chore: version bump to beta1 (#4827)

Co-authored-by: Soumil Rathi <soumilrathi@gmail.com>
Co-authored-by: Saket Aryan <saketaryan2002@gmail.com>
Co-authored-by: chaithanyak42 <chaithanya.kumar42a@gmail.com>
Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

docs/components/vectordbs/dbs/valkey.mdx

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

docs/cookbooks/frameworks/chrome-extension.mdx

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

docs/cookbooks/overview.mdx

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

docs/docs.json

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

docs/llms.txt

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

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

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

mem0-plugin/hooks/hooks.json

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

mem0-ts/package.json

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

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

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

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

@@ -195,10 +195,7 @@ export class Memory {
         ...this.config.vectorStore.config,
         collectionName: entityCollectionName,
       };
-      // For file-based stores (memory/SQLite), use a separate DB path for entities.
-      // If dbPath was set explicitly, derive the entity path from it. If it's unset,
-      // leave it unset — the vector store's default-path logic now scopes by
-      // collectionName, so the entity store will land in its own file automatically.
+      // For file-based stores (memory/SQLite), use a separate DB path for entities
       if (entityConfig.dbPath) {
         entityConfig.dbPath = entityConfig.dbPath.replace(
           /\.db$/,

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

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

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

@@ -21,10 +21,7 @@ export class MemoryVectorStore implements VectorStore {
 
   constructor(config: VectorStoreConfig) {
     this.dimension = config.dimension || 1536; // Default OpenAI dimension
-    // Scope the default path by collectionName so that parallel stores
-    // (e.g. memory vs entity) don't share the same `vectors` table.
-    this.dbPath =
-      config.dbPath || getDefaultVectorStoreDbPath(config.collectionName);
+    this.dbPath = config.dbPath || getDefaultVectorStoreDbPath();
 
     if (!config.dbPath) {
       const oldDefault = path.join(process.cwd(), "vector_store.db");

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

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

mem0/client/main.py

@@ -883,7 +883,7 @@ class MemoryClient:
 
         response = self.client.post("/v1/feedback/", json=data)
         response.raise_for_status()
-        capture_client_event("client.feedback", self, data, {"sync_type": "sync"})
+        capture_client_event("client.feedback", self, {**data, "sync_type": "sync"})
         return response.json()
 
     def _prepare_payload(self, messages: List[Dict[str, str]], kwargs: Dict[str, Any]) -> Dict[str, Any]:
@@ -1761,5 +1761,5 @@ class AsyncMemoryClient:
 
         response = await self.async_client.post("/v1/feedback/", json=data)
         response.raise_for_status()
-        capture_client_event("client.feedback", self, data, {"sync_type": "async"})
+        capture_client_event("client.feedback", self, {**data, "sync_type": "async"})
         return response.json()

mem0/configs/llms/openai.py

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

mem0/configs/vector_stores/valkey.py

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

mem0/llms/openai.py

@@ -126,11 +126,12 @@ class OpenAILLM(LLMBase):
             params.update(**openrouter_params)
         
         else:
-            openai_specific_generation_params = ["store"]
-            for param in openai_specific_generation_params:
-                if hasattr(self.config, param):
-                    params[param] = getattr(self.config, param)
-            
+            # Only send OpenAI-specific parameters when the user has explicitly
+            # configured them. OpenAI-compatible backends (Gemini, Groq, vLLM, etc.)
+            # reject unknown fields, so `store` must be opt-in, not opt-out.
+            if self.config.store is not None:
+                params["store"] = self.config.store
+
         if response_format:
             params["response_format"] = response_format
         if tools:  # TODO: Remove tools if no issues found with new memory addition logic

mem0/reranker/sentence_transformer_reranker.py

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

mem0/vector_stores/s3_vectors.py

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

mem0/vector_stores/valkey.py

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

openclaw/config.ts

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

openclaw/openclaw.plugin.json

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

openclaw/tests/config.test.ts

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

openclaw/types.ts

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

pyproject.toml

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

tests/llms/test_openai.py

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

tests/test_client_feedback.py

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

tests/test_server_params.py

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

tests/vector_stores/test_valkey.py

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