Merge remote-tracking branch 'origin/main' into docs/stale-vectordbs

Each fix verified against mem0/vector_stores/ source and configs:

- baidu: table_name default mem0_table → mem0 (BaiduDBConfig)
- langchain: TS import mem0ai → mem0ai/oss; fix undefined LangchainVectorStore
  variable to use MemoryVectorStore directly
- mongodb: collection_name default mem0_collection → mem0; mongo_uri default
  corrected to mongodb://localhost:27017 (MongoDBConfig)
- neon: hnsw default False → True (PGVectorConfig.hnsw = True)
- neptune_analytics: extra name vector_stores → vector-stores (pyproject.toml)
- pgvector: diskann default True → False; hnsw default False → True
  (PGVectorConfig)
- upstash-vector: move enable_embeddings inside config: {} block
  (UpstashVectorConfig field, not top-level VectorStoreConfig)
- valkey: remove non-existent distance_metric row (not in ValkeyConfig);
  fix extra name vector_stores → vector-stores
- vertex_ai: region is required (no default in GoogleMatchingEngineConfig)
- weaviate: install pip install weaviate weaviate-client → weaviate-client
  (weaviate is not a separate package)
- overview: update TS supported vector DBs list to reflect actual
  mem0-ts/src/oss/src/vector_stores/ files

docs/changelog/sdk.mdx

@@ -7,21 +7,6 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
-<Update label="2026-06-27" description="v2.0.10">
-
-**New Features:**
-- **Client:** Expose `expiration_date` on `MemoryClient.update()` and `AsyncMemoryClient.update()` — callers can now set or clear a memory's expiration date; `None` is preserved and forwarded to the API ([#5874](https://github.com/mem0ai/mem0/pull/5874))
-
-**Bug Fixes:**
-- **Memory (OSS):** Apply `remove_code_blocks()` to the LangChain path in async `_create_procedural_memory` so code fences are stripped consistently ([#5711](https://github.com/mem0ai/mem0/pull/5711))
-- **Rerankers:** Score HuggingFace cross-encoder results with per-document sigmoid instead of set-relative min-max, preventing a single low-score document from collapsing all relevance scores to zero ([#5715](https://github.com/mem0ai/mem0/pull/5715))
-- **Core:** Validate and trim entity IDs (`user_id`, `agent_id`, `run_id`) in `delete_all()` for both sync and async `Memory` ([#5735](https://github.com/mem0ai/mem0/pull/5735))
-- **Vector Stores:** Use `.get()` for `hash` and `created_at` in the Redis `insert()` and `update()` paths so entity payloads that omit those fields no longer raise `KeyError` ([#5709](https://github.com/mem0ai/mem0/pull/5709))
-- **Memory:** Fix scale-threshold notices not firing for Redis and search-engine backends by resolving `col_info()` signature differences and adding `num_docs` to the count-extraction lookup ([#5687](https://github.com/mem0ai/mem0/pull/5687))
-- **Vector Stores:** Escape special characters in Valkey FT.SEARCH tag filter values to prevent wildcard and operator injection through tenant-isolation filters ([#5750](https://github.com/mem0ai/mem0/pull/5750))
-
-</Update>
-
 <Update label="2026-06-24" description="v2.0.9">
 
 **Bug Fixes:**
@@ -1085,23 +1070,6 @@ See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-
 
 <Tab title="TypeScript">
 
-<Update label="2026-06-27" description="v3.0.12">
-
-**New Features:**
-- **Client:** Add `expirationDate` to `AddMemoryOptions`, `update()`, and the `Memory` interface; add `showExpired` to `SearchMemoryOptions` and `GetAllMemoryOptions` ([#5874](https://github.com/mem0ai/mem0/pull/5874))
-- **LLMs:** Add `MiniMaxLLM` provider backed by the OpenAI-compatible MiniMax API (`api.minimax.io/v1`, default model `MiniMax-M2.7`) ([#5858](https://github.com/mem0ai/mem0/pull/5858))
-- **LLMs:** Add `LiteLLM` provider for routing requests through a local or hosted LiteLLM proxy ([#5830](https://github.com/mem0ai/mem0/pull/5830))
-- **Vector Stores:** Add `connectionString` and `ssl` options to the PGVector config, allowing connection via URI instead of individual host/user/password/port fields ([#5789](https://github.com/mem0ai/mem0/pull/5789))
-
-**Bug Fixes:**
-- **Memory (OSS):** Validate and trim entity IDs (`userId`, `agentId`, `runId`) in `deleteAll()` via `validateAndTrimEntityId` ([#5735](https://github.com/mem0ai/mem0/pull/5735))
-- **Vector Stores:** Use nullish coalescing for `hash` and timestamps in the Redis `insert()` and `update()` paths so entity payloads that omit those fields no longer crash ([#5860](https://github.com/mem0ai/mem0/pull/5860))
-
-**Security:**
-- **Dependencies:** Bump `undici` to `>=6.27.0` via pnpm override to remediate CVE-2026-12151 ([#5861](https://github.com/mem0ai/mem0/pull/5861))
-
-</Update>
-
 <Update label="2026-06-24" description="v3.0.11">
 
 **Bug Fixes:**

docs/components/embedders/models/aws_bedrock.mdx

@@ -59,9 +59,5 @@ Here are the parameters available for configuring AWS Bedrock embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
-| `aws_region` | AWS region for the Bedrock client | `us-west-2` |
-| `aws_access_key_id` | AWS access key ID for authentication | `None` |
-| `aws_secret_access_key` | AWS secret access key for authentication | `None` |
-| `aws_session_token` | AWS session token for temporary credentials | `None` |
 </Tab>
 </Tabs>

docs/components/embedders/models/google_AI.mdx

@@ -67,15 +67,14 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter        | Description                          | Default Value           |
 | ---------------- | ------------------------------------ | ----------------------- |
 | `model`          | The name of the embedding model to use| `models/gemini-embedding-001` |
-| `embedding_dims` | Dimensions of the embedding model     | `768`                   |
+| `embedding_dims` | Dimensions of the embedding model     | `1536`                  |
 | `api_key`        | The Google API key                   | `None`                  |
-| `output_dimensionality` | Output dimensionality for the embedding model (Gemini-specific; used when `embedding_dims` is not set) | `None` |
 </Tab>
 <Tab title="TypeScript">
 | Parameter         | Description                                   | Default Value              |
 | ----------------- | --------------------------------------------- | -------------------------- |
 | `model`           | The name of the embedding model to use        | `gemini-embedding-001`     |
-| `embeddingDims`   | Dimensions of the embedding model. When not set, uses the model's native output dimensionality (3072 for `gemini-embedding-001`; MRL truncation to 768, 1536, or 3072 is supported) | `None`                     |
+| `embeddingDims`   | Dimensions of the embedding model             | `1536`                     |
 | `apiKey`          | Google API key                                | `None`                     |
 </Tab>
 </Tabs>

docs/components/embedders/models/lmstudio.mdx

@@ -16,7 +16,7 @@ config = {
     "embedder": {
         "provider": "lmstudio",
         "config": {
-            "model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
+            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
         }
     }
 }
@@ -37,6 +37,6 @@ Here are the parameters available for configuring LM Studio embedder:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the LM Studio model to use | `nomic-ai/nomic-embed-text-v1.5-GGUF` |
+| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |

docs/components/embedders/overview.mdx

@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.
 
 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, and **Ollama**.
+  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
 </Note>
 
 <CardGroup cols={4}>
@@ -24,7 +24,6 @@ See the list of supported embedders below.
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
   <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
   <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
-  <Card title="FastEmbed" href="/components/embedders/models/fastembed"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -98,7 +98,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `max_tokens`         | Tokens to generate                            | All               |
     | `top_p`              | Probability threshold for nucleus sampling    | All               |
     | `top_k`              | Number of highest probability tokens to keep  | All               |
-    | `http_client_proxies`| Allow proxy server settings                   | All               |
+    | `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
     | `models`             | List of models                                | Openrouter        |
     | `route`              | Routing strategy                              | Openrouter        |
     | `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
@@ -110,7 +110,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
     | `sarvam_base_url`    | Base URL for Sarvam API                       | Sarvam            |
-    | `reasoning_effort`   | Reasoning level (low, medium, high)           | All               |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | Sarvam            |
     | `frequency_penalty`  | Penalize frequent tokens (-2.0 to 2.0)        | Sarvam            |
     | `presence_penalty`   | Penalize existing tokens (-2.0 to 2.0)        | Sarvam            |
     | `seed`               | Seed for deterministic sampling               | Sarvam            |

docs/components/llms/models/anthropic.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-sonnet-4-6",
+            "model": "claude-sonnet-4-20250514",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -45,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-sonnet-4-6',
+      model: 'claude-sonnet-4-20250514',
       temperature: 0.1,
       maxTokens: 2000,
     },

docs/components/llms/models/aws_bedrock.mdx

@@ -6,7 +6,7 @@ description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authenti
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
 - You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
-- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
+- You will have to export `AWS_REGION`, `AWS_ACCESS_KEY`, and `AWS_SECRET_ACCESS_KEY` to set environment variables.
 
 ### Usage
 

docs/components/llms/models/groq.mdx

@@ -21,7 +21,7 @@ config = {
     "llm": {
         "provider": "groq",
         "config": {
-            "model": "llama-3.3-70b-versatile",
+            "model": "mixtral-8x7b-32768",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -46,7 +46,7 @@ const config = {
     provider: 'groq',
     config: {
       apiKey: process.env.GROQ_API_KEY || '',
-      model: 'llama3-70b-8192',
+      model: 'mixtral-8x7b-32768',
       temperature: 0.1,
       maxTokens: 1000,
     },

docs/components/llms/models/xAI.mdx

@@ -20,7 +20,7 @@ config = {
     "llm": {
         "provider": "xai",
         "config": {
-            "model": "grok-4.3",
+            "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
         }

docs/components/llms/overview.mdx

@@ -16,7 +16,7 @@ For a comprehensive list of available parameters for llm configuration, please r
 See the list of supported LLMs below.
 
 <Note>
-  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
 </Note>
 
 <CardGroup cols={4}>

docs/cookbooks/companions/nodejs-companion.mdx

@@ -31,7 +31,7 @@ const openaiClient = new OpenAI();
 const memory = new Memory();
 
 async function chatWithMemories(message, userId = "default_user") {
-  const relevantMemories = await memory.search(message, { filters: { user_id: userId } });
+  const relevantMemories = await memory.search(message, { userId: userId });
   
   const memoriesStr = relevantMemories.results
     .map(entry => `- ${entry.memory}`)

docs/cookbooks/essentials/building-ai-companion.mdx

@@ -289,7 +289,8 @@ print([m["memory"] for m in constraints["results"]])
 ```python
 constraints = memory.search(
     query="injury concerns",
-    filters={"user_id": "max", "memory_bucket": {"in": ["constraints"]}},
+    user_id="max",
+    filters={"memory_bucket": {"in": ["constraints"]}},
     threshold=0.0  # optional: widen recall for short phrases
 )
 print([m["memory"] for m in constraints["results"]])
@@ -735,7 +736,8 @@ mem0_client.add(messages, user_id="max", run_id="nyc-2025")
 # Retrieve only Boston memories
 boston_memories = mem0_client.search(
     "training plan",
-    filters={"user_id": "max", "run_id": "boston-2025"}
+    user_id="max",
+    run_id="boston-2025"
 )
 ```
   </Tab>
@@ -747,7 +749,8 @@ memory.add(messages, user_id="max", run_id="nyc-2025")
 # Retrieve only Boston memories
 boston_memories = memory.search(
     "training plan",
-    filters={"user_id": "max", "run_id": "boston-2025"},
+    user_id="max",
+    run_id="boston-2025",
 )
 ```
   </Tab>
@@ -843,7 +846,8 @@ Prioritize recent training over old data:
 ```python
 recent = mem0_client.search(
     "training progress",
-    filters={"user_id": "max", "created_at": {"gte": "2025-10-01"}}
+    user_id="max",
+    filters={"created_at": {"gte": "2025-10-01"}}
 )
 ```
   </Tab>
@@ -862,7 +866,8 @@ memory.add(
 cutoff = int(datetime(2025, 10, 1).timestamp())
 recent = memory.search(
     "training progress",
-    filters={"user_id": "max", "logged_epoch": {"gte": cutoff}},
+    user_id="max",
+    filters={"logged_epoch": {"gte": cutoff}},
 )
 ```
   </Tab>
@@ -884,7 +889,8 @@ mem0_client.add(
 # Later, find all speed workouts
 speed_sessions = mem0_client.search(
     "speed work",
-    filters={"user_id": "max", "metadata": {"workout_type": "speed"}}
+    user_id="max",
+    filters={"metadata": {"workout_type": "speed"}}
 )
 ```
   </Tab>
@@ -899,7 +905,8 @@ memory.add(
 # Later, find all speed workouts
 speed_sessions = memory.search(
     "speed work",
-    filters={"user_id": "max", "workout_type": "speed"},
+    user_id="max",
+    filters={"workout_type": "speed"},
 )
 ```
   </Tab>

docs/cookbooks/essentials/exporting-memories.mdx

@@ -58,7 +58,6 @@ Use `get_all()` with filters to retrieve everything for a specific user:
 ```python
 dev_memories = client.get_all(
     filters={"user_id": "dev"},
-    page=1,
     page_size=50
 )
 

docs/cookbooks/frameworks/llamaindex-multiagent.mdx

@@ -211,8 +211,8 @@ class MultiAgentLearningSystem:
         try:
             # Search memory for learning patterns
             memories = self.memory.search(
-                query="learning machine learning",
+                user_id=self.student_id,
-                filters={"user_id": self.student_id}
+                query="learning machine learning"
             )
 
             if memories and memories.get('results'):

docs/cookbooks/integrations/healthcare-google-adk.mdx

@@ -50,7 +50,7 @@ load_dotenv()
 USER_ID = "Alex"
 
 # Initialize Mem0 client
-mem0_client = MemoryClient()
+mem0 = MemoryClient()
 ```
 
 ## Define Memory Tools
@@ -76,7 +76,7 @@ def retrieve_patient_info(query: str) -> dict:
     # Search Mem0
     results = mem0_client.search(
         query,
-        filters={"user_id": USER_ID},
+        user_id=USER_ID,
         top_k=5,
         threshold=0.7  # Higher threshold for more relevant results
     )

docs/cookbooks/integrations/openai-tool-calls.mdx

@@ -53,12 +53,34 @@ async function addUserPreferences() {
 await addUserPreferences();
 ```
 
-```json Output
+```json Output (Memories)
-{
+ [
-  "message": "Memory processing has been queued for background execution",
+  {
-  "status": "PENDING",
+    "id": "ff9f3367-9e83-415d-b9c5-dc8befd9a4b4",
-  "event_id": "9f8c2b1a-4e7d-4c3a-9b21-1a2b3c4d5e6f"
+    "data": { "memory": "Loves BMW, Audi, and Porsche" },
-}
+    "event": "ADD"
+  },
+  {
+    "id": "04172ce6-3d7b-45a3-b4a1-ee9798593cb4",
+    "data": { "memory": "Hates Mercedes" },
+    "event": "ADD"
+  },
+  {
+    "id": "db363a5d-d258-4953-9e4c-777c120de34d",
+    "data": { "memory": "Loves red cars and maroon cars" },
+    "event": "ADD"
+  },
+  {
+    "id": "5519aaad-a2ac-4c0d-81d7-0d55c6ecdba8",
+    "data": { "memory": "Has a budget of 120K to 150K USD" },
+    "event": "ADD"
+  },
+  {
+    "id": "523b7693-7344-4563-922f-5db08edc8634",
+    "data": { "memory": "Likes Audi the most" },
+    "event": "ADD"
+  }
+]
 ```
 </CodeGroup>
 ## Retrieving Memories
@@ -66,7 +88,7 @@ await addUserPreferences();
 Search for relevant memories based on the current user input:
 
 ```javascript
-const relevantMemories = await mem0Client.search(userInput, { filters: { user_id: USER_ID } });
+const relevantMemories = await mem0Client.search(userInput, { userId: USER_ID });
 ```
 
 ## Structured Responses with Zod
@@ -172,7 +194,7 @@ async function main(memory = false) {
   // Search for relevant memories
   let relevantMemories = []
   if (memory) {
-    relevantMemories = await mem0Client.search(input, { filters: { user_id: USER_ID } });
+    relevantMemories = await mem0Client.search(input, { userId: USER_ID });
   }
 
   const response = await openAIClient.responses.create({

docs/cookbooks/integrations/tavily-search.mdx

@@ -4,6 +4,8 @@ description: "Blend Tavily's realtime results with personal context stored in Me
 ---
 
 
+<Snippet file="security-compliance.mdx" />
+
 Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great WiFi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a 7-year-old daughter and recommends peanut-free options that align with her allergy.
 
 That's what we are going to build today, a Personalized Search Assistant powered by Mem0 for memory and [Tavily](https://tavily.com) for real-time search.

docs/cookbooks/operations/content-writing.mdx

@@ -217,7 +217,8 @@ def apply_writing_style(original_content):
 
     results = memory.search(
         query="What are my writing style preferences?",
-        filters={"user_id": USER_ID, "run_id": RUN_ID},
+        user_id=USER_ID,
+        run_id=RUN_ID,
     )
 
     if not results:

docs/cookbooks/operations/email-automation.mdx

@@ -314,16 +314,18 @@ class EmailProcessor:
             user_id (str): User identifier
             sender (str, optional): Filter by sender email address
         """
+        # In OSS, user_id is an explicit parameter (not inside filters)
         if not sender:
             results = self.memory.search(
                 query=query,
-                filters={"user_id": user_id, "memory_category": "email"},
+                user_id=user_id,
+                filters={"memory_category": "email"},
             )
         else:
             results = self.memory.search(
                 query=query,
+                user_id=user_id,
                 filters={
-                    "user_id": user_id,
                     "AND": [
                         {"memory_category": "email"},
                         {"sender": sender},
@@ -341,9 +343,10 @@ class EmailProcessor:
             subject (str): Email subject to match
             user_id (str): User identifier
         """
+        # In OSS, user_id is an explicit parameter
         thread = self.memory.get_all(
+            user_id=user_id,
             filters={
-                "user_id": user_id,
                 "AND": [
                     {"memory_category": "email"},
                     {"subject": {"icontains": subject}},

docs/cookbooks/operations/support-inbox.mdx

@@ -57,7 +57,7 @@ class CustomerSupportAIAgent:
         """
         # Start a streaming chat completion request to the AI
         stream = self.client.chat.completions.create(
-            model="gpt-5-mini",
+            model="gpt-4",
             stream=True,
             messages=[
                 {"role": "system", "content": "You are a customer support AI agent."},

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

@@ -238,11 +238,11 @@ client.search("preferences", filters={
 - **Use natural language**: Mem0 understands intent, so describe what you're looking for naturally
 - **Scope with user ID**: Always provide `user_id` to scope search to relevant memories
   - **Platform API**: Use `filters={"user_id": "alice"}`
-  - **OSS**: Use `filters={"user_id": "alice"}` (passing `user_id` as a top-level kwarg raises `ValueError` in v3)
+  - **OSS**: Use `user_id="alice"` as parameter
 - **Combine filters**: Use AND/OR logic to create precise queries (Platform)
 - **Consider wildcard filters**: Use wildcard filters (e.g., `run_id: "*"`) for broader matches
 - **Tune parameters**: Adjust `top_k` for result count, `threshold` for relevance cutoff
-- **Enable reranking**: Use `rerank=True` (default is `False`) when you have a reranker configured
+- **Enable reranking**: Use `rerank=True` (default) when you have a reranker configured
 
 <Callout type="tip" icon="plug">
   **MCP Alternative**: With <Link href="/platform/mem0-mcp">Mem0 MCP</Link>, AI agents can search their own memories proactively when needed.

docs/core-concepts/memory-types.mdx

@@ -60,7 +60,7 @@ import os
 
 from mem0 import Memory
 
-memory = Memory()
+memory = Memory(api_key=os.environ["MEM0_API_KEY"])
 
 # Sticky note: conversation memory
 memory.add(
@@ -72,7 +72,8 @@ memory.add(
 # Later in the session, pull long-term + session context
 results = memory.search(
     "Any hotel preferences?",
-    filters={"user_id": "alex", "run_id": "trip-planning-2025"},
+    user_id="alex",
+    run_id="trip-planning-2025",
 )
 ```
 

docs/platform/advanced-memory-operations.mdx

@@ -9,6 +9,7 @@ description: "Run richer add/search/update/delete flows on the managed platform
   **Prerequisites**
   - Platform workspace with API key
   - Python 3.10+ and Node.js 18+
+  - Async memories enabled in your dashboard (Settings → Memory Options)
 </Info>
 
 <Tip>
@@ -20,9 +21,9 @@ description: "Run richer add/search/update/delete flows on the managed platform
 <Tabs>
   <Tab title="Python">
 <Steps>
-<Step title="Install the SDK">
+<Step title="Install the SDK with async extras">
 ```bash
-pip install mem0ai
+pip install "mem0ai[async]"
 ```
 </Step>
 <Step title="Export your API key">
@@ -54,9 +55,9 @@ export MEM0_API_KEY="sk-platform-..."
 </Step>
 <Step title="Instantiate the client">
 ```typescript
-import MemoryClient from 'mem0ai';
+import { Memory } from "mem0ai";
 
-const memory = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
+const memory = new Memory({ apiKey: process.env.MEM0_API_KEY!, async: true });
 ```
 </Step>
 </Steps>
@@ -120,8 +121,10 @@ const result = await memory.add(conversation, {
 ```python
 matches = await memory.search(
     "Any food alerts?",
-    filters={"user_id": "traveler-42", "metadata.trip": "japan-2025"},
+    user_id="traveler-42",
+    filters={"metadata.trip": "japan-2025"},
     rerank=True,
+    include_vectors=False,
 )
 ```
 </Step>
@@ -129,7 +132,7 @@ matches = await memory.search(
 ```python
 await memory.update(
     memory_id=matches["results"][0]["id"],
-    data="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+    content="Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 )
 ```
 </Step>
@@ -140,15 +143,17 @@ await memory.update(
 <Step title="Search with metadata filters">
 ```typescript
 const matches = await memory.search("Any food alerts?", {
-  filters: { user_id: "traveler-42", "metadata.trip": "japan-2025" },
+  userId: "traveler-42",
+  filters: { "metadata.trip": "japan-2025" },
   rerank: true,
+  includeVectors: false,
 });
 ```
 </Step>
 <Step title="Apply an update">
 ```typescript
 await memory.update(matches.results[0].id, {
-  text: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
+  content: "Morgan avoids shellfish and prefers boutique hotels in central Tokyo.",
 });
 ```
 </Step>

docs/platform/features/advanced-retrieval.mdx

@@ -26,7 +26,7 @@ Reorders results using deep semantic understanding to put the most relevant memo
 results = client.search(
     query="What are my upcoming travel plans?",
     rerank=True,
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 
 # Before reranking:        After reranking:
@@ -52,7 +52,7 @@ results = client.search(
 results = client.search(
     query="How do I like my bedroom temperature?",
     rerank=True,           # Get most recent preferences first
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 
 # Finds: "Keep bedroom at 68°F", "Too cold last night at 65°F", etc.
@@ -63,7 +63,7 @@ results = client.search(
 # Find specific product issues with high precision
 results = client.search(
     query="Problems with premium subscription billing",
-    filters={"user_id": "customer456"},
+    user_id="customer456"
 )
 
 # Returns only relevant billing problems, not general questions
@@ -75,7 +75,7 @@ results = client.search(
 results = client.search(
     query="Patient allergies and contraindications",
     rerank=True,            # Most important info first
-    filters={"user_id": "patient789"},
+    user_id="patient789"
 )
 
 # Ensures critical allergy info appears first
@@ -87,7 +87,7 @@ results = client.search(
 results = client.search(
     query="Python programming progress and difficulties",
     rerank=True,           # Recent progress first
-    filters={"user_id": "student123"},
+    user_id="student123"
 )
 
 # Gets comprehensive view of Python learning journey
@@ -105,7 +105,7 @@ results = client.search(
 def quick_search(query, user_id):
     return client.search(
         query=query,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 
 # Reranked search - good for most applications
@@ -113,7 +113,7 @@ def standard_search(query, user_id):
     return client.search(
         query=query,
         rerank=True,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 
 # Reranked search - good for critical applications
@@ -121,7 +121,7 @@ def precise_search(query, user_id):
     return client.search(
         query=query,
         rerank=True,
-        filters={"user_id": user_id},
+        user_id=user_id
     )
 ```
 
@@ -129,23 +129,23 @@ def precise_search(query, user_id):
 // Basic search - good for exploration
 function quickSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId
     });
 }
 
 // Reranked search - good for most applications
 function standardSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId,
-        rerank: true,
+        rerank: true
     });
 }
 
 // Reranked search - good for critical applications
 function preciseSearch(query, userId) {
     return client.search(query, {
-        filters: { user_id: userId },
+        user_id: userId,
-        rerank: true,
+        rerank: true
     });
 }
 ```
@@ -178,7 +178,7 @@ start_time = time.time()
 results = client.search(
     query="user preferences",
     rerank=True,         # +150ms
-    filters={"user_id": "user123"},
+    user_id="user123"
 )
 latency = time.time() - start_time
 print(f"Search completed in {latency:.2f}s")

docs/platform/features/contextual-add.mdx

@@ -25,7 +25,7 @@ const messages = [
     {"role": "assistant", "content": "Great! I'll remember your preference for Italian cuisine."}
 ];
 
-await client.add(messages, { userId: "user123" });
+await client.add(messages, { userId: "user123", version: "v2" });
 ```
 </CodeGroup>
 
@@ -65,14 +65,14 @@ const messages1 = [
     {"role": "user", "content": "Hi, I'm Sarah from New York"},
     {"role": "assistant", "content": "Hello Sarah! Nice to meet you."}
 ];
-await client.add(messages1, { userId: "sarah" });
+await client.add(messages1, { userId: "sarah", version: "v2" });
 
 // Later interaction - just send new messages
 const messages2 = [
     {"role": "user", "content": "I'm planning a trip to Italy next month"},
     {"role": "assistant", "content": "How exciting! Italy is beautiful this time of year."}
 ];
-await client.add(messages2, { userId: "sarah" });
+await client.add(messages2, { userId: "sarah", version: "v2" });
 // Mem0 automatically knows Sarah is from New York and can use this context
 ```
 </CodeGroup>
@@ -104,7 +104,7 @@ const messages = [
     {"role": "assistant", "content": "I've noted your allergies for future reference."}
 ];
 
-await client.add(messages, { userId: "user123" });
+await client.add(messages, { userId: "user123", version: "v2" });
 // This allergy info will be available in ALL future interactions
 ```
 </CodeGroup>
@@ -143,21 +143,21 @@ const messages1 = [
     {"role": "user", "content": "I want to plan a 5-day trip to Tokyo"},
     {"role": "assistant", "content": "Perfect! Let's plan your Tokyo adventure."}
 ];
-await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024" });
+await client.add(messages1, { userId: "user123", runId: "tokyo-trip-2024", version: "v2" });
 
 // Later in the same trip planning session
 const messages2 = [
     {"role": "user", "content": "I prefer staying near Shibuya"},
     {"role": "assistant", "content": "Great choice! Shibuya is very convenient."}
 ];
-await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024" });
+await client.add(messages2, { userId: "user123", runId: "tokyo-trip-2024", version: "v2" });
 
 // Different session for work project (separate context)
 const workMessages = [
     {"role": "user", "content": "Let's discuss the Q4 marketing strategy"},
     {"role": "assistant", "content": "Sure! What are your main goals for Q4?"}
 ];
-await client.add(workMessages, { userId: "user123", runId: "q4-marketing" });
+await client.add(workMessages, { userId: "user123", runId: "q4-marketing", version: "v2" });
 ```
 </CodeGroup>
 

docs/platform/features/criteria-retrieval.mdx

@@ -182,7 +182,7 @@ If no criteria are defined for a project, search behaves normally based on seman
 This lets you prioritize memories that align with your agent's goals and not just those that look similar to the query.
 
 <Note>
-Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query. `use_criteria` is a server-side parameter passed through to the Platform API — it is not a typed option in the SDK's `SearchMemoryOptions` interface, but the server accepts and processes it when included in the request body.
+Criteria retrieval is automatically enabled when criteria are defined in your project. Use `use_criteria=False` in search to temporarily disable it for a specific query.
 </Note>
 
 

docs/platform/features/feedback-mechanism.mdx

@@ -150,7 +150,7 @@ Handle potential errors when submitting feedback:
 
 ```python Python
 from mem0 import MemoryClient
-from mem0.exceptions import MemoryNotFoundError, NetworkError
+from mem0.exceptions import MemoryNotFoundError, APIError
 
 client = MemoryClient(api_key="your_api_key")
 
@@ -163,8 +163,8 @@ try:
     print("Feedback submitted successfully")
 except MemoryNotFoundError:
     print("Memory not found")
-except NetworkError as e:
+except APIError as e:
-    print(f"Network error: {e}")
+    print(f"API error: {e}")
 except Exception as e:
     print(f"Unexpected error: {e}")
 ```

docs/platform/features/group-chat.mdx

@@ -111,37 +111,32 @@ print(all_memories)
 ```
 
 ```json Output
-{
+[
-    "count": 3,
+    {
-    "next": null,
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-    "previous": null,
+        "memory": "suggests considering Angular because it has great enterprise support",
-    "results": [
+        "user_id": "charlie",
-        {
+        "run_id": "group_chat_1",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00"
-            "user_id": "charlie",
+    },
-            "run_id": "group_chat_1",
+    {
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+        "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
+        "memory": "prefers Vue.js for our use case",
-        },
+        "user_id": "bob",
-        {
+        "run_id": "group_chat_1",
-            "id": "1d8b8f39-7b17-4d18-8632-ab1c64fa35b9",
+        "created_at": "2025-06-21T05:51:08.675301-07:00",
-            "memory": "prefers Vue.js for our use case",
+        "updated_at": "2025-06-21T05:51:09.319269-07:00",
-            "user_id": "bob",
+    },
-            "run_id": "group_chat_1",
+    {
-            "created_at": "2025-06-21T05:51:08.675301-07:00",
+        "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
-            "updated_at": "2025-06-21T05:51:09.319269-07:00"
+        "memory": "prefers using React for the frontend",
-        },
+        "user_id": "alice",
-        {
+        "run_id": "group_chat_1",
-            "id": "4d82478a-8d50-47e6-9324-1f65efff5829",
+        "created_at": "2025-06-21T05:51:05.943223-07:00",
-            "memory": "prefers using React for the frontend",
+        "updated_at": "2025-06-21T05:51:06.982539-07:00",
-            "user_id": "alice",
+    }
-            "run_id": "group_chat_1",
+]
-            "created_at": "2025-06-21T05:51:05.943223-07:00",
-            "updated_at": "2025-06-21T05:51:06.982539-07:00"
-        }
-    ]
-}
 ```
 
 </CodeGroup>
@@ -166,21 +161,17 @@ print(charlie_memories)
 ```
 
 ```json Output
-{
+[
-    "count": 1,
+    {
-    "next": null,
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-    "previous": null,
+        "memory": "suggests considering Angular because it has great enterprise support",
-    "results": [
+        "user_id": "charlie",
-        {
+        "run_id": "group_chat_1",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00",
-            "user_id": "charlie",
+
-            "run_id": "group_chat_1",
+    }
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+]
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
-        }
-    ]
-}
 ```
 
 </CodeGroup>
@@ -208,18 +199,16 @@ print(search_response)
 ```
 
 ```json Output
-{
+[
-    "results": [
+    {
-        {
+        "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
-            "id": "147559a8-c5f7-44d0-9418-91f53f7a89a4",
+        "memory": "suggests considering Angular because it has great enterprise support",
-            "memory": "suggests considering Angular because it has great enterprise support",
+        "user_id": "charlie",
-            "user_id": "charlie",
+        "run_id": "group_chat_1",
-            "run_id": "group_chat_1",
+        "created_at": "2025-06-21T05:51:11.007223-07:00",
-            "created_at": "2025-06-21T05:51:11.007223-07:00",
+        "updated_at": "2025-06-21T05:51:11.626562-07:00",
-            "updated_at": "2025-06-21T05:51:11.626562-07:00"
+    }
-        }
+]
-    ]
-}
 ```
 
 </CodeGroup>

docs/platform/features/memory-export.mdx

@@ -251,6 +251,7 @@ You can apply various filters to customize which memories are included in the ex
 - `user_id`: Filter memories by specific user
 - `agent_id`: Filter memories by specific agent
 - `run_id`: Filter memories by specific run
+- `session_id`: Filter memories by specific session
 - `created_at`: Filter memories by date
 
 <Note>

docs/platform/mem0-mcp.mdx

@@ -9,7 +9,7 @@ estimatedTime: "~2 minutes"
   **Prerequisites**
   - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Sign up here</a>)
   - API key (<a href="https://app.mem0.ai/settings/api-keys?utm_source=oss&utm_medium=platform-mem0-mcp" rel="nofollow">Get one from dashboard</a>)
-  - Node.js 18+ (for npx)
+  - Node.js 14+ (for npx)
   - An MCP-compatible client (Claude, Claude Code, Codex, Cursor, Windsurf, VS Code, OpenCode)
 </Info>
 

docs/platform/quickstart.mdx

@@ -15,7 +15,7 @@ Get started with Mem0 Platform's hosted API in under 5 minutes. This guide shows
 
 - Mem0 Platform account (<a href="https://app.mem0.ai?utm_source=oss&utm_medium=platform-quickstart" rel="nofollow">Sign up here</a>)
 - API key (<a href="https://app.mem0.ai/dashboard/settings?tab=api-keys&subtab=configuration" rel="nofollow">Get one from dashboard</a>)
-- Python 3.10+, Node.js 18+, or cURL
+- Python 3.10+, Node.js 14+, or cURL
 
 ## Installation
 

mem0-ts/package.json

@@ -1,6 +1,6 @@
 {
   "name": "mem0ai",
-  "version": "3.0.12",
+  "version": "3.0.11",
   "description": "The Memory Layer For Your AI Apps",
   "main": "./dist/index.js",
   "module": "./dist/index.mjs",

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "mem0ai"
-version = "2.0.10"
+version = "2.0.9"
 description = "Long-term memory for AI Agents"
 authors = [
     { name = "Mem0", email = "support@mem0.ai" }