_id"] = _validate_and_trim_entity_id(
+                effective_filters["run_id"], "run_id"
+            )
+
         # Validate filters contains at least one entity ID
-        effective_filters = filters or {}
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError(
                 "filters must contain at least one of: user_id, agent_id, run_id. "
@@ -2207,7 +2333,7 @@ class AsyncMemory(MemoryBase):
         self,
         query: str,
         *,
-        top_k: int = 100,
+        top_k: int = 20,
         filters: Optional[Dict[str, Any]] = None,
         threshold: float = 0.1,
         rerank: bool = False,
@@ -2218,7 +2344,7 @@ class AsyncMemory(MemoryBase):
 
         Args:
             query (str): Query to search for.
-            top_k (int, optional): Maximum number of results to return. Defaults to 100.
+            top_k (int, optional): Maximum number of results to return. Defaults to 20.
             filters (dict): Filter dict containing entity IDs and optional metadata filters.
                 Must contain at least one of: user_id, agent_id, run_id.
                 Example: filters={"user_id": "u1", "agent_id": "a1"}
@@ -2247,13 +2373,31 @@ class AsyncMemory(MemoryBase):
                   Example for v1.1+: `{"results": [{"id": "...", "memory": "...", "score": 0.8, ...}]}`
 
         Raises:
-            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id.
+            ValueError: If filters doesn't contain at least one of user_id, agent_id, run_id,
+                or if threshold/top_k values are invalid.
         """
         # Reject top-level entity params - must use filters instead
         _reject_top_level_entity_params(kwargs, "search")
 
-        # Validate filters contains at least one entity ID
+        # Validate search parameters (before applying defaults)
+        _validate_search_params(threshold=threshold, top_k=top_k)
+
+        # Validate and trim entity IDs in filters
         effective_filters = filters.copy() if filters else {}
+        if "user_id" in effective_filters:
+            effective_filters["user_id"] = _validate_and_trim_entity_id(
+                effective_filters["user_id"], "user_id"
+            )
+        if "agent_id" in effective_filters:
+            effective_filters["agent_id"] = _validate_and_trim_entity_id(
+                effective_filters["agent_id"], "agent_id"
+            )
+        if "run_id" in effective_filters:
+            effective_filters["run_id"] = _validate_and_trim_entity_id(
+                effective_filters["run_id"], "run_id"
+            )
+
+        # Validate filters contains at least one entity ID
         if not any(key in effective_filters for key in ("user_id", "agent_id", "run_id")):
             raise ValueError(
                 "filters must contain at least one of: user_id, agent_id, run_id. "
@@ -2480,8 +2624,6 @@ class AsyncMemory(MemoryBase):
                 score=scored["score"],
             ).model_dump()
 
-            memory_item_dict["score_breakdown"] = scored.get("score_breakdown", {})
-
             for key in promoted_payload_keys:
                 if key in payload:
                     memory_item_dict[key] = payload[key]

mem0/memory/setup.py

@@ -32,15 +32,26 @@ def get_user_id():
         return "anonymous_user"
 
 
-def get_or_create_user_id(vector_store):
+def get_or_create_user_id(vector_store=None):
-    """Store user_id in vector store and return it."""
+    """Store user_id in vector store and return it.
+
+    If vector_store is None, simply returns the user_id from config.
+    This ensures telemetry initialization never fails due to missing vector store.
+    """
     user_id = get_user_id()
 
+    # If no vector store provided, just return the user_id
+    if vector_store is None:
+        return user_id
+
     # Try to get existing user_id from vector store
     try:
         existing = vector_store.get(vector_id=user_id)
         if existing and hasattr(existing, "payload") and existing.payload and "user_id" in existing.payload:
-            return existing.payload["user_id"]
+            stored_id = existing.payload["user_id"]
+            # Ensure we never return None from vector store
+            if stored_id is not None:
+                return stored_id
     except Exception:
         pass
 

mem0/memory/telemetry.py

@@ -88,6 +88,12 @@ class AnonymousTelemetry:
         if self.posthog is None:
             return
 
+        # Determine distinct_id, skip if None to prevent crashes
+        distinct_id = self.user_id if user_email is None else user_email
+        if distinct_id is None:
+            _logger.debug("Skipping telemetry event %r: no distinct_id available", event_name)
+            return
+
         if properties is None:
             properties = {}
         properties = {
@@ -101,8 +107,10 @@ class AnonymousTelemetry:
             "machine": platform.machine(),
             **properties,
         }
-        distinct_id = self.user_id if user_email is None else user_email
+        try:
-        self.posthog.capture(distinct_id=distinct_id, event=event_name, properties=properties)
+            self.posthog.capture(distinct_id=distinct_id, event=event_name, properties=properties)
+        except Exception as e:
+            _logger.debug("Failed to capture telemetry event %r: %s", event_name, e)
 
     def close(self):
         if self.posthog is not None:
@@ -158,36 +166,52 @@ atexit.register(client_telemetry.close)
 
 
 def capture_event(event_name, memory_instance, additional_data=None):
+    """Capture telemetry event for OSS Memory instances.
+
+    This function is designed to never raise exceptions - telemetry failures
+    should not affect the main application flow.
+    """
     if not MEM0_TELEMETRY:
         return
 
-    oss_telemetry = _get_oss_telemetry()
+    try:
-    if oss_telemetry is None:
+        oss_telemetry = _get_oss_telemetry()
-        return
+        if oss_telemetry is None:
+            return
 
-    event_data = {
+        event_data = {
-        "collection": memory_instance.collection_name,
+            "collection": memory_instance.collection_name,
-        "vector_size": memory_instance.embedding_model.config.embedding_dims,
+            "vector_size": memory_instance.embedding_model.config.embedding_dims,
-        "history_store": "sqlite",
+            "history_store": "sqlite",
-        "vector_store": f"{memory_instance.vector_store.__class__.__module__}.{memory_instance.vector_store.__class__.__name__}",
+            "vector_store": f"{memory_instance.vector_store.__class__.__module__}.{memory_instance.vector_store.__class__.__name__}",
-        "llm": f"{memory_instance.llm.__class__.__module__}.{memory_instance.llm.__class__.__name__}",
+            "llm": f"{memory_instance.llm.__class__.__module__}.{memory_instance.llm.__class__.__name__}",
-        "embedding_model": f"{memory_instance.embedding_model.__class__.__module__}.{memory_instance.embedding_model.__class__.__name__}",
+            "embedding_model": f"{memory_instance.embedding_model.__class__.__module__}.{memory_instance.embedding_model.__class__.__name__}",
-        "function": f"{memory_instance.__class__.__module__}.{memory_instance.__class__.__name__}.{memory_instance.api_version}",
+            "function": f"{memory_instance.__class__.__module__}.{memory_instance.__class__.__name__}.{memory_instance.api_version}",
-    }
+        }
-    if additional_data:
+        if additional_data:
-        event_data.update(additional_data)
+            event_data.update(additional_data)
 
-    oss_telemetry.capture_event(event_name, event_data)
+        oss_telemetry.capture_event(event_name, event_data)
+    except Exception as e:
+        _logger.debug("Failed to capture OSS telemetry event %r: %s", event_name, e)
 
 
 def capture_client_event(event_name, instance, additional_data=None):
+    """Capture telemetry event for hosted MemoryClient instances.
+
+    This function is designed to never raise exceptions - telemetry failures
+    should not affect the main application flow.
+    """
     if not MEM0_TELEMETRY:
         return
 
-    event_data = {
+    try:
-        "function": f"{instance.__class__.__module__}.{instance.__class__.__name__}",
+        event_data = {
-    }
+            "function": f"{instance.__class__.__module__}.{instance.__class__.__name__}",
-    if additional_data:
+        }
-        event_data.update(additional_data)
+        if additional_data:
+            event_data.update(additional_data)
 
-    client_telemetry.capture_event(event_name, event_data, instance.user_email)
+        client_telemetry.capture_event(event_name, event_data, instance.user_email)
+    except Exception as e:
+        _logger.debug("Failed to capture client telemetry event %r: %s", event_name, e)

mem0/reranker/sentence_transformer_reranker.py

@@ -6,7 +6,7 @@ from mem0.configs.rerankers.base import BaseRerankerConfig
 from mem0.configs.rerankers.sentence_transformer import SentenceTransformerRerankerConfig
 
 try:
-    from sentence_transformers import SentenceTransformer
+    from sentence_transformers import CrossEncoder
     SENTENCE_TRANSFORMERS_AVAILABLE = True
 except ImportError:
     SENTENCE_TRANSFORMERS_AVAILABLE = False
@@ -41,7 +41,7 @@ class SentenceTransformerReranker(BaseReranker):
             )
 
         self.config = config
-        self.model = SentenceTransformer(self.config.model, device=self.config.device)
+        self.model = CrossEncoder(self.config.model, device=self.config.device)
         
     def rerank(self, query: str, documents: List[Dict[str, Any]], top_k: int = None) -> List[Dict[str, Any]]:
         """
@@ -74,8 +74,11 @@ class SentenceTransformerReranker(BaseReranker):
             # Create query-document pairs
             pairs = [[query, doc_text] for doc_text in doc_texts]
             
-            # Get similarity scores
+            scores = self.model.predict(
-            scores = self.model.predict(pairs)
+                pairs,
+                batch_size=self.config.batch_size,
+                show_progress_bar=self.config.show_progress_bar,
+            )
             if isinstance(scores, np.ndarray):
                 scores = scores.tolist()
             

mem0/utils/scoring.py

@@ -113,11 +113,6 @@ def score_and_rank(
             {
                 "id": mem_id_str,
                 "score": combined,
-                "score_breakdown": {
-                    "semantic": semantic_score,
-                    "bm25": bm25_score,
-                    "entity_boost": entity_boost,
-                },
                 "payload": result.get("payload"),
             }
         )

mem0/vector_stores/faiss.py

@@ -1,10 +1,11 @@
+import json
 import logging
 import os
 import pickle
 import uuid
 import warnings
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
 import numpy as np
 from pydantic import BaseModel
@@ -30,6 +31,93 @@ from mem0.vector_stores.base import VectorStoreBase
 logger = logging.getLogger(__name__)
 
 
+class SafeUnpickler(pickle.Unpickler):
+    """
+    Restricted unpickler that only allows safe built-in types.
+
+    This prevents arbitrary code execution via pickle deserialization by only
+    allowing a whitelist of safe types (dict, list, str, int, float, bool, tuple, None).
+    """
+
+    # Only allow builtins module
+    SAFE_MODULES = frozenset({"builtins", "__builtin__"})
+    # Only allow safe basic types
+    SAFE_NAMES = frozenset({"dict", "list", "str", "int", "float", "bool", "tuple", "set", "frozenset", "NoneType"})
+
+    def find_class(self, module: str, name: str) -> Any:
+        """Override find_class to only allow safe types."""
+        if module in self.SAFE_MODULES and name in self.SAFE_NAMES:
+            import builtins
+
+            if hasattr(builtins, name):
+                return getattr(builtins, name)
+            # NoneType special case
+            if name == "NoneType":
+                return type(None)
+        raise pickle.UnpicklingError(
+            f"Unsafe pickle: attempted to load '{module}.{name}'. "
+            f"Only basic Python types are allowed for security reasons."
+        )
+
+
+def _safe_pickle_load(file_path: str) -> Any:
+    """
+    Safely load a pickle file using restricted unpickler.
+
+    Args:
+        file_path: Path to the pickle file.
+
+    Returns:
+        The deserialized object (only basic Python types allowed).
+
+    Raises:
+        pickle.UnpicklingError: If the pickle contains unsafe types.
+    """
+    with open(file_path, "rb") as f:
+        return SafeUnpickler(f).load()
+
+
+def _validate_docstore_structure(data: Any) -> tuple:
+    """
+    Validate that loaded data has the expected structure.
+
+    Args:
+        data: The loaded data to validate.
+
+    Returns:
+        Tuple of (docstore, index_to_id) if valid.
+
+    Raises:
+        ValueError: If the data structure is invalid.
+    """
+    if not isinstance(data, tuple) or len(data) != 2:
+        raise ValueError("Invalid docstore format: expected tuple of (docstore, index_to_id)")
+
+    docstore, index_to_id = data
+
+    if not isinstance(docstore, dict):
+        raise ValueError("Invalid docstore format: docstore must be a dict")
+
+    if not isinstance(index_to_id, dict):
+        raise ValueError("Invalid docstore format: index_to_id must be a dict")
+
+    # Validate docstore entries
+    for key, value in docstore.items():
+        if not isinstance(key, str):
+            raise ValueError(f"Invalid docstore key type: {type(key)}, expected str")
+        if not isinstance(value, dict):
+            raise ValueError(f"Invalid docstore value type: {type(value)}, expected dict")
+
+    # Validate index_to_id entries
+    for key, value in index_to_id.items():
+        if not isinstance(key, int):
+            raise ValueError(f"Invalid index_to_id key type: {type(key)}, expected int")
+        if not isinstance(value, str):
+            raise ValueError(f"Invalid index_to_id value type: {type(value)}, expected str")
+
+    return docstore, index_to_id
+
+
 class OutputData(BaseModel):
     id: Optional[str]  # memory id
     score: Optional[float]  # distance
@@ -73,9 +161,13 @@ class FAISS(VectorStoreBase):
 
             # Try to load existing index if available
             index_path = f"{self.path}/{collection_name}.faiss"
-            docstore_path = f"{self.path}/{collection_name}.pkl"
+            json_docstore_path = f"{self.path}/{collection_name}.json"
-            if os.path.exists(index_path) and os.path.exists(docstore_path):
+            pkl_docstore_path = f"{self.path}/{collection_name}.pkl"
-                self._load(index_path, docstore_path)
+
+            # Check for index file and either JSON (preferred) or legacy pickle docstore
+            if os.path.exists(index_path) and (os.path.exists(json_docstore_path) or os.path.exists(pkl_docstore_path)):
+                # _load will prefer JSON over pickle and auto-migrate
+                self._load(index_path, pkl_docstore_path)
             else:
                 self.create_col(collection_name)
 
@@ -83,34 +175,76 @@ class FAISS(VectorStoreBase):
         """
         Load FAISS index and docstore from disk.
 
+        Supports both JSON (preferred) and legacy pickle formats. Pickle files are loaded
+        using a restricted unpickler that only allows basic Python types to prevent
+        arbitrary code execution (CVE mitigation).
+
         Args:
             index_path (str): Path to FAISS index file.
-            docstore_path (str): Path to docstore pickle file.
+            docstore_path (str): Path to docstore file (.json or legacy .pkl).
         """
         try:
             self.index = faiss.read_index(index_path)
-            with open(docstore_path, "rb") as f:
+
-                self.docstore, self.index_to_id = pickle.load(f)
+            # Determine docstore format - prefer JSON over pickle
-            logger.info(f"Loaded FAISS index from {index_path} with {self.index.ntotal} vectors")
+            json_docstore_path = docstore_path.replace(".pkl", ".json")
+
+            if os.path.exists(json_docstore_path):
+                # Load from JSON (safe, preferred format)
+                with open(json_docstore_path, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                self.docstore = data.get("docstore", {})
+                # JSON keys are always strings, convert back to int
+                self.index_to_id = {int(k): v for k, v in data.get("index_to_id", {}).items()}
+                logger.info(f"Loaded FAISS index from {index_path} with {self.index.ntotal} vectors (JSON format)")
+
+            elif os.path.exists(docstore_path):
+                # Load from legacy pickle using safe unpickler
+                # This prevents arbitrary code execution from malicious pickle files
+                logger.warning(
+                    f"Loading legacy pickle docstore from {docstore_path}. "
+                    f"Consider migrating to JSON format for better security."
+                )
+                data = _safe_pickle_load(docstore_path)
+                self.docstore, self.index_to_id = _validate_docstore_structure(data)
+                logger.info(f"Loaded FAISS index from {index_path} with {self.index.ntotal} vectors (pickle format)")
+
+                # Auto-migrate to JSON format
+                self._save()
+                logger.info(f"Migrated docstore to JSON format: {json_docstore_path}")
+
+            else:
+                raise FileNotFoundError(f"No docstore found at {docstore_path} or {json_docstore_path}")
+
+        except pickle.UnpicklingError as e:
+            logger.error(f"Security error loading FAISS docstore: {e}")
+            raise ValueError(f"Failed to load FAISS docstore: potentially malicious pickle file. {e}") from e
         except Exception as e:
             logger.warning(f"Failed to load FAISS index: {e}")
-
             self.docstore = {}
             self.index_to_id = {}
 
     def _save(self):
-        """Save FAISS index and docstore to disk."""
+        """Save FAISS index and docstore to disk using JSON format (secure)."""
         if not self.path or not self.index:
             return
 
         try:
             os.makedirs(self.path, exist_ok=True)
             index_path = f"{self.path}/{self.collection_name}.faiss"
-            docstore_path = f"{self.path}/{self.collection_name}.pkl"
+            json_docstore_path = f"{self.path}/{self.collection_name}.json"
 
             faiss.write_index(self.index, index_path)
-            with open(docstore_path, "wb") as f:
+
-                pickle.dump((self.docstore, self.index_to_id), f)
+            # Save docstore as JSON (safe format, no code execution risk)
+            # JSON keys must be strings, so convert int keys to str
+            data = {
+                "docstore": self.docstore,
+                "index_to_id": {str(k): v for k, v in self.index_to_id.items()},
+            }
+            with open(json_docstore_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, indent=2)
+
         except Exception as e:
             logger.warning(f"Failed to save FAISS index: {e}")
 
@@ -417,12 +551,16 @@ class FAISS(VectorStoreBase):
         if self.path:
             try:
                 index_path = f"{self.path}/{self.collection_name}.faiss"
-                docstore_path = f"{self.path}/{self.collection_name}.pkl"
+                json_docstore_path = f"{self.path}/{self.collection_name}.json"
+                pkl_docstore_path = f"{self.path}/{self.collection_name}.pkl"
 
                 if os.path.exists(index_path):
                     os.remove(index_path)
-                if os.path.exists(docstore_path):
+                if os.path.exists(json_docstore_path):
-                    os.remove(docstore_path)
+                    os.remove(json_docstore_path)
+                # Also clean up legacy pickle files if they exist
+                if os.path.exists(pkl_docstore_path):
+                    os.remove(pkl_docstore_path)
 
                 logger.info(f"Deleted collection {self.collection_name}")
             except Exception as e:

mem0/vector_stores/milvus.py

@@ -46,6 +46,9 @@ class MilvusDB(VectorStoreBase):
         self.embedding_model_dims = embedding_model_dims
         self.metric_type = metric_type
         self.client = MilvusClient(uri=url, token=token, db_name=db_name)
+        # Whether this collection has the `text` + `sparse` fields for v3 BM25.
+        # Pre-v3 collections lack them; writing a top-level `text` field is rejected.
+        self._has_bm25_schema = False
         self.create_col(
             collection_name=self.collection_name,
             vector_size=self.embedding_model_dims,
@@ -68,6 +71,15 @@ class MilvusDB(VectorStoreBase):
 
         if self.client.has_collection(collection_name):
             logger.info(f"Collection {collection_name} already exists. Skipping creation.")
+            desc = self.client.describe_collection(collection_name=collection_name)
+            field_names = {f.get("name") for f in desc.get("fields", [])}
+            self._has_bm25_schema = "text" in field_names and "sparse" in field_names
+            if not self._has_bm25_schema:
+                logger.warning(
+                    f"Collection '{collection_name}' predates v3 hybrid search (no 'text'/'sparse' fields). "
+                    "BM25 keyword scoring will be disabled for this collection; semantic search works normally. "
+                    "To enable hybrid search, use a fresh collection."
+                )
         else:
             fields = [
                 FieldSchema(name="id", dtype=DataType.VARCHAR, is_primary=True, max_length=512),
@@ -101,6 +113,7 @@ class MilvusDB(VectorStoreBase):
                 index_name="sparse_index",
             )
             self.client.create_collection(collection_name=collection_name, schema=schema, index_params=index_params)
+            self._has_bm25_schema = True
 
     def insert(self, ids, vectors, payloads, **kwargs: Optional[dict[str, any]]):
         """Insert vectors into a collection.
@@ -110,17 +123,17 @@ class MilvusDB(VectorStoreBase):
             payloads (List[Dict], optional): List of payloads corresponding to vectors.
             ids (List[str], optional): List of IDs corresponding to vectors.
         """
-        # Batch insert all records at once for better performance and consistency
+        # Batch insert all records at once for better performance and consistency.
-        data = [
+        # Only include the `text` field when the collection's schema has it — legacy
-            {
+        # collections created pre-v3 reject unknown top-level fields.
-                "id": idx,
+        def _build_record(idx, embedding, metadata):
-                "vectors": embedding,
+            record = {"id": idx, "vectors": embedding, "metadata": metadata}
-                "metadata": metadata,
+            if self._has_bm25_schema:
                 # Populate the text field for BM25 sparse search; prefer lemmatized text, fall back to raw data
-                "text": (metadata.get("text_lemmatized") or metadata.get("data", ""))[:65535] if metadata else "",
+                record["text"] = (metadata.get("text_lemmatized") or metadata.get("data", ""))[:65535] if metadata else ""
-            }
+            return record
-            for idx, embedding, metadata in zip(ids, vectors, payloads)
+
-        ]
+        data = [_build_record(idx, embedding, metadata) for idx, embedding, metadata in zip(ids, vectors, payloads)]
         self.client.insert(collection_name=self.collection_name, data=data, **kwargs)
 
     def _create_filter(self, filters: dict):
@@ -179,13 +192,21 @@ class MilvusDB(VectorStoreBase):
             list: Search results.
         """
         query_filter = self._create_filter(filters) if filters else None
-        hits = self.client.search(
+        # v3 collections carry both a dense `vectors` field and a sparse `sparse`
-            collection_name=self.collection_name,
+        # field (for BM25), which makes anns_field ambiguous — Milvus rejects the
-            data=[vectors],
+        # query otherwise with "multiple anns_fields exist". Legacy single-vector
-            limit=top_k,
+        # collections don't need the hint, so only pass it when the hybrid schema
-            filter=query_filter,
+        # is present.
-            output_fields=["*"],
+        search_kwargs = {
-        )
+            "collection_name": self.collection_name,
+            "data": [vectors],
+            "limit": top_k,
+            "filter": query_filter,
+            "output_fields": ["*"],
+        }
+        if self._has_bm25_schema:
+            search_kwargs["anns_field"] = "vectors"
+        hits = self.client.search(**search_kwargs)
         result = self._parse_output(data=hits[0])
         return result
 
@@ -206,6 +227,8 @@ class MilvusDB(VectorStoreBase):
             list: Search results in the same format as search(), or None if sparse search
                   is not supported on this collection.
         """
+        if not self._has_bm25_schema:
+            return None
         try:
             query_filter = self._create_filter(filters) if filters else None
             hits = self.client.search(

mem0/vector_stores/qdrant.py

@@ -79,6 +79,10 @@ class Qdrant(VectorStoreBase):
         self.embedding_model_dims = embedding_model_dims
         self.on_disk = on_disk
         self._bm25_encoder = None
+        # Whether this collection has the `bm25` named sparse vector slot.
+        # Pre-v3 collections lack it; writing a `bm25` sparse vector into such a
+        # collection is rejected by Qdrant ("Not existing vector name error: bm25").
+        self._has_bm25_slot = False
         self.create_col(embedding_model_dims, on_disk)
 
     def _get_bm25_encoder(self):
@@ -127,6 +131,15 @@ class Qdrant(VectorStoreBase):
         for collection in response.collections:
             if collection.name == self.collection_name:
                 logger.debug(f"Collection {self.collection_name} already exists. Skipping creation.")
+                info = self.client.get_collection(self.collection_name)
+                sparse_cfg = info.config.params.sparse_vectors
+                self._has_bm25_slot = bool(sparse_cfg and "bm25" in sparse_cfg)
+                if not self._has_bm25_slot:
+                    logger.warning(
+                        f"Collection '{self.collection_name}' predates v3 hybrid search (no 'bm25' sparse slot). "
+                        "BM25 keyword scoring will be disabled for this collection; semantic search works normally. "
+                        "To enable hybrid search, use a fresh collection."
+                    )
                 self._create_filter_indexes()
                 return
 
@@ -139,6 +152,7 @@ class Qdrant(VectorStoreBase):
                 ),
             },
         )
+        self._has_bm25_slot = True
         self._create_filter_indexes()
 
     def _create_filter_indexes(self):
@@ -177,13 +191,14 @@ class Qdrant(VectorStoreBase):
             payload = payloads[idx] if payloads else {}
             point_id = idx if ids is None else ids[idx]
 
-            # Build named vectors: dense + optional BM25 sparse
+            # Build named vectors: dense + optional BM25 sparse (only if collection has the slot).
             named_vectors = {"": vector}
-            text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
+            if self._has_bm25_slot:
-            if text_for_bm25:
+                text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
-                sparse = self._encode_bm25(text_for_bm25)
+                if text_for_bm25:
-                if sparse is not None:
+                    sparse = self._encode_bm25(text_for_bm25)
-                    named_vectors["bm25"] = sparse
+                    if sparse is not None:
+                        named_vectors["bm25"] = sparse
 
             points.append(PointStruct(id=point_id, vector=named_vectors, payload=payload))
 
@@ -382,7 +397,7 @@ class Qdrant(VectorStoreBase):
         """Batch search using Qdrant's query_batch_points for efficiency."""
         query_filter = self._create_filter(filters) if filters else None
         requests = [
-            models.QueryRequest(query=vec, filter=query_filter, limit=top_k)
+            models.QueryRequest(query=vec, filter=query_filter, limit=top_k, with_payload=True)
             for vec in vectors_list
         ]
         try:
@@ -407,6 +422,8 @@ class Qdrant(VectorStoreBase):
         Returns:
             list: Search results, or None if BM25 is not available.
         """
+        if not self._has_bm25_slot:
+            return None
         sparse_query = self._encode_bm25(query)
         if sparse_query is None:
             return None
@@ -449,13 +466,14 @@ class Qdrant(VectorStoreBase):
             payload (dict, optional): Updated payload. Defaults to None.
         """
         if vector is not None and payload is not None:
-            # Full update: attach BM25 sparse vector alongside dense vector
+            # Full update: attach BM25 sparse vector alongside dense vector (only if slot exists).
             named_vectors = {"": vector}
-            text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
+            if self._has_bm25_slot:
-            if text_for_bm25:
+                text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
-                sparse = self._encode_bm25(text_for_bm25)
+                if text_for_bm25:
-                if sparse is not None:
+                    sparse = self._encode_bm25(text_for_bm25)
-                    named_vectors["bm25"] = sparse
+                    if sparse is not None:
+                        named_vectors["bm25"] = sparse
             point = PointStruct(id=vector_id, vector=named_vectors, payload=payload)
             self.client.upsert(collection_name=self.collection_name, points=[point])
         else:

mem0/vector_stores/s3_vectors.py

@@ -122,7 +122,37 @@ class S3Vectors(VectorStoreBase):
         )
 
     def update(self, vector_id, vector=None, payload=None):
-        # S3 Vectors uses put_vectors for updates (overwrite)
+        # S3 Vectors uses put_vectors for updates (overwrite).
+        # When vector=None (e.g. metadata-only update triggered by event=NONE),
+        # fetch the existing vector data first to avoid passing None to boto3
+        # which causes a parameter validation error:
+        #   "Invalid type for parameter vectors[0].data.float32, value: None"
+        if vector is None:
+            existing = self.get(vector_id)
+            if existing is None:
+                logger.warning(f"update called with vector=None but {vector_id} not found; skipping")
+                return
+            try:
+                response = self.client.get_vectors(
+                    vectorBucketName=self.vector_bucket_name,
+                    indexName=self.collection_name,
+                    keys=[vector_id],
+                    returnData=True,
+                    returnMetadata=True,
+                )
+                vectors = response.get("vectors", [])
+                if not vectors:
+                    logger.warning(f"update: no vector data found for {vector_id}; skipping")
+                    return
+                vector = vectors[0].get("data", {}).get("float32")
+                if vector is None:
+                    logger.warning(f"update: float32 data is None for {vector_id}; skipping")
+                    return
+                if payload is None:
+                    payload = existing.payload
+            except Exception as e:
+                logger.error(f"update: failed to fetch existing vector for {vector_id}: {e}")
+                return
         self.insert(vectors=[vector], payloads=[payload], ids=[vector_id])
 
     def get(self, vector_id) -> Optional[OutputData]:

mem0/vector_stores/valkey.py

@@ -52,6 +52,7 @@ class ValkeyDB(VectorStoreBase):
         hnsw_m: int = 16,
         hnsw_ef_construction: int = 200,
         hnsw_ef_runtime: int = 10,
+        cluster_mode: bool = False,
     ):
         """
         Initialize the Valkey vector store.
@@ -65,6 +66,7 @@ class ValkeyDB(VectorStoreBase):
             hnsw_m (int, optional): HNSW M parameter (connections per node). Defaults to 16.
             hnsw_ef_construction (int, optional): HNSW ef_construction parameter. Defaults to 200.
             hnsw_ef_runtime (int, optional): HNSW ef_runtime parameter. Defaults to 10.
+            cluster_mode (bool, optional): Enable cluster mode for Valkey cluster (CME) deployments. Defaults to False.
         """
         self.embedding_model_dims = embedding_model_dims
         self.collection_name = collection_name
@@ -74,6 +76,7 @@ class ValkeyDB(VectorStoreBase):
         self.hnsw_m = hnsw_m
         self.hnsw_ef_construction = hnsw_ef_construction
         self.hnsw_ef_runtime = hnsw_ef_runtime
+        self.cluster_mode = cluster_mode
 
         # Validate index type
         if self.index_type not in ["hnsw", "flat"]:
@@ -81,8 +84,13 @@ class ValkeyDB(VectorStoreBase):
 
         # Connect to Valkey
         try:
-            self.client = valkey.from_url(valkey_url)
+            if self.cluster_mode:
-            logger.debug(f"Successfully connected to Valkey at {valkey_url}")
+                from valkey.cluster import ValkeyCluster
+
+                self.client = ValkeyCluster.from_url(valkey_url)
+            else:
+                self.client = valkey.from_url(valkey_url)
+            logger.debug(f"Successfully connected to Valkey at {valkey_url} (cluster_mode={cluster_mode})")
         except Exception as e:
             logger.exception(f"Failed to connect to Valkey at {valkey_url}: {e}")
             raise
@@ -185,7 +193,6 @@ class ValkeyDB(VectorStoreBase):
         """
         # Check if the search module is available
         try:
-            # Try to execute a search command
             self.client.execute_command("FT._LIST")
         except ResponseError as e:
             if "unknown command" in str(e).lower():
@@ -353,6 +360,9 @@ class ValkeyDB(VectorStoreBase):
         """
         Execute a search query.
 
+        In cluster mode, the valkey-search module's built-in coordinator handles
+        fan-out across all shards and aggregates results server-side.
+
         Args:
             query (str): The search query to execute.
             params (dict): The query parameters.

openclaw/config.ts

@@ -147,6 +147,7 @@ export const DEFAULT_CUSTOM_CATEGORIES: Record<string, string> = {
 const ALLOWED_KEYS = [
   "mode",
   "apiKey",
+  "anonymousTelemetryId",
   "baseUrl",
   "userId",
   "userEmail",
@@ -215,6 +216,10 @@ export const mem0ConfigSchema = {
     return {
       mode,
       apiKey: resolvedApiKey,
+      anonymousTelemetryId:
+        typeof cfg.anonymousTelemetryId === "string"
+          ? cfg.anonymousTelemetryId
+          : undefined,
       baseUrl: resolvedBaseUrl,
       userId:
         typeof cfg.userId === "string" && cfg.userId

openclaw/openclaw.plugin.json

@@ -134,6 +134,10 @@
       "topK": {
         "type": "number"
       },
+      "anonymousTelemetryId": {
+        "type": "string",
+        "description": "Persistent anonymous telemetry identifier"
+      },
       "oss": {
         "type": "object",
         "properties": {

openclaw/tests/config.test.ts

@@ -88,6 +88,11 @@ describe("mem0ConfigSchema.parse() — defaults", () => {
     const cfg = mem0ConfigSchema.parse({ apiKey: "test-key" });
     expect(cfg.skills).toBeUndefined();
   });
+
+  it("allows anonymousTelemetryId", () => {
+    const cfg = mem0ConfigSchema.parse({ apiKey: "test-key", anonymousTelemetryId: "123" });
+    expect(cfg.anonymousTelemetryId).toBe("123");
+  });
 });
 
 // ---------------------------------------------------------------------------

openclaw/types.ts

@@ -8,6 +8,7 @@ export type Mem0Config = {
   mode: Mem0Mode;
   // Platform-specific
   apiKey?: string;
+  anonymousTelemetryId?: string;
   baseUrl?: string;
   customInstructions: string;
   customCategories: Record<string, string>;

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0ai"
-version = "1.0.11"
+version = "2.0.0b1"
 description = "Long-term memory for AI Agents"
 authors = [
     { name = "Mem0", email = "support@mem0.ai" }

tests/llms/test_azure_openai.py

@@ -286,8 +286,8 @@ def test_init_with_env_vars(monkeypatch):
             http_client=None,
             default_headers=None,
         )
-        # Should default to "gpt-4.1-nano-2025-04-14" if model is None
+        # Should default to "gpt-5-mini" if model is None
-        assert llm.config.model == "gpt-4.1-nano-2025-04-14"
+        assert llm.config.model == "gpt-5-mini"
 
 
 def test_init_with_default_azure_credential(monkeypatch):

tests/llms/test_azure_openai_structured.py

@@ -57,7 +57,7 @@ def test_init_with_default_credential(mock_credential, mock_token_provider, mock
     mock_token_provider.return_value = "token-provider"
     llm = AzureOpenAIStructuredLLM(config)
     # Should set default model if not provided
-    assert llm.config.model == "gpt-4.1-nano-2025-04-14"
+    assert llm.config.model == "gpt-5-mini"
     mock_credential.assert_called_once()
     mock_token_provider.assert_called_once_with(mock_credential.return_value, SCOPE)
     mock_azure_openai.assert_called_once()
@@ -92,7 +92,7 @@ def test_init_with_placeholder_api_key_uses_default_credential(
         config = DummyConfig(model=None, azure_kwargs=DummyAzureKwargs(api_key="your-api-key"))
         mock_token_provider.return_value = "token-provider"
         llm = AzureOpenAIStructuredLLM(config)
-        assert llm.config.model == "gpt-4.1-nano-2025-04-14"
+        assert llm.config.model == "gpt-5-mini"
         mock_credential.assert_called_once()
         mock_token_provider.assert_called_once_with(mock_credential.return_value, SCOPE)
         mock_azure_openai.assert_called_once()

tests/llms/test_openai.py

@@ -55,7 +55,7 @@ def test_generate_response_without_tools(mock_openai_client):
     response = llm.generate_response(messages)
 
     mock_openai_client.chat.completions.create.assert_called_once_with(
-        model="gpt-4.1-nano-2025-04-14", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0, store=False
+        model="gpt-4.1-nano-2025-04-14", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0
     )
     assert response == "I'm doing well, thank you for asking!"
 
@@ -97,7 +97,7 @@ def test_generate_response_with_tools(mock_openai_client):
     response = llm.generate_response(messages, tools=tools)
 
     mock_openai_client.chat.completions.create.assert_called_once_with(
-        model="gpt-4.1-nano-2025-04-14", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0, tools=tools, tool_choice="auto", store=False
+        model="gpt-4.1-nano-2025-04-14", messages=messages, temperature=0.7, max_tokens=100, top_p=1.0, tools=tools, tool_choice="auto"
     )
 
     assert response["content"] == "I've added the memory for you."
@@ -231,6 +231,60 @@ def test_reasoning_effort_config_values():
     assert config.reasoning_effort is None
 
 
+def test_store_not_sent_by_default(mock_openai_client):
+    """`store` must NOT be injected into requests when the user has not
+    explicitly configured it. Regression test for issue #4709, where
+    `store=False` was unconditionally sent and rejected by OpenAI-compatible
+    backends such as Google Gemini."""
+    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", temperature=0.1)
+    assert config.store is None  # new opt-in default
+    llm = OpenAILLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Response"))]
+    mock_openai_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_openai_client.chat.completions.create.call_args.kwargs
+    assert "store" not in call_kwargs
+
+
+def test_store_sent_when_explicitly_true(mock_openai_client):
+    """When the user explicitly sets `store=True`, the field must be forwarded."""
+    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", store=True)
+    llm = OpenAILLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Response"))]
+    mock_openai_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_openai_client.chat.completions.create.call_args.kwargs
+    assert call_kwargs["store"] is True
+
+
+def test_store_sent_when_explicitly_false(mock_openai_client):
+    """When the user explicitly sets `store=False`, the field must still be
+    forwarded — explicit opt-out is a valid configuration for users who rely on
+    it for OpenAI's zero-data-retention behavior."""
+    config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", store=False)
+    llm = OpenAILLM(config)
+    messages = [{"role": "user", "content": "Hello"}]
+
+    mock_response = Mock()
+    mock_response.choices = [Mock(message=Mock(content="Response"))]
+    mock_openai_client.chat.completions.create.return_value = mock_response
+
+    llm.generate_response(messages)
+
+    call_kwargs = mock_openai_client.chat.completions.create.call_args.kwargs
+    assert call_kwargs["store"] is False
+
+
 def test_callback_with_tools(mock_openai_client):
     mock_callback = Mock()
     config = OpenAIConfig(model="gpt-4.1-nano-2025-04-14", response_callback=mock_callback)

tests/test_client_feedback.py

@@ -0,0 +1,59 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from mem0.client.main import AsyncMemoryClient, MemoryClient
+
+
+def _build_memory_client(response_payload=None):
+    client = MemoryClient.__new__(MemoryClient)
+    client.user_email = "user@example.com"
+    client.client = MagicMock()
+    response = client.client.post.return_value
+    response.json.return_value = response_payload or {"message": "Feedback recorded"}
+    response.raise_for_status.return_value = None
+    return client
+
+
+def test_memory_client_feedback_uses_single_telemetry_payload():
+    client = _build_memory_client()
+
+    with patch("mem0.client.main.capture_client_event") as mock_capture:
+        result = client.feedback("mem_1", feedback="positive", feedback_reason="accurate")
+
+    assert result == {"message": "Feedback recorded"}
+    client.client.post.assert_called_once_with(
+        "/v1/feedback/",
+        json={"memory_id": "mem_1", "feedback": "POSITIVE", "feedback_reason": "accurate"},
+    )
+    mock_capture.assert_called_once_with(
+        "client.feedback",
+        client,
+        {"memory_id": "mem_1", "feedback": "POSITIVE", "feedback_reason": "accurate", "sync_type": "sync"},
+    )
+
+
+@pytest.mark.asyncio
+async def test_async_memory_client_feedback_uses_single_telemetry_payload():
+    client = AsyncMemoryClient.__new__(AsyncMemoryClient)
+    client.user_email = "user@example.com"
+    client.async_client = MagicMock()
+
+    response = MagicMock()
+    response.json.return_value = {"message": "Feedback recorded"}
+    response.raise_for_status.return_value = None
+    client.async_client.post = AsyncMock(return_value=response)
+
+    with patch("mem0.client.main.capture_client_event") as mock_capture:
+        result = await client.feedback("mem_1", feedback="negative", feedback_reason="outdated")
+
+    assert result == {"message": "Feedback recorded"}
+    client.async_client.post.assert_awaited_once_with(
+        "/v1/feedback/",
+        json={"memory_id": "mem_1", "feedback": "NEGATIVE", "feedback_reason": "outdated"},
+    )
+    mock_capture.assert_called_once_with(
+        "client.feedback",
+        client,
+        {"memory_id": "mem_1", "feedback": "NEGATIVE", "feedback_reason": "outdated", "sync_type": "async"},
+    )

tests/test_main.py

@@ -110,11 +110,10 @@ def test_search(memory_instance):
     assert result["results"][0]["user_id"] == "test_user"
     # Score is now combined score (semantic only since no BM25/entity), still 0.9
     assert result["results"][0]["score"] == pytest.approx(0.9)
-    assert "score_breakdown" in result["results"][0]
 
-    # Hybrid pipeline over-fetches: max(100*4, 60) = 400
+    # Hybrid pipeline over-fetches: max(20*4, 60) = 80 (top_k default is now 20)
     memory_instance.vector_store.search.assert_called_once_with(
-        query="test query", vectors=[0.1, 0.2, 0.3], top_k=400, filters={"user_id": "test_user"}
+        query="test query", vectors=[0.1, 0.2, 0.3], top_k=80, filters={"user_id": "test_user"}
     )
 
 
@@ -201,7 +200,7 @@ def test_get_all(memory_instance):
     assert result["results"][0]["memory"] == "Memory 1"
     assert result["results"][0]["user_id"] == "test_user"
 
-    memory_instance.vector_store.list.assert_called_once_with(filters={"user_id": "test_user"}, top_k=100)
+    memory_instance.vector_store.list.assert_called_once_with(filters={"user_id": "test_user"}, top_k=20)
 
 
 def test_no_telemetry_vector_store_when_disabled():
@@ -242,3 +241,110 @@ def test_telemetry_vector_store_created_when_enabled():
 
         # VectorStoreFactory.create should be called twice — user data + telemetry
         assert mock_vector_store.create.call_count == 2
+
+
+# =============================================================================
+# Input Validation Tests
+# =============================================================================
+
+
+class TestEntityIdValidation:
+    """Tests for entity ID validation (whitespace rejection and trimming)."""
+
+    def test_search_rejects_whitespace_only_user_id(self, memory_instance):
+        """Search should reject whitespace-only user_id in filters."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot be empty"):
+            memory_instance.search("test query", filters={"user_id": "   "})
+
+    def test_search_rejects_internal_whitespace_user_id(self, memory_instance):
+        """Search should reject user_id with internal whitespace."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
+            memory_instance.search("test query", filters={"user_id": "user 123"})
+
+    def test_search_rejects_tab_in_user_id(self, memory_instance):
+        """Search should reject user_id with tab character."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
+            memory_instance.search("test query", filters={"user_id": "user\t123"})
+
+    def test_get_all_rejects_whitespace_only_user_id(self, memory_instance):
+        """get_all should reject whitespace-only user_id in filters."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot be empty"):
+            memory_instance.get_all(filters={"user_id": "   "})
+
+    def test_get_all_rejects_internal_whitespace_user_id(self, memory_instance):
+        """get_all should reject user_id with internal whitespace."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
+            memory_instance.get_all(filters={"user_id": "user 123"})
+
+    def test_add_rejects_whitespace_only_user_id(self, memory_instance):
+        """add should reject whitespace-only user_id."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot be empty"):
+            memory_instance.add("test message", user_id="   ")
+
+    def test_add_rejects_internal_whitespace_user_id(self, memory_instance):
+        """add should reject user_id with internal whitespace."""
+        with pytest.raises(ValueError, match="Invalid user_id.*cannot contain whitespace"):
+            memory_instance.add("test message", user_id="user 123")
+
+
+class TestSearchParamValidation:
+    """Tests for search parameter validation (threshold and top_k)."""
+
+    def test_search_rejects_threshold_above_1(self, memory_instance):
+        """Search should reject threshold > 1."""
+        with pytest.raises(ValueError, match="Invalid threshold.*Must be between 0 and 1"):
+            memory_instance.search("test query", filters={"user_id": "test"}, threshold=1.5)
+
+    def test_search_rejects_negative_threshold(self, memory_instance):
+        """Search should reject negative threshold."""
+        with pytest.raises(ValueError, match="Invalid threshold.*Must be between 0 and 1"):
+            memory_instance.search("test query", filters={"user_id": "test"}, threshold=-0.5)
+
+    def test_search_rejects_negative_top_k(self, memory_instance):
+        """Search should reject negative top_k."""
+        with pytest.raises(ValueError, match="Invalid top_k.*Must be a non-negative"):
+            memory_instance.search("test query", filters={"user_id": "test"}, top_k=-5)
+
+    def test_get_all_rejects_negative_top_k(self, memory_instance):
+        """get_all should reject negative top_k."""
+        with pytest.raises(ValueError, match="Invalid top_k.*Must be a non-negative"):
+            memory_instance.get_all(filters={"user_id": "test"}, top_k=-1)
+
+    def test_search_accepts_threshold_zero(self, memory_instance):
+        """Search should accept threshold=0 (edge case)."""
+        mock_memories = []
+        memory_instance.vector_store.search = Mock(return_value=mock_memories)
+        memory_instance.vector_store.keyword_search = Mock(return_value=None)
+        memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+
+        with patch("mem0.memory.main.lemmatize_for_bm25", return_value="test"), \
+             patch("mem0.memory.main.extract_entities", return_value=[]):
+            result = memory_instance.search("test", filters={"user_id": "test"}, threshold=0)
+
+        assert "results" in result
+
+    def test_search_accepts_threshold_one(self, memory_instance):
+        """Search should accept threshold=1.0 (edge case)."""
+        mock_memories = []
+        memory_instance.vector_store.search = Mock(return_value=mock_memories)
+        memory_instance.vector_store.keyword_search = Mock(return_value=None)
+        memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+
+        with patch("mem0.memory.main.lemmatize_for_bm25", return_value="test"), \
+             patch("mem0.memory.main.extract_entities", return_value=[]):
+            result = memory_instance.search("test", filters={"user_id": "test"}, threshold=1.0)
+
+        assert "results" in result
+
+    def test_search_accepts_top_k_zero(self, memory_instance):
+        """Search should accept top_k=0."""
+        mock_memories = []
+        memory_instance.vector_store.search = Mock(return_value=mock_memories)
+        memory_instance.vector_store.keyword_search = Mock(return_value=None)
+        memory_instance.embedding_model.embed = Mock(return_value=[0.1, 0.2, 0.3])
+
+        with patch("mem0.memory.main.lemmatize_for_bm25", return_value="test"), \
+             patch("mem0.memory.main.extract_entities", return_value=[]):
+            result = memory_instance.search("test", filters={"user_id": "test"}, top_k=0)
+
+        assert "results" in result

tests/test_server_params.py

@@ -2,7 +2,7 @@
 
 Verifies that the Pydantic request models in server/main.py correctly accept
 and forward all parameters supported by the underlying Memory class methods,
-including limit, threshold, infer, memory_type, and prompt — which were
+including top_k, threshold, infer, memory_type, and prompt — which were
 previously silently dropped by Pydantic v2's default extra='ignore' behavior.
 """
 
@@ -55,31 +55,31 @@ def mock_memory(_mock_memory):
 
 
 # ===========================================================================
-# SearchRequest: limit parameter
+# SearchRequest: top_k parameter
 # ===========================================================================
 
 class TestSearchLimit:
-    """Verify that the limit parameter is accepted and forwarded to Memory.search()."""
+    """Verify that the top_k parameter is accepted and forwarded to Memory.search()."""
 
     def test_limit_forwarded(self, client, mock_memory):
-        resp = client.post("/search", json={"query": "food", "user_id": "u1", "limit": 5})
+        resp = client.post("/search", json={"query": "food", "user_id": "u1", "top_k": 5})
         assert resp.status_code == 200
         _, kwargs = mock_memory.search.call_args
-        assert kwargs["limit"] == 5
+        assert kwargs["top_k"] == 5
 
     def test_limit_one(self, client, mock_memory):
-        resp = client.post("/search", json={"query": "food", "user_id": "u1", "limit": 1})
+        resp = client.post("/search", json={"query": "food", "user_id": "u1", "top_k": 1})
         assert resp.status_code == 200
         _, kwargs = mock_memory.search.call_args
-        assert kwargs["limit"] == 1
+        assert kwargs["top_k"] == 1
 
     def test_limit_omitted_uses_memory_default(self, client, mock_memory):
-        """When limit is not sent, it should not appear in the kwargs,
+        """When top_k is not sent, it should not appear in the kwargs,
         allowing Memory.search() to use its own default (100)."""
         resp = client.post("/search", json={"query": "food", "user_id": "u1"})
         assert resp.status_code == 200
         _, kwargs = mock_memory.search.call_args
-        assert "limit" not in kwargs
+        assert "top_k" not in kwargs
 
 
 # ===========================================================================
@@ -110,18 +110,18 @@ class TestSearchThreshold:
 
 
 # ===========================================================================
-# SearchRequest: limit + threshold together
+# SearchRequest: top_k + threshold together
 # ===========================================================================
 
 class TestSearchLimitAndThreshold:
 
     def test_both_forwarded(self, client, mock_memory):
         resp = client.post("/search", json={
-            "query": "food", "user_id": "u1", "limit": 10, "threshold": 0.5
+            "query": "food", "user_id": "u1", "top_k": 10, "threshold": 0.5
         })
         assert resp.status_code == 200
         _, kwargs = mock_memory.search.call_args
-        assert kwargs["limit"] == 10
+        assert kwargs["top_k"] == 10
         assert kwargs["threshold"] == 0.5
 
 
@@ -334,8 +334,8 @@ class TestOpenAPISchema:
     def test_search_schema_includes_limit(self, client):
         schema = client.get("/openapi.json").json()
         search_props = schema["components"]["schemas"]["SearchRequest"]["properties"]
-        assert "limit" in search_props
+        assert "top_k" in search_props
-        assert search_props["limit"]["description"] == "Maximum number of results to return."
+        assert search_props["top_k"]["description"] == "Maximum number of results to return."
 
     def test_search_schema_includes_threshold(self, client):
         schema = client.get("/openapi.json").json()
@@ -367,7 +367,7 @@ class TestTypeValidation:
 
     def test_limit_string_rejected(self, client):
         resp = client.post("/search", json={
-            "query": "food", "user_id": "u1", "limit": "not_a_number",
+            "query": "food", "user_id": "u1", "top_k": "not_a_number",
         })
         assert resp.status_code == 422
 
@@ -399,7 +399,7 @@ class TestTypeValidation:
 
     def test_limit_float_rejected(self, client):
         resp = client.post("/search", json={
-            "query": "food", "user_id": "u1", "limit": 5.7,
+            "query": "food", "user_id": "u1", "top_k": 5.7,
         })
         assert resp.status_code == 422
 
@@ -422,11 +422,11 @@ class TestExplicitNull:
 
     def test_limit_null_uses_memory_default(self, client, mock_memory):
         resp = client.post("/search", json={
-            "query": "food", "user_id": "u1", "limit": None,
+            "query": "food", "user_id": "u1", "top_k": None,
         })
         assert resp.status_code == 200
         _, kwargs = mock_memory.search.call_args
-        assert "limit" not in kwargs
+        assert "top_k" not in kwargs
 
     def test_infer_null_uses_memory_default(self, client, mock_memory):
         resp = client.post("/memories", json={
@@ -462,12 +462,12 @@ class TestCallSignatureMatch:
         resp = client.post("/search", json={
             "query": "food", "user_id": "u1", "agent_id": "a1",
             "run_id": "r1", "filters": {"k": "v"},
-            "limit": 10, "threshold": 0.5,
+            "top_k": 10, "threshold": 0.5,
         })
         assert resp.status_code == 200
         # The handler passes query= as a keyword arg, so it appears in kwargs too
         _, kwargs = mock_memory.search.call_args
-        valid_params = {"query", "user_id", "agent_id", "run_id", "limit", "filters", "threshold", "rerank"}
+        valid_params = {"query", "user_id", "agent_id", "run_id", "top_k", "filters", "threshold", "rerank"}
         for key in kwargs:
             assert key in valid_params, f"Unexpected kwarg '{key}' forwarded to Memory.search()"
 

tests/test_telemetry.py

@@ -359,3 +359,63 @@ class TestTelemetryEnvVar:
     def test_env_var_parsing(self, value, expected):
         result = value.lower() in ("true", "1", "yes")
         assert result == expected
+
+
+class TestTelemetryNullUserIdHandling:
+    """Verify telemetry doesn't crash when user_id is None.
+
+    This is a regression test for the bug where Memory.from_config() crashed
+    with AssertionError because distinct_id was None in PostHog.capture().
+    """
+
+    def test_capture_event_skips_when_user_id_is_none(self):
+        """AnonymousTelemetry.capture_event should not crash when user_id is None."""
+        with patch.object(telemetry_module, "MEM0_TELEMETRY", True):
+            with patch("mem0.memory.telemetry.Posthog") as mock_posthog_cls:
+                at = telemetry_module.AnonymousTelemetry()
+                at.user_id = None  # Simulate the bug condition
+
+                # This should not raise, even with user_id=None
+                at.capture_event("test.event", {"key": "value"})
+
+                # PostHog.capture should NOT have been called since user_id is None
+                mock_posthog_cls.return_value.capture.assert_not_called()
+
+    def test_capture_event_does_not_crash_on_posthog_error(self):
+        """AnonymousTelemetry.capture_event should catch PostHog exceptions."""
+        with patch.object(telemetry_module, "MEM0_TELEMETRY", True):
+            with patch("mem0.memory.telemetry.Posthog") as mock_posthog_cls:
+                with patch("mem0.memory.telemetry.get_or_create_user_id", return_value="test-user"):
+                    at = telemetry_module.AnonymousTelemetry()
+                    mock_posthog_cls.return_value.capture.side_effect = Exception("PostHog error")
+
+                    # This should not raise, even when PostHog.capture fails
+                    at.capture_event("test.event", {"key": "value"})
+
+    def test_oss_capture_event_does_not_crash_memory_init(self):
+        """capture_event() should never raise, even if everything inside fails."""
+        with patch.object(telemetry_module, "MEM0_TELEMETRY", True):
+            # Make _get_oss_telemetry return a broken telemetry object
+            mock_at = MagicMock()
+            mock_at.capture_event.side_effect = Exception("Telemetry is broken")
+
+            with patch.object(telemetry_module, "_oss_telemetry_instance", mock_at):
+                mock_memory = MagicMock()
+                mock_memory.config.graph_store.config = None
+                mock_memory.api_version = "v1"
+
+                # This should not raise, even when telemetry fails
+                telemetry_module.capture_event("mem0.init", mock_memory)
+
+    def test_client_capture_event_does_not_crash(self):
+        """capture_client_event() should never raise exceptions."""
+        with patch.object(telemetry_module, "MEM0_TELEMETRY", True):
+            mock_client_telemetry = MagicMock()
+            mock_client_telemetry.capture_event.side_effect = Exception("Client telemetry broken")
+
+            with patch.object(telemetry_module, "client_telemetry", mock_client_telemetry):
+                mock_instance = MagicMock()
+                mock_instance.user_email = "test@example.com"
+
+                # This should not raise
+                telemetry_module.capture_client_event("test.event", mock_instance)

tests/utils/test_scoring.py

@@ -101,16 +101,6 @@ class TestScoreAndRank:
         scored = score_and_rank(results, {}, {}, threshold=0.1, top_k=5)
         assert len(scored) == 5
 
-    def test_score_breakdown_present(self):
-        results = [{"id": "a", "score": 0.8, "payload": {"data": "x"}}]
-        bm25 = {"a": 0.4}
-        entity = {"a": 0.2}
-        scored = score_and_rank(results, bm25, entity, threshold=0.1, top_k=10)
-        breakdown = scored[0]["score_breakdown"]
-        assert breakdown["semantic"] == 0.8
-        assert breakdown["bm25"] == 0.4
-        assert breakdown["entity_boost"] == 0.2
-
     def test_adaptive_divisor_semantic_only(self):
         results = [{"id": "a", "score": 0.8, "payload": {}}]
         scored = score_and_rank(results, {}, {}, threshold=0.1, top_k=10)

tests/vector_stores/test_faiss.py

@@ -1,4 +1,6 @@
+import json
 import os
+import pickle
 import tempfile
 from unittest.mock import Mock, patch
 
@@ -6,7 +8,13 @@ import faiss
 import numpy as np
 import pytest
 
-from mem0.vector_stores.faiss import FAISS, OutputData
+from mem0.vector_stores.faiss import (
+    FAISS,
+    OutputData,
+    SafeUnpickler,
+    _safe_pickle_load,
+    _validate_docstore_structure,
+)
 
 
 @pytest.fixture
@@ -273,8 +281,8 @@ def test_delete_col(faiss_instance):
             # Call delete_col
             faiss_instance.delete_col()
 
-            # Verify os.remove was called twice (for index and docstore files)
+            # Verify os.remove was called for index, json docstore, and legacy pkl files
-            assert mock_remove.call_count == 2
+            assert mock_remove.call_count == 3
 
             # Verify the internal state was reset
             assert faiss_instance.index is None
@@ -299,3 +307,360 @@ def test_normalize_L2(faiss_instance, mock_faiss_index):
 
             # Verify faiss.normalize_L2 was called
             mock_normalize.assert_called_once()
+
+
+# =============================================================================
+# Security Tests for Pickle Deserialization Vulnerability Fix
+# =============================================================================
+
+
+class TestSafeUnpickler:
+    """Tests for the SafeUnpickler class that prevents arbitrary code execution."""
+
+    def test_safe_unpickler_allows_basic_types(self):
+        """SafeUnpickler should allow basic Python types."""
+        # Create a legitimate pickle with basic types
+        data = (
+            {"key1": "value1", "key2": {"nested": "dict"}},
+            {0: "id1", 1: "id2"},
+        )
+        pickled = pickle.dumps(data)
+
+        # Should load successfully
+        import io
+
+        result = SafeUnpickler(io.BytesIO(pickled)).load()
+        assert result == data
+
+    def test_safe_unpickler_blocks_os_system(self):
+        """SafeUnpickler should block os.system execution attempts."""
+        # Generate the malicious payload dynamically to ensure correct format
+        import io
+
+        class Evil:
+            def __reduce__(self):
+                return (os.system, ("echo pwned",))
+
+        malicious_payload = pickle.dumps(Evil())
+
+        with pytest.raises(pickle.UnpicklingError) as exc_info:
+            SafeUnpickler(io.BytesIO(malicious_payload)).load()
+
+        assert "Unsafe pickle" in str(exc_info.value)
+        assert "posix.system" in str(exc_info.value)
+
+    def test_safe_unpickler_blocks_subprocess(self):
+        """SafeUnpickler should block subprocess execution attempts."""
+        import subprocess
+
+        # Create a malicious pickle that tries to use subprocess
+        class MaliciousSubprocess:
+            def __reduce__(self):
+                return (subprocess.call, (["echo", "pwned"],))
+
+        malicious_payload = pickle.dumps(MaliciousSubprocess())
+
+        import io
+
+        with pytest.raises(pickle.UnpicklingError) as exc_info:
+            SafeUnpickler(io.BytesIO(malicious_payload)).load()
+
+        assert "Unsafe pickle" in str(exc_info.value)
+
+    def test_safe_unpickler_blocks_eval(self):
+        """SafeUnpickler should block eval/exec attempts."""
+
+        # Create a malicious pickle that tries to use eval
+        class MaliciousEval:
+            def __reduce__(self):
+                return (eval, ("__import__('os').system('touch pwned')",))
+
+        malicious_payload = pickle.dumps(MaliciousEval())
+
+        import io
+
+        with pytest.raises(pickle.UnpicklingError) as exc_info:
+            SafeUnpickler(io.BytesIO(malicious_payload)).load()
+
+        assert "Unsafe pickle" in str(exc_info.value)
+
+    def test_safe_unpickler_blocks_arbitrary_modules(self):
+        """SafeUnpickler should block imports from arbitrary modules."""
+
+        # Create a pickle that tries to load a class from a non-builtins module
+        class ArbitraryClass:
+            def __reduce__(self):
+                return (type, ("Evil", (), {}))
+
+        malicious_payload = pickle.dumps(ArbitraryClass())
+
+        import io
+
+        # This should either work (type is a builtin) or fail safely
+        # The key is it shouldn't execute arbitrary code
+        try:
+            result = SafeUnpickler(io.BytesIO(malicious_payload)).load()
+            # If it loads, verify it's just a benign type object
+            assert isinstance(result, type)
+        except pickle.UnpicklingError:
+            # This is also acceptable - blocking unknown patterns
+            pass
+
+
+class TestSafePickleLoad:
+    """Tests for the _safe_pickle_load function."""
+
+    def test_safe_pickle_load_with_valid_file(self):
+        """_safe_pickle_load should load valid pickle files."""
+        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pkl", delete=False) as f:
+            data = ({"id1": {"data": "test"}}, {0: "id1"})
+            pickle.dump(data, f)
+            temp_path = f.name
+
+        try:
+            result = _safe_pickle_load(temp_path)
+            assert result == data
+        finally:
+            os.unlink(temp_path)
+
+    def test_safe_pickle_load_blocks_malicious_file(self):
+        """_safe_pickle_load should block malicious pickle files."""
+
+        # Generate the malicious payload dynamically
+        class Evil:
+            def __reduce__(self):
+                return (os.system, ("echo pwned",))
+
+        malicious_payload = pickle.dumps(Evil())
+
+        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pkl", delete=False) as f:
+            f.write(malicious_payload)
+            temp_path = f.name
+
+        try:
+            with pytest.raises(pickle.UnpicklingError) as exc_info:
+                _safe_pickle_load(temp_path)
+            assert "Unsafe pickle" in str(exc_info.value)
+        finally:
+            os.unlink(temp_path)
+
+
+class TestValidateDocstoreStructure:
+    """Tests for the _validate_docstore_structure function."""
+
+    def test_valid_structure(self):
+        """Should accept valid docstore structure."""
+        data = ({"id1": {"data": "test"}}, {0: "id1"})
+        docstore, index_to_id = _validate_docstore_structure(data)
+        assert docstore == {"id1": {"data": "test"}}
+        assert index_to_id == {0: "id1"}
+
+    def test_invalid_tuple_length(self):
+        """Should reject tuples with wrong length."""
+        with pytest.raises(ValueError, match="expected tuple"):
+            _validate_docstore_structure(({}, {}, {}))
+
+    def test_invalid_docstore_type(self):
+        """Should reject non-dict docstore."""
+        with pytest.raises(ValueError, match="docstore must be a dict"):
+            _validate_docstore_structure(("not a dict", {}))
+
+    def test_invalid_index_to_id_type(self):
+        """Should reject non-dict index_to_id."""
+        with pytest.raises(ValueError, match="index_to_id must be a dict"):
+            _validate_docstore_structure(({}, "not a dict"))
+
+    def test_invalid_docstore_key_type(self):
+        """Should reject non-string docstore keys."""
+        with pytest.raises(ValueError, match="Invalid docstore key type"):
+            _validate_docstore_structure(({123: {"data": "test"}}, {0: "id1"}))
+
+    def test_invalid_index_to_id_key_type(self):
+        """Should reject non-int index_to_id keys."""
+        with pytest.raises(ValueError, match="Invalid index_to_id key type"):
+            _validate_docstore_structure(({"id1": {"data": "test"}}, {"0": "id1"}))
+
+
+class TestFAISSSecurityIntegration:
+    """Integration tests for FAISS security fixes."""
+
+    def test_faiss_saves_as_json(self):
+        """FAISS should save docstore as JSON, not pickle."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            mock_index = Mock()
+            mock_index.d = 128
+            mock_index.ntotal = 0
+
+            with patch("mem0.vector_stores.faiss.faiss.IndexFlatL2", return_value=mock_index):
+                with patch("mem0.vector_stores.faiss.faiss.write_index"):
+                    faiss_store = FAISS(
+                        collection_name="test_security",
+                        path=os.path.join(temp_dir, "test_faiss"),
+                        distance_strategy="euclidean",
+                    )
+                    faiss_store.index = mock_index
+
+                    # Insert some data
+                    faiss_store.docstore = {"id1": {"data": "test"}}
+                    faiss_store.index_to_id = {0: "id1"}
+                    faiss_store._save()
+
+                    # Verify JSON file was created
+                    json_path = os.path.join(temp_dir, "test_faiss", "test_security.json")
+                    pkl_path = os.path.join(temp_dir, "test_faiss", "test_security.pkl")
+
+                    assert os.path.exists(json_path), "JSON docstore file should be created"
+                    assert not os.path.exists(pkl_path), "Pickle file should NOT be created"
+
+                    # Verify JSON content
+                    with open(json_path, "r") as f:
+                        data = json.load(f)
+                    assert data["docstore"] == {"id1": {"data": "test"}}
+                    assert data["index_to_id"] == {"0": "id1"}
+
+    def test_faiss_loads_json_preferentially(self):
+        """FAISS should prefer JSON over pickle when both exist."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            faiss_path = os.path.join(temp_dir, "test_faiss")
+            os.makedirs(faiss_path)
+
+            # Create both JSON and pickle files with different data
+            json_data = {"docstore": {"id1": {"source": "json"}}, "index_to_id": {"0": "id1"}}
+            pkl_data = ({"id1": {"source": "pickle"}}, {0: "id1"})
+
+            with open(os.path.join(faiss_path, "test_pref.json"), "w") as f:
+                json.dump(json_data, f)
+
+            with open(os.path.join(faiss_path, "test_pref.pkl"), "wb") as f:
+                pickle.dump(pkl_data, f)
+
+            mock_index = Mock()
+            mock_index.d = 128
+            mock_index.ntotal = 1
+
+            with patch("mem0.vector_stores.faiss.faiss.read_index", return_value=mock_index):
+                with patch("mem0.vector_stores.faiss.faiss.write_index"):
+                    faiss_store = FAISS.__new__(FAISS)
+                    faiss_store.collection_name = "test_pref"
+                    faiss_store.path = faiss_path
+                    faiss_store.index = None
+                    faiss_store.docstore = {}
+                    faiss_store.index_to_id = {}
+
+                    faiss_store._load(
+                        os.path.join(faiss_path, "test_pref.faiss"),
+                        os.path.join(faiss_path, "test_pref.pkl"),
+                    )
+
+                    # Should have loaded from JSON, not pickle
+                    assert faiss_store.docstore == {"id1": {"source": "json"}}
+
+    def test_faiss_blocks_malicious_pickle_on_load(self):
+        """FAISS should block loading of malicious pickle files."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            faiss_path = os.path.join(temp_dir, "test_faiss")
+            os.makedirs(faiss_path)
+
+            # Create a malicious pickle file (RCE payload)
+            class Evil:
+                def __reduce__(self):
+                    return (os.system, (f"touch {temp_dir}/pwned",))
+
+            malicious_payload = pickle.dumps(Evil())
+
+            with open(os.path.join(faiss_path, "malicious.pkl"), "wb") as f:
+                f.write(malicious_payload)
+
+            mock_index = Mock()
+            mock_index.ntotal = 1
+
+            with patch("mem0.vector_stores.faiss.faiss.read_index", return_value=mock_index):
+                faiss_store = FAISS.__new__(FAISS)
+                faiss_store.collection_name = "malicious"
+                faiss_store.path = faiss_path
+                faiss_store.index = None
+                faiss_store.docstore = {}
+                faiss_store.index_to_id = {}
+
+                # Should raise an error, not execute the malicious payload
+                with pytest.raises(ValueError) as exc_info:
+                    faiss_store._load(
+                        os.path.join(faiss_path, "malicious.faiss"),
+                        os.path.join(faiss_path, "malicious.pkl"),
+                    )
+
+                assert "malicious pickle" in str(exc_info.value).lower() or "unsafe" in str(exc_info.value).lower()
+
+                # Verify the malicious command was NOT executed
+                pwned_file = os.path.join(temp_dir, "pwned")
+                assert not os.path.exists(pwned_file), "Malicious payload should NOT have been executed!"
+
+    def test_faiss_migrates_legacy_pickle_to_json(self):
+        """FAISS should auto-migrate valid pickle files to JSON format."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            faiss_path = os.path.join(temp_dir, "test_faiss")
+            os.makedirs(faiss_path)
+
+            # Create a legitimate legacy pickle file
+            pkl_data = ({"id1": {"data": "legacy"}}, {0: "id1"})
+            with open(os.path.join(faiss_path, "legacy.pkl"), "wb") as f:
+                pickle.dump(pkl_data, f)
+
+            mock_index = Mock()
+            mock_index.d = 128
+            mock_index.ntotal = 1
+
+            with patch("mem0.vector_stores.faiss.faiss.read_index", return_value=mock_index):
+                with patch("mem0.vector_stores.faiss.faiss.write_index"):
+                    faiss_store = FAISS.__new__(FAISS)
+                    faiss_store.collection_name = "legacy"
+                    faiss_store.path = faiss_path
+                    faiss_store.index = None
+                    faiss_store.docstore = {}
+                    faiss_store.index_to_id = {}
+
+                    faiss_store._load(
+                        os.path.join(faiss_path, "legacy.faiss"),
+                        os.path.join(faiss_path, "legacy.pkl"),
+                    )
+
+                    # Data should be loaded correctly
+                    assert faiss_store.docstore == {"id1": {"data": "legacy"}}
+                    assert faiss_store.index_to_id == {0: "id1"}
+
+                    # JSON file should now exist (auto-migrated)
+                    json_path = os.path.join(faiss_path, "legacy.json")
+                    assert os.path.exists(json_path), "JSON file should be created during migration"
+
+    def test_delete_col_removes_json_and_pkl(self):
+        """delete_col should remove both JSON and legacy pickle files."""
+        with tempfile.TemporaryDirectory() as temp_dir:
+            faiss_path = os.path.join(temp_dir, "test_faiss")
+            os.makedirs(faiss_path)
+
+            # Create both file types
+            json_path = os.path.join(faiss_path, "test_del.json")
+            pkl_path = os.path.join(faiss_path, "test_del.pkl")
+            faiss_index_path = os.path.join(faiss_path, "test_del.faiss")
+
+            with open(json_path, "w") as f:
+                json.dump({"docstore": {}, "index_to_id": {}}, f)
+            with open(pkl_path, "wb") as f:
+                pickle.dump(({}, {}), f)
+            with open(faiss_index_path, "w") as f:
+                f.write("dummy")
+
+            with patch("faiss.IndexFlatL2"):
+                faiss_store = FAISS.__new__(FAISS)
+                faiss_store.collection_name = "test_del"
+                faiss_store.path = faiss_path
+                faiss_store.index = Mock()
+                faiss_store.docstore = {}
+                faiss_store.index_to_id = {}
+
+                faiss_store.delete_col()
+
+                # Both files should be deleted
+                assert not os.path.exists(json_path), "JSON file should be deleted"
+                assert not os.path.exists(pkl_path), "PKL file should be deleted"
+                assert not os.path.exists(faiss_index_path), "FAISS index should be deleted"

tests/vector_stores/test_valkey.py

@@ -222,9 +222,7 @@ def test_update_with_none_vector_preserves_embedding(valkey_db, mock_valkey_clie
 
     mock_valkey_client.hset.assert_called_once()
     args, kwargs = mock_valkey_client.hset.call_args
-    assert "embedding" not in kwargs["mapping"], (
+    assert "embedding" not in kwargs["mapping"], "embedding should not be in hash_data when vector is None"
-        "embedding should not be in hash_data when vector is None"
-    )
     assert kwargs["mapping"]["memory_id"] == "test_id"
     assert kwargs["mapping"]["memory"] == "updated_data"
 
@@ -243,9 +241,7 @@ def test_update_with_vector_includes_embedding(valkey_db, mock_valkey_client):
 
     mock_valkey_client.hset.assert_called_once()
     args, kwargs = mock_valkey_client.hset.call_args
-    assert "embedding" in kwargs["mapping"], (
+    assert "embedding" in kwargs["mapping"], "embedding should be in hash_data when vector is provided"
-        "embedding should be in hash_data when vector is provided"
-    )
     expected_bytes = np.array(vector, dtype=np.float32).tobytes()
     assert kwargs["mapping"]["embedding"] == expected_bytes
 
@@ -906,3 +902,121 @@ def test_list_with_missing_fields_and_defaults(valkey_db, mock_valkey_client):
     assert result.id == "fallback_id"
     assert "hash" in result.payload
     assert "data" in result.payload  # memory is renamed to data
+
+
+# Cluster mode tests
+
+
+@pytest.fixture
+def mock_valkey_cluster_client():
+    """Create a mock ValkeyCluster client."""
+    with patch("valkey.cluster.ValkeyCluster.from_url") as mock_from_url:
+        mock_client = MagicMock()
+        mock_ft = MagicMock()
+        mock_client.ft = MagicMock(return_value=mock_ft)
+        mock_client.execute_command = MagicMock()
+        mock_client.hset = MagicMock()
+        mock_client.hgetall = MagicMock()
+        mock_client.delete = MagicMock()
+        mock_from_url.return_value = mock_client
+        yield mock_client
+
+
+@pytest.fixture
+def valkey_db_cluster(mock_valkey_cluster_client):
+    """Create a ValkeyDB instance in cluster mode with a mock client."""
+    valkey_db = ValkeyDB(
+        valkey_url="valkey://localhost:7000",
+        collection_name="test_cluster",
+        embedding_model_dims=1536,
+        cluster_mode=True,
+    )
+    valkey_db.client = mock_valkey_cluster_client
+    return valkey_db
+
+
+def test_cluster_mode_init(mock_valkey_cluster_client):
+    """Test that cluster_mode=True uses ValkeyCluster client."""
+    db = ValkeyDB(
+        valkey_url="valkey://localhost:7000",
+        collection_name="test_cluster",
+        embedding_model_dims=1536,
+        cluster_mode=True,
+    )
+    assert db.cluster_mode is True
+
+
+def test_cluster_mode_create_index(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that index creation works in cluster mode (server handles propagation)."""
+    mock_valkey_cluster_client.execute_command.reset_mock()
+    mock_valkey_cluster_client.ft.return_value.info.side_effect = ResponseError("not found")
+
+    valkey_db_cluster._create_index(1536)
+
+    call_args = mock_valkey_cluster_client.execute_command.call_args
+    assert call_args is not None
+    assert "FT.CREATE" in call_args.args
+
+
+def test_cluster_mode_drop_index(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that dropping index works in cluster mode."""
+    mock_valkey_cluster_client.execute_command.reset_mock()
+
+    valkey_db_cluster._drop_index("test_cluster")
+
+    call_args = mock_valkey_cluster_client.execute_command.call_args
+    assert "FT.DROPINDEX" in call_args.args
+
+
+def test_cluster_mode_search(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that search in cluster mode uses ft().search() (server handles cross-shard fan-out)."""
+    ts = str(int(datetime.now().timestamp()))
+    mock_doc = MagicMock()
+    mock_doc.memory_id = "id1"
+    mock_doc.hash = "h1"
+    mock_doc.memory = "data1"
+    mock_doc.created_at = ts
+    mock_doc.metadata = "{}"
+    mock_doc.vector_score = "0.1"
+
+    mock_results = MagicMock()
+    mock_results.docs = [mock_doc]
+    mock_valkey_cluster_client.ft.return_value.search.return_value = mock_results
+
+    results = valkey_db_cluster.search("test", np.random.rand(1536).tolist(), top_k=5)
+
+    assert len(results) == 1
+    assert results[0].id == "id1"
+    mock_valkey_cluster_client.ft.return_value.search.assert_called_once()
+
+
+def test_cluster_mode_insert(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that insert works in cluster mode (ValkeyCluster handles routing)."""
+    vectors = [np.random.rand(1536).tolist()]
+    payloads = [{"hash": "h1", "data": "test", "user_id": "u1"}]
+    ids = ["id1"]
+
+    valkey_db_cluster.insert(vectors=vectors, payloads=payloads, ids=ids)
+
+    mock_valkey_cluster_client.hset.assert_called_once()
+
+
+def test_cluster_mode_get(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that get works in cluster mode."""
+    mock_valkey_cluster_client.hgetall.return_value = {
+        "memory_id": "id1",
+        "hash": "h1",
+        "memory": "test_data",
+        "created_at": str(int(datetime.now().timestamp())),
+        "metadata": "{}",
+    }
+
+    result = valkey_db_cluster.get("id1")
+    assert result.id == "id1"
+    assert result.payload["data"] == "test_data"
+
+
+def test_cluster_mode_delete(valkey_db_cluster, mock_valkey_cluster_client):
+    """Test that delete works in cluster mode."""
+    valkey_db_cluster.delete("id1")
+    mock_valkey_cluster_client.delete.assert_called_once_with("mem0:test_cluster:id1")