chore(release): bump SDK versions to next beta (#4859)

Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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="/cookbooks/essentials/memory-expiration-short-and-long-term">Expiration Policies</Link> to automate retention.
+- Pair deletes with <Link href="/platform/features/expiration-date">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="/cookbooks/essentials/memory-expiration-short-and-long-term"
+    href="/platform/features/expiration-date"
   />
 </CardGroup>

docs/docs.json

@@ -621,7 +621,7 @@
     },
     {
       "source": "/platform/features/expiration-date",
-      "destination": "/cookbooks/essentials/memory-expiration-short-and-long-term"
+      "destination": "/"
     },
     {
       "source": "/platform/features/async-mode-default-change",

docs/openapi.json

@@ -1048,8 +1048,8 @@
 											},
 											"expiration_date": {
 												"type": "string",
-												"format": "date-time",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"format": "date",
+												"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",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"format": "date",
+												"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",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"format": "date",
+												"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",
-												"description": "The date and time when the memory will expire. Format: YYYY-MM-DD.",
+												"format": "date",
+												"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-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "3.0.0-beta.1",
+  "version": "3.0.0-beta.2",
   "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);
-      expect(memories.length).toBeGreaterThanOrEqual(memoryIds.length);
+      // Paginated shape: { count, next, previous, results: [...] }
+      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 () => {
-      const memories = await client.getAll({
+    test("getAll for non-existent user returns empty results", async () => {
+      const response = await client.getAll({
         filters: { user_id: `nonexistent-user-${randomUUID()}` },
       });
 
-      expect(Array.isArray(memories)).toBe(true);
-      expect(memories.length).toBe(0);
+      expect(response).toHaveProperty("results");
+      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 {
-  return new MemoryClient({ apiKey: API_KEY! });
+export async function createTestClient(): Promise<MemoryClient> {
+  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/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: ${topK}. Must be a non-negative integer.`);
+    }
+  }
+}
+
 export class Memory {
   private config: MemoryConfig;
   private customInstructions: string | undefined;
@@ -195,12 +248,10 @@ export class Memory {
         ...this.config.vectorStore.config,
         collectionName: entityCollectionName,
       };
-      // For file-based stores (memory/SQLite), use a separate DB path for entities
-      if (entityConfig.dbPath) {
-        entityConfig.dbPath = entityConfig.dbPath.replace(
-          /\.db$/,
-          "_entities.db",
-        );
+      // For file-based stores (memory/SQLite), always use a separate DB for entities
+      if (this.config.vectorStore.provider === "memory") {
+        const basePath = entityConfig.dbPath || getDefaultVectorStoreDbPath();
+        entityConfig.dbPath = basePath.replace(/\.db$/, "_entities.db");
       }
       this._entityStore = VectorStoreFactory.create(
         this.config.vectorStore.provider,
@@ -211,6 +262,181 @@ export class Memory {
     return this._entityStore;
   }
 
+  /**
+   * Normalize a filters object for entity-store scoping: keeps only
+   * user_id/agent_id/run_id keys whose values are defined.
+   */
+  private _sessionFiltersFromPayload(
+    payload: Record<string, any>,
+  ): Record<string, any> {
+    const filters: Record<string, any> = {};
+    if (payload.user_id) filters.user_id = payload.user_id;
+    if (payload.agent_id) filters.agent_id = payload.agent_id;
+    if (payload.run_id) filters.run_id = payload.run_id;
+    return filters;
+  }
+
+  /**
+   * Remove `memoryId` from every entity record scoped to `filters`.
+   * If an entity's `linkedMemoryIds` becomes empty after removal, the
+   * entity record itself is deleted. Errors on individual entities are
+   * swallowed so one bad record does not break the whole operation.
+   *
+   * No-op if the entity store has not been initialized yet.
+   */
+  private async _removeMemoryFromEntityStore(
+    memoryId: string,
+    filters: Record<string, any>,
+  ): Promise<void> {
+    let entityStore: VectorStore;
+    try {
+      entityStore = await this.getEntityStore();
+    } catch (e) {
+      console.debug(`Entity store unavailable during cleanup: ${e}`);
+      return;
+    }
+
+    let rows: Array<{ id: string; payload: Record<string, any> }> = [];
+    try {
+      const listed = await entityStore.list(filters, 10000);
+      rows = (
+        Array.isArray(listed) && Array.isArray(listed[0])
+          ? listed[0]
+          : (listed as any)
+      ) as Array<{ id: string; payload: Record<string, any> }>;
+    } catch (e) {
+      console.debug(`Entity store list failed during cleanup: ${e}`);
+      return;
+    }
+
+    for (const row of rows) {
+      try {
+        const payload = row.payload || {};
+        const linked: string[] = Array.isArray(payload.linkedMemoryIds)
+          ? payload.linkedMemoryIds
+          : [];
+        if (!linked.includes(memoryId)) continue;
+
+        const remaining = linked.filter((id) => id !== memoryId);
+        if (remaining.length === 0) {
+          try {
+            await entityStore.delete(row.id);
+          } catch (e) {
+            console.debug(`Entity delete failed for id=${row.id}: ${e}`);
+          }
+        } else {
+          const newPayload = { ...payload, linkedMemoryIds: remaining };
+          // entityStore.update requires a vector — re-embed entity text.
+          const entityText =
+            typeof payload.data === "string" ? payload.data : "";
+          if (!entityText) {
+            // Can't re-embed without text; skip gracefully.
+            console.debug(
+              `Entity id=${row.id} missing 'data'; skipping update during cleanup`,
+            );
+            continue;
+          }
+          let vec: number[];
+          try {
+            vec = await this.embedder.embed(entityText);
+          } catch (e) {
+            console.debug(`Entity re-embed failed for '${entityText}': ${e}`);
+            continue;
+          }
+          try {
+            await entityStore.update(row.id, vec, newPayload);
+          } catch (e) {
+            console.debug(`Entity update failed for id=${row.id}: ${e}`);
+          }
+        }
+      } catch (e) {
+        console.debug(`Entity cleanup error for id=${row?.id}: ${e}`);
+      }
+    }
+  }
+
+  /**
+   * Extract entities from `text` and link them to `memoryId` in the
+   * entity store, scoped to `filters` (user_id / agent_id / run_id).
+   *
+   * Simpler single-memory variant of Phase 7 in add(): no cross-memory
+   * dedup, but still does per-entity "search for existing, update if
+   * match >= 0.95 else insert new". Non-fatal errors are swallowed.
+   */
+  private async _linkEntitiesForMemory(
+    memoryId: string,
+    text: string,
+    filters: Record<string, any>,
+  ): Promise<void> {
+    try {
+      const entities = extractEntities(text);
+      if (entities.length === 0) return;
+
+      const entityStore = await this.getEntityStore();
+
+      for (const entity of entities) {
+        try {
+          let entityVec: number[];
+          try {
+            entityVec = await this.embedder.embed(entity.text);
+          } catch (e) {
+            console.debug(`Entity embed failed for '${entity.text}': ${e}`);
+            continue;
+          }
+
+          let matches: Array<{
+            id: string;
+            score?: number;
+            payload: Record<string, any>;
+          }> = [];
+          try {
+            matches = await entityStore.search(entityVec, 1, filters);
+          } catch {}
+
+          if (matches.length > 0 && (matches[0].score ?? 0) >= 0.95) {
+            const match = matches[0];
+            const payload = match.payload || {};
+            const linked = new Set<string>(
+              Array.isArray(payload.linkedMemoryIds)
+                ? payload.linkedMemoryIds
+                : [],
+            );
+            linked.add(memoryId);
+            payload.linkedMemoryIds = Array.from(linked).sort();
+            try {
+              await entityStore.update(match.id, entityVec, payload);
+            } catch (e) {
+              console.debug(`Entity update failed for '${entity.text}': ${e}`);
+            }
+          } else {
+            const entityPayload: Record<string, any> = {
+              data: entity.text,
+              entityType: entity.type,
+              linkedMemoryIds: [memoryId],
+            };
+            if (filters.user_id) entityPayload.user_id = filters.user_id;
+            if (filters.agent_id) entityPayload.agent_id = filters.agent_id;
+            if (filters.run_id) entityPayload.run_id = filters.run_id;
+
+            try {
+              await entityStore.insert(
+                [entityVec],
+                [uuidv4()],
+                [entityPayload],
+              );
+            } catch (e) {
+              console.debug(`Entity insert failed for '${entity.text}': ${e}`);
+            }
+          }
+        } catch (e) {
+          console.debug(`Entity link error for '${entity.text}': ${e}`);
+        }
+      }
+    } catch (e) {
+      console.warn(`Entity linking failed during update: ${e}`);
+    }
+  }
+
   private buildSessionScope(filters: SearchFilters): string {
     const parts: string[] = [];
     for (const key of ["agent_id", "run_id", "user_id"].sort()) {
@@ -276,6 +502,13 @@ 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,
@@ -283,14 +516,12 @@ export class Memory {
       has_filters: !!config.filters,
       infer: config.infer,
     });
-    const {
-      userId,
-      agentId,
-      runId,
-      metadata = {},
-      filters = {},
-      infer = true,
-    } = config;
+    const { metadata = {}, filters = {}, infer = true } = config;
+
+    // Validate and trim entity IDs
+    const userId = validateAndTrimEntityId(config.userId, "userId");
+    const agentId = validateAndTrimEntityId(config.agentId, "agentId");
+    const runId = validateAndTrimEntityId(config.runId, "runId");
 
     // Convert camelCase entity params to snake_case for storage (matches API and search/getAll filters)
     if (userId) filters.user_id = metadata.user_id = userId;
@@ -804,14 +1035,38 @@ 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. Only include keys whose
+    // validated value is defined — otherwise downstream vector stores
+    // receive `agent_id: undefined` / `run_id: undefined` and fail
+    // (Qdrant rejects the malformed match, pgvector binds NULL, Redis
+    // emits a literal "undefined" string in TAG filters).
+    const normalizedFilters: Record<string, any> = config.filters
+      ? Object.fromEntries(
+          Object.entries({
+            ...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"),
+          }).filter(([, v]) => v !== undefined),
+        )
+      : {};
+
     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)) {
@@ -993,12 +1248,9 @@ export class Memory {
           createdAt: payload.createdAt,
           updatedAt: payload.updatedAt,
           score: scored.score,
-          metadata: {
-            ...Object.entries(payload)
-              .filter(([key]) => !excludedKeys.has(key))
-              .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),
-            scoreBreakdown: scored.scoreBreakdown,
-          },
+          metadata: Object.entries(payload)
+            .filter(([key]) => !excludedKeys.has(key))
+            .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),
           ...(payload.user_id && { user_id: payload.user_id }),
           ...(payload.agent_id && { agent_id: payload.agent_id }),
           ...(payload.run_id && { run_id: payload.run_id }),
@@ -1116,11 +1368,27 @@ 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. Drop keys that resolve to
+    // undefined so downstream vector stores don't receive
+    // `agent_id: undefined` / `run_id: undefined` and fail.
+    const filters: Record<string, any> = Object.fromEntries(
+      Object.entries({
+        ...(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"),
+      }).filter(([, v]) => v !== undefined),
+    );
 
     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,
@@ -1177,6 +1445,7 @@ export class Memory {
       ...metadata,
       data,
       hash: createHash("md5").update(data).digest("hex"),
+      textLemmatized: lemmatizeForBm25(data),
       createdAt: new Date().toISOString(),
     };
 
@@ -1234,6 +1503,16 @@ export class Memory {
       newMetadata.updatedAt,
     );
 
+    // Entity-store cleanup: strip this memory's id from old-text entities,
+    // then re-extract entities from the new text and link them back.
+    try {
+      const sessionFilters = this._sessionFiltersFromPayload(newMetadata);
+      await this._removeMemoryFromEntityStore(memoryId, sessionFilters);
+      await this._linkEntitiesForMemory(memoryId, data, sessionFilters);
+    } catch (e) {
+      console.warn(`Entity store cleanup/link failed during update: ${e}`);
+    }
+
     return memoryId;
   }
 
@@ -1244,6 +1523,9 @@ export class Memory {
     }
 
     const prevValue = existingMemory.payload.data;
+    const sessionFilters = this._sessionFiltersFromPayload(
+      existingMemory.payload || {},
+    );
     await this.vectorStore.delete(memoryId);
     await this.db.addHistory(
       memoryId,
@@ -1255,6 +1537,14 @@ export class Memory {
       1,
     );
 
+    // Entity-store cleanup: strip this memory's id from any entity records
+    // that linked to it. Non-fatal — log and continue on error.
+    try {
+      await this._removeMemoryFromEntityStore(memoryId, sessionFilters);
+    } catch (e) {
+      console.warn(`Entity store cleanup failed during delete: ${e}`);
+    }
+
     return memoryId;
   }
 

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/entity_extraction.ts

@@ -660,6 +660,9 @@ export function extractEntities(text: string): ExtractedEntity[] {
     txt = txt.replace(/\s*:+$/, "");
     // Strip leading numbered list markers
     txt = txt.replace(/^\d+\s*\.\s*/, "");
+    // Strip trailing sentence punctuation (".", ",", ";", "!", "?") — otherwise
+    // "Paris." and "Paris" produce different embeddings and break entity dedup.
+    txt = txt.replace(/[.,;!?]+$/, "").trim();
 
     if (!txt || txt.length <= 2 || hasArtifacts(txt)) {
       continue;

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/vector_stores/memory.ts

@@ -19,6 +19,24 @@ 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
     this.dbPath = config.dbPath || getDefaultVectorStoreDbPath();
@@ -246,7 +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(
@@ -260,7 +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) });
         }
       }
@@ -351,7 +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),
@@ -378,7 +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,
@@ -418,7 +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

@@ -9,6 +9,19 @@ import type {
 import { VectorStore } from "./base";
 import { SearchFilters, VectorStoreConfig, VectorStoreResult } from "../types";
 
+/**
+ * Escape RediSearch TAG filter special characters. Any punctuation in the
+ * value (including `-`, which appears in every UUID) must be backslash-
+ * escaped, otherwise RediSearch either parses it as an operator (`-` is
+ * minus, `|` is OR) or rejects the whole expression as a syntax error.
+ */
+function escapeRedisTagValue(value: unknown): string {
+  return String(value).replace(
+    /([,.<>{}\[\]"':;!@#$%^&*()\-+=~|/\\\s])/g,
+    "\\$1",
+  );
+}
+
 interface RedisConfig extends VectorStoreConfig {
   redisUrl: string;
   collectionName: string;
@@ -256,17 +269,25 @@ 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((module: any[]) => {
-        const moduleMap = new Map();
-        for (let i = 0; i < module.length; i += 2) {
-          moduleMap.set(module[i], module[i + 1]);
+      const hasSearch = modulesResponse.some((mod: any) => {
+        // node-redis v4+ returns objects: { name: "search", ver: ..., ... }
+        if (typeof mod === "object" && !Array.isArray(mod) && mod.name) {
+          const name = String(mod.name).toLowerCase();
+          return name === "search" || name === "searchlight";
         }
-        const moduleName = moduleMap.get("name");
-        return (
-          moduleName?.toLowerCase() === "search" ||
-          moduleName?.toLowerCase() === "searchlight"
-        );
+        // Fallback: legacy flat array format [key, value, key, value, ...]
+        if (Array.isArray(mod)) {
+          const moduleMap = new Map();
+          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) {
@@ -369,8 +390,8 @@ export class RedisDB implements VectorStore {
     const snakeFilters = filters ? toSnakeCase(filters) : undefined;
     const filterExpr = snakeFilters
       ? Object.entries(snakeFilters)
-          .filter(([_, value]) => value !== null)
-          .map(([key, value]) => `@${key}:{${value}}`)
+          .filter(([_, value]) => value !== null && value !== undefined)
+          .map(([key, value]) => `@${key}:{${escapeRedisTagValue(value)}}`)
           .join(" ")
       : "*";
 
@@ -607,8 +628,8 @@ export class RedisDB implements VectorStore {
     const snakeFilters = filters ? toSnakeCase(filters) : undefined;
     const filterExpr = snakeFilters
       ? Object.entries(snakeFilters)
-          .filter(([_, value]) => value !== null)
-          .map(([key, value]) => `@${key}:{${value}}`)
+          .filter(([_, value]) => value !== null && value !== undefined)
+          .map(([key, value]) => `@${key}:{${escapeRedisTagValue(value)}}`)
           .join(" ")
       : "*";
 

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 () => {
-    const results = await store.search(vec([1, 0, 0, 0]), 10, { userId: "u2" });
-    expect(results.every((r) => r.payload.userId === "u2")).toBe(true);
+  test("filters by user_id", async () => {
+    const results = await store.search(vec([1, 0, 0, 0]), 10, {
+      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 () => {
-    const [results, count] = await store.list({ userId: "u1" });
+  test("filters by user_id", async () => {
+    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
-    const filtered = await store.search(vec1, 2, { userId: "u1" });
+    // Search with filters (camelCase in payload is normalized to snake_case)
+    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()
-
-        # Ensure v1.1 format (wrap raw list if needed)
-        if isinstance(result, list):
-            return {"results": result}
-        return result
+        return response.json()
 
     @api_error_handler
     def search(self, query: str, options: Optional[SearchMemoryOptions] = None, **kwargs) -> 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()
-
-        # Ensure v1.1 format (wrap raw list if needed)
-        if isinstance(result, list):
-            return {"results": result}
-        return result
+        return response.json()
 
     @api_error_handler
     async def search(self, query: str, options: Optional[SearchMemoryOptions] = None, **kwargs) -> Dict[str, Any]:

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(

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
             )
@@ -382,6 +453,84 @@ class Memory(MemoryBase):
         except Exception as e:
             logger.warning(f"Entity upsert failed for '{entity_text}': {e}")
 
+    def _remove_memory_from_entity_store(self, memory_id, filters):
+        """Strip `memory_id` from every entity record scoped to `filters`.
+
+        For each entity whose `linked_memory_ids` contains `memory_id`:
+          - remove the id; if the list becomes empty, delete the entity record.
+          - otherwise re-embed the entity text and update the payload
+            (the vector store's update() requires a vector).
+
+        No-op if the entity store has never been initialized in this process.
+        Errors on individual entities are swallowed at debug level; outer
+        failures are swallowed at warning level so the primary delete/update
+        path is never broken by entity cleanup.
+        """
+        if self._entity_store is None:
+            return
+        search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
+        try:
+            listed = self.entity_store.list(filters=search_filters, top_k=10000)
+            rows = listed[0] if isinstance(listed, (list, tuple)) and listed and isinstance(listed[0], list) else listed
+            for row in rows or []:
+                try:
+                    payload = getattr(row, "payload", None) or {}
+                    linked = payload.get("linked_memory_ids", [])
+                    if not isinstance(linked, list) or memory_id not in linked:
+                        continue
+                    remaining = [mid for mid in linked if mid != memory_id]
+                    if not remaining:
+                        try:
+                            self.entity_store.delete(vector_id=row.id)
+                        except Exception as e:
+                            logger.debug(f"Entity delete failed for id={row.id}: {e}")
+                    else:
+                        entity_text = payload.get("data")
+                        if not isinstance(entity_text, str) or not entity_text:
+                            logger.debug(f"Entity id={row.id} missing 'data'; skipping update during cleanup")
+                            continue
+                        try:
+                            vec = self.embedding_model.embed(entity_text, "update")
+                        except Exception as e:
+                            logger.debug(f"Entity re-embed failed for '{entity_text}': {e}")
+                            continue
+                        new_payload = {**payload, "linked_memory_ids": remaining}
+                        try:
+                            self.entity_store.update(
+                                vector_id=row.id,
+                                vector=vec,
+                                payload=new_payload,
+                            )
+                        except Exception as e:
+                            logger.debug(f"Entity update failed for id={row.id}: {e}")
+                except Exception as e:
+                    logger.debug(f"Entity cleanup error: {e}")
+        except Exception as e:
+            logger.warning(f"Entity store cleanup failed for memory_id={memory_id}: {e}")
+
+    def _link_entities_for_memory(self, memory_id, text, filters):
+        """Extract entities from `text` and link them to `memory_id` in the
+        entity store, scoped to `filters`. Simpler single-memory variant of
+        Phase 7 in add(): per-entity search-then-update-or-insert via the
+        existing `_upsert_entity` helper. Non-fatal on any failure.
+        """
+        try:
+            entities = extract_entities(text)
+            if not entities:
+                return
+            seen = set()
+            for entity_type, entity_text in entities:
+                key = entity_text.strip().lower()
+                if not key or key in seen:
+                    continue
+                seen.add(key)
+                try:
+                    self._upsert_entity(entity_text, entity_type, memory_id, filters)
+                except Exception as e:
+                    logger.debug(f"Entity link failed for '{entity_text}': {e}")
+        except Exception as e:
+            logger.warning(f"Entity linking failed for memory_id={memory_id}: {e}")
+
     @classmethod
     def from_config(cls, config_dict: Dict[str, Any]):
         try:
@@ -868,7 +1017,7 @@ class Memory(MemoryBase):
         self,
         *,
         filters: Optional[Dict[str, Any]] = None,
-        top_k: int = 100,
+        top_k: int = 20,
         **kwargs,
     ):
         """
@@ -878,20 +1027,38 @@ 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 +1127,7 @@ 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 +1138,7 @@ 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 +1167,29 @@ 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 +1371,6 @@ 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 +1415,6 @@ 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]
@@ -1524,6 +1702,13 @@ class Memory(MemoryBase):
             actor_id=new_metadata.get("actor_id"),
             role=new_metadata.get("role"),
         )
+
+        # Entity-store cleanup: strip this memory's id from old-text entities,
+        # then re-extract entities from the new text and link them back.
+        session_filters = {k: new_metadata[k] for k in ("user_id", "agent_id", "run_id") if new_metadata.get(k)}
+        self._remove_memory_from_entity_store(memory_id, session_filters)
+        self._link_entities_for_memory(memory_id, data, session_filters)
+
         return memory_id
 
     def _delete_memory(self, memory_id, existing_memory=None):
@@ -1535,6 +1720,8 @@ class Memory(MemoryBase):
         prev_value = existing_memory.payload.get("data", "")
         created_at = _normalize_iso_timestamp_to_utc(existing_memory.payload.get("created_at"))
         updated_at = datetime.now(timezone.utc).isoformat()
+        payload = existing_memory.payload or {}
+        session_filters = {k: payload[k] for k in ("user_id", "agent_id", "run_id") if payload.get(k)}
         self.vector_store.delete(vector_id=memory_id)
         self.db.add_history(
             memory_id,
@@ -1547,6 +1734,11 @@ class Memory(MemoryBase):
             role=existing_memory.payload.get("role"),
             is_deleted=1,
         )
+
+        # Entity-store cleanup: strip this memory's id from any entity records
+        # that linked to it. Non-fatal — the helper swallows errors.
+        self._remove_memory_from_entity_store(memory_id, session_filters)
+
         return memory_id
 
     def reset(self):
@@ -1640,11 +1832,127 @@ 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
             )
         return self._entity_store
 
+    async def _upsert_entity_async(self, entity_text, entity_type, memory_id, filters):
+        """Async variant of `_upsert_entity` — per-entity search-then-update-or-insert."""
+        try:
+            entity_embedding = await asyncio.to_thread(self.embedding_model.embed, entity_text, "add")
+            search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
+
+            existing = await asyncio.to_thread(
+                self.entity_store.search,
+                query=entity_text,
+                vectors=entity_embedding,
+                top_k=1,
+                filters=search_filters,
+            )
+
+            if existing and existing[0].score >= 0.95:
+                match = existing[0]
+                payload = match.payload or {}
+                linked_ids = payload.get("linked_memory_ids", [])
+                if memory_id not in linked_ids:
+                    linked_ids.append(memory_id)
+                    payload["linked_memory_ids"] = linked_ids
+                    await asyncio.to_thread(
+                        self.entity_store.update,
+                        vector_id=match.id,
+                        vector=None,
+                        payload=payload,
+                    )
+            else:
+                entity_id = str(uuid.uuid4())
+                entity_payload = {
+                    "data": entity_text,
+                    "entity_type": entity_type,
+                    "linked_memory_ids": [memory_id],
+                    **{k: v for k, v in search_filters.items()},
+                }
+                await asyncio.to_thread(
+                    self.entity_store.insert,
+                    vectors=[entity_embedding],
+                    ids=[entity_id],
+                    payloads=[entity_payload],
+                )
+        except Exception as e:
+            logger.warning(f"Entity upsert failed for '{entity_text}' (async): {e}")
+
+    async def _remove_memory_from_entity_store(self, memory_id, filters):
+        """Async variant of `Memory._remove_memory_from_entity_store`."""
+        if self._entity_store is None:
+            return
+        search_filters = {k: v for k, v in filters.items() if k in ("user_id", "agent_id", "run_id") and v}
+        try:
+            listed = await asyncio.to_thread(self.entity_store.list, filters=search_filters, top_k=10000)
+            rows = listed[0] if isinstance(listed, (list, tuple)) and listed and isinstance(listed[0], list) else listed
+            for row in rows or []:
+                try:
+                    payload = getattr(row, "payload", None) or {}
+                    linked = payload.get("linked_memory_ids", [])
+                    if not isinstance(linked, list) or memory_id not in linked:
+                        continue
+                    remaining = [mid for mid in linked if mid != memory_id]
+                    if not remaining:
+                        try:
+                            await asyncio.to_thread(self.entity_store.delete, vector_id=row.id)
+                        except Exception as e:
+                            logger.debug(f"Entity delete failed for id={row.id} (async): {e}")
+                    else:
+                        entity_text = payload.get("data")
+                        if not isinstance(entity_text, str) or not entity_text:
+                            logger.debug(f"Entity id={row.id} missing 'data'; skipping update during cleanup (async)")
+                            continue
+                        try:
+                            vec = await asyncio.to_thread(self.embedding_model.embed, entity_text, "update")
+                        except Exception as e:
+                            logger.debug(f"Entity re-embed failed for '{entity_text}' (async): {e}")
+                            continue
+                        new_payload = {**payload, "linked_memory_ids": remaining}
+                        try:
+                            await asyncio.to_thread(
+                                self.entity_store.update,
+                                vector_id=row.id,
+                                vector=vec,
+                                payload=new_payload,
+                            )
+                        except Exception as e:
+                            logger.debug(f"Entity update failed for id={row.id} (async): {e}")
+                except Exception as e:
+                    logger.debug(f"Entity cleanup error (async): {e}")
+        except Exception as e:
+            logger.warning(f"Entity store cleanup failed for memory_id={memory_id} (async): {e}")
+
+    async def _link_entities_for_memory(self, memory_id, text, filters):
+        """Async variant of `Memory._link_entities_for_memory`."""
+        try:
+            entities = await asyncio.to_thread(extract_entities, text)
+            if not entities:
+                return
+            seen = set()
+            for entity_type, entity_text in entities:
+                key = entity_text.strip().lower()
+                if not key or key in seen:
+                    continue
+                seen.add(key)
+                try:
+                    await self._upsert_entity_async(entity_text, entity_type, memory_id, filters)
+                except Exception as e:
+                    logger.debug(f"Entity link failed for '{entity_text}' (async): {e}")
+        except Exception as e:
+            logger.warning(f"Entity linking failed for memory_id={memory_id} (async): {e}")
+
     @classmethod
     def from_config(cls, config_dict: Dict[str, Any]):
         try:
@@ -2115,7 +2423,7 @@ class AsyncMemory(MemoryBase):
         self,
         *,
         filters: Optional[Dict[str, Any]] = None,
-        top_k: int = 100,
+        top_k: int = 20,
         **kwargs,
     ):
         """
@@ -2125,20 +2433,38 @@ 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 +2533,7 @@ 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 +2544,7 @@ 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 +2573,31 @@ 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 +2824,6 @@ 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]
@@ -2784,6 +3126,13 @@ class AsyncMemory(MemoryBase):
             actor_id=new_metadata.get("actor_id"),
             role=new_metadata.get("role"),
         )
+
+        # Entity-store cleanup: strip this memory's id from old-text entities,
+        # then re-extract entities from the new text and link them back.
+        session_filters = {k: new_metadata[k] for k in ("user_id", "agent_id", "run_id") if new_metadata.get(k)}
+        await self._remove_memory_from_entity_store(memory_id, session_filters)
+        await self._link_entities_for_memory(memory_id, data, session_filters)
+
         return memory_id
 
     async def _delete_memory(self, memory_id, existing_memory=None):
@@ -2795,6 +3144,8 @@ class AsyncMemory(MemoryBase):
         prev_value = existing_memory.payload.get("data", "")
         created_at = _normalize_iso_timestamp_to_utc(existing_memory.payload.get("created_at"))
         updated_at = datetime.now(timezone.utc).isoformat()
+        payload = existing_memory.payload or {}
+        session_filters = {k: payload[k] for k in ("user_id", "agent_id", "run_id") if payload.get(k)}
 
         await asyncio.to_thread(self.vector_store.delete, vector_id=memory_id)
         await asyncio.to_thread(
@@ -2810,6 +3161,10 @@ class AsyncMemory(MemoryBase):
             is_deleted=1,
         )
 
+        # Entity-store cleanup: strip this memory's id from any entity records
+        # that linked to it. Non-fatal — the helper swallows errors.
+        await self._remove_memory_from_entity_store(memory_id, session_filters)
+
         return memory_id
 
     async def reset(self):

mem0/memory/setup.py

@@ -32,15 +32,26 @@ def get_user_id():
         return "anonymous_user"
 
 
-def get_or_create_user_id(vector_store):
-    """Store user_id in vector store and return it."""
+def get_or_create_user_id(vector_store=None):
+    """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
-        self.posthog.capture(distinct_id=distinct_id, event=event_name, properties=properties)
+        try:
+            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()
-    if oss_telemetry is None:
-        return
+    try:
+        oss_telemetry = _get_oss_telemetry()
+        if oss_telemetry is None:
+            return
 
-    event_data = {
-        "collection": memory_instance.collection_name,
-        "vector_size": memory_instance.embedding_model.config.embedding_dims,
-        "history_store": "sqlite",
-        "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__}",
-        "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}",
-    }
-    if additional_data:
-        event_data.update(additional_data)
+        event_data = {
+            "collection": memory_instance.collection_name,
+            "vector_size": memory_instance.embedding_model.config.embedding_dims,
+            "history_store": "sqlite",
+            "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__}",
+            "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}",
+        }
+        if 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 = {
-        "function": f"{instance.__class__.__module__}.{instance.__class__.__name__}",
-    }
-    if additional_data:
-        event_data.update(additional_data)
+    try:
+        event_data = {
+            "function": f"{instance.__class__.__module__}.{instance.__class__.__name__}",
+        }
+        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/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/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
-        data = [
-            {
-                "id": idx,
-                "vectors": embedding,
-                "metadata": metadata,
+        # Batch insert all records at once for better performance and consistency.
+        # Only include the `text` field when the collection's schema has it — legacy
+        # collections created pre-v3 reject unknown top-level fields.
+        def _build_record(idx, embedding, metadata):
+            record = {"id": idx, "vectors": embedding, "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 "",
-            }
-            for idx, embedding, metadata in zip(ids, vectors, payloads)
-        ]
+                record["text"] = (metadata.get("text_lemmatized") or metadata.get("data", ""))[:65535] if metadata else ""
+            return record
+
+        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(
-            collection_name=self.collection_name,
-            data=[vectors],
-            limit=top_k,
-            filter=query_filter,
-            output_fields=["*"],
-        )
+        # v3 collections carry both a dense `vectors` field and a sparse `sparse`
+        # field (for BM25), which makes anns_field ambiguous — Milvus rejects the
+        # query otherwise with "multiple anns_fields exist". Legacy single-vector
+        # collections don't need the hint, so only pass it when the hybrid schema
+        # is present.
+        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 text_for_bm25:
-                sparse = self._encode_bm25(text_for_bm25)
-                if sparse is not None:
-                    named_vectors["bm25"] = sparse
+            if self._has_bm25_slot:
+                text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
+                if text_for_bm25:
+                    sparse = self._encode_bm25(text_for_bm25)
+                    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 text_for_bm25:
-                sparse = self._encode_bm25(text_for_bm25)
-                if sparse is not None:
-                    named_vectors["bm25"] = sparse
+            if self._has_bm25_slot:
+                text_for_bm25 = payload.get("text_lemmatized") or payload.get("data", "")
+                if text_for_bm25:
+                    sparse = self._encode_bm25(text_for_bm25)
+                    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:

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0ai"
-version = "2.0.0b1"
+version = "2.0.0b2"
 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
-        assert llm.config.model == "gpt-4.1-nano-2025-04-14"
+        # Should default to "gpt-5-mini" if model is None
+        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/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_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)