chore: updating the sdk version and changelog (#4561)

Co-authored-by: Saket Aryan <saketaryan2002@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

.claude-plugin/marketplace.json

@@ -0,0 +1,18 @@
+{
+  "name": "mem0-plugins",
+  "owner": {
+    "name": "Mem0",
+    "email": "support@mem0.ai"
+  },
+  "metadata": {
+    "description": "Official Mem0 plugins for Claude"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": "./mem0-plugin",
+      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search to Claude workflows.",
+      "version": "0.1.0"
+    }
+  ]
+}

.cursor-plugin/marketplace.json

@@ -0,0 +1,18 @@
+{
+  "name": "mem0-plugins",
+  "owner": {
+    "name": "Mem0",
+    "email": "support@mem0.ai"
+  },
+  "metadata": {
+    "description": "Official Mem0 plugins for Cursor"
+  },
+  "plugins": [
+    {
+      "name": "mem0",
+      "source": "./mem0-plugin",
+      "description": "Mem0 memory layer for AI applications. Add persistent memory, personalization, and semantic search.",
+      "version": "0.1.0"
+    }
+  ]
+}

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,41 +1,55 @@
-name: 🐛 Bug Report
-description: Create a report to help us reproduce and fix the bug
+name: Bug Report
+description: Report a bug in mem0
+labels: ["bug"]
 
 body:
-- type: markdown
-  attributes:
-    value: >
-      #### Before submitting a bug, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/embedchain/embedchain/issues?q=is%3Aissue+sort%3Acreated-desc+).
-- type: textarea
-  attributes:
-    label: 🐛 Describe the bug
-    description: |
-      Please provide a clear and concise description of what the bug is.
+  - type: dropdown
+    id: component
+    attributes:
+      label: Component
+      description: Which part of mem0 is affected?
+      options:
+        - Core / Python SDK
+        - TypeScript SDK
+        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
+        - Graph Memory (Neo4j, Memgraph, etc.)
+        - Ollama / Local Models
+        - OpenClaw
+        - REST API
+        - Other
+    validations:
+      required: true
 
-      If relevant, add a minimal example so that we can reproduce the error by running the code. It is very important for the snippet to be as succinct (minimal) as possible, so please take time to trim down any irrelevant code to help us debug efficiently. We are going to copy-paste your code and we expect to get the same result as you did: avoid any external data, and include the relevant imports, etc. For example:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      value: |
+        ### Summary
 
-      ```python
-      # All necessary imports at the beginning
-      import embedchain as ec
-      # Your code goes here
+        A clear summary of the bug.
 
+        ### Steps to Reproduce
 
-      ```
+        ```python
+        from mem0 import Memory
 
-      Please also paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full** traceback of the exception. It may be relevant to wrap error messages in ```` ```triple quotes blocks``` ````.
-    placeholder: |
-      A clear and concise description of what the bug is.
+        m = Memory()
+        # Your code here...
+        ```
 
-      ```python
-      Sample code to reproduce the problem
-      ```
+        ### Expected Behavior
 
-      ```
-      The error message you got, with the full traceback.
-      ````
-  validations:
-    required: true
-- type: markdown
-  attributes:
-    value: >
-      Thanks for contributing 🎉!
+        What you expected to happen.
+
+        ### Actual Behavior
+
+        What actually happened. Paste the full error traceback if applicable.
+
+        ### Environment
+
+        - mem0 version:
+        - Python/Node version:
+        - OS:
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +1,8 @@
 blank_issues_enabled: true
 contact_links:
-  - name: 1-on-1 Session
-    url: https://cal.com/taranjeetio/ec
-    about: Speak directly with Taranjeet, the founder, to discuss issues, share feedback, or explore improvements for Embedchain
-  - name: Discord
+  - name: Discord Community
     url: https://discord.gg/6PzXDgEjG5
-    about: General community discussions
+    about: Ask questions and discuss with the community
+  - name: Documentation
+    url: https://docs.mem0.ai
+    about: Read the official mem0 documentation

.github/ISSUE_TEMPLATE/documentation_issue.yml

@@ -1,11 +1,23 @@
-name: Documentation
-description: Report an issue related to the Embedchain docs.
-title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
+name: Documentation Issue
+description: Report an issue or suggest an improvement to the mem0 docs
+labels: ["documentation"]
 
 body:
-- type: textarea
-  attributes:
-    label: "Issue with current documentation:"
-    description: >
-      Please make sure to leave a reference to the document/code you're
-      referring to.
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      value: |
+        ### Page
+
+        Link to the docs page: https://docs.mem0.ai/...
+
+        ### What's Wrong or Missing
+
+        Describe what's incorrect, unclear, or missing.
+
+        ### Suggested Fix
+
+        How should the docs be improved?
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,23 +1,41 @@
-name: 🚀 Feature request
-description: Submit a proposal/request for a new Embedchain feature
+name: Feature Request
+description: Suggest a new feature or improvement for mem0
+labels: ["enhancement"]
 
 body:
-- type: textarea
-  id: feature-request
-  attributes:
-    label: 🚀 The feature
-    description: >
-      A clear and concise description of the feature proposal
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: Motivation, pitch
-    description: >
-      Please outline the motivation for the proposal. Is your feature request related to a specific problem? e.g., *"I'm working on X and would like Y to be possible"*. If this is related to another GitHub issue, please link here too.
-  validations:
-    required: true
-- type: markdown
-  attributes:
-    value: >
-      Thanks for contributing 🎉!
+  - type: dropdown
+    id: component
+    attributes:
+      label: Component
+      description: Which part of mem0 does this relate to?
+      options:
+        - Core / Python SDK
+        - TypeScript SDK
+        - Vector Store (Qdrant, PGVector, Redis, Chroma, etc.)
+        - Graph Memory (Neo4j, Memgraph, etc.)
+        - Ollama / Local Models
+        - OpenClaw
+        - REST API
+        - Benchmarks / Evals
+        - Other
+    validations:
+      required: true
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Description
+      value: |
+        ### Use Case
+
+        What problem are you trying to solve?
+
+        ### Proposed Solution
+
+        How should this work? Include API examples or pseudocode if helpful.
+
+        ### Alternatives Considered
+
+        Any workarounds you've tried or other approaches considered.
+    validations:
+      required: true

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,41 +1,38 @@
+## Linked Issue
+
+Closes #<!-- issue number -->
+
 ## Description
 
-Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
+<!-- What does this PR do? Why is it needed? -->
 
-Fixes # (issue)
+## Type of Change
 
-## Type of change
-
-Please delete options that are not relevant.
-
-- [ ] Bug fix (non-breaking change which fixes an issue)
-- [ ] New feature (non-breaking change which adds functionality)
-- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
-- [ ] Refactor (does not change functionality, e.g. code style improvements, linting)
+- [ ] Bug fix (non-breaking change that fixes an issue)
+- [ ] New feature (non-breaking change that adds functionality)
+- [ ] Breaking change (fix or feature that would cause existing functionality to change)
+- [ ] Refactor (no functional changes)
 - [ ] Documentation update
 
-## How Has This Been Tested?
+## Breaking Changes
 
-Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration
+<!-- If this is a breaking change, describe what breaks and the migration path. Delete this section if not applicable. -->
 
-Please delete options that are not relevant.
+N/A
 
-- [ ] Unit Test
-- [ ] Test Script (please provide)
+## Test Coverage
 
-## Checklist:
+- [ ] I added/updated unit tests
+- [ ] I added/updated integration tests
+- [ ] I tested manually (describe below)
+- [ ] No tests needed (explain why)
 
-- [ ] My code follows the style guidelines of this project
-- [ ] I have performed a self-review of my own code
-- [ ] I have commented my code, particularly in hard-to-understand areas
-- [ ] I have made corresponding changes to the documentation
-- [ ] My changes generate no new warnings
-- [ ] I have added tests that prove my fix is effective or that my feature works
-- [ ] New and existing unit tests pass locally with my changes
-- [ ] Any dependent changes have been merged and published in downstream modules
-- [ ] I have checked my code and corrected any misspellings
+<!-- Describe how you tested this, or link to CI results. -->
 
-## Maintainer Checklist
+## Checklist
 
-- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
-- [ ] Made sure Checks passed
+- [ ] My code follows the project's style guidelines
+- [ ] I have performed a self-review of my code
+- [ ] I have added tests that prove my fix/feature works
+- [ ] New and existing tests pass locally
+- [ ] I have updated documentation if needed

.github/advanced-issue-labeler.yml

@@ -0,0 +1,18 @@
+# Maps dropdown selections to GitHub labels
+# Used by the advanced-issue-labeler GitHub Action
+
+component:
+  - label: "sdk-python"
+    matcher: "Core / Python SDK"
+  - label: "sdk-typescript"
+    matcher: "TypeScript SDK"
+  - label: "vector-store"
+    matcher: "Vector Store"
+  - label: "graph-memory"
+    matcher: "Graph Memory"
+  - label: "ollama"
+    matcher: "Ollama"
+  - label: "openclaw"
+    matcher: "OpenClaw"
+  - label: "rest-api"
+    matcher: "REST API"

.github/workflows/issue-labeler.yml

@@ -0,0 +1,39 @@
+name: Auto-label issues
+
+on:
+  issues:
+    types: [opened]
+
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  label:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: stefanbuck/github-issue-parser@v3
+        id: issue-parser
+        with:
+          template-path: .github/ISSUE_TEMPLATE/bug_report.yml
+
+      - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
+        with:
+          issue-form: ${{ steps.issue-parser.outputs.jsonString }}
+          section: component
+          token: ${{ secrets.GITHUB_TOKEN }}
+          config-path: .github/advanced-issue-labeler.yml
+
+      - uses: stefanbuck/github-issue-parser@v3
+        id: feature-parser
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
+        with:
+          template-path: .github/ISSUE_TEMPLATE/feature_request.yml
+
+      - uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
+        if: contains(github.event.issue.labels.*.name, 'enhancement')
+        with:
+          issue-form: ${{ steps.feature-parser.outputs.jsonString }}
+          section: component
+          token: ${{ secrets.GITHUB_TOKEN }}
+          config-path: .github/advanced-issue-labeler.yml

.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/stale.yml

@@ -0,0 +1,48 @@
+name: Close stale issues
+
+on:
+  schedule:
+    - cron: '0 0 * * *'
+  workflow_dispatch:
+
+permissions:
+  issues: write
+  pull-requests: write
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v9
+        with:
+          # Issue settings
+          days-before-issue-stale: 90
+          days-before-issue-close: 14
+          stale-issue-label: 'stale'
+          stale-issue-message: >
+            This issue has been automatically marked as stale because it has not
+            had any activity in 90 days. It will be closed in 14 days if no
+            further activity occurs. If this is still relevant, please leave a
+            comment or remove the `stale` label.
+          close-issue-message: >
+            This issue has been closed due to inactivity. If this is still
+            relevant, feel free to reopen it or create a new issue.
+
+          # PR settings — mark stale but never auto-close
+          days-before-pr-stale: 90
+          days-before-pr-close: -1
+          stale-pr-label: 'stale'
+          stale-pr-message: >
+            This pull request has been automatically marked as stale because it
+            has not had any activity in 90 days. Please update your branch and
+            address any review comments, or it may be closed in the future.
+
+          # Exempt these labels from stale processing
+          exempt-issue-labels: 'P0-critical,P1-high,good first issue,security'
+          exempt-pr-labels: 'P0-critical,P1-high'
+
+          # Remove stale label when there is new activity
+          remove-stale-when-updated: true
+
+          # Process up to 100 issues per run to stay within API limits
+          operations-per-run: 100

.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

README.md

@@ -15,8 +15,6 @@
   <a href="https://mem0.dev/DiG">Join Discord</a>
   ·
   <a href="https://mem0.dev/demo">Demo</a>
-  ·
-  <a href="https://mem0.dev/openmemory">OpenMemory</a>
 </p>
 
 <p align="center">

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 +8,80 @@ mode: "wide"
 <Tabs>
 <Tab title="Python">
 
+<Update label="2026-03-26" description="v1.0.8">
+
+**New Features & Updates:**
+- **Vector Stores:** Integrated Turbopuffer as a vector database provider ([#4428](https://github.com/mem0ai/mem0/pull/4428))
+- **LLMs:** Added MiniMax LLM provider ([#4431](https://github.com/mem0ai/mem0/pull/4431))
+
+**Bug Fixes:**
+- **Core:** Fixed merging of multiple filter operators for the same key ([#4559](https://github.com/mem0ai/mem0/pull/4559))
+- **Core:** Prevented in-place mutation of metadata in `_create_memory` ([#4529](https://github.com/mem0ai/mem0/pull/4529))
+- **Core:** Preserved custom metadata when updating memory ([#4495](https://github.com/mem0ai/mem0/pull/4495))
+- **Core:** Handled chatty LLM responses in JSON parsing ([#4525](https://github.com/mem0ai/mem0/pull/4525))
+- **Core:** Prevented double embedding in `mem0.add` ([#3996](https://github.com/mem0ai/mem0/pull/3996))
+- **Core:** Raised `ValueError` when deleting nonexistent memory ([#4455](https://github.com/mem0ai/mem0/pull/4455))
+- **Core:** Cleaned up graph store data on `Memory.delete()` ([#4505](https://github.com/mem0ai/mem0/pull/4505))
+- **Vector Stores:** Prevented SQL injection in Databricks vector store ([#4558](https://github.com/mem0ai/mem0/pull/4558))
+- **Vector Stores:** Upgraded MongoDB vector store from deprecated `knnVector` to GA `vectorSearch` ([#3995](https://github.com/mem0ai/mem0/pull/3995))
+- **Vector Stores:** Prevented embedding corruption in Valkey and Redis when vector is `None` ([#4362](https://github.com/mem0ai/mem0/pull/4362))
+- **Vector Stores:** Accepted default `/tmp/chroma` path in `ChromaDbConfig` validator ([#4179](https://github.com/mem0ai/mem0/pull/4179))
+- **Vector Stores:** Wrapped vector and payload in lists for `Langchain.update` ([#4446](https://github.com/mem0ai/mem0/pull/4446))
+- **Graph:** Soft-delete graph relationships instead of hard `DELETE` ([#4188](https://github.com/mem0ai/mem0/pull/4188))
+- **Graph:** Sanitized hyphens in Neo4j Cypher relationship names ([#4154](https://github.com/mem0ai/mem0/pull/4154))
+- **Graph:** Used root LLM config as fallback for graph store instead of hardcoded OpenAI default ([#4466](https://github.com/mem0ai/mem0/pull/4466))
+- **Qdrant:** Fixed `do not remove local path on init` ([#4475](https://github.com/mem0ai/mem0/pull/4475))
+- **Qdrant:** Implemented enhanced metadata filtering operators ([#4127](https://github.com/mem0ai/mem0/pull/4127))
+- **Embeddings:** Fixed OpenAI embedding dimensions ([#4481](https://github.com/mem0ai/mem0/pull/4481))
+- **LLMs:** Omitted `topP` for Anthropic Converse in Bedrock; used `AWSBedrockConfig` in `LlmFactory` ([#4469](https://github.com/mem0ai/mem0/pull/4469))
+- **LLMs:** Avoided sending both `temperature` and `top_p` to Anthropic API ([#4471](https://github.com/mem0ai/mem0/pull/4471))
+- **LLMs:** Handled `None` content and empty candidates in `GeminiLLM` parsing ([#4462](https://github.com/mem0ai/mem0/pull/4462))
+- **LLMs:** Added missing `_parse_response` to `AzureOpenAIStructuredLLM` ([#4434](https://github.com/mem0ai/mem0/pull/4434))
+- **History:** Added timestamps for `DELETE` operations in history ([#4492](https://github.com/mem0ai/mem0/pull/4492))
+
+**Improvements:**
+- **Vector Stores:** Added vector validation to OpenSearchDB to ensure non-null, non-empty, and correct-dimension vectors ([#4472](https://github.com/mem0ai/mem0/pull/4472))
+
+</Update>
+
+<Update label="2026-03-19" description="v1.0.7">
+
+**Bug Fixes:**
+- **Core:** Fixed control characters in LLM JSON responses causing parse failures ([#4420](https://github.com/mem0ai/mem0/pull/4420))
+- **Core:** Replaced hardcoded US/Pacific timezone references with `timezone.utc` ([#4404](https://github.com/mem0ai/mem0/pull/4404))
+- **Core:** Preserved `http_auth` in `_safe_deepcopy_config` for OpenSearch ([#4418](https://github.com/mem0ai/mem0/pull/4418))
+- **Core:** Normalized malformed LLM fact output before embedding ([#4224](https://github.com/mem0ai/mem0/pull/4224))
+- **Embeddings:** Pass `encoding_format='float'` in OpenAI embeddings for proxy compatibility ([#4058](https://github.com/mem0ai/mem0/pull/4058))
+- **LLMs:** Fixed Ollama to pass tools to `client.chat` and parse `tool_calls` from response ([#4176](https://github.com/mem0ai/mem0/pull/4176))
+- **Reranker:** Support nested LLM config in `LLMReranker` for non-OpenAI providers ([#4405](https://github.com/mem0ai/mem0/pull/4405))
+- **Vector Stores:** Cast `vector_distance` to float in Redis search ([#4377](https://github.com/mem0ai/mem0/pull/4377))
+
+**Improvements:**
+- **Embeddings:** Improved Ollama embedder with model name normalization and error handling ([#4403](https://github.com/mem0ai/mem0/pull/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.
+</Update>
+
 <Update label="2026-02-17" description="v1.0.4">
 
 **New Features & Updates:**
@@ -724,6 +799,74 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
+<Update label="2026-03-26" description="v2.4.3">
+
+**New Features & Updates:**
+- **OSS:** Added pgvector support to NodeJS OSS `VectorStoreFactory` ([#3997](https://github.com/mem0ai/mem0/pull/3997))
+
+**Bug Fixes:**
+- **OSS:** Made pgvector `pg` import compatible with ESM ([#4544](https://github.com/mem0ai/mem0/pull/4544))
+- **OSS:** Registered pgvector in `VectorStoreFactory` ([#4502](https://github.com/mem0ai/mem0/pull/4502))
+- **OSS:** Used root LLM config as fallback for graph store instead of hardcoded OpenAI default ([#4466](https://github.com/mem0ai/mem0/pull/4466))
+- **OSS:** Fixed `toCamelCase` in Redis `get` method for the payload ([#3172](https://github.com/mem0ai/mem0/pull/3172))
+- **Client:** Fixed Zod Schema incompatibility with OpenAI Structured Outputs API ([#3462](https://github.com/mem0ai/mem0/pull/3462))
+
+</Update>
+
+<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:**
+- **OSS Storage:** Fixed `SQLITE_CANTOPEN` errors when running as a LaunchAgent, systemd service, or in containers where `process.cwd()` is read-only (e.g. `/`). Default `vector_store.db` location changed from `process.cwd()/vector_store.db` to `~/.mem0/vector_store.db`.
+- **OSS Storage:** Fixed `historyDbPath` config being silently ignored — config merging always overwrote it with defaults. Top-level `historyDbPath` is now correctly propagated into `historyStore.config` with proper precedence.
+- **OSS Storage:** Added `ensureSQLiteDirectory()` — parent directories for SQLite database files are now auto-created before opening, preventing `SQLITE_CANTOPEN` when using nested paths.
+
+**Improvements:**
+- **Migration:** Added deprecation warning when an existing `vector_store.db` is found at the old `process.cwd()` location, guiding users to move it or set `vectorStore.config.dbPath` explicitly.
+- **Config:** Limited default SQLite config spreading to only SQLite history providers, preventing config leaking into Supabase or other providers.
+
+</Update>
+
+<Update label="2026-03-09" description="v2.3.0">
+
+**Breaking Changes:**
+- **Dependencies:** Minimum Node.js version for OSS sqlite features is now Node 20+ (due to `better-sqlite3` v12)
+
+**Bug Fixes:**
+- **OSS Storage:** Replaced `sqlite3` with `better-sqlite3` to fix native binding resolution failures under jiti-based loaders (e.g. OpenClaw plugin system). Fixes issues where the `bindings` module walked V8 stack frames with synthetic filenames, failing to locate the native `.node` addon.
+- **OSS Storage:** Fixed async init race condition in `SQLiteManager` — `init()` is now synchronous
+- **OSS Vector Store:** Migrated `MemoryVectorStore` from `sqlite3` to `better-sqlite3` with transactional batch inserts
+
+**Improvements:**
+- **Performance:** Cached prepared statements in `SQLiteManager` for faster history operations
+- **Performance:** Batch `insert()` in `MemoryVectorStore` wrapped in a transaction for atomicity
+- **Build:** Updated `tsup.config.ts` externals from `sqlite3` to `better-sqlite3`
+
+</Update>
+
 <Update label="2026-02-17" description="v2.2.3">
 
 **New Features & Updates:**

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

@@ -1,63 +1,57 @@
 ---
 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.
 
 ## Default Prompt
 
-The default LLM reranker prompt is designed to be general-purpose:
+The default LLM reranker prompt scores each memory individually on a 0.0-1.0 scale:
 
 ```
-Given a query and a list of memory entries, rank the memory entries based on their relevance to the query.
-Rate each memory on a scale of 1-10 where 10 is most relevant.
+You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
 
-Query: {query}
+Score the relevance on a scale from 0.0 to 1.0, where:
+- 1.0 = Perfectly relevant and directly answers the query
+- 0.8-0.9 = Highly relevant with good information
+- 0.6-0.7 = Moderately relevant with some useful information
+- 0.4-0.5 = Slightly relevant with limited useful information
+- 0.0-0.3 = Not relevant or no useful information
 
-Memory entries:
-{memories}
+Query: "{query}"
+Document: "{document}"
 
-Provide your ranking as a JSON array with scores for each memory.
+Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
 ```
 
 ## Custom Prompt Configuration
 
-You can provide a custom prompt template when configuring the LLM reranker:
+You can provide a custom prompt template using the `scoring_prompt` parameter:
 
 ```python
 from mem0 import Memory
 
 custom_prompt = """
-You are an expert at ranking memories for a personal AI assistant.
-Given a user query and a list of memory entries, rank each memory based on:
-1. Direct relevance to the query
-2. Temporal relevance (recent memories may be more important)
-3. Emotional significance
-4. Actionability
+You are an expert at evaluating memories for a personal AI assistant.
+Given a user query and a memory entry, score how relevant the memory is.
+Consider direct relevance, temporal relevance, and actionability.
 
-Query: {query}
-User Context: {user_context}
+Query: "{query}"
+Memory: "{document}"
 
-Memory entries:
-{memories}
-
-Rate each memory from 1-10 and provide reasoning.
-Return as JSON: {{"rankings": [{{"index": 0, "score": 8, "reason": "..."}}]}}
+Provide only a single numerical score between 0.0 and 1.0.
 """
 
 config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4.1-nano-2025-04-14",
-                    "api_key": "your-openai-key"
-                }
-            },
-            "custom_prompt": custom_prompt,
-            "top_n": 5
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-openai-key",
+            "scoring_prompt": custom_prompt,
+            "top_k": 5
         }
     }
 }
@@ -69,12 +63,14 @@ memory = Memory.from_config(config)
 
 Your custom prompt can use the following variables:
 
-| Variable         | Description                           |
-| ---------------- | ------------------------------------- |
-| `{query}`        | The search query                      |
-| `{memories}`     | The list of memory entries to rank    |
-| `{user_id}`      | The user ID (if available)            |
-| `{user_context}` | Additional user context (if provided) |
+| Variable     | Description                   |
+| ------------ | ----------------------------- |
+| `{query}`    | The search query              |
+| `{document}` | The memory entry being scored |
+
+<Note>
+  Both `{query}` and `{document}` are required in your custom prompt. The LLM reranker scores each memory individually against the query, so the prompt is called once per candidate memory.
+</Note>
 
 ## Domain-Specific Examples
 
@@ -88,13 +84,10 @@ Prioritize memories that:
 - Show previous resolution patterns
 - Indicate customer preferences or constraints
 
-Query: {query}
-Customer Context: Previous interactions with this customer
+Query: "{query}"
+Memory: "{document}"
 
-Memories:
-{memories}
-
-Rank each memory 1-10 based on support relevance.
+Score relevance from 0.0 to 1.0.
 """
 ```
 
@@ -102,19 +95,16 @@ Rank each memory 1-10 based on support relevance.
 
 ```python
 educational_prompt = """
-Rank these learning memories for a student query.
+Score this learning memory for relevance to a student query.
 Consider:
 - Prerequisite knowledge requirements
 - Learning progression and difficulty
 - Relevance to current learning objectives
 
-Student Query: {query}
-Learning Context: {user_context}
+Student Query: "{query}"
+Memory: "{document}"
 
-Available memories:
-{memories}
-
-Score each memory for educational value (1-10).
+Score educational relevance from 0.0 to 1.0.
 """
 ```
 
@@ -122,73 +112,64 @@ Score each memory for educational value (1-10).
 
 ```python
 personal_assistant_prompt = """
-Rank personal memories for relevance to the user's query.
+Score this personal memory for relevance to the user's query.
 Consider:
 - Recent vs. historical importance
 - Personal preferences and habits
-- Contextual relationships between memories
+- Contextual relationships
 
-Query: {query}
-Personal context: {user_context}
+Query: "{query}"
+Memory: "{document}"
 
-Memories to rank:
-{memories}
-
-Provide relevance scores (1-10) with brief explanations.
+Provide relevance score from 0.0 to 1.0.
 """
 ```
 
 ## Advanced Prompt Techniques
 
-### Multi-Criteria Ranking
+### Multi-Criteria Scoring
 
 ```python
 multi_criteria_prompt = """
-Evaluate memories using multiple criteria:
+Evaluate this memory using multiple criteria:
 
 1. RELEVANCE (40%): How directly related to the query
-2. RECENCY (20%): How recent the memory is
+2. RECENCY (20%): How recent the memory appears to be
 3. IMPORTANCE (25%): Personal or business significance
 4. ACTIONABILITY (15%): How useful for next steps
 
-Query: {query}
-Context: {user_context}
+Query: "{query}"
+Memory: "{document}"
 
-Memories:
-{memories}
-
-For each memory, provide:
-- Overall score (1-10)
-- Breakdown by criteria
-- Final ranking recommendation
-
-Format: JSON with detailed scoring
+Compute a weighted score from 0.0 to 1.0 based on these criteria.
+Provide only the final numerical score.
 """
 ```
 
-### Contextual Ranking
+### Chain-of-Thought Scoring
 
 ```python
-contextual_prompt = """
-Consider the following context when ranking memories:
-- Current user situation: {user_context}
-- Time of day: {current_time}
-- Recent activities: {recent_activities}
+reasoning_prompt = """
+Evaluate this memory's relevance step by step:
 
-Query: {query}
+1. What is the main intent of the query?
+2. What key information does the memory contain?
+3. How directly does the memory address the query?
 
-Rank these memories considering both direct relevance and contextual appropriateness:
-{memories}
+Based on this analysis, provide a single relevance score from 0.0 to 1.0.
 
-Provide contextually-aware relevance scores (1-10).
+Query: "{query}"
+Memory: "{document}"
+
+Score:
 """
 ```
 
 ## Best Practices
 
 1. **Be Specific**: Clearly define what makes a memory relevant for your use case
-2. **Use Examples**: Include examples in your prompt for better model understanding
-3. **Structure Output**: Specify the exact JSON format you want returned
+2. **Use 0.0-1.0 Scale**: The score extractor expects values between 0.0 and 1.0
+3. **Request Only the Score**: Ask for just the numerical score to improve extraction reliability
 4. **Test Iteratively**: Refine your prompt based on actual ranking performance
 5. **Consider Token Limits**: Keep prompts concise while being comprehensive
 
@@ -205,7 +186,7 @@ prompts = [
 ]
 
 for i, prompt in enumerate(prompts):
-    config["reranker"]["config"]["custom_prompt"] = prompt
+    config["reranker"]["config"]["scoring_prompt"] = prompt
     memory = Memory.from_config(config)
 
     results = memory.search("test query", user_id="test_user")
@@ -215,6 +196,6 @@ for i, prompt in enumerate(prompts):
 ## Common Issues
 
 - **Too Long**: Keep prompts under token limits for your chosen LLM
-- **Too Vague**: Be specific about ranking criteria
-- **Inconsistent Format**: Ensure JSON output format is clearly specified
-- **Missing Context**: Include relevant variables for your use case
+- **Too Vague**: Be specific about scoring criteria
+- **Wrong Scale**: Use 0.0-1.0 scale to match the default score extractor
+- **Extra Output**: Ask for only the numeric score — extra text can confuse score extraction

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/llm_reranker.mdx

@@ -18,13 +18,9 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4",
-                    "api_key": "your-openai-api-key"
-                }
-            }
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-openai-api-key"
         }
     }
 }
@@ -36,11 +32,14 @@ m = Memory.from_config(config)
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `llm` | dict | Required | LLM configuration object |
-| `top_k` | int | 10 | Number of results to rerank |
+| `provider` | str | `"openai"` | LLM provider (openai, anthropic, etc.) |
+| `model` | str | `"gpt-4o-mini"` | LLM model to use for reranking |
+| `api_key` | str | None | API key for the LLM provider |
+| `top_k` | int | None | Number of top documents to return after reranking |
 | `temperature` | float | 0.0 | LLM temperature for consistency |
-| `custom_prompt` | str | None | Custom reranking prompt |
-| `score_range` | tuple | (0, 10) | Score range for relevance |
+| `max_tokens` | int | 100 | Maximum tokens for LLM response |
+| `scoring_prompt` | str | None | Custom prompt template for scoring documents |
+| `llm` | dict | None | Optional nested LLM config for provider-specific fields (e.g., `ollama_base_url`, `azure_endpoint`). Overrides top-level `provider`/`model`/`api_key` when provided. |
 
 ### Advanced Configuration
 
@@ -49,20 +48,19 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "anthropic",
-                "config": {
-                    "model": "claude-3-sonnet-20240229",
-                    "api_key": "your-anthropic-api-key"
-                }
-            },
+            "provider": "anthropic",
+            "model": "claude-sonnet-4-20250514",
+            "api_key": "your-anthropic-api-key",
             "top_k": 15,
             "temperature": 0.0,
-            "score_range": (1, 5),
-            "custom_prompt": """
-            Rate the relevance of each memory to the query on a scale of 1-5.
+            "scoring_prompt": """
+            Rate the relevance of each memory to the query on a scale of 0.0-1.0.
             Consider semantic similarity, context, and practical utility.
             Only provide the numeric score.
+
+            Query: "{query}"
+            Document: "{document}"
+            Score:
             """
         }
     }
@@ -78,14 +76,10 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4",
-                    "api_key": "your-openai-api-key",
-                    "temperature": 0.0
-                }
-            }
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-openai-api-key",
+            "temperature": 0.0
         }
     }
 }
@@ -98,13 +92,9 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "anthropic",
-                "config": {
-                    "model": "claude-3-sonnet-20240229",
-                    "api_key": "your-anthropic-api-key"
-                }
-            }
+            "provider": "anthropic",
+            "model": "claude-sonnet-4-20250514",
+            "api_key": "your-anthropic-api-key"
         }
     }
 }
@@ -117,10 +107,12 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
+            "provider": "ollama",
+            "model": "llama3.2",
             "llm": {
                 "provider": "ollama",
                 "config": {
-                    "model": "llama2",
+                    "model": "llama3.2",
                     "ollama_base_url": "http://localhost:11434"
                 }
             }
@@ -129,6 +121,10 @@ config = {
 }
 ```
 
+<Note>
+  For providers like Ollama that need extra fields (e.g., `ollama_base_url`), use the optional nested `llm` key to pass provider-specific configuration. The nested `llm` config overrides top-level `provider`/`model`/`api_key` when provided.
+</Note>
+
 ### Azure OpenAI
 
 ```python
@@ -136,13 +132,16 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
+            "provider": "azure_openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-azure-api-key",
             "llm": {
                 "provider": "azure_openai",
                 "config": {
-                    "model": "gpt-4",
+                    "model": "gpt-4o-mini",
                     "api_key": "your-azure-api-key",
                     "azure_endpoint": "https://your-resource.openai.azure.com/",
-                    "azure_deployment": "gpt-4-deployment"
+                    "azure_deployment": "gpt-4o-mini-deployment"
                 }
             }
         }
@@ -154,16 +153,22 @@ config = {
 
 ### Default Prompt Behavior
 
-The default prompt asks the LLM to score relevance on a 0-10 scale:
+The default prompt asks the LLM to score relevance on a 0.0-1.0 scale:
 
 ```
-Given a query and a memory, rate how relevant the memory is to answering the query.
-Score from 0 (completely irrelevant) to 10 (perfectly relevant).
-Only provide the numeric score.
+You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
 
-Query: {query}
-Memory: {memory}
-Score:
+Score the relevance on a scale from 0.0 to 1.0, where:
+- 1.0 = Perfectly relevant and directly answers the query
+- 0.8-0.9 = Highly relevant with good information
+- 0.6-0.7 = Moderately relevant with some useful information
+- 0.4-0.5 = Slightly relevant with limited useful information
+- 0.0-0.3 = Not relevant or no useful information
+
+Query: "{query}"
+Document: "{document}"
+
+Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
 ```
 
 ### Custom Prompt Examples
@@ -174,14 +179,14 @@ Score:
 custom_prompt = """
 You are a medical information specialist. Rate how relevant each memory is for answering the medical query.
 Consider clinical accuracy, specificity, and practical applicability.
-Rate from 1-10 where:
-- 1-3: Irrelevant or potentially harmful
-- 4-6: Somewhat relevant but incomplete
-- 7-8: Relevant and helpful
-- 9-10: Highly relevant and clinically useful
+Rate from 0.0 to 1.0 where:
+- 0.0-0.3: Irrelevant or potentially harmful
+- 0.4-0.6: Somewhat relevant but incomplete
+- 0.7-0.8: Relevant and helpful
+- 0.9-1.0: Highly relevant and clinically useful
 
-Query: {query}
-Memory: {memory}
+Query: "{query}"
+Document: "{document}"
 Score:
 """
 
@@ -189,14 +194,10 @@ config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4",
-                    "api_key": "your-api-key"
-                }
-            },
-            "custom_prompt": custom_prompt
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-api-key",
+            "scoring_prompt": custom_prompt
         }
     }
 }
@@ -213,15 +214,15 @@ Consider:
 - Recency and accuracy
 - Practical usefulness
 
-Rate 1-5:
-1 = Not relevant
-2 = Slightly relevant
-3 = Moderately relevant
-4 = Very relevant
-5 = Perfectly answers the question
+Rate 0.0-1.0:
+0.0 = Not relevant
+0.25 = Slightly relevant
+0.5 = Moderately relevant
+0.75 = Very relevant
+1.0 = Perfectly answers the question
 
-Query: {query}
-Memory: {memory}
+Query: "{query}"
+Document: "{document}"
 Score:
 """
 ```
@@ -239,13 +240,17 @@ Consider:
 - Factual accuracy
 - Conversation flow
 
-Rate 0-10:
-Query: {query}
-Memory: {memory}
+Rate 0.0-1.0:
+Query: "{query}"
+Document: "{document}"
 Score:
 """
 ```
 
+<Note>
+  Custom prompts must include `{query}` and `{document}` placeholders. The LLM response should contain a numerical score which is automatically extracted.
+</Note>
+
 ## Usage Examples
 
 ### Basic Usage
@@ -295,9 +300,9 @@ results = safe_llm_rerank_search("What are my preferences?", "alice")
 
 | Model Type | Speed | Quality | Cost | Best For |
 |------------|-------|---------|------|----------|
-| GPT-3.5 Turbo | Fast | Good | Low | High-volume applications |
-| GPT-4 | Medium | Excellent | Medium | Quality-critical applications |
-| Claude 3 Sonnet | Medium | Excellent | Medium | Balanced performance |
+| GPT-4o mini | Fast | Good | Low | High-volume applications |
+| GPT-4o | Medium | Excellent | Medium | Quality-critical applications |
+| Claude Sonnet | Medium | Excellent | Medium | Balanced performance |
 | Ollama Local | Variable | Good | Free | Privacy-sensitive applications |
 
 ### Optimization Strategies
@@ -308,14 +313,10 @@ fast_config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-3.5-turbo",
-                    "api_key": "your-api-key"
-                }
-            },
-            "top_k": 5,  # Limit candidates
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-api-key",
+            "top_k": 5,
             "temperature": 0.0
         }
     }
@@ -326,13 +327,9 @@ quality_config = {
     "reranker": {
         "provider": "llm_reranker",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4",
-                    "api_key": "your-api-key"
-                }
-            },
+            "provider": "openai",
+            "model": "gpt-4o",
+            "api_key": "your-api-key",
             "top_k": 15,
             "temperature": 0.0
         }
@@ -353,10 +350,10 @@ Evaluate this memory's relevance using multi-step reasoning:
 3. How directly does the memory address the query?
 4. What additional context might be needed?
 
-Based on this analysis, rate relevance 1-10:
+Based on this analysis, rate relevance 0.0-1.0:
 
-Query: {query}
-Memory: {memory}
+Query: "{query}"
+Document: "{document}"
 
 Analysis:
 Step 1 (Intent):
@@ -367,42 +364,6 @@ Final Score:
 """
 ```
 
-### Comparative Ranking
-
-```python
-comparative_prompt = """
-You will see a query and multiple memories. Rank them in order of relevance.
-Consider which memories best answer the question and would be most helpful.
-
-Query: {query}
-
-Memories to rank:
-{memories}
-
-Provide scores 1-10 for each memory, considering their relative usefulness.
-"""
-```
-
-### Emotional Intelligence
-
-```python
-emotional_prompt = """
-Consider both factual relevance and emotional appropriateness.
-Rate how suitable this memory is for responding to the user's query.
-
-Factors to consider:
-- Factual accuracy and relevance
-- Emotional tone and sensitivity
-- User's likely emotional state
-- Appropriateness of response
-
-Query: {query}
-Memory: {memory}
-Emotional Context: {context}
-Score (1-10):
-"""
-```
-
 ## Error Handling and Fallbacks
 
 ```python
@@ -433,14 +394,22 @@ class RobustLLMReranker:
 primary_config = {
     "reranker": {
         "provider": "llm_reranker",
-        "config": {"llm": {"provider": "openai", "config": {"model": "gpt-4"}}}
+        "config": {
+            "provider": "openai",
+            "model": "gpt-4o",
+            "api_key": "your-api-key"
+        }
     }
 }
 
 fallback_config = {
     "reranker": {
         "provider": "llm_reranker",
-        "config": {"llm": {"provider": "openai", "config": {"model": "gpt-3.5-turbo"}}}
+        "config": {
+            "provider": "openai",
+            "model": "gpt-4o-mini",
+            "api_key": "your-api-key"
+        }
     }
 }
 
@@ -486,4 +455,4 @@ results = reranker.search("What are my preferences?", "alice")
   <Card title="Performance Optimization" icon="bolt" href="/components/rerankers/optimization">
     Optimize LLM reranker performance
   </Card>
-</CardGroup>
+</CardGroup>

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
@@ -13,8 +17,10 @@ config = {
             "workspace_url": "https://your-workspace.databricks.com",
             "access_token": "your-access-token",
             "endpoint_name": "your-vector-search-endpoint",
-            "index_name": "catalog.schema.index_name",
-            "source_table_name": "catalog.schema.source_table",
+            "catalog": "your_catalog",
+            "schema": "your_schema",
+            "table_name": "your_table",
+            "collection_name": "your_index_name",
             "embedding_dimension": 1536
         }
     }
@@ -38,17 +44,22 @@ Here are the parameters available for configuring Databricks Vector Search:
 | --- | --- | --- |
 | `workspace_url` | The URL of your Databricks workspace | **Required** |
 | `access_token` | Personal Access Token for authentication | `None` |
-| `service_principal_client_id` | Service principal client ID (alternative to access_token) | `None` |
-| `service_principal_client_secret` | Service principal client secret (required with client_id) | `None` |
+| `client_id` | Service principal client ID (alternative to access_token) | `None` |
+| `client_secret` | Service principal client secret (required with client_id) | `None` |
+| `azure_client_id` | Azure AD application client ID (for Azure Databricks) | `None` |
+| `azure_client_secret` | Azure AD application client secret (for Azure Databricks) | `None` |
 | `endpoint_name` | Name of the Vector Search endpoint | **Required** |
-| `index_name` | Name of the vector index (Unity Catalog format: catalog.schema.index) | **Required** |
-| `source_table_name` | Name of the source Delta table (Unity Catalog format: catalog.schema.table) | **Required** |
-| `embedding_dimension` | Dimension of self-managed embeddings | `1536` |
-| `embedding_source_column` | Column name for text when using Databricks-computed embeddings | `None` |
+| `catalog` | Unity Catalog catalog name | **Required** |
+| `schema` | Unity Catalog schema name | **Required** |
+| `table_name` | Source Delta table name | **Required** |
+| `collection_name` | Vector search index name | `mem0` |
+| `index_type` | Index type: `DELTA_SYNC` or `DIRECT_ACCESS` | `DELTA_SYNC` |
 | `embedding_model_endpoint_name` | Databricks serving endpoint for embeddings | `None` |
-| `embedding_vector_column` | Column name for self-managed embedding vectors | `embedding` |
+| `embedding_dimension` | Dimension of self-managed embeddings | `1536` |
 | `endpoint_type` | Type of endpoint (`STANDARD` or `STORAGE_OPTIMIZED`) | `STANDARD` |
-| `sync_computed_embeddings` | Whether to sync computed embeddings automatically | `True` |
+| `pipeline_type` | Sync pipeline type: `TRIGGERED` or `CONTINUOUS` | `TRIGGERED` |
+| `warehouse_name` | Databricks SQL warehouse name (if using SQL warehouse) | `None` |
+| `query_type` | Query type: `ANN` or `HYBRID` | `ANN` |
 
 ### Authentication
 
@@ -61,11 +72,13 @@ config = {
         "provider": "databricks",
         "config": {
             "workspace_url": "https://your-workspace.databricks.com",
-            "service_principal_client_id": "your-service-principal-id",
-            "service_principal_client_secret": "your-service-principal-secret",
+            "client_id": "your-service-principal-id",
+            "client_secret": "your-service-principal-secret",
             "endpoint_name": "your-endpoint",
-            "index_name": "catalog.schema.index_name",
-            "source_table_name": "catalog.schema.source_table"
+            "catalog": "your_catalog",
+            "schema": "your_schema",
+            "table_name": "your_table",
+            "collection_name": "your_index_name",
         }
     }
 }
@@ -80,8 +93,10 @@ config = {
             "workspace_url": "https://your-workspace.databricks.com",
             "access_token": "your-personal-access-token",
             "endpoint_name": "your-endpoint",
-            "index_name": "catalog.schema.index_name",
-            "source_table_name": "catalog.schema.source_table"
+            "catalog": "your_catalog",
+            "schema": "your_schema",
+            "table_name": "your_table",
+            "collection_name": "your_index_name",
         }
     }
 }
@@ -99,7 +114,6 @@ config = {
         "config": {
             # ... authentication config ...
             "embedding_dimension": 768,  # Match your embedding model
-            "embedding_vector_column": "embedding"
         }
     }
 }
@@ -114,7 +128,6 @@ config = {
         "provider": "databricks",
         "config": {
             # ... authentication config ...
-            "embedding_source_column": "text",
             "embedding_model_endpoint_name": "e5-small-v2"
         }
     }
@@ -123,8 +136,8 @@ config = {
 
 ### Important Notes
 
-- **Delta Sync Index**: This implementation uses Delta Sync Index, which automatically syncs with your source Delta table. Direct vector insertion/deletion/update operations will log warnings as they're not supported with Delta Sync.
-- **Unity Catalog**: Both the source table and index must be in Unity Catalog format (`catalog.schema.table_name`).
+- **Index Types**: This implementation supports both `DELTA_SYNC` (auto-syncs with source Delta table) and `DIRECT_ACCESS` (manage vectors directly) index types.
+- **Unity Catalog**: The source table and index are created under the specified `catalog.schema` namespace.
 - **Endpoint Auto-Creation**: If the specified endpoint doesn't exist, it will be created automatically.
 - **Index Auto-Creation**: If the specified index doesn't exist, it will be created automatically with the provided configuration.
 - **Filter Support**: Supports filtering by metadata fields, with different syntax for STANDARD vs STORAGE_OPTIMIZED endpoints.

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.