refactor: add close() to Memory and thread-safe telemetry singleton

Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.github/workflows/openclaw-checks.yml

@@ -0,0 +1,100 @@
+name: openclaw checks
+
+on:
+  workflow_dispatch:
+  push:
+    branches: [main]
+    paths:
+      - 'openclaw/**'
+      - '.github/workflows/openclaw-checks.yml'
+  pull_request:
+    paths:
+      - 'openclaw/**'
+      - '.github/workflows/openclaw-checks.yml'
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: 'pnpm'
+          cache-dependency-path: openclaw/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd openclaw && pnpm install --frozen-lockfile
+
+      - name: Type check
+        run: cd openclaw && pnpm exec tsc --noEmit
+
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        node-version: [20, 22]
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js ${{ matrix.node-version }}
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: 'pnpm'
+          cache-dependency-path: openclaw/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd openclaw && pnpm install --frozen-lockfile
+
+      - name: Run tests with coverage
+        run: cd openclaw && pnpm exec vitest run --coverage
+
+      - name: Upload coverage to Codecov
+        if: matrix.node-version == 20
+        uses: codecov/codecov-action@v4
+        with:
+          flags: openclaw
+          directory: openclaw/coverage
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+          cache: 'pnpm'
+          cache-dependency-path: openclaw/pnpm-lock.yaml
+
+      - name: Install dependencies
+        run: cd openclaw && pnpm install --frozen-lockfile
+
+      - name: Build
+        run: cd openclaw && pnpm build
+
+      - name: Verify dist output exists
+        run: |
+          test -f openclaw/dist/index.js || (echo "Build output missing: dist/index.js" && exit 1)
+          test -f openclaw/dist/index.d.ts || (echo "Build output missing: dist/index.d.ts" && exit 1)

.github/workflows/ts-sdk-ci.yml

@@ -0,0 +1,110 @@
+name: TypeScript SDK CI
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - 'mem0-ts/**'
+      - '.github/workflows/ts-sdk-ci.yml'
+  pull_request:
+    paths:
+      - 'mem0-ts/**'
+
+jobs:
+  check_changes:
+    runs-on: ubuntu-latest
+    outputs:
+      ts_sdk_changed: ${{ steps.filter.outputs.ts_sdk }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v2
+        id: filter
+        with:
+          filters: |
+            ts_sdk:
+              - 'mem0-ts/**'
+
+  build_ts_sdk:
+    needs: check_changes
+    if: needs.check_changes.outputs.ts_sdk_changed == 'true'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        node-version: [20, 22]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v4
+        with:
+          version: 10
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: 'pnpm'
+          cache-dependency-path: mem0-ts/pnpm-lock.yaml
+
+      - name: Install dependencies
+        working-directory: mem0-ts
+        run: pnpm install --frozen-lockfile
+
+      - name: Lint
+        working-directory: mem0-ts
+        run: npx prettier --check .
+
+      - name: Build
+        working-directory: mem0-ts
+        run: pnpm run build
+
+      - name: Run unit tests
+        working-directory: mem0-ts
+        run: pnpm run test:unit
+
+      - name: Verify package exports
+        working-directory: mem0-ts
+        run: |
+          node -e "const m = require('./dist/index.js'); console.log('Client exports:', Object.keys(m).length)"
+          node -e "const m = require('./dist/oss/index.js'); console.log('OSS exports:', Object.keys(m).length)"
+
+      - name: Upload coverage
+        if: matrix.node-version == 20
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-report
+          path: mem0-ts/coverage/
+
+  integration_ts_sdk:
+    needs: build_ts_sdk
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 1
+      matrix:
+        node-version: [20, 22]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v4
+        with:
+          version: 10
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: 'pnpm'
+          cache-dependency-path: mem0-ts/pnpm-lock.yaml
+
+      - name: Install dependencies
+        working-directory: mem0-ts
+        run: pnpm install --frozen-lockfile
+
+      - name: Build
+        working-directory: mem0-ts
+        run: pnpm run build
+
+      - name: Run integration tests (with cleanup)
+        working-directory: mem0-ts
+        env:
+          MEM0_API_KEY: ${{ secrets.MEM0_API_KEY }}
+        run: pnpm run test:integration

LLM.md

@@ -273,7 +273,7 @@ config = MemoryConfig(
 
 ### Supported Providers
 
-#### LLM Providers (19 supported)
+#### LLM Providers (20 supported)
 - **openai** - OpenAI GPT models (default)
 - **anthropic** - Claude models
 - **gemini** - Google Gemini
@@ -284,6 +284,7 @@ config = MemoryConfig(
 - **azure_openai** - Azure OpenAI
 - **litellm** - LiteLLM proxy
 - **deepseek** - DeepSeek models
+- **minimax** - MiniMax models
 - **xai** - xAI models
 - **sarvam** - Sarvam AI
 - **lmstudio** - LM Studio local server

docs/api-reference.mdx

@@ -20,8 +20,8 @@ Mem0 provides a comprehensive REST API for integrating advanced memory capabilit
 Get started with Mem0 API in three simple steps:
 
 1. **[Add Memories](/api-reference/memory/add-memories)** - Store information and context from user conversations
-2. **[Search Memories](/api-reference/memory/v2-search-memories)** - Retrieve relevant memories using semantic search
-3. **[Get Memories](/api-reference/memory/v2-get-memories)** - Fetch all memories for a specific entity
+2. **[Search Memories](/api-reference/memory/search-memories)** - Retrieve relevant memories using semantic search
+3. **[Get Memories](/api-reference/memory/get-memories)** - Fetch all memories for a specific entity
 
 ---
 
@@ -32,7 +32,7 @@ Get started with Mem0 API in three simple steps:
     Store new memories from conversations and interactions
   </Card>
 
-  <Card title="Search Memories" icon="magnifying-glass" href="/api-reference/memory/v2-search-memories">
+  <Card title="Search Memories" icon="magnifying-glass" href="/api-reference/memory/search-memories">
     Find relevant memories using semantic search with filters
   </Card>
 
@@ -102,7 +102,7 @@ Get your API key from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-key
     Start storing memories via the REST API
   </Card>
 
-  <Card title="Search with Filters" icon="filter" href="/api-reference/memory/v2-search-memories">
+  <Card title="Search with Filters" icon="filter" href="/api-reference/memory/search-memories">
     Learn advanced search and filtering techniques
   </Card>
 </CardGroup>

docs/api-reference/entities/delete-user.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete User'
+description: "Remove a user entity from the Mem0 platform by entity type and ID using the DELETE endpoint."
 openapi: delete /v2/entities/{entity_type}/{entity_id}/
 ---

docs/api-reference/entities/get-users.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Users'
+description: "Retrieve a list of all user entities stored in the Mem0 platform using the GET endpoint."
 openapi: get /v1/entities/
 ---

docs/api-reference/events/get-event.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Get Event'
+description: "Retrieve details of a specific event by ID, including status and payload for async memory operations."
 openapi: get /v1/event/{event_id}/
 ---
 

docs/api-reference/events/get-events.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Get Events'
+description: "List recent events for your organization and project, useful for dashboards, alerting, and audit logging."
 openapi: get /v1/events/
 ---
 

docs/api-reference/memory/add-memories.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Add Memories'
+description: "Add facts, messages, or metadata to a user memory store with support for async processing and event tracking."
 openapi: post /v1/memories/
 ---
 

docs/api-reference/memory/batch-delete.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Batch Delete Memories'
+description: "Delete multiple memories in a single batch request using the Mem0 API DELETE endpoint."
 openapi: delete /v1/batch/
 ---

docs/api-reference/memory/batch-update.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Batch Update Memories'
+description: "Update multiple memories in a single batch request using the Mem0 API PUT endpoint."
 openapi: put /v1/batch/
 ---

docs/api-reference/memory/create-memory-export.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Create Memory Export'
+description: "Submit an export job to create a structured memory export using a customizable Pydantic schema and filters."
 openapi: post /v1/exports/
 ---
 

docs/api-reference/memory/delete-memories.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete Memories'
+description: "Delete all memories matching specified filters from the Mem0 memory store using the DELETE endpoint."
 openapi: delete /v1/memories/
 ---

docs/api-reference/memory/delete-memory.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete Memory'
+description: "Delete a single memory by its unique memory ID from the Mem0 platform using the DELETE endpoint."
 openapi: delete /v1/memories/{memory_id}/
 ---

docs/api-reference/memory/feedback.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Feedback'
+description: "Submit positive or negative feedback on memory results to help improve memory accuracy and relevance."
 openapi: post /v1/feedback/
 ---

docs/api-reference/memory/get-memories.mdx

@@ -1,5 +1,6 @@
 ---
 title: "Get Memories"
+description: "Retrieve memories with advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
 openapi: post /v2/memories/
 ---
 

docs/api-reference/memory/get-memory-export.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Get Memory Export'
+description: "Retrieve the latest structured memory export after submitting an export job, with optional entity filters."
 openapi: post /v1/exports/get
 ---
 

docs/api-reference/memory/get-memory.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Memory'
+description: "Retrieve a single memory by its unique memory ID from the Mem0 platform using the GET endpoint."
 openapi: get /v1/memories/{memory_id}/
 ---

docs/api-reference/memory/history-memory.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Memory History'
+description: "Retrieve the full change history of a specific memory to track how it has evolved over time."
 openapi: get /v1/memories/{memory_id}/history/
 ---

docs/api-reference/memory/search-memories.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Search Memories'
+description: "Search memories with semantic queries and advanced filtering using logical and comparison operators."
 openapi: post /v2/memories/search/
 ---
 

docs/api-reference/memory/update-memory.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Update Memory'
+description: "Update the content or metadata of a single memory by its unique ID using the PUT endpoint."
 openapi: put /v1/memories/{memory_id}/
 ---

docs/api-reference/organization/add-org-member.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Add Member'
+description: "Add a new member to an organization with a specified role such as READER or OWNER access level."
 openapi: post /api/v1/orgs/organizations/{org_id}/members/
 ---
 

docs/api-reference/organization/create-org.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Create Organization'
+description: "Create a new organization on the Mem0 platform to manage projects, members, and memory resources."
 openapi: post /api/v1/orgs/organizations/
 ---

docs/api-reference/organization/delete-org.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete Organization'
+description: "Permanently delete an organization and its associated resources from the Mem0 platform."
 openapi: delete /api/v1/orgs/organizations/{org_id}/
 ---

docs/api-reference/organization/get-org-members.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Members'
+description: "Retrieve a list of all members belonging to a specific organization on the Mem0 platform."
 openapi: get /api/v1/orgs/organizations/{org_id}/members/
 ---

docs/api-reference/organization/get-org.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Organization'
+description: "Retrieve details of a specific organization by its ID from the Mem0 platform using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/
 ---

docs/api-reference/organization/get-orgs.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Organizations'
+description: "Retrieve a list of all organizations associated with your Mem0 account using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/
 ---

docs/api-reference/project/add-project-member.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Add Member'
+description: "Add a new member to a project with a specified role such as READER or OWNER access level."
 openapi: post /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
 ---
 

docs/api-reference/project/create-project.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Create Project'
+description: "Create a new project within an organization on the Mem0 platform to isolate memory resources."
 openapi: post /api/v1/orgs/organizations/{org_id}/projects/
 ---

docs/api-reference/project/delete-project.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete Project'
+description: "Permanently delete a project and its associated data from the Mem0 platform by project ID."
 openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
 ---

docs/api-reference/project/get-project-members.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Members'
+description: "Retrieve a list of all members belonging to a specific project on the Mem0 platform."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
 ---

docs/api-reference/project/get-project.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Project'
+description: "Retrieve details of a specific project by its organization and project ID using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
 ---

docs/api-reference/project/get-projects.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Get Projects'
+description: "Retrieve a list of all projects within an organization on the Mem0 platform using the GET endpoint."
 openapi: get /api/v1/orgs/organizations/{org_id}/projects/
 ---

docs/api-reference/webhook/create-webhook.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Create Webhook'
+description: "Create a new webhook for a project to receive real-time notifications about memory events."
 openapi: post /api/v1/webhooks/projects/{project_id}/
 ---
 

docs/api-reference/webhook/delete-webhook.mdx

@@ -1,4 +1,5 @@
 ---
 title: 'Delete Webhook'
+description: "Delete an existing webhook by its ID to stop receiving notifications for memory events."
 openapi: delete /api/v1/webhooks/{webhook_id}/
 ---

docs/api-reference/webhook/get-webhook.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Get Webhook'
+description: "Retrieve webhook configuration details for a specific project on the Mem0 platform."
 openapi: get /api/v1/webhooks/projects/{project_id}/
 ---
 

docs/api-reference/webhook/update-webhook.mdx

@@ -1,5 +1,6 @@
 ---
 title: 'Update Webhook'
+description: "Update an existing webhook configuration, such as its URL or event subscriptions, by webhook ID."
 openapi: put /api/v1/webhooks/{webhook_id}/
 ---
 

docs/changelog.mdx

@@ -1,5 +1,6 @@
 ---
 title: "Product Updates"
+description: "Latest releases, bug fixes, and improvements for the Mem0 Python and TypeScript SDKs."
 mode: "wide"
 ---
 
@@ -7,6 +8,39 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-03-19" description="v1.0.7">
+
+**Bug Fixes:**
+- **Core:** Fixed control characters in LLM JSON responses causing parse failures (#4420)
+- **Core:** Replaced hardcoded US/Pacific timezone references with `timezone.utc` (#4404)
+- **Core:** Preserved `http_auth` in `_safe_deepcopy_config` for OpenSearch (#4418)
+- **Core:** Normalized malformed LLM fact output before embedding (#4224)
+- **Embeddings:** Pass `encoding_format='float'` in OpenAI embeddings for proxy compatibility (#4058)
+- **LLMs:** Fixed Ollama to pass tools to `client.chat` and parse `tool_calls` from response (#4176)
+- **Reranker:** Support nested LLM config in `LLMReranker` for non-OpenAI providers (#4405)
+- **Vector Stores:** Cast `vector_distance` to float in Redis search (#4377)
+
+**Improvements:**
+- **Embeddings:** Improved Ollama embedder with model name normalization and error handling (#4403)
+
+</Update>
+
+<Update label="2026-03-16" description="v1.0.6">
+
+**Bug Fixes:**
+- **Telemetry:** Fixed telemetry vector store initialization still running when `MEM0_TELEMETRY` is disabled (#4351)
+- **Core:** Removed destructive `vector_store.reset()` call from `delete_all()` that was wiping the entire vector store instead of deleting only the target memories (#4349)
+- **OSS:** `OllamaLLM` now respects the configured URL instead of always falling back to localhost (#4320)
+- **Core:** Fixed `KeyError` when LLM omits the `entities` key in tool call response (#4313)
+- **Prompts:** Ensured JSON instruction is included in prompts when using `json_object` response format (#4271)
+- **Core:** Fixed incorrect database parameter handling (#3913)
+
+**Dependencies:**
+- Updated LangChain dependencies to v1.0.0 (#4353)
+- Bumped protobuf dependency to 5.29.6 and extended upper bound to `<7.0.0` (#4326)
+
+</Update>
+
 <Update label="2026-03-03" description="v1.0.5">
 - **Telemetry Fix**
   - Fixed an issue where the PostHog client was initialized even after telemetry was disabled. Although events were not captured, the client was unnecessarily initialized.
@@ -729,6 +763,30 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
+<Update label="2026-03-19" description="v2.4.2">
+
+**Bug Fixes:**
+- **Client:** Fixed webhook `createWebhook` and `updateWebhook` API serialization
+- **Client:** Added missing `MEMORY_CATEGORIZED` event type to `WebhookEvent` enum
+- **Types:** Added `WebhookCreatePayload` and `WebhookUpdatePayload` for better type safety
+
+**Tests:**
+- Added end-to-end unit test coverage for the platform client — CRUD, batch, search, webhooks, users, project, and initialization (#4357)
+- Added real API integration tests for memory CRUD, batch operations, search, user management, project configuration, and webhook lifecycle (#4395)
+- Deleted obsolete e2e test files replaced by the new structured test suite (#4419)
+
+</Update>
+
+<Update label="2026-03-16" description="v2.4.1">
+
+**Bug Fixes:**
+- **Core:** Fixed code block content extraction — content inside code blocks is now properly extracted instead of being deleted (#4317)
+
+**Improvements:**
+- **Code Quality:** Fixed linting issues across the SDK (#4334)
+
+</Update>
+
 <Update label="2026-03-14" description="v2.4.0">
 
 **Bug Fixes:**

docs/components/embedders/config.mdx

@@ -1,5 +1,6 @@
 ---
 title: Configurations
+description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
 ---
 
 

docs/components/embedders/models/aws_bedrock.mdx

@@ -1,5 +1,6 @@
 ---
 title: AWS Bedrock
+description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
 ---
 
 To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.

docs/components/embedders/models/azure_openai.mdx

@@ -1,5 +1,6 @@
 ---
 title: Azure OpenAI
+description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
 ---
 
 To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure.

docs/components/embedders/models/google_AI.mdx

@@ -1,5 +1,6 @@
 ---
 title: Google AI
+description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
 ---
 
 To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).

docs/components/embedders/models/huggingface.mdx

@@ -1,5 +1,6 @@
 ---
 title: Hugging Face
+description: "Configure Hugging Face as an embedding provider in Mem0 for local embedding generation with open-source models."
 ---
 
 You can use embedding models from Huggingface to run Mem0 locally.

docs/components/embedders/models/langchain.mdx

@@ -1,5 +1,6 @@
 ---
 title: LangChain
+description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
 ---
 
 Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.

docs/components/embedders/models/lmstudio.mdx

@@ -1,3 +1,7 @@
+---
+title: "LM Studio"
+description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
+---
 You can use embedding models from LM Studio to run Mem0 locally.
 
 ### Usage

docs/components/embedders/models/ollama.mdx

@@ -1,3 +1,7 @@
+---
+title: "Ollama"
+description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
+---
 You can use embedding models from Ollama to run Mem0 locally.
 
 ### Usage

docs/components/embedders/models/openai.mdx

@@ -1,5 +1,6 @@
 ---
 title: OpenAI
+description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
 ---
 
 To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).

docs/components/embedders/models/together.mdx

@@ -1,5 +1,6 @@
 ---
 title: Together
+description: "Configure Together AI as an embedding provider in Mem0 with support for 768-dimensional embedding models."
 ---
 
 To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.xyz/settings/api-keys).

docs/components/embedders/models/vertexai.mdx

@@ -1,3 +1,7 @@
+---
+title: "Vertex AI"
+description: "Configure Google Cloud Vertex AI as an embedding provider in Mem0 with support for task-specific embedding types."
+---
 ### Vertex AI
 
 To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).

docs/components/embedders/overview.mdx

@@ -1,5 +1,6 @@
 ---
 title: Overview
+description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
 ---
 
 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.

docs/components/llms/config.mdx

@@ -1,5 +1,6 @@
 ---
 title: Configurations
+description: "Reference for LLM configuration options in Mem0 for Python and TypeScript, including value precedence rules."
 ---
 
 ## How to define configurations?

docs/components/llms/models/anthropic.mdx

@@ -1,5 +1,6 @@
 ---
 title: Anthropic
+description: "Configure Anthropic Claude models as the LLM provider in Mem0 with API key setup and usage examples."
 ---
 
 

docs/components/llms/models/aws_bedrock.mdx

@@ -1,5 +1,6 @@
 ---
 title: AWS Bedrock
+description: "Configure AWS Bedrock as an LLM provider in Mem0 with IAM authentication and Claude model support."
 ---
 
 ### Setup

docs/components/llms/models/azure_openai.mdx

@@ -1,5 +1,6 @@
 ---
 title: Azure OpenAI
+description: "Configure Azure OpenAI as an LLM provider in Mem0 with Azure Identity authentication and deployment settings."
 ---
 
 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>

docs/components/llms/models/deepseek.mdx

@@ -1,5 +1,6 @@
 ---
 title: DeepSeek
+description: "Configure DeepSeek as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
 ---
 
 To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").

docs/components/llms/models/google_AI.mdx

@@ -1,5 +1,6 @@
 ---
 title: Google AI
+description: "Configure Google Gemini as an LLM provider in Mem0 using the google.genai SDK and GOOGLE_API_KEY variable."
 ---
 
 To use the Gemini model, set the `GOOGLE_API_KEY` environment variable. You can obtain the Google/Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).

docs/components/llms/models/groq.mdx

@@ -1,5 +1,6 @@
 ---
 title: Groq
+description: "Configure Groq as an LLM provider in Mem0 for high-speed inference using LPU-powered language models."
 ---
 
 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.

docs/components/llms/models/langchain.mdx

@@ -1,5 +1,6 @@
 ---
 title: LangChain
+description: "Use LangChain as an LLM provider in Mem0 to integrate with various chat models through a unified interface."
 ---
 
 

docs/components/llms/models/litellm.mdx

@@ -1,3 +1,7 @@
+---
+title: "LiteLLM"
+description: "Use LiteLLM as an LLM provider in Mem0 to access over 100 language models through a unified interface."
+---
 [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.
 
 ## Usage

docs/components/llms/models/lmstudio.mdx

@@ -1,5 +1,6 @@
 ---
 title: LM Studio
+description: "Configure LM Studio as an LLM provider in Mem0 for running local language models via an OpenAI-compatible API."
 ---
 
 To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.

docs/components/llms/models/minimax.mdx

@@ -0,0 +1,56 @@
+---
+title: MiniMax
+description: "Configure MiniMax as an LLM provider in Mem0 with API key setup and optional custom endpoint configuration."
+---
+
+To use MiniMax LLM models, you have to set the `MINIMAX_API_KEY` environment variable. You can also optionally set `MINIMAX_API_BASE` if you need to use a different API endpoint (defaults to "https://api.minimax.io/v1").
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["MINIMAX_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder model
+
+config = {
+    "llm": {
+        "provider": "minimax",
+        "config": {
+            "model": "MiniMax-M2.7",  # default model
+            "temperature": 0.2,
+            "max_tokens": 2000,
+            "top_p": 1.0
+        }
+    }
+}
+
+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="alice", metadata={"category": "movies"})
+```
+
+You can also configure the API base URL in the config:
+
+```python
+config = {
+    "llm": {
+        "provider": "minimax",
+        "config": {
+            "model": "MiniMax-M2.7",
+            "minimax_base_url": "https://your-custom-endpoint.com",
+            "api_key": "your-api-key"  # alternatively to using environment variable
+        }
+    }
+}
+```
+
+## Config
+
+All available parameters for the `minimax` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/mistral_AI.mdx

@@ -1,5 +1,6 @@
 ---
 title: Mistral AI
+description: "Configure Mistral AI as an LLM provider in Mem0 using the litellm integration and Mixtral model family."
 ---
 
 To use mistral's models, please obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/models/ollama.mdx

@@ -1,5 +1,6 @@
 ---
 title: Ollama
+description: "Configure Ollama as an LLM provider in Mem0 for running local language models with tool-calling support."
 ---
 
 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool calling.

docs/components/llms/models/openai.mdx

@@ -1,5 +1,6 @@
 ---
 title: OpenAI
+description: "Configure OpenAI as an LLM provider in Mem0 with support for GPT models and Openrouter compatibility."
 ---
 
 To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).

docs/components/llms/models/sarvam.mdx

@@ -1,5 +1,6 @@
 ---
 title: Sarvam AI
+description: "Configure Sarvam AI as an LLM provider in Mem0, specializing in Indian language support with the Sarvam-M model."
 ---
 
 **Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.

docs/components/llms/models/together.mdx

@@ -1,5 +1,6 @@
 ---
 title: Together
+description: "Configure Together AI as an LLM provider in Mem0 with API key setup and Mixtral model configuration."
 ---
 
 To use Together LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from their [Account settings page](https://api.together.xyz/settings/api-keys).

docs/components/llms/models/vllm.mdx

@@ -1,5 +1,6 @@
 ---
 title: vLLM
+description: "Configure vLLM as an LLM provider in Mem0 for high-performance local inference with GPU-optimized serving."
 ---
 
 [vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.

docs/components/llms/models/xAI.mdx

@@ -1,5 +1,6 @@
 ---
 title: xAI
+description: "Configure xAI Grok models as an LLM provider in Mem0 with API key setup and usage examples."
 ---
 
 [xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.

docs/components/llms/overview.mdx

@@ -1,5 +1,6 @@
 ---
 title: Overview
+description: "Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more."
 ---
 
 Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
@@ -30,6 +31,7 @@ See the list of supported LLMs below.
   <Card title="Google AI" href="/components/llms/models/google_AI" />
   <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
   <Card title="DeepSeek" href="/components/llms/models/deepseek" />
+  <Card title="MiniMax" href="/components/llms/models/minimax" />
   <Card title="xAI" href="/components/llms/models/xAI" />
   <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
   <Card title="LM Studio" href="/components/llms/models/lmstudio" />

docs/components/rerankers/config.mdx

@@ -1,6 +1,6 @@
 ---
 title: Config
-description: "Configuration options for rerankers in Mem0"
+description: "Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings."
 ---
 
 ## Common Configuration Parameters

docs/components/rerankers/custom-prompts.mdx

@@ -1,5 +1,6 @@
 ---
 title: Custom Prompts
+description: "Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored."
 ---
 
 When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.

docs/components/rerankers/models/cohere.mdx

@@ -1,6 +1,6 @@
 ---
 title: Cohere
-description: "Reranking with Cohere"
+description: "Configure Cohere as a reranker in Mem0 with support for English and multilingual reranking models."
 ---
 
 Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.

docs/components/rerankers/models/llm.mdx

@@ -1,6 +1,6 @@
 ---
 title: LLM as Reranker
-description: 'Flexible reranking using LLMs'
+description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
 ---
 
 <Warning>

docs/components/rerankers/models/zero_entropy.mdx

@@ -1,6 +1,6 @@
 ---
 title: Zero Entropy
-description: 'Neural reranking with Zero Entropy'
+description: "Configure Zero Entropy neural reranking models in Mem0 with zerank-1 and zerank-1-small support."
 ---
 
 [Zero Entropy](https://www.zeroentropy.dev) provides neural reranking models that significantly improve search relevance with fast performance.

docs/components/rerankers/optimization.mdx

@@ -1,5 +1,6 @@
 ---
 title: Performance Optimization
+description: "Best practices for optimizing reranker performance in Mem0, covering candidate sizing, batching, and tuning."
 ---
 
 Optimizing reranker performance is crucial for maintaining fast search response times while improving result quality. This guide covers best practices for different reranker types.

docs/components/vectordbs/config.mdx

@@ -1,5 +1,6 @@
 ---
 title: Configurations
+description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
 ---
 
 ## How to define configurations?

docs/components/vectordbs/dbs/azure.mdx

@@ -1,5 +1,6 @@
 ---
 title: Azure AI Search
+description: "Use Azure AI Search as a vector store in Mem0 for managed vector search with service name and API key setup."
 ---
 
 [Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.

docs/components/vectordbs/dbs/azure_mysql.mdx

@@ -1,5 +1,6 @@
 ---
 title: Azure MySQL
+description: "Use Azure Database for MySQL as a vector store in Mem0 with JSON-based vector storage for semantic search."
 ---
 
 [Azure Database for MySQL](https://azure.microsoft.com/products/mysql) is a fully managed relational database service that provides enterprise-grade reliability and security. It supports JSON-based vector storage for semantic search capabilities in AI applications.

docs/components/vectordbs/dbs/baidu.mdx

@@ -1,5 +1,6 @@
 ---
 title: Baidu VectorDB (Mochow)
+description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval."
 ---
 
 [Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.

docs/components/vectordbs/dbs/cassandra.mdx

@@ -1,5 +1,6 @@
 ---
 title: Apache Cassandra
+description: "Use Apache Cassandra as a distributed vector store in Mem0 with semantic search over large-scale datasets."
 ---
 
 [Apache Cassandra](https://cassandra.apache.org/) is a highly scalable, distributed NoSQL database designed for handling large amounts of data across many commodity servers with no single point of failure. It supports vector storage for semantic search capabilities in AI applications and can scale to massive datasets with linear performance improvements.

docs/components/vectordbs/dbs/chroma.mdx

@@ -1,3 +1,7 @@
+---
+title: "Chroma"
+description: "Use Chroma as a vector database in Mem0 for local or cloud-hosted vector storage with built-in embedding support."
+---
 [Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.
 
 ### Usage

docs/components/vectordbs/dbs/databricks.mdx

@@ -1,3 +1,7 @@
+---
+title: "Databricks"
+description: "Use Databricks Vector Search as a serverless vector store in Mem0 with auto-updating indexes from Delta tables."
+---
 [Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.
 
 ### Usage

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -1,3 +1,7 @@
+---
+title: "Elasticsearch"
+description: "Use Elasticsearch as a vector database in Mem0 for distributed vector search using dense vectors and k-NN queries."
+---
 [Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.
 
 ### Installation

docs/components/vectordbs/dbs/faiss.mdx

@@ -1,3 +1,7 @@
+---
+title: "FAISS"
+description: "Use Facebook FAISS as a high-performance vector store in Mem0, optimized for memory usage and fast similarity search."
+---
 [FAISS](https://github.com/facebookresearch/faiss) is a library for efficient similarity search and clustering of dense vectors. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. FAISS is optimized for memory usage and search speed, making it an excellent choice for production environments.
 
 ### Usage

docs/components/vectordbs/dbs/langchain.mdx

@@ -1,5 +1,6 @@
 ---
 title: LangChain
+description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
 ---
 
 Mem0 supports LangChain as a provider for vector store integration. LangChain provides a unified interface to various vector databases, making it easy to integrate different vector store providers through a consistent API.

docs/components/vectordbs/dbs/milvus.mdx

@@ -1,3 +1,7 @@
+---
+title: "Milvus"
+description: "Use Milvus as an open-source vector database in Mem0, scalable from local development to production workloads."
+---
 [Milvus](https://milvus.io/) is an open-source vector database that suits AI applications of every size, from running a demo chatbot in a Jupyter notebook to building web-scale search that serves billions of users.
 
 ### Usage

docs/components/vectordbs/dbs/mongodb.mdx

@@ -1,3 +1,7 @@
+---
+title: "MongoDB"
+description: "Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries."
+---
 # MongoDB
 
 [MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -1,3 +1,7 @@
+---
+title: "Neptune Analytics"
+description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
+---
 # Neptune Analytics Vector Store
 
 [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.

docs/components/vectordbs/dbs/opensearch.mdx

@@ -1,3 +1,7 @@
+---
+title: "OpenSearch"
+description: "Use OpenSearch as a vector database in Mem0 with k-NN search support via AWS OpenSearch Service serverless collections."
+---
 [OpenSearch](https://opensearch.org/) is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
 
 ### Installation

docs/components/vectordbs/dbs/pgvector.mdx

@@ -1,3 +1,7 @@
+---
+title: "pgvector"
+description: "Use pgvector as a vector store in Mem0 for PostgreSQL-based vector similarity search with open-source simplicity."
+---
 [pgvector](https://github.com/pgvector/pgvector) is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run `CREATE EXTENSION IF NOT EXISTS vector;` to create the vector extension.
 
 ### Usage

docs/components/vectordbs/dbs/pinecone.mdx

@@ -1,3 +1,7 @@
+---
+title: "Pinecone"
+description: "Use Pinecone as a fully managed vector database in Mem0 with serverless deployment and namespace-based multi-tenancy."
+---
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
 
 > **New**: Pinecone integration now supports custom namespaces! Use the `namespace` parameter to logically separate data within the same index. This is especially useful for multi-tenant or multi-user applications.

docs/components/vectordbs/dbs/qdrant.mdx

@@ -1,3 +1,7 @@
+---
+title: "Qdrant"
+description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance similarity search at scale."
+---
 [Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.
 
 ### Usage

docs/components/vectordbs/dbs/redis.mdx

@@ -1,3 +1,7 @@
+---
+title: "Redis"
+description: "Use Redis as a real-time vector database in Mem0 for fast vector search using Redis Stack and redisvl."
+---
 [Redis](https://redis.io/) is a scalable, real-time database that can store, search, and analyze vector data.
 
 ### Installation

docs/components/vectordbs/dbs/s3_vectors.mdx

@@ -1,5 +1,6 @@
 ---
 title: Amazon S3 Vectors
+description: "Use Amazon S3 Vectors as a cost-optimized vector storage service in Mem0 with AWS credential authentication."
 ---
 
 [Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/) is a purpose-built, cost-optimized vector storage and query service for semantic search and AI applications. It provides S3-level elasticity and durability with sub-second query performance.

docs/components/vectordbs/dbs/supabase.mdx

@@ -1,3 +1,7 @@
+---
+title: "Supabase"
+description: "Use Supabase as a vector store in Mem0, powered by PostgreSQL and pgvector with HNSW indexing support."
+---
 [Supabase](https://supabase.com/) is an open-source Firebase alternative that provides a PostgreSQL database with pgvector extension for vector similarity search. It offers a powerful and scalable solution for storing and querying vector embeddings.
 
 Create a [Supabase](https://supabase.com/dashboard/projects) account and project, then get your connection string from Project Settings > Database. See the [docs](https://supabase.github.io/vecs/hosting/) for details.

docs/components/vectordbs/dbs/turbopuffer.mdx

@@ -0,0 +1,79 @@
+---
+title: "Turbopuffer"
+description: "Use Turbopuffer as a serverless vector database in Mem0 for low-latency search at scale with native metadata filtering."
+---
+[Turbopuffer](https://turbopuffer.com) is a serverless vector database optimized for low-latency search at scale. It offers cost-effective vector storage with native metadata filtering.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+os.environ["TURBOPUFFER_API_KEY"] = "tpuf_xxxxxxxxxxxx"
+
+config = {
+    "vector_store": {
+        "provider": "turbopuffer",
+        "config": {
+            "collection_name": "movie_preferences",
+            "embedding_model_dims": 1536,
+            "region": "gcp-us-central1",
+        }
+    }
+}
+
+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 thrillers but I love sci-fi."},
+    {"role": "assistant", "content": "Got it! I'll suggest sci-fi movies instead."}
+]
+
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+
+# Search memories
+results = m.search(query="sci-fi recommendations", user_id="alice")
+```
+
+### Config
+
+Here are the parameters available for configuring Turbopuffer:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | Name of the namespace/collection | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model (must match your chosen embedding model) | `1536` |
+| `api_key` | Turbopuffer API key | Environment variable: `TURBOPUFFER_API_KEY` |
+| `region` | Turbopuffer region | `gcp-us-central1` |
+| `distance_metric` | Distance metric for vector similarity (`cosine_distance` or `euclidean_squared`) | `cosine_distance` |
+| `batch_size` | Batch size for bulk operations | `100` |
+| `extra_params` | Additional parameters for the Turbopuffer client | `None` |
+
+### Regions
+
+| Region | Location |
+| --- | --- |
+| `gcp-us-central1` | Iowa, USA (Default) |
+| `aws-us-west-2` | Oregon, USA |
+
+### Config Example
+
+```python
+config = {
+    "vector_store": {
+        "provider": "turbopuffer",
+        "config": {
+            "collection_name": "my_memories",
+            "embedding_model_dims": 1536,
+            "api_key": "tpuf_xxxxxxxxxxxx",
+            "region": "aws-us-west-2",
+            "distance_metric": "cosine_distance",
+            "batch_size": 200,
+        }
+    }
+}
+```

docs/components/vectordbs/dbs/upstash-vector.mdx

@@ -1,3 +1,7 @@
+---
+title: "Upstash Vector"
+description: "Use Upstash Vector as a serverless vector database in Mem0 with optional built-in embedding models."
+---
 [Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database with built-in embedding models.
 
 ### Usage with Upstash embeddings

docs/components/vectordbs/dbs/valkey.mdx

@@ -1,3 +1,7 @@
+---
+title: "Valkey"
+description: "Use Valkey as an open-source vector store in Mem0 for high-performance key-value storage with vector search."
+---
 # Valkey Vector Store
 
 [Valkey](https://valkey.io/) is an open source (BSD) high-performance key/value datastore that supports a variety of workloads and rich datastructures including vector search.

docs/components/vectordbs/dbs/vectorize.mdx

@@ -1,3 +1,7 @@
+---
+title: "Cloudflare Vectorize"
+description: "Use Cloudflare Vectorize as a vector database in Mem0 for building AI-powered applications at the edge."
+---
 [Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) is a vector database offering from Cloudflare, allowing you to build AI-powered applications with vector embeddings.
 
 ### Usage

docs/components/vectordbs/dbs/vertex_ai.mdx

@@ -1,5 +1,6 @@
 ---
 title: Vertex AI Vector Search
+description: "Use Google Cloud Vertex AI Vector Search as a managed vector store in Mem0 with endpoint and index configuration."
 ---