refactor: add legacy_path override to legacy data migration helper

Remove unused imports (os, tempfile), unused variables, and
extraneous f-string prefixes flagged by ruff.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.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/ci.yml

@@ -7,6 +7,8 @@ on:
       - 'mem0/**'
       - 'tests/**'
       - 'embedchain/**'
+      - '.github/workflows/**'
+      - 'pyproject.toml'
   pull_request:
     paths:
       - 'mem0/**'
@@ -28,6 +30,8 @@ jobs:
           mem0:
             - 'mem0/**'
             - 'tests/**'
+            - '.github/workflows/**'
+            - 'pyproject.toml'
           embedchain:
             - 'embedchain/**'
 
@@ -44,6 +48,13 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+      - name: Clean up disk space
+        run: |
+          df -h
+          sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL
+          sudo docker image prune --all --force
+          sudo docker builder prune -a
+          df -h
       - name: Install Hatch
         run: pip install hatch
       - name: Load cached venv

.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

@@ -103,7 +103,7 @@ memory = Memory()
 # With custom configuration
 config = MemoryConfig(
     vector_store={"provider": "qdrant", "config": {"host": "localhost"}},
-    llm={"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    llm={"provider": "openai", "config": {"model": "gpt-4.1-nano-2025-04-14"}},
     embedder={"provider": "openai", "config": {"model": "text-embedding-3-small"}}
 )
 memory = Memory(config)
@@ -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
@@ -339,7 +340,7 @@ config = MemoryConfig(
     llm={
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.1,
             "max_tokens": 1000
         }
@@ -527,7 +528,7 @@ const memory = new Memory({
   },
   llm: {
     provider: 'openai',
-    config: { model: 'gpt-4o-mini' }
+    config: { model: 'gpt-4.1-nano' }
   }
 });
 

MIGRATION_GUIDE_v1.0.md

@@ -0,0 +1,221 @@
+# Migration Guide: Upgrading to mem0 1.0.0
+
+## TL;DR
+
+**What changed?** We simplified the API by removing confusing version parameters. Now everything returns a consistent format: `{"results": [...]}`.
+
+**What you need to do:**
+1. Upgrade: `pip install mem0ai==1.0.0`
+2. Remove `version` and `output_format` parameters from your code
+3. Update response handling to use `result["results"]` instead of treating responses as lists
+
+**Time needed:** ~5-10 minutes for most projects
+
+---
+
+## Quick Migration Guide
+
+### 1. Install the Update
+
+```bash
+pip install mem0ai==1.0.0
+```
+
+### 2. Update Your Code
+
+**If you're using the Memory API:**
+
+```python
+# Before
+memory = Memory(config=MemoryConfig(version="v1.1"))
+result = memory.add("I like pizza")
+
+# After
+memory = Memory()  # That's it - version is automatic now
+result = memory.add("I like pizza")
+```
+
+**If you're using the Client API:**
+
+```python
+# Before
+client.add(messages, output_format="v1.1")
+client.search(query, version="v2", output_format="v1.1")
+
+# After
+client.add(messages)  # Just remove those extra parameters
+client.search(query)
+```
+
+### 3. Update How You Handle Responses
+
+All responses now use the same format: a dictionary with `"results"` key.
+
+```python
+# Before - you might have done this
+result = memory.add("I like pizza")
+for item in result:  # Treating it as a list
+    print(item)
+
+# After - do this instead
+result = memory.add("I like pizza")
+for item in result["results"]:  # Access the results key
+    print(item)
+
+# Graph relations (if you use them)
+if "relations" in result:
+    for relation in result["relations"]:
+        print(relation)
+```
+
+---
+
+## Enhanced Message Handling
+
+The platform client (MemoryClient) now supports the same flexible message formats as the OSS version:
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# All three formats now work:
+
+# 1. Single string (automatically converted to user message)
+client.add("I like pizza", user_id="alice")
+
+# 2. Single message dictionary
+client.add({"role": "user", "content": "I like pizza"}, user_id="alice")
+
+# 3. List of messages (conversation)
+client.add([
+    {"role": "user", "content": "I like pizza"},
+    {"role": "assistant", "content": "I'll remember that!"}
+], user_id="alice")
+```
+
+### Async Mode Configuration
+
+The `async_mode` parameter now defaults to `True` but can be configured:
+
+```python
+# Default behavior (async_mode=True)
+client.add(messages, user_id="alice")
+
+# Explicitly set async mode
+client.add(messages, user_id="alice", async_mode=True)
+
+# Disable async mode if needed
+client.add(messages, user_id="alice", async_mode=False)
+```
+
+**Note:** `async_mode=True` provides better performance for most use cases. Only set it to `False` if you have specific synchronous processing requirements.
+
+---
+
+## That's It!
+
+For most users, that's all you need to know. The changes are:
+- ✅ No more `version` or `output_format` parameters
+- ✅ Consistent `{"results": [...]}` response format
+- ✅ Cleaner, simpler API
+
+---
+
+## Common Issues
+
+**Getting `KeyError: 'results'`?**
+
+Your code is still treating the response as a list. Update it:
+```python
+# Change this:
+for memory in response:
+
+# To this:
+for memory in response["results"]:
+```
+
+**Getting `TypeError: unexpected keyword argument`?**
+
+You're still passing old parameters. Remove them:
+```python
+# Change this:
+client.add(messages, output_format="v1.1")
+
+# To this:
+client.add(messages)
+```
+
+**Seeing deprecation warnings?**
+
+Remove any explicit `version="v1.0"` from your config:
+```python
+# Change this:
+memory = Memory(config=MemoryConfig(version="v1.0"))
+
+# To this:
+memory = Memory()
+```
+
+---
+
+## What's New in 1.0.0
+
+- **Better vector stores:** Fixed OpenSearch and improved reliability across all stores
+- **Cleaner API:** One way to do things, no more confusing options
+- **Enhanced GCP support:** Better Vertex AI configuration options
+- **Flexible message input:** Platform client now accepts strings, dicts, and lists (aligned with OSS)
+- **Configurable async_mode:** Now defaults to `True` but users can override if needed
+
+---
+
+## Need Help?
+
+- Check [GitHub Issues](https://github.com/mem0ai/mem0/issues)
+- Read the [documentation](https://docs.mem0.ai/)
+- Open a new issue if you're stuck
+
+---
+
+## Advanced: Configuration Changes
+
+**If you configured vector stores with version:**
+
+```python
+# Before
+config = MemoryConfig(
+    version="v1.1",
+    vector_store=VectorStoreConfig(...)
+)
+
+# After
+config = MemoryConfig(
+    vector_store=VectorStoreConfig(...)
+)
+```
+
+---
+
+## Testing Your Migration
+
+Quick sanity check:
+
+```python
+from mem0 import Memory
+
+memory = Memory()
+
+# Add should return a dict with "results"
+result = memory.add("I like pizza", user_id="test")
+assert "results" in result
+
+# Search should return a dict with "results"
+search = memory.search("food", user_id="test")
+assert "results" in search
+
+# Get all should return a dict with "results"
+all_memories = memory.get_all(user_id="test")
+assert "results" in all_memories
+
+print("✅ Migration successful!")
+```

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">
@@ -47,6 +45,8 @@
   <strong>⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens</strong>
 </p>
 
+> **🎉 mem0ai v1.0.0 is now available!** This major release includes API modernization, improved vector store support, and enhanced GCP integration. [See migration guide →](MIGRATION_GUIDE_v1.0.md)
+
 ##  🔥 Research Highlights
 - **+26% Accuracy** over OpenAI Memory on the LOCOMO benchmark
 - **91% Faster Responses** than full-context, ensuring low-latency at scale
@@ -95,7 +95,7 @@ npm install mem0ai
 
 ### Basic Usage
 
-Mem0 requires an LLM to function, with `gpt-4o-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).
+Mem0 requires an LLM to function, with `gpt-4.1-nano-2025-04-14 from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).
 
 First step is to instantiate the memory:
 
@@ -114,7 +114,7 @@ def chat_with_memories(message: str, user_id: str = "default_user") -> str:
     # Generate Assistant response
     system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
     messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
-    response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
+    response = openai_client.chat.completions.create(model="gpt-4.1-nano-2025-04-14", messages=messages)
     assistant_response = response.choices[0].message.content
 
     # Create new memories from the conversation
@@ -166,4 +166,4 @@ We now have a paper you can cite:
 
 ## ⚖️ License
 
-Apache 2.0 — see the [LICENSE](LICENSE) file for details.
+Apache 2.0 — see the [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.

docs/_snippets/paper-release.mdx

@@ -1,3 +1,3 @@
 <Note type="info">
-  📢 Announcing our research paper: Mem0 achieves <strong>26%</strong> higher accuracy than OpenAI Memory, <strong>91%</strong> lower latency, and <strong>90%</strong> token savings! [Read the paper](https://mem0.ai/research) to learn how we're revolutionizing AI agent memory.
+  <strong>🎉 Mem0 1.0.0 is here!</strong> Enhanced filtering, reranking, and smarter memory management.
 </Note>

docs/api-reference.mdx

@@ -1,191 +1,108 @@
 ---
-title: Overview
-icon: "info"
+title: "Overview"
+icon: "terminal"
 iconType: "solid"
+description: "REST APIs for memory management, search, and entity operations"
 ---
 
-Mem0 provides a powerful set of APIs that allow you to integrate advanced memory management capabilities into your applications. Our APIs are designed to be intuitive, efficient, and scalable, enabling you to create, retrieve, update, and delete memories across various entities such as users, agents, apps, and runs.
+## Mem0 REST API
 
-## Key Features
+Mem0 provides a comprehensive REST API for integrating advanced memory capabilities into your applications. Create, search, update, and manage memories across users, agents, and custom entities with simple HTTP requests.
 
-- **Memory Management**: Add, retrieve, update, and delete memories with ease.
-- **Entity-based Operations**: Perform operations on memories associated with specific users, agents, apps, or runs.
-- **Advanced Search**: Utilize our search API to find relevant memories based on various criteria.
-- **History Tracking**: Access the history of memory interactions for comprehensive analysis.
-- **User Management**: Manage user entities and their associated memories.
+<Info>
+**Quick start:** Get your API key from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-keys) and make your first memory operation in minutes.
+</Info>
 
-## API Structure
+---
 
-Our API is organized into several main categories:
+## Quick Start Guide
 
-1. **Memory APIs**: Core operations for managing individual memories and collections.
-2. **Entities APIs**: Manage different entity types (users, agents, etc.) and their associated memories.
-3. **Search API**: Advanced search functionality to retrieve relevant memories.
-4. **History API**: Track and retrieve the history of memory interactions.
+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/search-memories)** - Retrieve relevant memories using semantic search
+3. **[Get Memories](/api-reference/memory/get-memories)** - Fetch all memories for a specific entity
+
+---
+
+## Core Operations
+
+<CardGroup cols={2}>
+  <Card title="Add Memories" icon="plus" href="/api-reference/memory/add-memories">
+    Store new memories from conversations and interactions
+  </Card>
+
+  <Card title="Search Memories" icon="magnifying-glass" href="/api-reference/memory/search-memories">
+    Find relevant memories using semantic search with filters
+  </Card>
+
+  <Card title="Update Memory" icon="pen" href="/api-reference/memory/update-memory">
+    Modify existing memory content and metadata
+  </Card>
+
+  <Card title="Delete Memory" icon="trash" href="/api-reference/memory/delete-memory">
+    Remove specific memories or batch delete operations
+  </Card>
+</CardGroup>
+
+---
+
+## API Categories
+
+Explore the full API organized by functionality:
+
+<CardGroup cols={2}>
+  <Card title="Memory APIs" icon="microchip" href="/api-reference/memory/add-memories">
+    Core and advanced operations: CRUD, search, batch updates, history, and exports
+  </Card>
+
+  <Card title="Events APIs" icon="clock" href="/api-reference/events/get-events">
+    Track and monitor the status of asynchronous memory operations
+  </Card>
+
+  <Card title="Entities APIs" icon="users" href="/api-reference/entities/get-users">
+    Manage users, agents, and their associated memory data
+  </Card>
+
+  <Card title="Organizations & Projects" icon="building" href="/api-reference/organizations-projects">
+    Multi-tenant support, access control, and team collaboration
+  </Card>
+
+  <Card title="Webhooks" icon="webhook" href="/api-reference/webhook/create-webhook">
+    Real-time notifications for memory events and updates
+  </Card>
+</CardGroup>
+
+<Note>
+**Building multi-tenant apps?** Learn about [Organizations & Projects](/api-reference/organizations-projects) for team isolation and access control.
+</Note>
+
+---
 
 ## Authentication
 
-All API requests require authentication using HTTP Basic Auth. Ensure you include your API key in the Authorization header of each request.
+All API requests require authentication using Token-based authentication. Include your API key in the Authorization header:
 
-## Organizations and projects (optional)
-
-Organizations and projects provide the following capabilities:
-
-- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
-- **Member Management**: Control access to data through organization and project membership
-- **Access Control**: Only members can access memories and data within their organization/project scope
-- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
-
-Example with the mem0 Python package:
-
-<Tabs>
-  <Tab title="Python">
-
-```python
-from mem0 import MemoryClient
-client = MemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+```bash
+Authorization: Token <your-api-key>
 ```
 
-  </Tab>
+Get your API key from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-keys).
 
-  <Tab title="Node.js">
+<Warning>
+**Keep your API key secure.** Never expose it in client-side code or public repositories. Use environment variables and server-side requests only.
+</Warning>
 
-```javascript
-import { MemoryClient } from "mem0ai";
-const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR_PROJECT_ID"});
-```
+---
 
-  </Tab>
-</Tabs>
+## Next Steps
 
-### Project Management Methods
+<CardGroup cols={2}>
+  <Card title="Add Your First Memory" icon="rocket" href="/api-reference/memory/add-memories">
+    Start storing memories via the REST API
+  </Card>
 
-The Mem0 client provides comprehensive project management capabilities through the `client.project` interface:
-
-#### Get Project Details
-
-Retrieve information about the current project:
-
-```python
-# Get all project details
-project_info = client.project.get()
-
-# Get specific fields only
-project_info = client.project.get(fields=["name", "description", "custom_categories"])
-```
-
-#### Create a New Project
-
-Create a new project within your organization:
-
-```python
-# Create a project with name and description
-new_project = client.project.create(
-    name="My New Project",
-    description="A project for managing customer support memories"
-)
-```
-
-#### Update Project Settings
-
-Modify project configuration including custom instructions, categories, and graph settings:
-
-```python
-# Update project with custom categories
-client.project.update(
-    custom_categories=[
-        {"customer_preferences": "Customer likes, dislikes, and preferences"},
-        {"support_history": "Previous support interactions and resolutions"}
-    ]
-)
-
-# Update project with custom instructions
-client.project.update(
-    custom_instructions="..."
-)
-
-# Enable graph memory for the project
-client.project.update(enable_graph=True)
-
-# Update multiple settings at once
-client.project.update(
-    custom_instructions="...",
-    custom_categories=[
-        {"personal_info": "User personal information and preferences"},
-        {"work_context": "Professional context and work-related information"}
-    ],
-    enable_graph=True
-)
-```
-
-#### Delete Project
-
-<Note>
-This action will remove all memories, messages, and other related data in the project. This operation is irreversible.
-</Note>
-
-Remove a project and all its associated data:
-
-```python
-# Delete the current project (irreversible)
-result = client.project.delete()
-```
-
-#### Member Management
-
-Manage project members and their access levels:
-
-```python
-# Get all project members
-members = client.project.get_members()
-
-# Add a new member as a reader
-client.project.add_member(
-    email="colleague@company.com",
-    role="READER"  # or "OWNER"
-)
-
-# Update a member's role
-client.project.update_member(
-    email="colleague@company.com",
-    role="OWNER"
-)
-
-# Remove a member from the project
-client.project.remove_member(email="colleague@company.com")
-```
-
-#### Member Roles
-
-- **READER**: Can view and search memories, but cannot modify project settings or manage members
-- **OWNER**: Full access including project modification, member management, and all reader permissions
-
-#### Async Support
-
-All project methods are also available in async mode:
-
-```python
-from mem0 import AsyncMemoryClient
-
-async def manage_project():
-    client = AsyncMemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
-
-    # All methods support async/await
-    project_info = await client.project.get()
-    await client.project.update(enable_graph=True)
-    members = await client.project.get_members()
-
-# To call the async function properly
-import asyncio
-asyncio.run(manage_project())
-```
-
-## Getting Started
-
-To begin using the Mem0 API, you'll need to:
-
-1. Sign up for a [Mem0 account](https://app.mem0.ai) and obtain your API key.
-2. Familiarize yourself with the API endpoints and their functionalities.
-3. Make your first API call to add or retrieve a memory.
-
-Explore the detailed documentation for each API endpoint to learn more about request/response formats, parameters, and example usage.
+  <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'
-openapi: delete /v1/entities/{entity_type}/{entity_id}/
+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

@@ -0,0 +1,7 @@
+---
+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}/
+---
+
+Retrieve details about a specific event by passing its `event_id`. This endpoint is particularly helpful for tracking the status, payload, and completion details of asynchronous memory operations.

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

@@ -0,0 +1,14 @@
+---
+title: 'Get Events'
+description: "List recent events for your organization and project, useful for dashboards, alerting, and audit logging."
+openapi: get /v1/events/
+---
+
+List recent events for your organization and project.
+
+## Use Cases
+
+- **Dashboards**: Summarize adds/searches over time by paging through events.
+- **Alerting**: Poll for `FAILED` events and trigger follow-up workflows.
+- **Audit**: Store the returned payload/metadata for compliance logs.
+

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

@@ -1,4 +1,98 @@
 ---
 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/
----
+---
+
+Add new facts, messages, or metadata to a user’s memory store. The Add Memories endpoint accepts either raw text or conversational turns and commits them asynchronously so the memory is ready for later search, retrieval, and graph queries.
+
+## Endpoint
+
+- **Method**: `POST`
+- **URL**: `/v1/memories/`
+- **Content-Type**: `application/json`
+
+Memories are processed asynchronously by default. The response contains queued events you can track while the platform finalizes enrichment.
+
+## Required headers
+
+| Header | Required | Description |
+| --- | --- | --- |
+| `Authorization: Token <MEM0_API_KEY>` | Yes | API key scoped to your workspace. |
+| `Accept: application/json` | Yes | Ensures a JSON response. |
+
+## Request body
+
+Provide at least one message or direct memory string. Most callers supply `messages` so Mem0 can infer structured memories as part of ingestion.
+
+<CodeGroup>
+```json Basic request
+{
+  "user_id": "alice",
+  "messages": [
+    { "role": "user", "content": "I moved to Austin last month." }
+  ],
+  "metadata": {
+    "source": "onboarding_form"
+  }
+}
+```
+</CodeGroup>
+
+### Common fields
+
+| Field | Type | Required | Description |
+| --- | --- | --- | --- |
+| `user_id` | string | No* | Associates the memory with a user. Provide when you want the memory scoped to a specific identity. |
+| `messages` | array | No* | Conversation turns for Mem0 to infer memories from. Each object should include `role` and `content`. |
+| `metadata` | object | Optional | Custom key/value metadata (e.g., `{"topic": "preferences"}`). |
+| `infer` | boolean (default `true`) | Optional | Set to `false` to skip inference and store the provided text as-is. |
+| `async_mode` | boolean (default `true`) | Optional | Controls asynchronous processing. Most clients leave this enabled. |
+| `output_format` | string (default `v1.1`) | Optional | Response format. `v1.1` wraps results in a `results` array. |
+
+> \* Provide at least one `messages` entry to describe what you are storing. For scoped memories, include `user_id`. You can also attach `agent_id`, `app_id`, `run_id`, `project_id`, or `org_id` to refine ownership.
+
+## Response
+
+Successful requests return an array of events queued for processing. Each event includes the generated memory text and an identifier you can persist for auditing.
+
+<CodeGroup>
+```json 200 response
+[
+  {
+    "id": "mem_01JF8ZS4Y0R0SPM13R5R6H32CJ",
+    "event": "ADD",
+    "data": {
+      "memory": "The user moved to Austin in 2025."
+    }
+  }
+]
+```
+
+```json 400 response
+{
+  "error": "400 Bad Request",
+  "details": {
+    "message": "Invalid input data. Please refer to the memory creation documentation at https://docs.mem0.ai/platform/quickstart#4-1-create-memories for correct formatting and required fields."
+  }
+}
+```
+</CodeGroup>
+
+## Graph relationships
+
+Add Memories can enrich the knowledge graph on write. Set `enable_graph: true` to create entity nodes and relationships for the stored memory. Use this when you want downstream `get_all` or search calls to traverse connected entities.
+
+<CodeGroup>
+```json Graph-aware request
+{
+  "user_id": "alice",
+  "messages": [
+    { "role": "user", "content": "I met with Dr. Lee at General Hospital." }
+  ],
+  "enable_graph": true
+}
+```
+</CodeGroup>
+
+The response follows the same format, and related entities become available in [Graph Memory](/platform/features/graph-memory) queries.

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

@@ -1,6 +1,7 @@
 ---
 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/
 ---
 
-Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., user_id, agent_id, run_id, or session_id) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you're exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

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

@@ -0,0 +1,100 @@
+---
+title: "Get Memories"
+description: "Retrieve memories with advanced filtering using logical operators like AND, OR, NOT, and comparison queries."
+openapi: post /v2/memories/
+---
+
+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
+
+- `in`: Matches any of the values specified
+- `gte`: Greater than or equal to
+- `lte`: Less than or equal to
+- `gt`: Greater than
+- `lt`: Less than
+- `ne`: Not equal to
+- `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
+
+<CodeGroup>
+```python Code
+memories = client.get_all(
+    filters={
+        "AND": [
+            {
+                "user_id": "alex"
+            },
+            {
+                "created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}
+            }
+        ]
+    }
+)
+```
+
+```python Output
+{
+    "results": [
+        {
+            "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
+            "memory": "Alex is planning a trip to San Francisco from July 1st to July 10th",
+            "created_at": "2024-07-01T12:00:00Z",
+            "updated_at": "2024-07-01T12:00:00Z"
+        },
+        {
+            "id": "a2b8c3d4-5e6f-7g8h-9i0j-1k2l3m4n5o6p",
+            "memory": "Alex prefers vegetarian restaurants",
+            "created_at": "2024-07-05T15:30:00Z",
+            "updated_at": "2024-07-05T15:30:00Z"
+        }
+    ],
+    "total": 2
+}
+```
+
+</CodeGroup>
+
+## Graph Memory
+
+To retrieve graph memory relationships between entities, pass `output_format="v1.1"` in your request. This will return memories with entity and relationship information from the knowledge graph.
+
+<CodeGroup>
+```python Code
+memories = client.get_all(
+    filters={
+        "user_id": "alex"
+    },
+    output_format="v1.1"
+)
+```
+
+```python Output
+{
+    "results": [
+        {
+            "id": "f4cbdb08-7062-4f3e-8eb2-9f5c80dfe64c",
+            "memory": "Alex is planning a trip to San Francisco",
+            "entities": [
+                {
+                    "id": "entity-1",
+                    "name": "Alex",
+                    "type": "person"
+                },
+                {
+                    "id": "entity-2",
+                    "name": "San Francisco",
+                    "type": "location"
+                }
+            ],
+            "relations": [
+                {
+                    "source": "entity-1",
+                    "target": "entity-2",
+                    "relationship": "traveling_to"
+                }
+            ]
+        }
+    ]
+}
+```
+
+</CodeGroup>

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

@@ -0,0 +1,105 @@
+---
+title: 'Search Memories'
+description: "Search memories with semantic queries and advanced filtering using logical and comparison operators."
+openapi: post /v2/memories/search/
+---
+
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
+- `in`: Matches any of the values specified
+- `gte`: Greater than or equal to
+- `lte`: Less than or equal to
+- `gt`: Greater than
+- `lt`: Less than
+- `ne`: Not equal to
+- `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
+
+<CodeGroup>
+```python Platform API Example
+related_memories = client.search(
+    query="What are Alice's hobbies?",
+    filters={
+        "OR": [
+            {
+              "user_id": "alice"
+            },
+            {
+              "agent_id": {"in": ["travel-agent", "sports-agent"]}
+            }
+        ]
+    },
+)
+```
+
+```json Output
+{
+  "memories": [
+    {
+      "id": "ea925981-272f-40dd-b576-be64e4871429",
+      "memory": "Likes to play cricket and plays cricket on weekends.",
+      "metadata": {
+        "category": "hobbies"
+      },
+      "score": 0.32116443111457704,
+      "created_at": "2024-07-26T10:29:36.630547-07:00",
+      "updated_at": null,
+      "user_id": "alice",
+      "agent_id": "sports-agent"
+    }
+  ],
+}
+```
+</CodeGroup>
+
+<CodeGroup>
+```python Wildcard Example
+# Using wildcard to match all run_ids for a specific user
+all_memories = client.search(
+    query="What are Alice's hobbies?",
+    filters={
+        "AND": [
+            {
+                "user_id": "alice"
+            },
+            {
+                "run_id": "*"
+            }
+        ]
+    },
+)
+```
+</CodeGroup>
+
+<CodeGroup>
+```python Categories Filter Examples
+# Example 1: Using 'contains' for partial matching
+finance_memories = client.search(
+    query="What are my financial goals?",
+    filters={
+        "AND": [
+            { "user_id": "alice" },
+            {
+                "categories": {
+                    "contains": "finance"
+                }
+            }
+        ]
+    },
+)
+
+# Example 2: Using 'in' for exact matching
+personal_memories = client.search(
+    query="What personal information do you have?",
+    filters={
+        "AND": [
+            { "user_id": "alice" },
+            {
+                "categories": {
+                    "in": ["personal_information"]
+                }
+            }
+        ]
+    },
+)
+```
+</CodeGroup>

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/memory/v1-get-memories.mdx

@@ -1,4 +0,0 @@
----
-title: 'Get Memories (v1 - Deprecated)'
-openapi: get /v1/memories/
----

docs/api-reference/memory/v1-search-memories.mdx

@@ -1,4 +0,0 @@
----
-title: 'Search Memories (v1 - Deprecated)'
-openapi: post /v1/memories/search/
----

docs/api-reference/memory/v2-get-memories.mdx

@@ -1,65 +0,0 @@
----
-title: 'Get Memories (v2)'
-openapi: post /v2/memories/
----
-
-The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
-- `in`: Matches any of the values specified
-- `gte`: Greater than or equal to
-- `lte`: Less than or equal to
-- `gt`: Greater than
-- `lt`: Less than
-- `ne`: Not equal to
-- `icontains`: Case-insensitive containment check
-- `*`: Wildcard character that matches everything
-
-<CodeGroup>
-```python Code
-memories = m.get_all(
-    filters={
-        "AND": [
-            {
-                "user_id": "alex"
-            },
-            {
-                "created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}
-            }
-        ]
-    },
-    version="v2"
-)
-```
-
-```json Output
-[
-{
-    "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
-    "memory":"Name: Alex. Vegetarian. Allergic to nuts.",
-    "user_id":"alex",
-    "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
-    "metadata":null,
-    "created_at":"2024-07-25T23:57:00.108347-07:00",
-    "updated_at":"2024-07-25T23:57:00.108367-07:00"
-}
-]
-```
-</CodeGroup>
-
-<CodeGroup>
-```python Wildcard Example
-# Using wildcard to get all memories for a specific user across all run_ids
-memories = m.get_all(
-    filters={
-        "AND": [
-            {
-                "user_id": "alex"
-            },
-            {
-                "run_id": "*"
-            }
-        ]
-    },
-    version="v2"
-)
-```
-</CodeGroup>

docs/api-reference/memory/v2-search-memories.mdx

@@ -1,108 +0,0 @@
----
-title: 'Search Memories (v2)'
-openapi: post /v2/memories/search/
----
-
-The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
-- `in`: Matches any of the values specified
-- `gte`: Greater than or equal to
-- `lte`: Less than or equal to
-- `gt`: Greater than
-- `lt`: Less than
-- `ne`: Not equal to
-- `icontains`: Case-insensitive containment check
-- `*`: Wildcard character that matches everything
-
-  <CodeGroup>
-  ```python Code
-  related_memories = m.search(
-      query="What are Alice's hobbies?",
-      version="v2",
-      filters={
-          "OR": [
-              {
-                "user_id": "alice"
-              },
-              {
-                "agent_id": {"in": ["travel-agent", "sports-agent"]}
-              }
-          ]
-      },
-  )
-  ```
-
-  ```json Output
-  {
-    "memories": [
-      {
-        "id": "ea925981-272f-40dd-b576-be64e4871429",
-        "memory": "Likes to play cricket and plays cricket on weekends.",
-        "metadata": {
-          "category": "hobbies"
-        },
-        "score": 0.32116443111457704,
-        "created_at": "2024-07-26T10:29:36.630547-07:00",
-        "updated_at": null,
-        "user_id": "alice",
-        "agent_id": "sports-agent"
-      }
-    ],
-  }
-  ```
-  </CodeGroup>
-
-  <CodeGroup>
-  ```python Wildcard Example
-  # Using wildcard to match all run_ids for a specific user
-  all_memories = m.search(
-      query="What are Alice's hobbies?",
-      version="v2",
-      filters={
-          "AND": [
-              {
-                  "user_id": "alice"
-              },
-              {
-                  "run_id": "*"
-              }
-          ]
-      },
-  )
-  ```
-  </CodeGroup>
-
-  <CodeGroup>
-  ```python Categories Filter Examples
-  # Example 1: Using 'contains' for partial matching
-  finance_memories = m.search(
-      query="What are my financial goals?",
-      version="v2",
-      filters={
-          "AND": [
-              { "user_id": "alice" },
-              {
-                  "categories": {
-                      "contains": "finance"
-                  }
-              }
-          ]
-      },
-  )
-
-  # Example 2: Using 'in' for exact matching
-  personal_memories = m.search(
-      query="What personal information do you have?",
-      version="v2",
-      filters={
-          "AND": [
-              { "user_id": "alice" },
-              {
-                  "categories": {
-                      "in": ["personal_information"]
-                  }
-              }
-          ]
-      },
-  )
-  ```
-  </CodeGroup>

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/organizations-projects.mdx

@@ -0,0 +1,197 @@
+---
+title: Organizations & Projects
+icon: "building"
+description: "Manage multi-tenant applications with organization and project APIs"
+---
+
+## Overview
+
+Organizations and projects provide multi-tenant support, access control, and team collaboration capabilities for Mem0 Platform. Use these APIs to build applications that support multiple teams, customers, or isolated environments.
+
+<Info>
+Organizations and projects are **optional** features. You can use Mem0 without them for single-user or simple multi-user applications.
+</Info>
+
+## Key Capabilities
+
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
+- **Member Management**: Control access to data through organization and project membership
+- **Access Control**: Only members can access memories and data within their organization/project scope
+- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
+
+---
+
+## Using Organizations & Projects
+
+### Initialize with Org/Project Context
+
+Example with the mem0 Python package:
+
+<Tabs>
+  <Tab title="Python">
+
+```python
+from mem0 import MemoryClient
+client = MemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+```
+
+  </Tab>
+
+  <Tab title="Node.js">
+
+```javascript
+import { MemoryClient } from "mem0ai";
+const client = new MemoryClient({
+  organizationId: "YOUR_ORG_ID",
+  projectId: "YOUR_PROJECT_ID"
+});
+```
+
+  </Tab>
+</Tabs>
+
+---
+
+## Project Management
+
+The Mem0 client provides comprehensive project management through the `client.project` interface:
+
+### Get Project Details
+
+Retrieve information about the current project:
+
+```python
+# Get all project details
+project_info = client.project.get()
+
+# Get specific fields only
+project_info = client.project.get(fields=["name", "description", "custom_categories"])
+```
+
+### Create a New Project
+
+Create a new project within your organization:
+
+```python
+# Create a project with name and description
+new_project = client.project.create(
+    name="My New Project",
+    description="A project for managing customer support memories"
+)
+```
+
+### Update Project Settings
+
+Modify project configuration including custom instructions, categories, and graph settings:
+
+```python
+# Update project with custom categories
+client.project.update(
+    custom_categories=[
+        {"customer_preferences": "Customer likes, dislikes, and preferences"},
+        {"support_history": "Previous support interactions and resolutions"}
+    ]
+)
+
+# Update project with custom instructions
+client.project.update(
+    custom_instructions="..."
+)
+
+# Enable graph memory for the project
+client.project.update(enable_graph=True)
+
+# Update multiple settings at once
+client.project.update(
+    custom_instructions="...",
+    custom_categories=[
+        {"personal_info": "User personal information and preferences"},
+        {"work_context": "Professional context and work-related information"}
+    ],
+    enable_graph=True
+)
+```
+
+### Delete Project
+
+<Warning>
+This action will remove all memories, messages, and other related data in the project. **This operation is irreversible.**
+</Warning>
+
+Remove a project and all its associated data:
+
+```python
+# Delete the current project (irreversible)
+result = client.project.delete()
+```
+
+---
+
+## Member Management
+
+Manage project members and their access levels:
+
+```python
+# Get all project members
+members = client.project.get_members()
+
+# Add a new member as a reader
+client.project.add_member(
+    email="colleague@company.com",
+    role="READER"  # or "OWNER"
+)
+
+# Update a member's role
+client.project.update_member(
+    email="colleague@company.com",
+    role="OWNER"
+)
+
+# Remove a member from the project
+client.project.remove_member(email="colleague@company.com")
+```
+
+### Member Roles
+
+| Role | Permissions |
+|------|-------------|
+| **READER** | Can view and search memories, but cannot modify project settings or manage members |
+| **OWNER** | Full access including project modification, member management, and all reader permissions |
+
+---
+
+## Async Support
+
+All project methods are available in async mode:
+
+```python
+from mem0 import AsyncMemoryClient
+
+async def manage_project():
+    client = AsyncMemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+
+    # All methods support async/await
+    project_info = await client.project.get()
+    await client.project.update(enable_graph=True)
+    members = await client.project.get_members()
+
+# To call the async function properly
+import asyncio
+asyncio.run(manage_project())
+```
+
+---
+
+## API Reference
+
+For complete API specifications and additional endpoints, see:
+
+<CardGroup cols={2}>
+  <Card title="Organizations APIs" icon="building" href="/api-reference/organization/create-org">
+    Create, get, and manage organizations
+  </Card>
+
+  <Card title="Project APIs" icon="folder" href="/api-reference/project/create-project">
+    Full project CRUD and member management endpoints
+  </Card>
+</CardGroup>

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

@@ -1,9 +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}/
 ---
 
-## Create Webhook
-
-Create a webhook by providing the project ID and the webhook details.
-

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

@@ -1,8 +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}/
 ---
-
-## Delete Webhook
-
-Delete a webhook by providing the webhook ID.

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

@@ -1,9 +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}/
 ---
 
-## Get Webhook
-
-Get a webhook by providing the project ID.
-

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

@@ -1,9 +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}/
 ---
 
-## Update Webhook
-
-Update a webhook by providing the webhook ID and the fields to update.
-

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 +8,121 @@ 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.
+</Update>
+
+<Update label="2026-02-17" description="v1.0.4">
+
+**New Features & Updates:**
+- **Memory Update:**
+  - Added `timestamp` parameter to `update()` — accepts Unix epoch (int/float) or ISO 8601 string
+
+</Update>
+
+<Update label="2026-01-29" description="v1.0.3">
+
+**New Features & Updates:**
+- **Project Settings:**
+  - Added inclusion prompt, exclusion prompt, memory depth, and usecase setting
+
+</Update>
+
+<Update label="2026-01-13" description="v1.0.2">
+
+**New Features & Updates:**
+- **Vector Stores:**
+  - Added DriverInfo metadata to MongoDB vector store
+
+</Update>
+
+<Update label="2025-11-14" description="v1.0.1">
+
+**New Features & Updates:**
+- **Vector Stores:**
+  - Added Apache Cassandra vector store support
+- **Embeddings:**
+  - Added FastEmbed embedding support for local embeddings
+- **Graph Store:**
+  - Added configurable embedding similarity threshold for graph store node matching
+
+**Bug Fixes:**
+- **Core:**
+  - Fixed condition check for memories_result type in Memory class
+  - Fixed list_memories endpoint Pydantic validation error
+  - Fixed memory deletion not removing from vector store
+
+</Update>
+
+<Update label="2025-10-16" description="v1.0.0">
+
+**New Features & Updates:**
+- **Vector Stores:**
+  - Added Azure MySQL support
+  - Added Azure AI Search Vector Store support
+- **LLMs:**
+  - Added Tool Call support for LangchainLLM
+  - Enabled custom model and parameters for Hugging Face with huggingface_base_url
+  - Updated default LLM configuration
+- **Rerankers:**
+  - Added reranker support: Cohere, ZeroEntropy, Hugging Face, Sentence Transformers, and LLMs
+- **Core:**
+  - Added metadata filtering for OSS
+  - Added Assistant memory retrieval
+  - Enabled async mode as default
+
+**Improvements:**
+- **Prompts:**
+  - Improved prompt for better memory retrieval
+- **Dependencies:**
+  - Updated dependency compatibility with OpenAI 2.x
+- **Validation:**
+  - Validated embedding_dims for Kuzu integration
+
+**Bug Fixes:**
+- **Vector Stores:**
+  - Fixed Databricks Vector Store integration
+  - Fixed Milvus DB bug and added test coverage
+  - Fixed Weaviate search method
+- **LLMs:**
+  - Fixed bug with thinking LLM in vLLM
+
+</Update>
+
 <Update label="2025-09-25" description="v0.1.118">
 
 **New Features & Updates:**
@@ -647,6 +763,98 @@ 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:**
+- **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:**
+- **Memory Update:**
+  - Added `timestamp` parameter to `update()` — accepts Unix epoch or ISO 8601 string
+
+</Update>
+
+<Update label="2026-01-29" description="v2.2.2">
+
+**New Features & Updates:**
+- **Project Settings:**
+  - Added inclusion prompt, exclusion prompt, memory depth, and usecase setting
+
+</Update>
+
+<Update label="2025-12-30" description="v2.2.1">
+
+**Improvements:**
+- **Client:** Added support for keyword arguments in `add` and `search` methods, allowing additional properties beyond defined options for experimental features
+
+</Update>
+
+<Update label="2025-12-29" description="v2.2.0">
+
+**New Features:**
+- **Vector Stores:** Added Azure AI Search vector store support
+
+**Improvements:**
+- **Config:** Fixed embedder config schema to support `embeddingDims` and `url` parameters
+- **Graph Memory:** Replaced hardcoded LLM provider with provider from configuration
+
+**Bug Fixes:**
+- **Embedders:** Fixed hardcoded `embeddingDims` values in embedders (OpenAI, Ollama, Google, Azure)
+- **Build:** Fixed TypeScript build errors
+
+</Update>
+
 <Update label="2025-09-04" description="v2.1.38">
 **New Features:**
 - **Client:** Added `metadata` param to `update` method.
@@ -673,17 +881,17 @@ mode: "wide"
 </Update>
 
 <Update label="2025-06-24" description="v2.1.33">
-**Improvement :**
+**Improvement:**
 - **Client:** Added `immutable` param to `add` method.
 </Update>
 
 <Update label="2025-06-20" description="v2.1.32">
-**Improvement :**
+**Improvement:**
 - **Client:** Made `api_version` V2 as default.
 </Update>
 
 <Update label="2025-06-17" description="v2.1.31">
-**Improvement :**
+**Improvement:**
 - **Client:** Added param `filter_memories`.
 </Update>
 
@@ -1123,6 +1331,16 @@ mode: "wide"
 
 <Tab title="Vercel AI SDK">
 
+<Update label="2025-12-26" description="v2.0.5">
+**Bug Fix:**
+- **Vercel AI SDK:** Removed unnecessary dependencies to make the package lighter.
+</Update>
+
+<Update label="2025-09-25" description="v2.0.4">
+**Bug Fix:**
+- **Vercel AI SDK:** Fixed version parameter in the AI SDK to use V2 for addition.
+</Update>
+
 <Update label="2025-09-25" description="v2.0.3">
 **New Features:**
 - **Vercel AI SDK:** Added file support for multimodal capabilities with memory context

docs/components/embedders/config.mdx

@@ -1,7 +1,6 @@
 ---
 title: Configurations
-icon: "gear"
-iconType: "solid"
+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.
@@ -41,7 +42,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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.
@@ -23,7 +24,7 @@ config = {
     "embedder": {
         "provider": "azure_openai",
         "config": {
-            "model": "text-embedding-3-large"
+            "model": "text-embedding-3-large",
             "azure_kwargs": {
                   "api_version": "",
                   "azure_deployment": "",
@@ -40,7 +41,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -68,7 +69,7 @@ const memory = new Memory(config);
 
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -117,9 +118,20 @@ Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sd
 ### Config
 
 Here are the parameters available for configuring Azure OpenAI embedder:
-
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `text-embedding-3-small` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `azure_kwargs` | The Azure OpenAI configs | `config_keys` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter         | Description                                   | Default Value              |
+| ----------------- | --------------------------------------------- | -------------------------- |
+| `model`           | The name of the embedding model to use        | `text-embedding-3-small`   |
+| `embeddingDims`   | Dimensions of the embedding model             | `1536`                     |
+| `apiKey`          | Azure OpenAI API key                          | `None`                     |
+| `modelProperties` | Object containing endpoint and other settings | `{ endpoint: "",...rest   }`|
+</Tab>
+</Tabs>

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).
@@ -26,7 +27,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -38,19 +39,19 @@ import { Memory } from 'mem0ai/oss';
 
 const config = {
   embedder: {
-    provider: 'google',
-    config: {
-      apiKey: process.env.GOOGLE_API_KEY || '',
-      model: 'text-embedding-004',
-      // The output dimensionality is fixed at 768 for Google AI embeddings
+      provider: "google",
+      config: {
+        apiKey: process.env["GOOGLE_API_KEY"],
+        model: "gemini-embedding-001",
+        embeddingDims: 1536,
+      },
     },
-  },
 };
 
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -61,9 +62,19 @@ await memory.add(messages, { userId: "john" });
 ### Config
 
 Here are the parameters available for configuring Gemini embedder:
-
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `model` | The name of the embedding model to use | `models/text-embedding-004` |
-| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
-| `api_key` | The Google API key | `None` |
+<Tabs>
+<Tab title="Python">
+| Parameter        | Description                          | Default Value           |
+| ---------------- | ------------------------------------ | ----------------------- |
+| `model`          | The name of the embedding model to use| `models/text-embedding-004` |
+| `embedding_dims` | Dimensions of the embedding model     | `1536`                  |
+| `api_key`        | The Google API key                   | `None`                  |
+</Tab>
+<Tab title="TypeScript">
+| Parameter         | Description                                   | Default Value              |
+| ----------------- | --------------------------------------------- | -------------------------- |
+| `model`           | The name of the embedding model to use        | `gemini-embedding-001`     |
+| `embeddingDims`   | Dimensions of the embedding model             | `1536`                     |
+| `apiKey`          | Google API key                                | `None`                     |
+</Tab>
+</Tabs>

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.
@@ -24,7 +25,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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.
@@ -36,7 +37,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -66,7 +67,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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
@@ -20,7 +24,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -29,10 +33,10 @@ m.add(messages, user_id="john")
 
 ### Config
 
-Here are the parameters available for configuring Ollama embedder:
+Here are the parameters available for configuring LM Studio embedder:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the OpenAI model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
+| `model` | The name of the LM Studio model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |

docs/components/embedders/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
@@ -21,7 +25,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -44,7 +48,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -69,5 +73,6 @@ Here are the parameters available for configuring Ollama embedder:
 | --- | --- | --- |
 | `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
 | `url` | Base URL for Ollama server | `http://localhost:11434` |
+| `embeddingDims` | Dimensions of the embedding model | 768
 </Tab>
 </Tabs>

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).
@@ -25,7 +26,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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).
@@ -27,7 +28,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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/).
@@ -27,7 +31,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

docs/components/embedders/overview.mdx

@@ -1,7 +1,6 @@
 ---
 title: Overview
-icon: "info"
-iconType: "solid"
+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.
@@ -29,6 +28,6 @@ See the list of supported embedders below.
 
 ## Usage
 
-To utilize a embedder, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the embedder.
+To utilize an embedding model, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `OpenAI` will be used as the embedding model.
 
-For a comprehensive list of available parameters for embedder configuration, please refer to [Config](./config).
+For a comprehensive list of available parameters for embedding model configuration, please refer to [Config](./config).

docs/components/llms/config.mdx

@@ -1,7 +1,6 @@
 ---
 title: Configurations
-icon: "gear"
-iconType: "solid"
+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."
 ---
 
 
@@ -29,7 +30,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -54,7 +55,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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
@@ -31,7 +32,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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>
@@ -48,7 +49,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -77,7 +78,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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").
@@ -28,7 +29,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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.
@@ -30,7 +31,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -55,7 +56,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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."
 ---
 
 
@@ -20,7 +21,7 @@ os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 # Initialize a LangChain model directly
 openai_model = ChatOpenAI(
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
     temperature=0.2,
     max_tokens=2000
 )
@@ -38,7 +39,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -69,7 +70,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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
@@ -12,7 +16,7 @@ config = {
     "llm": {
         "provider": "litellm",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -22,7 +26,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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.
@@ -29,7 +30,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -57,7 +58,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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.
@@ -28,7 +29,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -53,7 +54,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

docs/components/llms/models/ollama.mdx

@@ -1,4 +1,9 @@
-You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.
+---
+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.
 
 ## Usage
 
@@ -23,7 +28,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -47,7 +52,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

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).
@@ -19,7 +20,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -40,7 +41,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -65,7 +66,7 @@ const config = {
 const memory = new Memory(config);
 const messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -85,7 +86,7 @@ config = {
     "llm": {
         "provider": "openai_structured",
         "config": {
-            "model": "gpt-4o-2024-08-06",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.0,
         }
     }

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.
@@ -28,7 +29,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

docs/components/llms/models/together.mdx

@@ -1,4 +1,9 @@
-To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
+---
+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).
 
 ## Usage
 
@@ -23,7 +28,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -32,4 +37,4 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 
 ## Config
 
-All available parameters for the `togetherai` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `together` config are present in [Master List of All Params in Config](../config).

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.
@@ -29,7 +30,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]

docs/components/llms/overview.mdx

@@ -1,7 +1,6 @@
 ---
 title: Overview
-icon: "info"
-iconType: "solid"
+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.
@@ -32,6 +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

@@ -1,145 +1,105 @@
 ---
-title: Configuration
-icon: "gear"
-iconType: "solid"
+title: Config
+description: "Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings."
 ---
 
-## How to define configurations?
+## Common Configuration Parameters
 
-The `reranker` configuration is defined as an object with two main keys:
-- `provider`: The name of the reranker provider (e.g., "cohere", "sentence_transformer", "huggingface", "llm_reranker")
-- `config`: A nested dictionary containing provider-specific settings
+All rerankers share these common configuration parameters:
 
-## Basic Configuration
+| Parameter  | Description                                         | Type  | Default  |
+| ---------- | --------------------------------------------------- | ----- | -------- |
+| `provider` | Reranker provider name                              | `str` | Required |
+| `top_k`    | Maximum number of results to return after reranking | `int` | `None`   |
+| `api_key`  | API key for the reranker service                    | `str` | `None`   |
 
-Here's how to configure a reranker with Mem0:
+## Provider-Specific Configuration
 
-```python
-from mem0 import Memory
+### Zero Entropy
 
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-api-key",
-            "top_n": 10,
-            "model": "rerank-english-v3.0"
-        }
-    }
-}
-
-memory = Memory.from_config(config)
-```
-
-## Configuration Parameters
-
-| Parameter | Description | Required | Default |
-|-----------|-------------|----------|---------|
-| `provider` | Reranker provider name | Yes | - |
-| `config` | Provider-specific configuration | Yes | - |
-
-### Common Config Parameters
-
-| Parameter | Description | Providers |
-|-----------|-------------|-----------|
-| `api_key` | API key for the service | Cohere, Hugging Face |
-| `model` | Model name to use | All |
-| `top_n` | Number of results to return | All |
-| `device` | Device to run on (cpu/cuda/mps) | Sentence Transformer, Hugging Face |
-
-## Provider-Specific Examples
+| Parameter | Description                                  | Type  | Default      |
+| --------- | -------------------------------------------- | ----- | ------------ |
+| `model`   | Model to use: `zerank-1` or `zerank-1-small` | `str` | `"zerank-1"` |
+| `api_key` | Zero Entropy API key                         | `str` | `None`       |
 
 ### Cohere
-```python
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-cohere-api-key",
-            "model": "rerank-english-v3.0",
-            "top_n": 5
-        }
-    }
-}
-```
+
+| Parameter            | Description                                  | Type   | Default                 |
+| -------------------- | -------------------------------------------- | ------ | ----------------------- |
+| `model`              | Cohere rerank model                          | `str`  | `"rerank-english-v3.0"` |
+| `api_key`            | Cohere API key                               | `str`  | `None`                  |
+| `return_documents`   | Whether to return document texts in response | `bool` | `False`                 |
+| `max_chunks_per_doc` | Maximum chunks per document                  | `int`  | `None`                  |
 
 ### Sentence Transformer
-```python
-config = {
-    "reranker": {
-        "provider": "sentence_transformer",
-        "config": {
-            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
-            "device": "cpu",
-            "top_n": 10
-        }
-    }
-}
-```
+
+| Parameter           | Description                                  | Type   | Default                                  |
+| ------------------- | -------------------------------------------- | ------ | ---------------------------------------- |
+| `model`             | HuggingFace cross-encoder model name         | `str`  | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
+| `device`            | Device to run model on (`cpu`, `cuda`, etc.) | `str`  | `None`                                   |
+| `batch_size`        | Batch size for processing                    | `int`  | `32`                                     |
+| `show_progress_bar` | Show progress during processing              | `bool` | `False`                                  |
 
 ### Hugging Face
-```python
-config = {
-    "reranker": {
-        "provider": "huggingface",
-        "config": {
-            "api_key": "your-hf-token",
-            "model": "BAAI/bge-reranker-large",
-            "top_n": 8
-        }
-    }
-}
-```
+
+| Parameter | Description                                  | Type  | Default                     |
+| --------- | -------------------------------------------- | ----- | --------------------------- |
+| `model`   | HuggingFace reranker model name              | `str` | `"BAAI/bge-reranker-large"` |
+| `api_key` | HuggingFace API token                        | `str` | `None`                      |
+| `device`  | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None`                      |
+
+### LLM-based
+
+| Parameter        | Description                                | Type    | Default                |
+| ---------------- | ------------------------------------------ | ------- | ---------------------- |
+| `model`          | LLM model to use for scoring               | `str`   | `"gpt-4o-mini"`        |
+| `provider`       | LLM provider (`openai`, `anthropic`, etc.) | `str`   | `"openai"`             |
+| `api_key`        | API key for LLM provider                   | `str`   | `None`                 |
+| `temperature`    | Temperature for LLM generation             | `float` | `0.0`                  |
+| `max_tokens`     | Maximum tokens for LLM response            | `int`   | `100`                  |
+| `scoring_prompt` | Custom prompt template for scoring         | `str`   | Default scoring prompt |
 
 ### LLM Reranker
-```python
+
+| Parameter      | Description                 | Type   | Default  |
+| -------------- | --------------------------- | ------ | -------- |
+| `llm.provider` | LLM provider for reranking  | `str`  | Required |
+| `llm.config`   | LLM configuration object    | `dict` | Required |
+| `top_n`        | Number of results to return | `int`  | `None`   |
+
+## Environment Variables
+
+You can set API keys using environment variables:
+
+- `ZERO_ENTROPY_API_KEY` - Zero Entropy API key
+- `COHERE_API_KEY` - Cohere API key
+- `HUGGINGFACE_API_KEY` - HuggingFace API token
+- `OPENAI_API_KEY` - OpenAI API key (for LLM-based reranker)
+- `ANTHROPIC_API_KEY` - Anthropic API key (for LLM-based reranker)
+
+## Basic Configuration Example
+
+```python Python
 config = {
-    "reranker": {
-        "provider": "llm_reranker",
+    "vector_store": {
+        "provider": "chroma",
         "config": {
-            "llm": {
-                "provider": "openai",
-                "config": {
-                    "model": "gpt-4",
-                    "api_key": "your-openai-key"
-                }
-            },
-            "top_n": 5
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
         }
-    }
-}
-```
-
-## Advanced Configuration
-
-You can combine rerankers with other components:
-
-```python
-config = {
+    },
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4",
-            "api_key": "your-openai-key"
-        }
-    },
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "collection_name": "memories",
-            "host": "localhost",
-            "port": 6333
+            "model": "gpt-4.1-nano-2025-04-14"
         }
     },
     "reranker": {
-        "provider": "cohere",
+        "provider": "zero_entropy",
         "config": {
-            "api_key": "your-cohere-key",
-            "model": "rerank-english-v3.0",
-            "top_n": 10
+            "model": "zerank-1",
+            "top_k": 5
         }
     }
 }
 ```
-
-For provider-specific configuration details, visit the individual reranker pages.

docs/components/rerankers/custom-prompts.mdx

@@ -1,7 +1,6 @@
 ---
 title: Custom Prompts
-icon: "pencil"
-iconType: "solid"
+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.
@@ -54,7 +53,7 @@ config = {
             "llm": {
                 "provider": "openai",
                 "config": {
-                    "model": "gpt-4",
+                    "model": "gpt-4.1-nano-2025-04-14",
                     "api_key": "your-openai-key"
                 }
             },
@@ -71,16 +70,17 @@ 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) |
+| 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) |
 
 ## Domain-Specific Examples
 
 ### Customer Support
+
 ```python
 customer_support_prompt = """
 You are ranking customer support conversation memories.
@@ -100,6 +100,7 @@ Rank each memory 1-10 based on support relevance.
 ```
 
 ### Educational Content
+
 ```python
 educational_prompt = """
 Rank these learning memories for a student query.
@@ -119,6 +120,7 @@ Score each memory for educational value (1-10).
 ```
 
 ### Personal Assistant
+
 ```python
 personal_assistant_prompt = """
 Rank personal memories for relevance to the user's query.
@@ -140,6 +142,7 @@ Provide relevance scores (1-10) with brief explanations.
 ## Advanced Prompt Techniques
 
 ### Multi-Criteria Ranking
+
 ```python
 multi_criteria_prompt = """
 Evaluate memories using multiple criteria:
@@ -165,6 +168,7 @@ Format: JSON with detailed scoring
 ```
 
 ### Contextual Ranking
+
 ```python
 contextual_prompt = """
 Consider the following context when ranking memories:
@@ -214,4 +218,4 @@ for i, prompt in enumerate(prompts):
 - **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
+- **Missing Context**: Include relevant variables for your use case

docs/components/rerankers/models/cohere.mdx

@@ -1,116 +1,145 @@
 ---
 title: Cohere
+description: "Configure Cohere as a reranker in Mem0 with support for English and multilingual reranking models."
 ---
 
-Cohere provides state-of-the-art reranking models that can significantly improve the relevance of search results. Cohere's rerankers are optimized for various languages and use cases.
+Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
 
-## Usage
+## Models
 
-To use Cohere's reranker with Mem0:
+Cohere offers several reranking models:
 
-```python
-import os
+- **`rerank-english-v3.0`**: Latest English reranker with best performance
+- **`rerank-multilingual-v3.0`**: Multilingual support for global applications
+- **`rerank-english-v2.0`**: Previous generation English reranker
+
+## Installation
+
+```bash
+pip install cohere
+```
+
+## Configuration
+
+```python Python
 from mem0 import Memory
 
-os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
-
 config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4.1-nano-2025-04-14"
+        }
+    },
     "reranker": {
         "provider": "cohere",
         "config": {
-            "api_key": "your-cohere-api-key",  # Can also use environment variable
             "model": "rerank-english-v3.0",
-            "top_n": 10
+            "api_key": "your-cohere-api-key",  # or set COHERE_API_KEY
+            "top_k": 5,
+            "return_documents": False,
+            "max_chunks_per_doc": None
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## Environment Variables
+
+Set your API key as an environment variable:
+
+```bash
+export COHERE_API_KEY="your-api-key"
+```
+
+## Usage Example
+
+```python Python
+import os
+from mem0 import Memory
+
+# Set API key
+os.environ["COHERE_API_KEY"] = "your-api-key"
+
+# Initialize memory with Cohere reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "rerank": {
+        "provider": "cohere",
+        "config": {
+            "model": "rerank-english-v3.0",
+            "top_k": 3
         }
     }
 }
 
 memory = Memory.from_config(config)
 
-# Use memory as usual
-memory.add("I love playing basketball", user_id="alice")
-memory.add("I enjoy watching movies", user_id="alice")
+# Add memories
+messages = [
+    {"role": "user", "content": "I work as a data scientist at Microsoft"},
+    {"role": "user", "content": "I specialize in machine learning and NLP"},
+    {"role": "user", "content": "I enjoy playing tennis on weekends"}
+]
 
-# Search will now use Cohere reranking
-results = memory.search("What sports does Alice like?", user_id="alice")
+memory.add(messages, user_id="bob")
+
+# Search with reranking
+results = memory.search("What is the user's profession?", user_id="bob")
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
 ```
 
-## Configuration
+## Multilingual Support
 
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `api_key` | Cohere API key | Required |
-| `model` | Cohere rerank model | `rerank-english-v3.0` |
-| `top_n` | Number of results to return | `10` |
+For multilingual applications, use the multilingual model:
 
-## Available Models
-
-- `rerank-english-v3.0`: Latest English reranking model
-- `rerank-multilingual-v3.0`: Multilingual reranking model
-- `rerank-english-v2.0`: Previous English model
-- `rerank-multilingual-v2.0`: Previous multilingual model
-
-## Example with Different Models
-
-### English Reranker
-```python
+```python Python
 config = {
-    "reranker": {
+    "rerank": {
         "provider": "cohere",
         "config": {
-            "api_key": "your-cohere-api-key",
-            "model": "rerank-english-v3.0",
-            "top_n": 5
-        }
-    }
-}
-```
-
-### Multilingual Reranker
-```python
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-cohere-api-key",
             "model": "rerank-multilingual-v3.0",
-            "top_n": 8
+            "top_k": 5
         }
     }
 }
 ```
 
-## Environment Variables
+## Configuration Parameters
 
-You can set your Cohere API key as an environment variable:
+| Parameter            | Description                      | Type   | Default                 |
+| -------------------- | -------------------------------- | ------ | ----------------------- |
+| `model`              | Cohere rerank model to use       | `str`  | `"rerank-english-v3.0"` |
+| `api_key`            | Cohere API key                   | `str`  | `None`                  |
+| `top_k`              | Maximum documents to return      | `int`  | `None`                  |
+| `return_documents`   | Whether to return document texts | `bool` | `False`                 |
+| `max_chunks_per_doc` | Maximum chunks per document      | `int`  | `None`                  |
 
-```bash
-export COHERE_API_KEY="your-cohere-api-key"
-```
+## Features
 
-Then use the config without specifying the API key:
+- **High Quality**: Enterprise-grade relevance scoring
+- **Multilingual**: Support for 100+ languages
+- **Scalable**: Production-ready with high throughput
+- **Reliable**: SLA-backed service with 99.9% uptime
 
-```python
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "model": "rerank-english-v3.0",
-            "top_n": 10
-        }
-    }
-}
-```
+## Best Practices
 
-## Getting Your API Key
-
-1. Sign up at [Cohere](https://cohere.ai/)
-2. Navigate to the API keys section in your dashboard
-3. Generate a new API key
-4. Use this key in your configuration
-
-## Performance Considerations
-
-- Cohere rerankers work best with 10-100 candidate documents
-- Higher `top_n` values provide more comprehensive reranking but may increase latency
-- The v3.0 models generally provide better performance than v2.0 models
+1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
+2. **Batch Processing**: Process multiple queries efficiently
+3. **Error Handling**: Implement retry logic for production systems
+4. **Monitoring**: Track reranking performance and costs

docs/components/rerankers/models/huggingface.mdx

@@ -1,8 +1,6 @@
 ---
 title: Hugging Face Reranker
 description: 'Access thousands of reranking models from Hugging Face Hub'
-icon: "face-smile"
-iconType: "solid"
 ---
 
 ## Overview

docs/components/rerankers/models/llm.mdx

@@ -0,0 +1,226 @@
+---
+title: LLM as Reranker
+description: "Use any LLM as a flexible reranker in Mem0 with custom prompts and domain-specific scoring logic."
+---
+
+<Warning>
+**This page has been superseded.** Please see [LLM Reranker](/components/rerankers/models/llm_reranker) for the complete and up-to-date documentation on using LLMs for reranking.
+</Warning>
+
+LLM-based reranker provides maximum flexibility by using any Large Language Model to score document relevance. This approach allows for custom prompts and domain-specific scoring logic.
+
+## Supported LLM Providers
+
+Any LLM provider supported by Mem0 can be used for reranking:
+
+- **OpenAI**: GPT-4, GPT-3.5-turbo, etc.
+- **Anthropic**: Claude models
+- **Together**: Open-source models
+- **Groq**: Fast inference
+- **Ollama**: Local models
+- And more...
+
+## Configuration
+
+```python Python
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "api_key": "your-openai-api-key",  # or set OPENAI_API_KEY
+            "top_k": 5,
+            "temperature": 0.0
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## Custom Scoring Prompt
+
+You can provide a custom prompt for relevance scoring:
+
+```python Python
+custom_prompt = """You are a relevance scoring assistant. Rate how well this document answers the query.
+
+Query: "{query}"
+Document: "{document}"
+
+Score from 0.0 to 1.0 where:
+- 1.0: Perfect match, directly answers the query
+- 0.8-0.9: Highly relevant, good match  
+- 0.6-0.7: Moderately relevant, partial match
+- 0.4-0.5: Slightly relevant, limited useful information
+- 0.0-0.3: Not relevant or no useful information
+
+Provide only a single numerical score between 0.0 and 1.0."""
+
+config["reranker"]["config"]["scoring_prompt"] = custom_prompt
+```
+
+## Usage Example
+
+```python Python
+import os
+from mem0 import Memory
+
+# Set API key
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize memory with LLM reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "temperature": 0.0
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+
+# Add memories
+messages = [
+    {"role": "user", "content": "I'm learning Python programming"},
+    {"role": "user", "content": "I find object-oriented programming challenging"}, 
+    {"role": "user", "content": "I love hiking in national parks"}
+]
+
+memory.add(messages, user_id="david")
+
+# Search with LLM reranking
+results = memory.search("What programming topics is the user studying?", user_id="david")
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
+```
+
+```text Output
+Memory: I'm learning Python programming
+Vector Score: 0.856
+Rerank Score: 0.920
+
+Memory: I find object-oriented programming challenging
+Vector Score: 0.782
+Rerank Score: 0.850
+```
+
+## Domain-Specific Scoring
+
+Create specialized scoring for your domain:
+
+```python Python
+medical_prompt = """You are a medical relevance expert. Score how relevant this medical record is to the clinical query.
+
+Clinical Query: "{query}"
+Medical Record: "{document}"
+
+Consider:
+- Clinical relevance and accuracy
+- Patient safety implications
+- Diagnostic value
+- Treatment relevance
+
+Score from 0.0 to 1.0. Provide only the numerical score."""
+
+config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "gpt-4o-mini",
+            "provider": "openai",
+            "scoring_prompt": medical_prompt,
+            "temperature": 0.0
+        }
+    }
+}
+```
+
+## Multiple LLM Providers
+
+Use different LLM providers for reranking:
+
+```python Python
+# Using Anthropic Claude
+anthropic_config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "claude-3-haiku-20240307",
+            "provider": "anthropic",
+            "temperature": 0.0
+        }
+    }
+}
+
+# Using local Ollama model
+ollama_config = {
+    "reranker": {
+        "provider": "llm",
+        "config": {
+            "model": "llama2:7b",
+            "provider": "ollama",
+            "temperature": 0.0
+        }
+    }
+}
+```
+
+## Configuration Parameters
+
+| Parameter | Description | Type | Default |
+|-----------|-------------|------|---------|
+| `model` | LLM model to use for scoring | `str` | `"gpt-4o-mini"` |
+| `provider` | LLM provider name | `str` | `"openai"` |
+| `api_key` | API key for the LLM provider | `str` | `None` |
+| `top_k` | Maximum documents to return | `int` | `None` |
+| `temperature` | Temperature for LLM generation | `float` | `0.0` |
+| `max_tokens` | Maximum tokens for LLM response | `int` | `100` |
+| `scoring_prompt` | Custom prompt template | `str` | Default prompt |
+
+## Advantages
+
+- **Maximum Flexibility**: Custom prompts for any use case
+- **Domain Expertise**: Leverage LLM knowledge for specialized domains
+- **Interpretability**: Understand scoring through prompt engineering
+- **Multi-criteria**: Score based on multiple relevance factors
+
+## Considerations
+
+- **Latency**: Higher latency than specialized rerankers
+- **Cost**: LLM API costs per reranking operation
+- **Consistency**: May have slight variations in scoring
+- **Prompt Engineering**: Requires careful prompt design
+
+## Best Practices
+
+1. **Temperature**: Use 0.0 for consistent scoring
+2. **Prompt Design**: Be specific about scoring criteria
+3. **Token Efficiency**: Keep prompts concise to reduce costs
+4. **Caching**: Cache results for repeated queries when possible
+5. **Fallback**: Handle API errors gracefully

docs/components/rerankers/models/llm_reranker.mdx

@@ -1,8 +1,6 @@
 ---
 title: LLM Reranker
 description: 'Use any language model as a reranker with custom prompts'
-icon: "robot"
-iconType: "solid"
 ---
 
 ## Overview

docs/components/rerankers/models/sentence_transformer.mdx

@@ -1,162 +1,159 @@
 ---
 title: Sentence Transformer
+description: 'Local reranking with HuggingFace cross-encoder models'
 ---
 
-Sentence Transformer rerankers use cross-encoder models that are specifically designed for ranking tasks. These models can run locally and provide good reranking performance without external API calls.
+Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
 
-## Usage
+## Models
 
-To use Sentence Transformer reranker with Mem0:
+Any HuggingFace cross-encoder model can be used. Popular choices include:
 
-```python
+- **`cross-encoder/ms-marco-MiniLM-L-6-v2`**: Default, good balance of speed and accuracy
+- **`cross-encoder/ms-marco-TinyBERT-L-2-v2`**: Fastest, smaller model size
+- **`cross-encoder/ms-marco-electra-base`**: Higher accuracy, larger model
+- **`cross-encoder/stsb-distilroberta-base`**: Good for semantic similarity tasks
+
+## Installation
+
+```bash
+pip install sentence-transformers
+```
+
+## Configuration
+
+```python Python
 from mem0 import Memory
 
 config = {
-    "reranker": {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "rerank": {
         "provider": "sentence_transformer",
         "config": {
             "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
-            "device": "cpu",
-            "top_n": 10
+            "device": "cpu",  # or "cuda" for GPU
+            "batch_size": 32,
+            "show_progress_bar": False,
+            "top_k": 5
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## GPU Acceleration
+
+For better performance, use GPU acceleration:
+
+```python Python
+config = {
+    "rerank": {
+        "provider": "sentence_transformer",
+        "config": {
+            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "device": "cuda",  # Use GPU
+            "batch_size": 64   # high batch size for high memory GPUs
+        }
+    }
+}
+```
+
+## Usage Example
+
+```python Python
+from mem0 import Memory
+
+# Initialize memory with local reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "rerank": {
+        "provider": "sentence_transformer",
+        "config": {
+            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "device": "cpu"
         }
     }
 }
 
 memory = Memory.from_config(config)
 
-# Use memory as usual
-memory.add("I love playing basketball", user_id="alice")
-memory.add("I enjoy watching movies", user_id="alice")
+# Add memories
+messages = [
+    {"role": "user", "content": "I love reading science fiction novels"},
+    {"role": "user", "content": "My favorite author is Isaac Asimov"},
+    {"role": "user", "content": "I also enjoy watching sci-fi movies"}
+]
 
-# Search will now use Sentence Transformer reranking
-results = memory.search("What sports does Alice like?", user_id="alice")
-```
+memory.add(messages, user_id="charlie")
 
-## Configuration
+# Search with local reranking
+results = memory.search("What books does the user like?", user_id="charlie")
 
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `model` | Sentence Transformer cross-encoder model | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
-| `device` | Device to run on (`cpu`, `cuda`, `mps`) | `cpu` |
-| `top_n` | Number of results to return | `10` |
-
-## Popular Models
-
-### Lightweight Models
-- `cross-encoder/ms-marco-MiniLM-L-6-v2`: Fast and efficient
-- `cross-encoder/ms-marco-MiniLM-L-4-v2`: Even faster, slightly lower accuracy
-- `cross-encoder/ms-marco-MiniLM-L-2-v2`: Fastest, good for real-time applications
-
-### High-Performance Models
-- `cross-encoder/ms-marco-electra-base`: Better accuracy, larger model
-- `ms-marco-MiniLM-L-12-v2`: Balanced performance and speed
-- `cross-encoder/qnli-electra-base`: Good for question-answering tasks
-
-## Device Configuration
-
-### CPU Usage
-```python
-config = {
-    "reranker": {
-        "provider": "sentence_transformer",
-        "config": {
-            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
-            "device": "cpu",
-            "top_n": 10
-        }
-    }
-}
-```
-
-### GPU Usage (CUDA)
-```python
-config = {
-    "reranker": {
-        "provider": "sentence_transformer",
-        "config": {
-            "model": "cross-encoder/ms-marco-electra-base",
-            "device": "cuda",
-            "top_n": 15
-        }
-    }
-}
-```
-
-### Apple Silicon (MPS)
-```python
-config = {
-    "reranker": {
-        "provider": "sentence_transformer",
-        "config": {
-            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
-            "device": "mps",
-            "top_n": 10
-        }
-    }
-}
-```
-
-## Installation
-
-The sentence-transformers library is required:
-
-```bash
-pip install sentence-transformers
-```
-
-For GPU support with CUDA:
-```bash
-pip install sentence-transformers torch
-```
-
-## Performance Optimization
-
-### Model Selection
-- Use MiniLM models for faster inference
-- Use larger models (electra-base) for better accuracy
-- Consider the trade-off between speed and quality
-
-### Device Optimization
-- Use GPU (`cuda` or `mps`) for larger models
-- CPU is sufficient for MiniLM models
-- Batch processing improves GPU utilization
-
-### Memory Considerations
-```python
-# For memory-constrained environments
-config = {
-    "reranker": {
-        "provider": "sentence_transformer",
-        "config": {
-            "model": "cross-encoder/ms-marco-MiniLM-L-2-v2",  # Smallest model
-            "device": "cpu",
-            "top_n": 5  # Fewer results to process
-        }
-    }
-}
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
 ```
 
 ## Custom Models
 
-You can use any Sentence Transformer cross-encoder model:
+You can use any HuggingFace cross-encoder model:
 
-```python
+```python Python
+# Using a different model
 config = {
-    "reranker": {
-        "provider": "sentence_transformer",
+    "rerank": {
+        "provider": "sentence_transformer", 
         "config": {
-            "model": "your-custom-model-name",
-            "device": "cpu",
-            "top_n": 10
+            "model": "cross-encoder/stsb-distilroberta-base",
+            "device": "cpu"
         }
     }
 }
 ```
 
+## Configuration Parameters
+
+| Parameter | Description | Type | Default |
+|-----------|-------------|------|---------|
+| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
+| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
+| `batch_size` | Batch size for processing documents | `int` | `32` |
+| `show_progress_bar` | Show progress bar during processing | `bool` | `False` |
+| `top_k` | Maximum documents to return | `int` | `None` |
+
 ## Advantages
 
-- **Local Processing**: No external API calls required
-- **Privacy**: Data stays on your infrastructure
-- **Cost Effective**: No per-request charges
-- **Fast**: Especially with GPU acceleration
-- **Customizable**: Can fine-tune on your specific data
+- **Privacy**: Complete local processing, no external API calls
+- **Cost**: No per-token charges after initial model download
+- **Customization**: Use any HuggingFace cross-encoder model
+- **Offline**: Works without internet connection after model download
+
+## Performance Considerations
+
+- **First Run**: Model download may take time initially
+- **Memory Usage**: Models require GPU/CPU memory
+- **Batch Size**: Optimize batch size based on available memory
+- **Device**: GPU acceleration significantly improves speed
+
+## Best Practices
+
+1. **Model Selection**: Choose model based on accuracy vs speed requirements
+2. **Device Management**: Use GPU when available for better performance
+3. **Batch Processing**: Process multiple documents together for efficiency
+4. **Memory Monitoring**: Monitor system memory usage with larger models

docs/components/rerankers/models/zero_entropy.mdx

@@ -0,0 +1,117 @@
+---
+title: 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.
+
+## Models
+
+Zero Entropy offers two reranking models:
+
+- **`zerank-1`**: Flagship state-of-the-art reranker (non-commercial license)
+- **`zerank-1-small`**: Open-source model (Apache 2.0 license)
+
+## Installation
+
+```bash
+pip install zeroentropy
+```
+
+## Configuration
+
+```python Python
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "rerank": {
+        "provider": "zero_entropy",
+        "config": {
+            "model": "zerank-1",  # or "zerank-1-small"
+            "api_key": "your-zero-entropy-api-key",  # or set ZERO_ENTROPY_API_KEY
+            "top_k": 5
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## Environment Variables
+
+Set your API key as an environment variable:
+
+```bash
+export ZERO_ENTROPY_API_KEY="your-api-key"
+```
+
+## Usage Example
+
+```python Python
+import os
+from mem0 import Memory
+
+# Set API key
+os.environ["ZERO_ENTROPY_API_KEY"] = "your-api-key"
+
+# Initialize memory with Zero Entropy reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
+    "rerank": {"provider": "zero_entropy", "config": {"model": "zerank-1"}}
+}
+
+memory = Memory.from_config(config)
+
+# Add memories
+messages = [
+    {"role": "user", "content": "I love Italian pasta, especially carbonara"},
+    {"role": "user", "content": "Japanese sushi is also amazing"},
+    {"role": "user", "content": "I enjoy cooking Mediterranean dishes"}
+]
+
+memory.add(messages, user_id="alice")
+
+# Search with reranking
+results = memory.search("What Italian food does the user like?", user_id="alice")
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
+```
+
+## Configuration Parameters
+
+| Parameter | Description | Type | Default |
+|-----------|-------------|------|---------|
+| `model` | Model to use: `"zerank-1"` or `"zerank-1-small"` | `str` | `"zerank-1"` |
+| `api_key` | Zero Entropy API key | `str` | `None` |
+| `top_k` | Maximum documents to return after reranking | `int` | `None` |
+
+## Performance
+
+- **Fast**: Optimized neural architecture for low latency
+- **Accurate**: State-of-the-art relevance scoring
+- **Cost-effective**: ~$0.025/1M tokens processed
+
+## Best Practices
+
+1. **Model Selection**: Use `zerank-1` for best quality, `zerank-1-small` for faster processing
+2. **Batch Size**: Process multiple queries together when possible
+3. **Top-k Limiting**: Set reasonable `top_k` values (5-20) for best performance
+4. **API Key Management**: Use environment variables for secure key storage

docs/components/rerankers/optimization.mdx

@@ -1,7 +1,6 @@
 ---
 title: Performance Optimization
-icon: "bolt"
-iconType: "solid"
+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/rerankers/overview.mdx

@@ -1,52 +1,78 @@
 ---
 title: Overview
-icon: "info"
-iconType: "solid"
+description: 'Pick the right reranker path to boost Mem0 search relevance.'
 ---
 
-Rerankers enhance the quality of search results by re-ordering the initial retrieval results using more sophisticated scoring mechanisms. They act as a secondary ranking layer that can significantly improve the relevance of retrieved memories.
+Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.
 
-## How Rerankers Work
+<Info>
+Reranking trades extra latency for better precision. Start once you have baseline search working and measure before/after relevance.
+</Info>
 
-1. **Initial Retrieval**: Vector search returns candidate memories based on semantic similarity
-2. **Reranking**: The reranker evaluates and re-scores these candidates using more complex criteria
-3. **Final Results**: Returns the top-k memories with improved relevance ordering
-
-## Benefits
-
-- **Improved Precision**: Better ranking of relevant memories
-- **Context Awareness**: More sophisticated understanding of query-memory relationships
-- **Performance**: Can improve results without changing the underlying vector store
-
-## Supported Rerankers
-
-Mem0 supports several reranker models:
-
-<CardGroup cols={2}>
-  <Card title="Cohere" href="/components/rerankers/models/cohere" />
-  <Card title="Sentence Transformer" href="/components/rerankers/models/sentence_transformer" />
-  <Card title="Hugging Face" href="/components/rerankers/models/huggingface" />
-  <Card title="LLM Reranker" href="/components/rerankers/models/llm_reranker" />
+<CardGroup cols={3}>
+  <Card
+    title="Understand Reranking"
+    description="See how reranker-enhanced search changes your retrieval flow."
+    icon="search"
+    href="/open-source/features/reranker-search"
+  />
+  <Card
+    title="Configure Providers"
+    description="Add reranker blocks to your memory configuration."
+    icon="settings"
+    href="/components/rerankers/config"
+  />
+  <Card
+    title="Optimize Performance"
+    description="Balance relevance, latency, and cost with tuning tactics."
+    icon="speedometer"
+    href="/components/rerankers/optimization"
+  />
+  <Card
+    title="Custom Prompts"
+    description="Shape LLM-based reranking with tailored instructions."
+    icon="code"
+    href="/components/rerankers/custom-prompts"
+  />
+  <Card
+    title="Zero Entropy Guide"
+    description="Adopt the managed neural reranker for production workloads."
+    icon="sparkles"
+    href="/components/rerankers/models/zero_entropy"
+  />
+  <Card
+    title="Sentence Transformers"
+    description="Keep reranking on-device with cross-encoder models."
+    icon="cpu"
+    href="/components/rerankers/models/sentence_transformer"
+  />
 </CardGroup>
 
-## Usage
+## Picking the Right Reranker
 
-Rerankers are configured as part of the memory configuration:
+- **API-first** when you need top quality and can absorb request costs (Cohere, Zero Entropy).  
+- **Self-hosted** for privacy-sensitive deployments that must stay on your hardware (Sentence Transformer, Hugging Face).  
+- **LLM-driven** when you need bespoke scoring logic or complex prompts.  
+- **Hybrid** by enabling reranking only on premium journeys to control spend.
 
-```python
-from mem0 import Memory
+## Implementation Checklist
 
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-api-key",
-            "top_n": 10
-        }
-    }
-}
+1. Confirm baseline search KPIs so you can measure uplift.  
+2. Select a provider and add the `reranker` block to your config.  
+3. Test latency impact with production-like query batches.  
+4. Decide whether to enable reranking globally or per-search via the `rerank` flag.
 
-memory = Memory.from_config(config)
-```
-
-For detailed configuration options, see the [Config](./config) page.
+<CardGroup cols={2}>
+  <Card
+    title="Set Up Reranking"
+    description="Walk through the configuration fields and defaults."
+    icon="settings"
+    href="/components/rerankers/config"
+  />
+  <Card
+    title="Example: Reranker Search"
+    description="Follow the feature guide to see reranking in action."
+    icon="rocket"
+    href="/open-source/features/reranker-search"
+  />
+</CardGroup>

docs/components/vectordbs/config.mdx

@@ -1,7 +1,6 @@
 ---
 title: Configurations
-icon: "gear"
-iconType: "solid"
+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.
@@ -27,7 +28,7 @@ config = {
 m = Memory.from_config(config)
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
     {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
@@ -107,13 +108,13 @@ To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these ste
     - Click **Add** > **Add role assignment**.
 6. **Choose Role:**
     - Mem0 requires the **Search Index Data Contributor** and **Search Service Contributor** role.
-7. **Choose Member** 
-    - To assign to a User, Group, Service Principle or Managed Identity:
+7. **Choose Member**
+    - To assign to a User, Group, Service Principal or Managed Identity:
         - For production it is recommended to use a service principal or managed identity.
             - For a service principal: select **User, group, or service principal** and search for the service principal.
             - For a managed identity: select **Managed identity** and choose the managed identity.
         - For development, you can assign the role to a user account.
-            - For development: select ***User, group, or service principal** and pick a Azure Entra ID account (the same used with `az login`).
+            - For development: select **User, group, or service principal** and pick an Azure Entra ID account (the same used with `az login`).
 8. **Complete the Assignment:**
     - Click **Review + Assign**.
 
@@ -133,7 +134,7 @@ config = {
 }
 ```
 
-### Environment Variables to set to use Azure Identity Credential:
+### Environment Variables to Use Azure Identity Credential
 * For an Environment Credential, you will need to setup a Service Principal and set the following environment variables:
   - `AZURE_TENANT_ID`: Your Azure Active Directory tenant ID.
   - `AZURE_CLIENT_ID`: The client ID of your service principal or managed identity.
@@ -142,7 +143,7 @@ config = {
   - `AZURE_CLIENT_ID`: The client ID of the user-assigned managed identity.
 * For a System-Assigned Managed Identity, no additional environment variables are needed.
 
-### Developer logins to use for a Azure Identity Credential:
+### Developer Logins for Azure Identity Credential
 * For an Azure CLI Credential, you need to have the Azure CLI installed and logged in with `az login`.
 * For an Azure PowerShell Credential, you need to have the Azure PowerShell module installed and logged in with `Connect-AzAccount`.
 * For an Azure Developer CLI Credential, you need to have the Azure Developer CLI installed and logged in with `azd auth login`.

docs/components/vectordbs/dbs/azure_mysql.mdx

@@ -0,0 +1,129 @@
+---
+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.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "port": 3306,
+            "user": "your_username",
+            "password": "your_password",
+            "database": "mem0_db",
+            "collection_name": "memories",
+        }
+    }
+}
+
+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"})
+```
+
+#### Using Azure Managed Identity
+
+For production deployments, use Azure Managed Identity instead of passwords:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_mysql",
+        "config": {
+            "host": "your-server.mysql.database.azure.com",
+            "user": "your_username",
+            "database": "mem0_db",
+            "collection_name": "memories",
+            "use_azure_credential": True,  # Uses DefaultAzureCredential
+            "ssl_disabled": False
+        }
+    }
+}
+```
+
+<Note>
+When `use_azure_credential` is enabled, the password is obtained via Azure DefaultAzureCredential (supports Managed Identity, Azure CLI, etc.)
+</Note>
+
+### Config
+
+Here are the parameters available for configuring Azure MySQL:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `host` | MySQL server hostname | Required |
+| `port` | MySQL server port | `3306` |
+| `user` | Database user | Required |
+| `password` | Database password (optional with Azure credential) | `None` |
+| `database` | Database name | Required |
+| `collection_name` | Table name for storing vectors | `"mem0"` |
+| `embedding_model_dims` | Dimensions of embedding vectors | `1536` |
+| `use_azure_credential` | Use Azure DefaultAzureCredential | `False` |
+| `ssl_ca` | Path to SSL CA certificate | `None` |
+| `ssl_disabled` | Disable SSL (not recommended) | `False` |
+| `minconn` | Minimum connections in pool | `1` |
+| `maxconn` | Maximum connections in pool | `5` |
+
+### Setup
+
+#### Create MySQL Flexible Server using Azure CLI:
+
+```bash
+# Create resource group
+az group create --name mem0-rg --location eastus
+
+# Create MySQL Flexible Server
+az mysql flexible-server create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --location eastus \
+    --admin-user myadmin \
+    --admin-password <YourPassword> \
+    --version 8.0.21
+
+# Create database
+az mysql flexible-server db create \
+    --resource-group mem0-rg \
+    --server-name mem0-mysql-server \
+    --database-name mem0_db
+
+# Configure firewall
+az mysql flexible-server firewall-rule create \
+    --resource-group mem0-rg \
+    --name mem0-mysql-server \
+    --rule-name AllowMyIP \
+    --start-ip-address <YourIP> \
+    --end-ip-address <YourIP>
+```
+
+#### Enable Azure AD Authentication:
+
+1. In Azure Portal, navigate to your MySQL Flexible Server
+2. Go to **Security** > **Authentication** and enable Azure AD
+3. Add your application's managed identity as a MySQL user:
+
+```sql
+CREATE AADUSER 'your-app-identity' IDENTIFIED BY 'your-client-id';
+GRANT ALL PRIVILEGES ON mem0_db.* TO 'your-app-identity'@'%';
+FLUSH PRIVILEGES;
+```
+
+<Tip>
+For production, use [Managed Identity](https://learn.microsoft.com/azure/active-directory/managed-identities-azure-resources/) to eliminate password management.
+</Tip>

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.
@@ -37,7 +38,7 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 
 ### Config
 
-Here are the available parameters for the `mochow` config:
+Here are the parameters available for configuring Baidu VectorDB:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |