docs: add SECURITY.md and refresh contribution guidelines

- Add SECURITY.md with a private disclosure policy (GitHub Security Advisories + support@mem0.ai, 72h acknowledgement window). - Expand CONTRIBUTING.md: issue-first workflow, mandatory CLA, separate Python (hatch) and TypeScript (pnpm) SDK setup, good-practices section, and a PR checklist. - Update docs/contributing/development.mdx to match, adding the missing TypeScript SDK environment setup.
2026-06-25 14:41:42 +05:30
126 changed files with 1260 additions and 1982 deletions
@@ -50,7 +50,6 @@ Provide conversation messages for Mem0 to extract memories from. At least one en
 | `app_id` | string | No* | Associates the memory with an app. |
 | `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
 | `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
 | `expiration_date` | string | Optional | Date in `YYYY-MM-DD` format. The memory is visible through this date and hidden by default after it passes. |
 > \* At least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`) is required.
@@ -84,11 +83,3 @@ The request is queued for background processing. The response contains an `event
 <Info>
 Poll the event status via `GET /v1/event/{event_id}/`. Status will be `SUCCEEDED` or `FAILED` once processing completes.
 </Info>
 <Info>
 Memories with `expiration_date` remain stored after they expire. Search and get-all hide them by default; pass `show_expired: true` to include them.
 </Info>
 <Info>
 Python uses `expiration_date`; TypeScript uses `expirationDate`.
 </Info>
@@ -4,4 +4,4 @@ description: "Submit an export job to create a structured memory export using a
 openapi: post /v1/exports/
 ---
-Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `app_id`, or `run_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
@@ -6,10 +6,6 @@ openapi: post /v3/memories/
 List memories scoped by filters with paginated results. Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400.
 Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
 Python uses `show_expired`; TypeScript uses `showExpired`.
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 - `in`: Matches any of the values specified
@@ -36,7 +32,6 @@ memories = client.get_all(
            }
        ]
    },
    show_expired=False,
    page=1,
    page_size=50
 )
@@ -51,14 +46,12 @@ memories = client.get_all(
        {
            "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
            "memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
            "expiration_date": null,
            "created_at": "2024-07-01T12:00:00Z",
            "updated_at": "2024-07-01T12:00:00Z"
        },
        {
            "id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
            "memory": "Alex prefers vegetarian restaurants",
            "expiration_date": null,
            "created_at": "2024-07-05T15:30:00Z",
            "updated_at": "2024-07-05T15:30:00Z"
        }
@@ -4,4 +4,4 @@ description: "Retrieve the latest structured memory export after submitting an e
 openapi: post /v1/exports/get
 ---
-Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `agent_id`, `app_id`, `run_id`, `created_at`, or `updated_at` to get the most recent export matching your filters.
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.
@@ -8,10 +8,6 @@ Relevance-ranked hybrid search across stored memories. V3 uses multi-signal retr
 Entity IDs (`user_id`, `agent_id`, `app_id`, `run_id`) **must** be passed inside the `filters` object — top-level entity IDs are rejected with 400. At least one entity ID is required.
 Expired memories are hidden by default. Pass `show_expired: true` to include memories whose `expiration_date` has passed.
 Python uses `show_expired`; TypeScript uses `showExpired`.
 The `filters` object supports complex logical operations (AND, OR, NOT) and comparison operators:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
@@ -24,17 +20,16 @@ The `filters` object supports complex logical operations (AND, OR, NOT) and comp
 ### Search parameter defaults
-| Parameter | Default |
+| Parameter | V1/V2 | V3 |
-| --- | --- |
+| --- | --- | --- |
-| `top_k` | `10` (range 1–1000) |
+| `top_k` | Supported (default 10) | Supported (1-1000, default 10) |
-| `threshold` | `0.1` (pass `0.0` to disable) |
+| `threshold` | No default | Default `0.1` (pass `0.0` to disable) |
-| `rerank` | `false` (pass `true` to enable) |
+| `rerank` | Default `true` | Default `false` (pass `true` to enable) |
 <CodeGroup>
 ```python Platform API Example
 related_memories = client.search(
    query="What are Alice's hobbies?",
    show_expired=False,
    filters={
        "OR": [
            {
@@ -59,7 +54,6 @@ related_memories = client.search(
        "category": "hobbies"
      },
      "score": 0.82,
      "expiration_date": null,
      "created_at": "2024-07-26T10:29:36.630547-07:00",
      "updated_at": null,
      "categories": ["hobbies"]
@@ -1,14 +1,5 @@
 ---
 title: 'Update Memory'
-description: "Update the content, metadata, timestamp, or expiration date of a single memory by its unique ID using the PUT endpoint."
+description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
---
+---
 Use this endpoint to update mutable memory fields. To make a memory expire, set `expiration_date` to a `YYYY-MM-DD` date. To make it permanent again, send `expiration_date: null`.
 ```python
 client.update("mem_123", expiration_date="2030-01-31")
 client.update("mem_123", expiration_date=None)
 ```
 TypeScript uses `expirationDate`.
@@ -1,5 +0,0 @@
 ---
 title: "Remove Organization Member"
 description: "Remove a member from an organization to revoke their access to its projects and resources."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/members/"
 ---
@@ -1,5 +0,0 @@
 ---
 title: "Update Organization Member"
 description: "Update an existing member's role within an organization to change their permissions and access level."
 openapi: "put /api/v1/orgs/organizations/{org_id}/members/"
 ---
@@ -14,7 +14,7 @@ Organizations and projects are **optional** features. You can use Mem0 without t
 ## Key Capabilities
- **Multi-org/project Support**: Organization and project are resolved automatically from your API key via `/v1/ping/` — no org or project params are accepted by `MemoryClient.__init__`. Use a project-specific API key to target a particular project.
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
 - **Member Management**: Control access to data through organization and project membership
 - **Access Control**: Only members can access memories and data within their organization/project scope
 - **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
@@ -79,7 +79,7 @@ new_project = client.project.create(
 ### Update Project Settings
-Modify project configuration including custom instructions, categories, language preferences, retrieval criteria, and memory decay:
+Modify project configuration including custom instructions, categories, and language preferences:
 ```python
 # Update project with custom categories
@@ -98,17 +98,6 @@ client.project.update(
 # Use the input language for memory storage and retrieval
 client.project.update(multilingual=True)
 # Set retrieval criteria to control which memories are surfaced in search
 client.project.update(
    retrieval_criteria=[
        {"name": "relevance", "description": "How directly relevant this memory is to the current topic or user query", "weight": 3},
        {"name": "access_frequency", "description": "How often this memory has been accessed or surfaced recently", "weight": 1}
    ]
 )
 # Enable Memory Decay (boosts recently-accessed memories at search time)
 client.project.update(decay=True)
 # Update multiple settings at once
 client.project.update(
    custom_instructions="...",
@@ -120,34 +109,6 @@ client.project.update(
 )
 ```
 #### Set Retrieval Criteria
 `retrieval_criteria` is a per-project list of dictionaries (`List[Dict]`) that shapes how memories are ranked and filtered during search. Each dictionary has three fields: `name` (identifier), `description` (interpreted by the LLM to score each memory), and `weight` (relative influence on the final score). Use this to focus retrieval on intent-aligned or signal-specific memories:
 ```python
 client.project.update(
    retrieval_criteria=[
        {
            "name": "joy",
            "description": "Measure the intensity of positive emotions such as happiness, excitement, or amusement expressed in the memory. A higher score reflects greater joy.",
            "weight": 3
        },
        {
            "name": "curiosity",
            "description": "Assess the extent to which the memory reflects inquisitiveness or interest in exploring new information. A higher score reflects stronger curiosity.",
            "weight": 2
        },
        {
            "name": "access_frequency",
            "description": "How often this memory has been accessed or surfaced recently.",
            "weight": 1
        }
    ]
 )
 ```
 Pass an empty list to clear all criteria and restore default retrieval behaviour.
 #### Toggle Memory Decay
 `decay` is a per-project boolean that turns on [Memory Decay](/platform/features/memory-decay) — a search-time ranking bias that reinforces recently-accessed memories and gently dampens stale ones. The flag is `false` by default; set it via the same project-update endpoint:
@@ -1,5 +0,0 @@
 ---
 title: "Remove Project Member"
 description: "Remove a member from a project to revoke their access to its memories, configuration, and resources."
 openapi: "delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
 ---
@@ -1,5 +0,0 @@
 ---
 title: "Update Project Member"
 description: "Update an existing member's role within a project to change their permissions and access level."
 openapi: "put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/"
 ---
@@ -1,5 +0,0 @@
 ---
 title: "Update Project"
 description: "Update a project's settings, including name, custom instructions, and other configuration options."
 openapi: "patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/"
 ---
@@ -113,7 +113,7 @@ Launched a unified Mem0 plugin across three major AI development environments
 Major expansion of the provider ecosystem:
- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
+- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE)
 - **Turbopuffer** — New vector database provider for Python SDK
 - **MiniMax** — New LLM provider with dedicated AWS Bedrock support
 - **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK
@@ -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:**
@@ -136,7 +121,7 @@ mode: "wide"
 **New Features:**
 - **Memory:** Warn at init time when hybrid/BM25 search silently degrades to semantic-only because the configured vector store does not implement `keyword_search`. Affected stores: Chroma, FAISS, Cassandra, LangChain, Neptune Analytics, S3 Vectors, Supabase, TurboPuffer, Valkey ([#5444](https://github.com/mem0ai/mem0/pull/5444))
- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_details` dict with `semantic_score`, `bm25_score`, `entity_boost`, `raw_score`, `max_possible_score`, `final_score`, and `threshold` so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
+- **Memory:** Add opt-in `explain=True` parameter to `Memory.search()` and `AsyncMemory.search()`. When enabled, each result includes a `score_breakdown` dict with `semantic`, `keyword` (normalized BM25), `entity_boost`, and `temporal_boost` signals so callers can understand and tune retrieval ranking ([#5102](https://github.com/mem0ai/mem0/pull/5102))
 **Bug Fixes:**
 - **Vector Stores:** Normalize similarity scores to `[0, 1]` (higher = better) consistently across all backends. 11 adapters previously returned raw distance metrics (lower = better) — FAISS, Chroma, Milvus, Redis, Cassandra, PGVector, S3 Vectors, Supabase, Valkey, Azure MySQL, and Vertex AI Vector Search — causing incorrect ranking in multi-store setups ([#5391](https://github.com/mem0ai/mem0/pull/5391))
@@ -244,7 +229,7 @@ mode: "wide"
 **Improvements:**
 - **Telemetry:** Sample OSS hot-path events at 10% via PostHog `before_send` hook to reduce event volume ([#4771](https://github.com/mem0ai/mem0/pull/4771))
-See the [OSS v2 to v3 migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
+See the [OSS v1 to v2 migration guide](https://docs.mem0.ai/migration/oss-v1-to-v2) and [Platform migration guide](https://docs.mem0.ai/migration/platform-v2-to-v3) for upgrade instructions.
 </Update>
@@ -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:**
@@ -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>
@@ -1,50 +0,0 @@
 ---
 title: "FastEmbed"
 description: "Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU."
 ---
 You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.
 ### Installation
 ```bash
 pip install fastembed
 ```
 ### Usage
 <CodeGroup>
 ```python Python
 import os
 from mem0 import Memory
 os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
 config = {
    "embedder": {
        "provider": "fastembed",
        "config": {
            "model": "thenlper/gte-large"
        }
    }
 }
 m = Memory.from_config(config)
 messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="john")
 ```
 </CodeGroup>
 ### Config
 Here are the parameters available for configuring FastEmbed embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
 | `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |
@@ -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>
@@ -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` |
@@ -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
@@ -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            |
@@ -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,
    },
@@ -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
@@ -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,
    },
@@ -4,12 +4,9 @@ description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language
 ---
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 In the TypeScript SDK, run LiteLLM as a [proxy server](https://docs.litellm.ai/docs/simple_proxy) (an OpenAI-compatible endpoint) and point Mem0 at it via `LITELLM_API_BASE` (defaults to `http://localhost:4000`).
 ## Usage
-<CodeGroup>
+```python
 ```python Python
 import os
 from mem0 import Memory
@@ -36,33 +33,6 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 ```typescript TypeScript
 import { Memory } from 'mem0ai/oss';
 // Point Mem0 at your LiteLLM proxy. apiKey defaults to "sk-anything"
 // (the proxy handles real auth); baseURL defaults to http://localhost:4000.
 const config = {
  llm: {
    provider: 'litellm',
    config: {
      apiKey: process.env.LITELLM_API_KEY || 'sk-anything',
      baseURL: process.env.LITELLM_API_BASE || 'http://localhost:4000',
      model: 'gpt-5-mini',
    },
  },
 };
 const memory = new Memory(config);
 const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ];
 await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
 ```
 </CodeGroup>
 ## Config
 All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).
@@ -7,8 +7,7 @@ To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment var
 ## Usage
-<CodeGroup>
+```python
 ```python Python
 import os
 from mem0 import Memory
@@ -37,37 +36,9 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 ```typescript TypeScript
 import { Memory } from 'mem0ai/oss';
 const config = {
  llm: {
    provider: 'minimax',
    config: {
      apiKey: process.env.MINIMAX_API_KEY || '',
      model: 'MiniMax-M2.7',
      temperature: 0.2,
      maxTokens: 2000,
      topP: 1.0,
    },
  },
 };
 const memory = new Memory(config);
 const messages = [
  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
 ];
 await memory.add(messages, { userId: 'alice', metadata: { category: 'movies' } });
 ```
 </CodeGroup>
 You can also configure the API base URL in the config:
-<CodeGroup>
+```python
 ```python Python
 config = {
    "llm": {
        "provider": "minimax",
@@ -80,20 +51,6 @@ config = {
 }
 ```
 ```typescript TypeScript
 const config = {
  llm: {
    provider: 'minimax',
    config: {
      model: 'MiniMax-M2.7',
      baseURL: 'https://your-custom-endpoint.com',
      apiKey: 'your-api-key', // alternatively to using the environment variable
    },
  },
 };
 ```
 </CodeGroup>
 ## Config
 All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).
@@ -20,7 +20,7 @@ config = {
    "llm": {
        "provider": "xai",
        "config": {
-            "model": "grok-4.3",
+            "model": "grok-3-beta",
            "temperature": 0.1,
            "max_tokens": 2000,
        }
@@ -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}>
@@ -0,0 +1,226 @@
 ---
 title: LLM as Reranker
 description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
 ---
 <Warning>
 **This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
 </Warning>
 LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
 ## Supported LLM Providers
 Any LLM provider supported by Mem0 can be used for reranking:
 - **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
 - **Anthropic**: Claude models
 - **Together**: Open-source models
 - **Groq**: Fast inference
 - **Ollama**: Local models
 - And more...
 ## Configuration
 ```python Python
 from mem0 import Memory
 config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_memories",
            "path": "./chroma_db"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o-mini"
        }
    },
    "reranker": {
        "provider": "llm",
        "config": {
            "model": "gpt-4o-mini",
            "provider": "openai",
            "api_key": "your-openai-api-key",  # or set OPENAI_API_KEY
            "top_k": 5,
            "temperature": 0.0
        }
    }
 }
 memory = Memory.from_config(config)
 ```
 ## Custom Scoring Prompt
 You can provide a custom prompt for relevance scoring:
 ```python Python
 custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
 Query: "{query}"
 Document: "{document}"
 Score from 0.0 to 1.0 where:
 - 1.0: Perfect match, directly answers the query
 - 0.8-0.9: Highly relevant, good match  
 - 0.6-0.7: Moderately relevant, partial match
 - 0.4-0.5: Slightly relevant, limited useful information
 - 0.0-0.3: Not relevant or no useful information
 Provide only a single numerical score between 0.0 and 1.0."""
 config["reranker"]["config"]["scoring_prompt"] = custom_prompt
 ```
 ## Usage Example
 ```python Python
 import os
 from mem0 import Memory
 # Set API key
 os.environ["OPENAI_API_KEY"] = "your-api-key"
 # Initialize memory with LLM reranker
 config = {
    "vector_store": {"provider": "chroma"},
    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
    "reranker": {
        "provider": "llm",
        "config": {
            "model": "gpt-4o-mini",
            "provider": "openai",
            "temperature": 0.0
        }
    }
 }
 memory = Memory.from_config(config)
 # Add memories
 messages = [
    {"role": "user", "content": "I'm learning Python programming"},
    {"role": "user", "content": "I find object-oriented programming challenging"}, 
    {"role": "user", "content": "I love hiking in national parks"}
 ]
 memory.add(messages, user_id="david")
 # Search with LLM reranking
 results = memory.search("What programming topics is the user studying?", filters={"user_id": "david"})
 for result in results['results']:
    print(f"Memory: {result['memory']}")
    print(f"Vector Score: {result['score']:.3f}")
    print(f"Rerank Score: {result['rerank_score']:.3f}")
    print()
 ```
 ```text Output
 Memory: I'm learning Python programming
 Vector Score: 0.856
 Rerank Score: 0.920
 Memory: I find object-oriented programming challenging
 Vector Score: 0.782
 Rerank Score: 0.850
 ```
 ## Domain-Specific Scoring
 Create specialized scoring for your domain:
 ```python Python
 medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
 Clinical Query: "{query}"
 Medical Record: "{document}"
 Consider:
 - Clinical relevance and accuracy
 - Patient safety implications
 - Diagnostic value
 - Treatment relevance
 Score from 0.0 to 1.0. Provide only the numerical score."""
 config = {
    "reranker": {
        "provider": "llm",
        "config": {
            "model": "gpt-4o-mini",
            "provider": "openai",
            "scoring_prompt": medical_prompt,
            "temperature": 0.0
        }
    }
 }
 ```
 ## Multiple LLM Providers
 Use different LLM providers for reranking:
 ```python Python
 # Using Anthropic Claude
 anthropic_config = {
    "reranker": {
        "provider": "llm",
        "config": {
            "model": "claude-3-haiku-20240307",
            "provider": "anthropic",
            "temperature": 0.0
        }
    }
 }
 # Using local Ollama model
 ollama_config = {
    "reranker": {
        "provider": "llm",
        "config": {
            "model": "llama2:7b",
            "provider": "ollama",
            "temperature": 0.0
        }
    }
 }
 ```
 ## Configuration Parameters
 | Parameter | Description | Type | Default |
 |-----------|-------------|------|---------|
 | `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
 | `provider` | LLM provider name | `str` | `"openai"` |
 | `api_key` | API key for the LLM provider | `str` | `None` |
 | `top_k` | Maximum documents to return | `int` | `None` |
 | `temperature` | Temperature for LLM generation | `float` | `0.0` |
 | `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
 | `scoring_prompt` | Custom prompt template | `str` | Default prompt |
 ## Advantages
 - **Maximum Flexibility**: Custom prompts for any use case
 - **Domain Expertise**: Leverage LLM knowledge for specialized domains
 - **Interpretability**: Understand scoring through prompt engineering
 - **Multi-criteria**: Score based on multiple relevance factors
 ## Considerations
 - **Latency**: Higher latency than specialized rerankers
 - **Cost**: LLM API costs per reranking operation
 - **Consistency**: May have slight variations in scoring
 - **Prompt Engineering**: Requires careful prompt design
 ## Best Practices
 1. **Temperature**: Use 0.0 for consistent scoring
 2. **Prompt Design**: Be specific about scoring criteria
 3. **Token Efficiency**: Keep prompts concise to reduce costs
 4. **Caching**: Cache results for repeated queries when possible
 5. **Fallback**: Handle API errors gracefully
@@ -46,7 +46,7 @@ Here are the parameters available for configuring Baidu VectorDB:
 | `account` | Baidu VectorDB account name | `root` |
 | `api_key` | API key for accessing Baidu VectorDB | Required |
 | `database_name` | Name of the database | `mem0` |
-| `table_name` | Name of the table | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Distance metric for similarity search | `L2` |
@@ -56,8 +56,6 @@ Here are the parameters available for configuring Elasticsearch:
 | `api_key`              | API key for authentication                         | `None`        |
 | `user`                 | Username for basic authentication                  | `None`        |
 | `password`             | Password for basic authentication                  | `None`        |
 | `use_ssl`              | Whether to use SSL for the connection              | `True`        |
 | `ca_certs`             | Path to CA bundle for SSL certificate verification | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
 | `custom_search_query`  | Function returning a custom search query           | `None`        |
@@ -55,7 +55,6 @@ Here are the parameters available for configuring FAISS:
 | `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
 | `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
 | `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 ### Performance Considerations
@@ -47,12 +47,12 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 ```typescript TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 import { OpenAIEmbeddings } from "@langchain/openai";
-import { MemoryVectorStore } from "langchain/vectorstores/memory";
+import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
 const embeddings = new OpenAIEmbeddings();
-const vectorStore = new MemoryVectorStore(embeddings);
+const vectorStore = new LangchainVectorStore(embeddings);
 const config = {
    "vector_store": {
@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | db_name | Name of the MongoDB database | `"mem0_db"` |
-| collection_name | Name of the MongoDB collection | `"mem0"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
 | embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
+| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
-> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
+> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
@@ -21,33 +21,34 @@ from mem0 import Memory
 load_dotenv()
 config = {
-    "vector_store": {
+"vector_store": {
-        "provider": "pgvector",
+"provider": "pgvector",
-        "config": {
+"config": {
-            "connection_string": os.environ["DATABASE_URL"],
+"connection_string": os.environ["DATABASE_URL"],
-            "collection_name": "memories",
+"collection_name": "memories",
-            "embedding_model_dims": 1536,
+"embedding_model_dims": 1536,
-            "hnsw": True,
+"hnsw": True,
-        },
+},
-    },
+},
 }
 m = Memory.from_config(config)
 messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
+{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 results = m.search(
-    "What movies should I recommend?",
+"What movies should I recommend?",
-    filters={"user_id": "alice"},
+filters={"user_id": "alice"},
 )
 print(results)
-```
+
 ````
 ```typescript TypeScript
 import "dotenv/config";
@@ -86,7 +87,7 @@ const results = await m.search("What movies should I recommend?", {
 });
 console.log(results);
-```
+````
 </CodeGroup>
@@ -10,7 +10,7 @@ description: "Use AWS Neptune Analytics as a vector store in Mem0, combining gra
 ## Installation
 ```bash
-pip install mem0ai[vector-stores]
+pip install mem0ai[vector_stores]
 ```
 ## Usage
@@ -15,27 +15,27 @@ from mem0 import Memory
 os.environ["OPENAI_API_KEY"] = "sk-xx"
 config = {
-    "vector_store": {
+"vector_store": {
-        "provider": "pgvector",
+"provider": "pgvector",
-        "config": {
+"config": {
-            "user": "test",
+"user": "test",
-            "password": "123",
+"password": "123",
-            "host": "127.0.0.1",
+"host": "127.0.0.1",
-            "port": "5432",
+"port": "5432",
-        },
+},
-    }
+}
 }
 m = Memory.from_config(config)
 messages = [
-    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
+{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."},
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
-```
+````
 ```typescript TypeScript
 import { Memory } from "mem0ai/oss";
@@ -62,7 +62,7 @@ const messages = [
 ];
 await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
-```
+````
 </CodeGroup>
@@ -18,9 +18,7 @@ os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
 config = {
    "vector_store": {
        "provider": "upstash_vector",
-        "config": {
+        "enable_embeddings": True,
            "enable_embeddings": True,
        }
    }
 }
@@ -9,7 +9,7 @@ description: "Use Valkey as an open-source vector store in Mem0 for high-perform
 ## Installation
 ```bash
-pip install mem0ai[vector-stores]
+pip install mem0ai[vector_stores]
 ```
 ## Usage
@@ -51,7 +51,7 @@ Here are the parameters available for configuring Valkey:
 | `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` |
-| `timezone` | Timezone for timestamp handling | `UTC` |
+| `distance_metric` | Distance metric for vector similarity | `cosine` |
 ## Cluster Mode
@@ -24,7 +24,7 @@ config = {
            "deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID",  # Required: Deployment-specific ID
            "project_id": "YOUR_PROJECT_ID",              # Required: Google Cloud project ID
            "project_number": "YOUR_PROJECT_NUMBER",      # Required: Google Cloud project number
-            "region": "YOUR_REGION",                      # Required: Google Cloud region
+            "region": "YOUR_REGION",                      # Optional: Defaults to GOOGLE_CLOUD_REGION
            "credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
            "vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
        }
@@ -45,6 +45,5 @@ m.add("Your text here", user_id="user", metadata={"category": "example"})
 | `project_id` | Google Cloud project ID | Yes |
 | `project_number` | Google Cloud project number | Yes |
 | `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
-| `region` | Google Cloud region | Yes |
+| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
 | `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |
 | `service_account_json` | Service account credentials as a dictionary (alternative to `credentials_path`) | `None` |
@@ -7,7 +7,7 @@ description: "Use Weaviate as an open-source vector search engine in Mem0 for st
 ### Installation
 ```bash
-pip install weaviate-client
+pip install weaviate weaviate-client
 ```
 ### Usage
@@ -48,5 +48,4 @@ Here are the parameters available for configuring Weaviate:
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `cluster_url` | URL for the Weaviate server | `None` |
-| `auth_client_secret` | API key for Weaviate authentication | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |
 | `additional_headers` | Additional headers to include in requests (`Dict[str, str]`) | `None` |
@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, and an in-memory store.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
 </Note>
 <CardGroup cols={3}>
@@ -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}`)
@@ -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>
@@ -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
 )
@@ -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'):
@@ -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
    )
@@ -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({
@@ -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.
@@ -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:
@@ -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}},
@@ -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."},
@@ -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.
@@ -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",
 )
 ```
@@ -123,7 +123,8 @@
                    "icon": "arrow-right",
                    "pages": [
                      "migration/platform-v2-to-v3",
-                      "migration/oss-to-platform"
+                      "migration/oss-to-platform",
                      "migration/api-changes"
                    ]
                  },
                  {
@@ -272,8 +273,7 @@
                              "components/embedders/models/lmstudio",
                              "components/embedders/models/together",
                              "components/embedders/models/langchain",
-                              "components/embedders/models/aws_bedrock",
+                              "components/embedders/models/aws_bedrock"
                              "components/embedders/models/fastembed"
                            ]
                          }
                        ]
@@ -533,8 +533,6 @@
                      "api-reference/organization/get-org",
                      "api-reference/organization/get-org-members",
                      "api-reference/organization/add-org-member",
                      "api-reference/organization/update-org-member",
                      "api-reference/organization/remove-org-member",
                      "api-reference/organization/delete-org"
                    ]
                  },
@@ -547,9 +545,6 @@
                      "api-reference/project/get-project",
                      "api-reference/project/get-project-members",
                      "api-reference/project/add-project-member",
                      "api-reference/project/update-project",
                      "api-reference/project/update-project-member",
                      "api-reference/project/remove-project-member",
                      "api-reference/project/delete-project"
                    ]
                  },
@@ -629,10 +624,6 @@
    ]
  },
  "redirects": [
    {
      "source": "/components/rerankers/models/llm",
      "destination": "/components/rerankers/models/llm_reranker"
    },
    {
      "source": "/migration/breaking-changes",
      "destination": "/"
@@ -641,10 +632,6 @@
      "source": "/migration/v0-to-v1",
      "destination": "/"
    },
    {
      "source": "/migration/api-changes",
      "destination": "/migration/oss-v2-to-v3"
    },
    {
      "source": "/platform/features/expiration-date",
      "destination": "/"
@@ -73,7 +73,7 @@ client = MemoryClient()
 # Define the agent
 agent = Agent(
    name="Personal Agent",
-    model=OpenAIChat(id="gpt-5-mini"),
+    model=OpenAIChat(id="gpt-4"),
    description="You are a helpful personal agent that helps me with day to day activities."
                "You can process both text and images.",
    markdown=True
@@ -43,7 +43,7 @@ OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
 memory_client = MemoryClient()
 agent = ConversableAgent(
    "chatbot",
-    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
    code_execution_config=False,
    human_input_mode="NEVER",
 )
@@ -99,7 +99,7 @@ For more complex scenarios, you can create multiple agents:
 manager = ConversableAgent(
    "manager",
    system_message="You are a manager who helps in resolving complex customer issues.",
-    llm_config={"config_list": [{"model": "gpt-5-mini", "api_key": OPENAI_API_KEY}]},
+    llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},
    human_input_mode="NEVER"
 )
@@ -138,10 +138,9 @@ When installed via the plugin marketplace, Mem0 hooks into Claude Code's lifecyc
 | Hook | Event | What it does |
 |------|-------|-------------|
 | **Setup** | `Setup` | Installs the mem0 SDK and dependencies (runs on init and maintenance) |
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
+| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
@@ -123,7 +123,7 @@ When installed via the plugin marketplace, Mem0 hooks into Codex's lifecycle to
 |------|-------|-------------|
 | **Session start** | `SessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `UserPromptSubmit` | Searches relevant memories before each message |
-| **Pre-tool (3 handlers)** | `PreToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
+| **Pre-tool** | `PreToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool** | `PostToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `Stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `PreCompact` | Stores a summary before the context is compacted |
@@ -108,7 +108,7 @@ When installed via the Cursor Marketplace, Mem0 hooks into Cursor's lifecycle:
 |------|-------|-------------|
 | **Session start** | `sessionStart` | Loads prior memories and displays status banner |
 | **User prompt** | `beforeSubmitPrompt` | Searches relevant memories before each message; skips short prompts |
-| **Pre-tool (3 handlers)** | `preToolUse` | Blocks MEMORY.md writes; enforces `user_id`/`app_id` on mem0 tool calls; scans files being read for relevant memory context |
+| **Pre-tool (2 handlers)** | `preToolUse` | Blocks MEMORY.md writes, enforces `user_id`/`app_id` on mem0 tool calls |
 | **Post-tool (2 handlers)** | `postToolUse` | Tracks stats, scans bash errors for related memories |
 | **Stop** | `stop` | Stores a session summary when the session ends |
 | **Pre-compact** | `preCompact` | Stores a summary before the context is compacted |
@@ -98,9 +98,20 @@ add_result = add_tool.invoke(add_input)
 ```json Output
 {
-  "message": "Memory processing has been queued for background execution",
+  "results": [
-  "status": "PENDING",
+    {
-  "event_id": "3a1b2c3d-4e5f-6789-abcd-ef0123456789"
+      "memory": "Name is Alex",
      "event": "ADD"
    },
    {
      "memory": "Is a vegetarian", 
      "event": "ADD"
    },
    {
      "memory": "Is allergic to nuts",
      "event": "ADD"
    }
  ]
 }
 ```
 </CodeGroup>
@@ -162,25 +173,23 @@ result = search_tool.invoke(search_input)
 ```
 ```json Output
-{
+[
-  "results": [
+  {
-    {
+    "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
-      "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
+    "memory": "Name is Alex",
-      "memory": "Name is Alex",
+    "user_id": "alex", 
-      "user_id": "alex",
+    "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
-      "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
+    "metadata": {
-      "metadata": {
+      "food": "vegan"
-        "food": "vegan"
+    },
-      },
+    "categories": [
-      "categories": [
+      "personal_details"
-        "personal_details"
+    ],
-      ],
+    "created_at": "2024-11-27T16:53:43.276872-08:00",
-      "created_at": "2024-11-27T16:53:43.276872-08:00",
+    "updated_at": "2024-11-27T16:53:43.276885-08:00",
-      "updated_at": "2024-11-27T16:53:43.276885-08:00",
+    "score": 0.3810526501504994
-      "score": 0.3810526501504994
+  }
-    }
+]
  ]
 }
 ```
 </CodeGroup>
@@ -41,7 +41,7 @@ load_dotenv()
 # MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-5-mini")
+llm = ChatOpenAI(model="gpt-4")
 mem0 = MemoryClient()
 ```
@@ -121,7 +121,7 @@ async def websocket_endpoint(websocket: WebSocket):
    # LLM for response generation
    llm = OpenAILLMService(
        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-5-mini",
+        model="gpt-3.5-turbo",
        system_prompt="You are a helpful assistant that remembers past conversations."
    )
@@ -26,12 +26,12 @@ Install the SDK provider and AI SDK:
 npm install @mem0/vercel-ai-provider ai@^6
 ```
-### Dependencies
+### Peer Dependencies
-`@mem0/vercel-ai-provider` bundles `ai`, all `@ai-sdk/*` provider packages, and `@ai-sdk/provider` as regular dependencies — you do **not** need to install them separately. The install command above (`npm install @mem0/vercel-ai-provider ai@^6`) is sufficient.
+`@mem0/vercel-ai-provider` v3.0.0 requires:
-
+- `ai` v6+ (`^6.0.199`)
-The only true peer dependency is `zod` (optional):
+- `@ai-sdk/provider` v3+ (`^3.0.10`)
- `zod` v3+ (`^3.0.0`) — required only if you use Zod schemas in tool definitions
+- Provider packages at v3+: `@ai-sdk/openai@^3`, `@ai-sdk/anthropic@^3`, `@ai-sdk/google@^3`, `@ai-sdk/groq@^3`, `@ai-sdk/cohere@^3`
 ## Getting Started
@@ -305,8 +305,6 @@ These options can be passed per-request when creating a model instance:
 | `rerank` | `boolean` | Enable reranking of results |
 | `page` | `number` | Page number for pagination |
 | `page_size` | `number` | Results per page |
 | `mem0ApiKey` | `string` | Mem0 API key; overrides the `MEM0_API_KEY` env var |
 | `host` | `string` | Custom Mem0 API base URL for self-hosted deployments |
 ## Key Features
@@ -314,7 +312,6 @@ These options can be passed per-request when creating a model instance:
 - `retrieveMemories()`: Retrieves memory context for prompts as a formatted system prompt string.
 - `getMemories()`: Get memories from your profile in array format.
 - `addMemories()`: Adds user memories to enhance contextual responses.
 - `searchMemories()`: Searches memories and returns the raw results array (semantic search rather than the full retrieval pipeline).
 ## Migrating from v2.x
@@ -228,6 +228,7 @@ If the user is on a pre-current major (Python < 2, TS < 3, or Platform `output_f
 - [OSS to Platform Migration](https://docs.mem0.ai/migration/oss-to-platform) [Both]: Use when moving from self-hosted to managed.
 - [OSS v2 to v3 Migration](https://docs.mem0.ai/migration/oss-v2-to-v3) [OSS]: Use when upgrading a self-hosted deployment across major versions.
 - [Platform v2 to v3 Migration](https://docs.mem0.ai/migration/platform-v2-to-v3) [Platform]: Use when upgrading a Platform integration across major versions.
 - [API Changes](https://docs.mem0.ai/migration/api-changes) [Both]: Use when the upgrade involves API surface changes.
 - [Server pgvector Image Upgrade](https://docs.mem0.ai/migration/server-pgvector-upgrade) [OSS]: Use when upgrading the self-hosted server Docker image from ankane/pgvector to pgvector/pgvector.
 - [Changelog](https://docs.mem0.ai/changelog/highlights) [Both]: Use when the user asks what shipped recently.
@@ -366,8 +367,6 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Organization](https://docs.mem0.ai/api-reference/organization/get-org) [Platform]: Use when fetching one org.
 - [Get Organization Members](https://docs.mem0.ai/api-reference/organization/get-org-members) [Platform]: Use when listing org members.
 - [Add Organization Member](https://docs.mem0.ai/api-reference/organization/add-org-member) [Platform]: Use when inviting a member to an org.
 - [Update Organization Member](https://docs.mem0.ai/api-reference/organization/update-org-member) [Platform]: Use when updating an org member's role.
 - [Remove Organization Member](https://docs.mem0.ai/api-reference/organization/remove-org-member) [Platform]: Use when removing a member from an organization.
 - [Delete Organization](https://docs.mem0.ai/api-reference/organization/delete-org) [Platform]: Use when removing an org.
 ### Projects
@@ -376,9 +375,6 @@ All API Reference docs describe Mem0 Platform REST endpoints (requires API key).
 - [Get Project](https://docs.mem0.ai/api-reference/project/get-project) [Platform]: Use when fetching one project.
 - [Get Project Members](https://docs.mem0.ai/api-reference/project/get-project-members) [Platform]: Use when listing project members.
 - [Add Project Member](https://docs.mem0.ai/api-reference/project/add-project-member) [Platform]: Use when inviting a member to a project.
 - [Update Project](https://docs.mem0.ai/api-reference/project/update-project) [Platform]: Use when updating project settings.
 - [Update Project Member](https://docs.mem0.ai/api-reference/project/update-project-member) [Platform]: Use when updating a project member's role.
 - [Remove Project Member](https://docs.mem0.ai/api-reference/project/remove-project-member) [Platform]: Use when removing a member from a project.
 - [Delete Project](https://docs.mem0.ai/api-reference/project/delete-project) [Platform]: Use when removing a project.
 ### Webhooks
@@ -464,7 +460,6 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [LM Studio Embeddings](https://docs.mem0.ai/components/embedders/models/lmstudio) [OSS]: Use when embeddings run through LM Studio.
 - [Together Embeddings](https://docs.mem0.ai/components/embedders/models/together) [OSS]: Use when embeddings run on Together.
 - [LangChain Embeddings](https://docs.mem0.ai/components/embedders/models/langchain) [OSS]: Use when embeddings are wrapped behind a LangChain adapter.
 - [FastEmbed](https://docs.mem0.ai/components/embedders/models/fastembed) [OSS]: Use when embeddings run locally via FastEmbed (ONNX).
 ### Vector Databases [OSS]
 - [Vector Database Overview](https://docs.mem0.ai/components/vectordbs/overview) [OSS]: Use when choosing a vector store.
@@ -503,5 +498,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
 - [Custom Reranker Prompts](https://docs.mem0.ai/components/rerankers/custom-prompts) [OSS]: Use when rewriting reranker prompts.
 - [Cohere Reranker](https://docs.mem0.ai/components/rerankers/models/cohere) [OSS]: Use for Cohere Rerank.
 - [Sentence Transformer Reranker](https://docs.mem0.ai/components/rerankers/models/sentence_transformer) [OSS]: Use for local cross-encoder rerankers.
- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.- [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
+- [Hugging Face Reranker](https://docs.mem0.ai/components/rerankers/models/huggingface) [OSS]: Use for HF-hosted reranker models.
 - [LLM Reranker (prompt)](https://docs.mem0.ai/components/rerankers/models/llm) [OSS]: Use when the reranker is a prompted LLM (config guide).
 - [LLM Reranker](https://docs.mem0.ai/components/rerankers/models/llm_reranker) [OSS]: Use when the reranker is a prompted LLM (implementation reference).
 - [Zero Entropy Reranker](https://docs.mem0.ai/components/rerankers/models/zero_entropy) [OSS]: Use for the Zero Entropy reranker.
@@ -0,0 +1,574 @@
 ---
 title: API Reference Changes
 description: "Comprehensive reference of all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method."
 icon: "code"
 iconType: "solid"
 ---
 ## Overview
 This page documents all API changes between Mem0 v0.x and v1.0.0 Beta, organized by component and method.
 ## Memory Class Changes
 ### Constructor
 #### v0.x
 ```python
 from mem0 import Memory
 # Basic initialization
 m = Memory()
 # With configuration
 config = {
    "version": "v1.0",  # Supported in v0.x
    "vector_store": {...}
 }
 m = Memory.from_config(config)
 ```
 #### v1.0.0 
 ```python
 from mem0 import Memory
 # Basic initialization (same)
 m = Memory()
 # With configuration
 config = {
    "version": "v1.1",  # v1.1+ only
    "vector_store": {...},
    # New optional features
    "reranker": {
        "provider": "cohere",
        "config": {...}
    }
 }
 m = Memory.from_config(config)
 ```
 ### add() Method
 #### v0.x Signature
 ```python
 def add(
    self,
    messages,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    metadata: dict = None,
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
 ) -> Union[List[dict], dict]:
    ...
 ```
 #### v1.0.0  Signature
 ```python
 def add(
    self,
    messages,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    metadata: dict = None,
    filters: dict = None,
    infer: bool = True          # ✅ NEW: Control memory inference
 ) -> dict:  # Always returns dict with "results" key
    ...
 ```
 #### Changes Summary
 | Parameter | v0.x | v1.0.0  | Change |
 |-----------|------|-----------|---------|
 | `messages` | ✅ | ✅ | Unchanged |
 | `user_id` | ✅ | ✅ | Unchanged |
 | `agent_id` | ✅ | ✅ | Unchanged |
 | `run_id` | ✅ | ✅ | Unchanged |
 | `metadata` | ✅ | ✅ | Unchanged |
 | `filters` | ✅ | ✅ | Unchanged |
 | `output_format` | ✅ | ❌ | **REMOVED** |
 | `version` | ✅ | ❌ | **REMOVED** |
 | `infer` | ❌ | ✅ | **NEW** |
 #### Response Format Changes
 **v0.x Response (variable format):**
 ```python
 # With output_format="v1.0"
 [
    {
        "id": "mem_123",
        "memory": "User loves pizza",
        "event": "ADD"
    }
 ]
 # With output_format="v1.1"
 {
    "results": [
        {
            "id": "mem_123",
            "memory": "User loves pizza",
            "event": "ADD"
        }
    ]
 }
 ```
 **v1.0.0  Response (standardized):**
 ```python
 # Always returns this format
 {
    "results": [
        {
            "id": "mem_123",
            "memory": "User loves pizza",
            "metadata": {...},
            "event": "ADD"
        }
    ]
 }
 ```
 ### search() Method
 #### v0.x Signature
 ```python
 def search(
    self,
    query: str,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    limit: int = 100,
    filters: dict = None,       # Basic key-value only
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
 ) -> Union[List[dict], dict]:
    ...
 ```
 #### v1.0.0  Signature
 ```python
 def search(
    self,
    query: str,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    limit: int = 100,
    filters: dict = None,       # ✅ ENHANCED: Advanced operators
    rerank: bool = True         # ✅ NEW: Reranking support
 ) -> dict:  # Always returns dict with "results" key
    ...
 ```
 #### Enhanced Filtering
 **v0.x Filters (basic):**
 ```python
 # Simple key-value filtering only
 filters = {
    "category": "food",
    "user_id": "alice"
 }
 ```
 **v1.0.0  Filters (enhanced):**
 ```python
 # Advanced filtering with operators
 filters = {
    "AND": [
        {"category": "food"},
        {"score": {"gte": 0.8}},
        {
            "OR": [
                {"priority": "high"},
                {"urgent": True}
            ]
        }
    ]
 }
 # Comparison operators
 filters = {
    "score": {"gt": 0.5},      # Greater than
    "priority": {"gte": 5},     # Greater than or equal
    "rating": {"lt": 3},        # Less than
    "confidence": {"lte": 0.9}, # Less than or equal
    "status": {"eq": "active"}, # Equal
    "archived": {"ne": True},   # Not equal
    "tags": {"in": ["work", "personal"]},     # In list
    "category": {"nin": ["spam", "deleted"]}  # Not in list
 }
 ```
 ### get_all() Method
 #### v0.x Signature
 ```python
 def get_all(
    self,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    filters: dict = None,
    output_format: str = None,  # ❌ REMOVED
    version: str = None         # ❌ REMOVED
 ) -> Union[List[dict], dict]:
    ...
 ```
 #### v1.0.0  Signature
 ```python
 def get_all(
    self,
    user_id: str = None,
    agent_id: str = None,
    run_id: str = None,
    filters: dict = None        # ✅ ENHANCED: Advanced operators
 ) -> dict:  # Always returns dict with "results" key
    ...
 ```
 ### update() Method
 #### No Breaking Changes
 ```python
 # Same signature in both versions
 def update(
    self,
    memory_id: str,
    data: str
 ) -> dict:
    ...
 ```
 ### delete() Method
 #### No Breaking Changes
 ```python
 # Same signature in both versions
 def delete(
    self,
    memory_id: str
 ) -> dict:
    ...
 ```
 ### delete_all() Method
 #### Breaking Change — Empty filter no longer silently deletes everything
 **Before:** calling `delete_all()` with no filters silently deleted **all memories in the project**.
 **After:**
 - No filters → raises a validation error (prevents accidental full-project wipe).
 - Concrete ID (e.g. `user_id="alice"`) → deletes memories for that entity (unchanged).
 - `"*"` for a filter → deletes all memories for that entity type across the project (new).
 - All four filters set to `"*"` → explicit full project wipe (new, requires opt-in on every parameter).
 This change replaces the silent full-project delete (triggered by an empty or missing filter) with a validation error, and introduces `"*"` wildcards as the intentional path for bulk deletion.
 ```python
 # v0.x — no filter silently wiped all project memories
 m.delete_all()                      # DANGER: deleted everything
 m.delete_all(user_id="alice")       # deleted alice's memories
 # v1.x — no filter now raises an error; use "*" for intentional bulk deletes
 m.delete_all()                                                       # ERROR: at least one filter required
 m.delete_all(user_id="alice")                                        # unchanged
 m.delete_all(user_id="*")                                            # NEW — delete all users' memories
 m.delete_all(user_id="*", agent_id="*", app_id="*", run_id="*")     # NEW — full project wipe
 ```
 ## Platform Client (MemoryClient) Changes
 ### async_mode Default Changed
 #### v0.x
 ```python
 from mem0 import MemoryClient
 client = MemoryClient(api_key="your-key")
 # async_mode had to be explicitly set or had different default
 result = client.add("content", user_id="alice", async_mode=True)
 ```
 #### v1.0.0
 ```python
 from mem0 import MemoryClient
 client = MemoryClient(api_key="your-key")
 # async_mode defaults to True now (better performance)
 result = client.add("content", user_id="alice")  # Uses async_mode=True by default
 # Can still override if needed
 result = client.add("content", user_id="alice", async_mode=False)
 ```
 ## Configuration Changes
 ### Memory Configuration
 #### v0.x Config Options
 ```python
 config = {
    "vector_store": {...},
    "llm": {...},
    "embedder": {...},
    "graph_store": {...},
    "version": "v1.0",              # ❌ v1.0 no longer supported
    "history_db_path": "...",
    "custom_instructions": "..."
 }
 ```
 #### v1.0.0  Config Options
 ```python
 config = {
    "vector_store": {...},
    "llm": {...},
    "embedder": {...},
    "graph_store": {...},
    "reranker": {                   # ✅ NEW: Reranker support
        "provider": "cohere",
        "config": {...}
    },
    "version": "v1.1",              # ✅ v1.1+ only
    "history_db_path": "...",
    "custom_instructions": "...",
    "custom_update_memory_prompt": "..."  # ✅ NEW: Custom update prompt
 }
 ```
 ### New Configuration Options
 #### Reranker Configuration
 ```text
 # Cohere reranker
 "reranker": {
    "provider": "cohere",
    "config": {
        "model": "rerank-english-v3.0",
        "api_key": "your-api-key",
        "top_k": 10
    }
 }
 # Sentence Transformer reranker
 "reranker": {
    "provider": "sentence_transformer",
    "config": {
        "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
        "device": "cuda"
    }
 }
 # Hugging Face reranker
 "reranker": {
    "provider": "huggingface",
    "config": {
        "model": "BAAI/bge-reranker-base",
        "device": "cuda"
    }
 }
 # LLM-based reranker
 "reranker": {
    "provider": "llm_reranker",
    "config": {
        "llm": {
            "provider": "openai",
            "config": {
                "model": "gpt-4",
                "api_key": "your-api-key"
            }
        }
    }
 }
 ```
 ## Error Handling Changes
 ### New Error Types
 #### v0.x Errors
 ```python
 # Generic exceptions
 try:
    result = m.add("content", user_id="alice", version="v1.0")
 except Exception as e:
    print(f"Error: {e}")
 ```
 #### v1.0.0  Errors
 ```python
 # More specific error handling
 try:
    result = m.add("content", user_id="alice")
 except ValueError as e:
    if "v1.0 API format is no longer supported" in str(e):
        # Handle version compatibility error
        pass
    elif "Invalid filter operator" in str(e):
        # Handle filter syntax error
        pass
 except TypeError as e:
    # Handle parameter errors
    pass
 except Exception as e:
    # Handle unexpected errors
    pass
 ```
 ### Validation Changes
 #### Stricter Parameter Validation
 **v0.x (Lenient):**
 ```python
 # Unknown parameters might be ignored
 result = m.add("content", user_id="alice", unknown_param="value")
 ```
 **v1.0.0  (Strict):**
 ```python
 # Unknown parameters raise TypeError
 try:
    result = m.add("content", user_id="alice", unknown_param="value")
 except TypeError as e:
    print(f"Invalid parameter: {e}")
 ```
 ## Response Schema Changes
 ### Memory Object Schema
 #### v0.x Schema
 ```python
 {
    "id": "mem_123",
    "memory": "User loves pizza",
    "user_id": "alice",
    "metadata": {...},
    "created_at": "2024-01-01T00:00:00Z",
    "updated_at": "2024-01-01T00:00:00Z",
    "score": 0.95  # In search results
 }
 ```
 #### v1.0.0  Schema (Enhanced)
 ```python
 {
    "id": "mem_123",
    "memory": "User loves pizza",
    "user_id": "alice",
    "agent_id": "assistant",     # ✅ More context
    "run_id": "session_001",     # ✅ More context
    "metadata": {...},
    "categories": ["food"],      # ✅ NEW: Auto-categorization
    "immutable": false,          # ✅ NEW: Immutability flag
    "created_at": "2024-01-01T00:00:00Z",
    "updated_at": "2024-01-01T00:00:00Z",
    "score": 0.95,              # In search results
    "rerank_score": 0.98        # ✅ NEW: If reranking used
 }
 ```
 ## Migration Code Examples
 ### Simple Migration
 #### Before (v0.x)
 ```python
 from mem0 import Memory
 m = Memory()
 # Add with deprecated parameters
 result = m.add(
    "I love pizza",
    user_id="alice",
    output_format="v1.1",
    version="v1.0"
 )
 # Handle variable response format
 if isinstance(result, list):
    memories = result
 else:
    memories = result.get("results", [])
 for memory in memories:
    print(memory["memory"])
 ```
 #### After (v1.0.0 )
 ```python
 from mem0 import Memory
 m = Memory()
 # Add without deprecated parameters
 result = m.add(
    "I love pizza",
    user_id="alice"
 )
 # Always dict format with "results" key
 for memory in result["results"]:
    print(memory["memory"])
 ```
 ### Advanced Migration
 #### Before (v0.x)
 ```python
 # Basic filtering
 results = m.search(
    "food preferences",
    user_id="alice",
    filters={"category": "food"},
    output_format="v1.1"
 )
 ```
 #### After (v1.0.0 )
 ```python
 # Enhanced filtering with reranking
 results = m.search(
    "food preferences",
    user_id="alice",
    filters={
        "AND": [
            {"category": "food"},
            {"score": {"gte": 0.8}}
        ]
    },
    rerank=True
 )
 ```
 ## Summary
 | Component | v0.x | v1.0.0  | Status |
 |-----------|------|-----------|---------|
 | `add()` method | Variable response | Standardized response | ⚠️ Breaking |
 | `search()` method | Basic filtering | Enhanced filtering + reranking | ⚠️ Breaking |
 | `get_all()` method | Variable response | Standardized response | ⚠️ Breaking |
 | Response format | Variable | Always `{"results": [...]}` | ⚠️ Breaking |
 | Reranking | ❌ Not available | ✅ Full support | ✅ New feature |
 | Advanced filtering | ❌ Basic only | ✅ Full operators | ✅ Enhancement |
 | Error handling | Generic | Specific error types | ✅ Improvement |
 <Info>
 Use this reference to systematically update your codebase. Test each change thoroughly before deploying to production.
 </Info>
@@ -62,7 +62,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `MemoryClient(api_key, org_id, project_id)` | `MemoryClient(api_key)` | Remove `org_id`, `project_id` from constructor |
 | Method options | `client.add(messages, **kwargs)` | `client.add(messages, options=AddMemoryOptions(...))` | Use typed option classes (or `**kwargs` still works) |
-| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `filter_memories`, `expiration_date`, `keyword_search`, `force_add_only`, `batch_size`, `immutable`, `includes`, `excludes`, `enable_graph`, `org_name`, `project_name` | — | Remove from all calls |
 ### TypeScript Client SDK
@@ -70,7 +70,7 @@ These changes produce a **+20 point improvement on LoCoMo** (71.4 → 91.6) and
 |---|---|---|---|
 | Constructor | `new MemoryClient({ apiKey, organizationId, projectId })` | `new MemoryClient({ apiKey })` | Remove `organizationId`, `projectId`, `organizationName`, `projectName` |
 | All params | snake_case: `user_id`, `agent_id`, `top_k` | camelCase: `userId`, `agentId`, `topK` | Rename all params to camelCase |
-| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
+| Removed params | `api_version`, `output_format`, `async_mode`, `enable_graph`, `org_id`, `project_id`, `org_name`, `project_name`, `filter_memories`, `batch_size`, `force_add_only`, `immutable`, `expiration_date`, `includes`, `excludes`, `keyword_search` | — | Remove from all calls |
 | Output format enum | `OutputFormat.V1`, `OutputFormat.V1_1` | Removed | v1.1 is now always used |
 | API version enum | `API_VERSION.V1`, `API_VERSION.V2` | Removed | Handled internally |
@@ -423,7 +423,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 **All methods:** `api_version`, `output_format`, `async_mode`, `org_name`, `project_name`, `org_id`, `project_id`
-**add():** `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
+**add():** `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`
 **search():** `enable_graph`
@@ -437,7 +437,7 @@ These parameters have been removed across all SDKs. Remove them from your code:
 **All methods:** `OutputFormat` enum, `API_VERSION` enum
-**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
+**add():** `enable_graph` / `enableGraph`, `async_mode` / `asyncMode`, `output_format` / `outputFormat`, `immutable`, `expiration_date` / `expirationDate`, `filter_memories` / `filterMemories`, `batch_size` / `batchSize`, `force_add_only` / `forceAddOnly`, `includes`, `excludes`, `keyword_search` / `keywordSearch`
 **search():** `enable_graph` / `enableGraph`
@@ -215,7 +215,7 @@ client.add(messages, user_id="alice")
 # async_mode and output_format removed (async by default, v1.1 always)
 ```
-**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
+**Removed parameters:** `org_id`, `project_id`, `api_version`, `output_format`, `async_mode`, `enable_graph`, `immutable`, `expiration_date`, `filter_memories`, `batch_size`, `force_add_only`, `includes`, `excludes`, `keyword_search`, `org_name`, `project_name`
 ### TypeScript Client SDK
@@ -242,7 +242,7 @@ await client.search("query", {
 });
 ```
-**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
+**Removed:** `OutputFormat` enum, `API_VERSION` enum, `organizationId`, `projectId`, `organizationName`, `projectName`, `enableGraph`, `asyncMode`, `outputFormat`, `immutable`, `expirationDate`, `filterMemories`, `batchSize`, `forceAddOnly`, `includes`, `excludes`, `keywordSearch`
 <Info>
 For the full list of parameter changes across all SDKs, see the [OSS migration guide](/migration/oss-v2-to-v3#removed-parameters-reference).
@@ -130,7 +130,7 @@ memory = Memory.from_config_file("config.yaml")
 </Tabs>
 <Info icon="check">
-  Run `memory.add("Remember my favorite cafe in Tokyo.", user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
+  Run `memory.add(["Remember my favorite cafe in Tokyo."], user_id="alex")` and then `memory.search("favorite cafe", filters={"user_id": "alex"})`. You should see the Qdrant collection populate and the reranker mark the memory as a top hit.
 </Info>
 ## Tune component settings
@@ -18,7 +18,7 @@ icon: "bolt"
 </Warning>
 <Note>
-  Working in TypeScript? The OSS `Memory` class in the Node SDK (`mem0ai/oss`) is also fully async — every method returns a `Promise` and must be `await`ed. Python’s `AsyncMemory` serves the same purpose within Python async frameworks like FastAPI. Both runtimes support awaited memory operations; choose the SDK that matches your language.
+  Working in TypeScript? The Node SDK still uses synchronous calls—use `Memory` there and rely on Python’s `AsyncMemory` when you need awaited operations.
 </Note>
 ## Feature anatomy
@@ -165,7 +165,8 @@ await memory.add("Yesterday, I ordered a laptop, the order id is 12345", { userI
    {"memory": "Ordered a laptop", "event": "ADD"},
    {"memory": "Order ID: 12345", "event": "ADD"},
    {"memory": "Order placed yesterday", "event": "ADD"}
-  ]
+  ],
  "relations": []
 }
 ```
 </CodeGroup>
@@ -187,7 +188,8 @@ await memory.add("I like going to hikes", { userId: "user123" });
 ```json Output
 {
-  "results": []
+  "results": [],
  "relations": []
 }
 ```
 </CodeGroup>
@@ -41,14 +41,6 @@ Multimodal support lets Mem0 extract facts from images alongside regular text. A
 ## Configure it
 <Warning>
  You must set `enable_vision: True` in your LLM config for image content to be processed. Without it, image turns are silently dropped and no vision memories are created. Example:
  ```python
  config = {"llm": {"provider": "openai", "config": {"enable_vision": True, "vision_details": "auto"}}}
  client = Memory.from_config(config)
  ```
 </Warning>
 ### Add image messages from URLs
 <CodeGroup>
@@ -74,7 +66,7 @@ client.add(messages, user_id="alice")
 ```
 ```ts TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 const client = new Memory();
@@ -131,7 +123,7 @@ client.add(messages, user_id="alice")
 ```ts TypeScript
 import fs from "fs";
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 function encodeImage(imagePath: string) {
  const buffer = fs.readFileSync(imagePath);
@@ -234,7 +226,7 @@ client.add(messages, user_id="user123")
 <CodeGroup>
 ```python Python
 from mem0 import Memory
-from mem0.exceptions import ValidationError
+from mem0.exceptions import InvalidImageError, FileSizeError
 client = Memory()
@@ -250,14 +242,16 @@ try:
    client.add(messages, user_id="user123")
    print("Image processed successfully")
-except ValidationError as exc:
+except InvalidImageError:
-    print(f"Image validation error: {exc}")
+    print("Invalid image format or corrupted file")
 except FileSizeError:
    print("Image file too large")
 except Exception as exc:
    print(f"Unexpected error: {exc}")
 ```
 ```ts TypeScript
-import { Memory } from "mem0ai/oss";
+import { Memory } from "mem0ai";
 const client = new Memory();
@@ -124,7 +124,7 @@ config = {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
-            "model": "gpt-5-mini",
+            "model": "gpt-4o-mini",
            "api_key": "your-openai-api-key",
            "top_k": 5
        }
@@ -150,7 +150,7 @@ config = {
    "llm": {
        "provider": "openai",
        "config": {
-            "model": "gpt-5-mini",
+            "model": "gpt-4",
            "api_key": "your-openai-api-key"
        }
    },
@@ -1779,11 +1779,6 @@
 										"type": "object",
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`).",
 										"additionalProperties": true
 									},
 									"show_expired": {
 										"type": "boolean",
 										"default": false,
 										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
 									}
 								}
 							},
@@ -1982,12 +1977,6 @@
 										"additionalProperties": true,
 										"description": "User-supplied metadata to attach to each extracted memory."
 									},
 									"expiration_date": {
 										"type": "string",
 										"format": "date",
 										"nullable": true,
 										"description": "Optional expiration date in YYYY-MM-DD format. After this date, memories are hidden from search and get-all unless `show_expired` is true."
 									},
 									"custom_instructions": {
 										"type": "string",
 										"description": "Project-level instructions that guide extraction for this call."
@@ -2105,11 +2094,6 @@
 										"description": "Entity and metadata filters. Must include at least one entity ID (`user_id`, `agent_id`, `app_id`, or `run_id`). Supports `AND`, `OR`, `NOT`, and comparison operators (`in`, `gte`, `lte`, `gt`, `lt`, `contains`, `icontains`, `ne`).",
 										"additionalProperties": true
 									},
 									"show_expired": {
 										"type": "boolean",
 										"default": false,
 										"description": "When true, include memories whose `expiration_date` has passed. Expired memories are hidden by default."
 									},
 									"top_k": {
 										"type": "integer",
 										"minimum": 1,
@@ -2448,12 +2432,6 @@
 									"metadata": {
 										"type": "object",
 										"description": "Additional metadata associated with the memory"
 									},
 									"expiration_date": {
 										"type": "string",
 										"format": "date",
 										"nullable": true,
 										"description": "Expiration date in YYYY-MM-DD format, or null to clear the expiration date."
 									}
 								}
 							}
@@ -4883,7 +4861,8 @@
 										"items": {
 											"type": "object",
 											"required": [
-												"memory_id"
+												"memory_id",
 												"text"
 											],
 											"properties": {
 												"memory_id": {
@@ -4894,11 +4873,6 @@
 												"text": {
 													"type": "string",
 													"description": "The new text content for the memory"
 												},
 												"metadata": {
 													"type": "object",
 													"additionalProperties": true,
 													"description": "Updated metadata to associate with the memory."
 												}
 											}
 										},
@@ -4974,27 +4948,18 @@
 							"schema": {
 								"type": "object",
 								"properties": {
-									"memories": {
+									"memory_ids": {
 										"type": "array",
 										"items": {
-											"type": "object",
+											"type": "string",
-											"properties": {
+											"format": "uuid"
 												"memory_id": {
 													"type": "string",
 													"format": "uuid",
 													"description": "The unique identifier of the memory to delete."
 												}
 											},
 											"required": [
 												"memory_id"
 											]
 										},
 										"maxItems": 1000,
-										"description": "Array of memory objects to delete."
+										"description": "Array of memory IDs to delete."
 									}
 								},
 								"required": [
-									"memories"
+									"memory_ids"
 								]
 							}
 						}
@@ -6291,4 +6256,4 @@
 		}
 	},
 	"x-original-swagger-version": "2.0"
-}
+}
@@ -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>
@@ -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")
@@ -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>
@@ -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>
@@ -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}")
 ```
@@ -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>
@@ -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>
@@ -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>
@@ -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
@@ -71,8 +71,7 @@
      "picomatch@<2.3.2": "^2.3.2",
      "@qdrant/js-client-rest": "^1.18.0",
      "uuid@<11.1.1": ">=11.1.1",
-      "esbuild": ">=0.28.1",
+      "esbuild": ">=0.28.1"
      "undici@<6.27.0": ">=6.27.0 <8.0.0"
    }
  }
 }
@@ -13,7 +13,6 @@ overrides:
  '@qdrant/js-client-rest': ^1.18.0
  uuid@<11.1.1: '>=11.1.1'
  esbuild: '>=0.28.1'
  undici@<6.27.0: '>=6.27.0 <8.0.0'
 importers:
@@ -1997,9 +1996,9 @@ packages:
  undici-types@6.21.0:
    resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
-  undici@7.28.0:
+  undici@6.26.0:
-    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=20.18.1'}
+    engines: {node: '>=18.17'}
  util-deprecate@1.0.2:
    resolution: {integrity: sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==}
@@ -2510,7 +2509,7 @@ snapshots:
    dependencies:
      '@qdrant/openapi-typescript-fetch': 1.2.6
      typescript: 5.9.3
-      undici: 7.28.0
+      undici: 6.26.0
  '@qdrant/openapi-typescript-fetch@1.2.6': {}
@@ -4064,7 +4063,7 @@ snapshots:
  undici-types@6.21.0: {}
-  undici@7.28.0: {}
+  undici@6.26.0: {}
  util-deprecate@1.0.2: {}
@@ -21,4 +21,3 @@ overrides:
  "@qdrant/js-client-rest": "^1.18.0"
  "uuid@<11.1.1": ">=11.1.1"
  "esbuild": ">=0.28.1"
  "undici@<6.27.0": ">=6.27.0 <8.0.0"
@@ -4,12 +4,10 @@
 import { describe, it, expect } from "vitest";
 import {
  safePath,
  normalizeModuleUrlToPath,
  loadSkill,
  loadTriagePrompt,
  loadCompactTriagePrompt,
 } from "./skill-loader.ts";
 import { fileURLToPath, pathToFileURL } from "node:url";
 // ---------------------------------------------------------------------------
 // safePath — path containment
@@ -76,31 +74,6 @@ describe("loadSkill path traversal", () => {
  });
 });
 describe("normalizeModuleUrlToPath", () => {
  it("normalizes raw Windows paths before fileURLToPath conversion", () => {
    const rawWindowsMetaUrl = "C:\\Users\\example\\openclaw\\index.ts";
    const result = normalizeModuleUrlToPath(rawWindowsMetaUrl);
    // Assert the decoded property directly rather than reconstructing via the function body
    expect(typeof result).toBe("string");
    expect(result).not.toContain("%5C");
  });
  it("leaves already-correct file URLs unchanged", () => {
    const fileMetaUrl = "file:///C:/Users/example/openclaw/index.ts";
    const expected = fileURLToPath(fileMetaUrl);
    expect(normalizeModuleUrlToPath(fileMetaUrl)).toBe(expected);
  });
  it.skipIf(process.platform === "win32")(
    "passes POSIX absolute paths through unchanged",
    () => {
      const posixPath = "/home/user/openclaw/index.ts";
      expect(normalizeModuleUrlToPath(posixPath)).toBe(posixPath);
    },
  );
 });
 describe("loadCompactTriagePrompt", () => {
  it("keeps the core triage instructions without inlining the full skill body", () => {
    const prompt = loadCompactTriagePrompt();
@@ -4,7 +4,7 @@
 */
 import * as path from "node:path";
-import { fileURLToPath, pathToFileURL } from "node:url";
+import { fileURLToPath } from "node:url";
 import type { SkillsConfig, CategoryConfig } from "./types.ts";
 import { readText, exists } from "./fs-safe.ts";
@@ -84,14 +84,6 @@ function parseSkillFile(content: string): ParsedSkill {
  };
 }
 /** @internal — exported for testing only */
 export function normalizeModuleUrlToPath(moduleUrl: string): string {
  const normalizedUrl = moduleUrl.startsWith("file:")
    ? moduleUrl
    : pathToFileURL(moduleUrl).toString();
  return fileURLToPath(normalizedUrl);
 }
 // ============================================================================
 // Skill Loader
 // ============================================================================
@@ -104,7 +96,7 @@ function resolveSkillsDir(): string {
  // Strategy 1: import.meta.url (works in native ESM)
  try {
-    const metaDir = path.dirname(normalizeModuleUrlToPath(import.meta.url));
+    const metaDir = path.dirname(fileURLToPath(import.meta.url));
    candidates.push(path.join(metaDir, "skills"));
    candidates.push(path.join(metaDir, "..", "skills"));
  } catch {
@@ -74,7 +74,6 @@
      "form-data@<4.0.6": ">=4.0.6",
      "uuid@<11.1.1": ">=11.1.1",
      "esbuild": ">=0.28.1",
      "undici@<6.27.0": ">=6.27.0 <8.0.0",
      "undici@>=8.0.0 <8.5.0": ">=8.5.0"
    }
  }
@@ -8,7 +8,6 @@ overrides:
  form-data@<4.0.6: '>=4.0.6'
  uuid@<11.1.1: '>=11.1.1'
  esbuild: '>=0.28.1'
  undici@<6.27.0: '>=6.27.0 <8.0.0'
  undici@>=8.0.0 <8.5.0: '>=8.5.0'
 importers:
@@ -2352,9 +2351,9 @@ packages:
  undici-types@7.24.6:
    resolution: {integrity: sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg==}
-  undici@7.28.0:
+  undici@6.26.0:
-    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=20.18.1'}
+    engines: {node: '>=18.17'}
  undici@8.5.0:
    resolution: {integrity: sha512-xamtWoB1EshgjpmlXd7GGm2VfdDtw1+rD8uhry8pSNW3If6S8E0m2T2+orSKeZXEn/aPJMviCpDBA65WJt8zhg==}
@@ -3203,7 +3202,7 @@ snapshots:
    dependencies:
      '@qdrant/openapi-typescript-fetch': 1.2.6
      typescript: 6.0.3
-      undici: 7.28.0
+      undici: 6.26.0
  '@qdrant/openapi-typescript-fetch@1.2.6': {}
@@ -4895,7 +4894,7 @@ snapshots:
  undici-types@7.24.6: {}
-  undici@7.28.0: {}
+  undici@6.26.0: {}
  undici@8.5.0: {}
@@ -5,5 +5,4 @@ overrides:
  "form-data@<4.0.6": ">=4.0.6"
  "uuid@<11.1.1": ">=11.1.1"
  "esbuild": ">=0.28.1"
  "undici@<6.27.0": ">=6.27.0 <8.0.0"
  "undici@>=8.0.0 <8.5.0": ">=8.5.0"
@@ -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",
@@ -156,8 +156,7 @@
      "tar-fs@>=2.0.0 <2.1.4": "^2.1.4",
      "glob@>=10.2.0 <10.5.0": "^10.5.0",
      "@modelcontextprotocol/sdk": "^1.25.4",
-      "esbuild": ">=0.28.1",
+      "esbuild": ">=0.28.1"
      "undici@<6.27.0": ">=6.27.0 <8.0.0"
    }
  }
 }
@@ -23,7 +23,6 @@ overrides:
  glob@>=10.2.0 <10.5.0: ^10.5.0
  '@modelcontextprotocol/sdk': ^1.25.4
  esbuild: '>=0.28.1'
  undici@<6.27.0: '>=6.27.0 <8.0.0'
 importers:
@@ -2926,9 +2925,9 @@ packages:
  undici-types@6.21.0:
    resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
-  undici@7.28.0:
+  undici@6.26.0:
-    resolution: {integrity: sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==}
+    resolution: {integrity: sha512-4yqz8a3n5HmGTlsbADNtr/dJlhkh/55Rq798G6ibiULcXbDtaLpTl1pvdqcbFfeoj3iSi52lePFM7h9H21cw/A==}
-    engines: {node: '>=20.18.1'}
+    engines: {node: '>=18.17'}
  update-browserslist-db@1.2.3:
    resolution: {integrity: sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==}
@@ -3748,7 +3747,7 @@ snapshots:
    dependencies:
      '@qdrant/openapi-typescript-fetch': 1.2.6
      typescript: 5.5.4
-      undici: 7.28.0
+      undici: 6.26.0
  '@qdrant/openapi-typescript-fetch@1.2.6': {}
@@ -6114,7 +6113,7 @@ snapshots:
  undici-types@6.21.0: {}
-  undici@7.28.0: {}
+  undici@6.26.0: {}
  update-browserslist-db@1.2.3(browserslist@4.28.2):
    dependencies:
@@ -24,4 +24,3 @@ overrides:
  "glob@>=10.2.0 <10.5.0": "^10.5.0"
  "@modelcontextprotocol/sdk": "^1.25.4"
  "esbuild": ">=0.28.1"
  "undici@<6.27.0": ">=6.27.0 <8.0.0"
@@ -281,22 +281,19 @@ export default class MemoryClient {
      text,
      metadata,
      timestamp,
      expirationDate,
    }: {
      text?: string;
      metadata?: Record<string, any>;
      timestamp?: number | string;
      expirationDate?: string | null;
    },
  ): Promise<Array<Memory>> {
    if (
      text === undefined &&
      metadata === undefined &&
-      timestamp === undefined &&
+      timestamp === undefined
      expirationDate === undefined
    ) {
      throw new Error(
-        "At least one of text, metadata, timestamp, or expirationDate must be provided for update.",
+        "At least one of text, metadata, or timestamp must be provided for update.",
      );
    }
@@ -305,7 +302,6 @@ export default class MemoryClient {
    if (text !== undefined) payload.text = text;
    if (metadata !== undefined) payload.metadata = metadata;
    if (timestamp !== undefined) payload.timestamp = timestamp;
    if (expirationDate !== undefined) payload.expiration_date = expirationDate;
    const payloadKeys = Object.keys(payload);
    this._captureEvent("update", [payloadKeys]);
@@ -13,7 +13,6 @@ export interface AddMemoryOptions extends EntityOptions {
  customCategories?: custom_categories[];
  customInstructions?: string;
  timestamp?: number;
  expirationDate?: string;
  structuredDataSchema?: Record<string, any>;
 }
@@ -26,7 +25,6 @@ export interface SearchMemoryOptions {
  latestOnly?: boolean;
  fields?: string[];
  categories?: string[];
  showExpired?: boolean;
 }
 export interface GetAllMemoryOptions {
@@ -37,7 +35,6 @@ export interface GetAllMemoryOptions {
  endDate?: string;
  latestOnly?: boolean;
  categories?: string[];
  showExpired?: boolean;
 }
 export interface DeleteAllMemoryOptions extends EntityOptions {}
@@ -122,7 +119,6 @@ export interface Memory {
  memoryType?: string;
  score?: number;
  metadata?: any | null;
  expirationDate?: string | null;
  owner?: string | null;
  agentId?: string | null;
  appId?: string | null;
@@ -59,21 +59,6 @@ describe("MemoryClient - add()", () => {
    expect(getFetchBody(call!).user_id).toBe("user_1");
  });
  test("serializes expirationDate as expiration_date", async () => {
    const extra = new Map<string, { status: number; body: unknown }>();
    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: "test" }], {
      userId: "u1",
      expirationDate: "2030-01-31",
    });
    const call = findFetchCall(mock, "/v3/memories/add/", "POST");
    expect(getFetchBody(call!).expiration_date).toBe("2030-01-31");
  });
  test("throws an error when given an empty messages array", async () => {
    setupMockFetch();
@@ -191,26 +176,11 @@ describe("MemoryClient - update()", () => {
    expect(body.timestamp).toBe(1710600000);
  });
  test("sends expirationDate as expiration_date, including null", async () => {
    const extra = new Map<string, { status: number; body: unknown }>();
    extra.set("/v1/memories/mem_123/", {
      status: 200,
      body: createMockMemory(),
    });
    const mock = setupMockFetch(extra);
    const client = new MemoryClient({ apiKey: TEST_API_KEY });
    await client.update("mem_123", { expirationDate: null });
    const call = findFetchCall(mock, "/v1/memories/mem_123/", "PUT");
    expect(getFetchBody(call!).expiration_date).toBeNull();
  });
  test("throws when no fields provided", async () => {
    setupMockFetch();
    const client = new MemoryClient({ apiKey: TEST_API_KEY });
    await expect(client.update("mem_123", {})).rejects.toThrow(
-      "At least one of text, metadata, timestamp, or expirationDate must be provided",
+      "At least one of text, metadata, or timestamp must be provided",
    );
  });
 });
@@ -81,24 +81,6 @@ describe("MemoryClient - search()", () => {
    expect(getFetchBody(call!).latest_only).toBe(true);
  });
  test("serializes showExpired as show_expired", async () => {
    const extra = new Map<string, { status: number; body: unknown }>();
    extra.set("/v3/memories/search/", {
      status: 200,
      body: { results: [] },
    });
    const mock = setupMockFetch(extra);
    const client = new MemoryClient({ apiKey: TEST_API_KEY });
    await client.search("test", {
      filters: { user_id: "u1" },
      showExpired: true,
    });
    const call = findFetchCall(mock, "/v3/memories/search/", "POST");
    expect(getFetchBody(call!).show_expired).toBe(true);
  });
  test("passes complex OR filters through to the API body", async () => {
    const extra = new Map<string, { status: number; body: unknown }>();
    extra.set("/v3/memories/search/", {
@@ -318,22 +300,4 @@ describe("MemoryClient - getAll() entity param rejection", () => {
    const call = findFetchCall(mock, "/v3/memories/", "POST");
    expect(getFetchBody(call!).latest_only).toBe(true);
  });
  test("serializes showExpired as show_expired", async () => {
    const extra = new Map<string, { status: number; body: unknown }>();
    extra.set("/v3/memories/", {
      status: 200,
      body: { results: [] },
    });
    const mock = setupMockFetch(extra);
    const client = new MemoryClient({ apiKey: TEST_API_KEY });
    await client.getAll({
      filters: { user_id: "u1" },
      showExpired: true,
    });
    const call = findFetchCall(mock, "/v3/memories/", "POST");
    expect(getFetchBody(call!).show_expired).toBe(true);
  });
 });
@@ -18,7 +18,6 @@ export * from "./llms/ollama";
 export * from "./llms/lmstudio";
 export * from "./llms/mistral";
 export * from "./llms/langchain";
 export * from "./llms/litellm";
 export * from "./vector_stores/base";
 export * from "./vector_stores/memory";
 export * from "./vector_stores/qdrant";
@@ -1,39 +0,0 @@
 import { OpenAILLM } from "./openai";
 import { LLMConfig, Message } from "../types";
 import { LLMResponse } from "./base";
 export class LiteLLM extends OpenAILLM {
  constructor(config: LLMConfig) {
    super({
      ...config,
      apiKey: config.apiKey || process.env.LITELLM_API_KEY || "sk-anything",
      baseURL:
        config.baseURL ||
        process.env.LITELLM_API_BASE ||
        "http://localhost:4000",
      model: config.model || "gpt-5-mini",
    });
  }
  async generateResponse(
    messages: Message[],
    responseFormat?: { type: string },
    tools?: any[],
  ): Promise<string | LLMResponse> {
    try {
      return await super.generateResponse(messages, responseFormat, tools);
    } catch (err) {
      const message = err instanceof Error ? err.message : String(err);
      throw new Error(`LiteLLM failed: ${message}`);
    }
  }
  async generateChat(messages: Message[]): Promise<LLMResponse> {
    try {
      return await super.generateChat(messages);
    } catch (err) {
      const message = err instanceof Error ? err.message : String(err);
      throw new Error(`LiteLLM failed: ${message}`);
    }
  }
 }
@@ -1,43 +0,0 @@
 import { OpenAILLM } from "./openai";
 import { LLMConfig, Message } from "../types";
 import { LLMResponse } from "./base";
 export class MiniMaxLLM extends OpenAILLM {
  constructor(config: LLMConfig) {
    const apiKey = config.apiKey || process.env.MINIMAX_API_KEY;
    if (!apiKey) {
      throw new Error("MiniMax API key is required");
    }
    super({
      ...config,
      apiKey,
      baseURL:
        config.baseURL ||
        process.env.MINIMAX_API_BASE ||
        "https://api.minimax.io/v1",
      model: config.model || "MiniMax-M2.7",
    });
  }
  async generateResponse(
    messages: Message[],
    responseFormat?: { type: string },
    tools?: any[],
  ): Promise<string | LLMResponse> {
    try {
      return await super.generateResponse(messages, responseFormat, tools);
    } catch (err) {
      const message = err instanceof Error ? err.message : String(err);
      throw new Error(`MiniMax LLM failed: ${message}`);
    }
  }
  async generateChat(messages: Message[]): Promise<LLMResponse> {
    try {
      return await super.generateChat(messages);
    } catch (err) {
      const message = err instanceof Error ? err.message : String(err);
      throw new Error(`MiniMax LLM failed: ${message}`);
    }
  }
 }
--- a/Show More
+++ b/Show More