Merge branch 'main' into mem0-1.0.0

Signed-off-by: Andy Kwok <andy.kwok@improving.com>

.github/workflows/ci.yml

@@ -37,7 +37,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
@@ -56,14 +56,12 @@ jobs:
         run: sudo apt-get update && sudo apt-get install -y libgeos-dev
       - name: Install dependencies
         run: |
-          make install_all
-          pip install -e ".[test]"
-          pip install pinecone pinecone-text
+          pip install --upgrade pip
+          pip install -e ".[test,graph,vector_stores,llms,extras]"
+          pip install ruff
         if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
-      - name: Run Formatting
-        run: |
-          mkdir -p .ruff_cache && chmod -R 777 .ruff_cache
-          hatch run format
+      - name: Run Linting
+        run: make lint
       - name: Run tests and generate coverage report
         run: make test
 
@@ -73,7 +71,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}

CONTRIBUTING.md

@@ -25,6 +25,7 @@ We use `hatch` for managing development environments. To set up:
 hatch shell dev_py_3_9   # Python 3.9
 hatch shell dev_py_3_10  # Python 3.10  
 hatch shell dev_py_3_11  # Python 3.11
+hatch shell dev_py_3_12  # Python 3.12
 
 # The environment will automatically install all dev dependencies
 # Run tests within the activated shell:
@@ -51,6 +52,7 @@ make test
 make test-py-3.9   # Python 3.9 environment
 make test-py-3.10  # Python 3.10 environment
 make test-py-3.11  # Python 3.11 environment
+make test-py-3.12  # Python 3.12 environment
 
 # When using hatch shells, run tests with:
 make test  # After activating a shell with hatch shell test_XX

MIGRATION_GUIDE_v1.0.md

@@ -0,0 +1,347 @@
+# Migration Guide: Upgrading to mem0ai 1.0.0
+
+This guide will help you migrate from mem0ai 0.x to the new 1.0.0 version.
+
+## Breaking Changes
+
+### 1. API Version Changes
+
+**Before (0.x):**
+```python
+# Multiple API versions supported
+memory = Memory(config=MemoryConfig(version="v1.1"))
+
+# Client with output_format parameter
+client.add(messages, output_format="v1.1")
+client.search(query, version="v1", output_format="v1.1") 
+client.get_all(version="v1", output_format="v1.1")
+```
+
+**After (1.0.0):**
+```python
+# v1.1 format is default (v1.0 is deprecated)
+memory = Memory()  # Defaults to v1.1 format
+
+# Client API with correct versioning behavior:
+client.add(messages)  # Uses v1 API endpoint, returns v1.1 format
+client.search(query)  # Uses v2 API endpoint, returns v1.1 format  
+client.get_all()  # Uses v2 API endpoint, returns v1.1 format
+```
+
+### 2. API Versioning Strategy Clarification
+
+**IMPORTANT: Understanding the New Versioning Strategy**
+
+The API versioning strategy in mem0ai 1.0.0 has been unified and simplified:
+
+#### **Endpoint vs Format Distinction**
+- **API Endpoints** (`/v1/`, `/v2/`): Control which REST API version to use
+- **Response Formats** (v1.0, v1.1): Control the structure of the returned data
+
+#### **New Unified Strategy:**
+- **Add operations**: Always use `/v1/` endpoint with v1.1 response format (no more output_format parameter)
+- **Search operations**: Always use `/v2/` endpoint with v1.1 response format  
+- **Get_all operations**: Always use `/v2/` endpoint with v1.1 response format
+- **Response format**: All operations now return v1.1 format (`{"results": [...]}`)
+
+#### **What Changed:**
+- ✅ **Consistent response format**: Everything returns v1.1 format
+- ✅ **Simplified API**: No more `output_format` or `version` parameters to manage
+- ✅ **Endpoint optimization**: Add uses v1, Search/Get use v2 for best performance
+- ❌ **Removed v1.0 support**: v1.0 response format is no longer supported
+
+### 3. Response Format Standardization
+
+**Before (0.x):**
+```python
+# Inconsistent response formats based on api_version
+result = memory.add(messages)
+# Could return list or dict depending on version
+
+memories = memory.get_all()  
+# Could return list or dict depending on version
+```
+
+**After (1.0.0):**
+```python
+# v1.1 format is now default (consistent dict format)
+result = memory.add(messages)
+# Returns: {"results": [...], "relations": [...] (if graph enabled)}
+
+memories = memory.get_all()
+# Returns: {"results": [...], "relations": [...] (if graph enabled)}
+
+# v1.0 format still works but shows deprecation warning
+memory_v1 = Memory(config=MemoryConfig(version="v1.0"))
+result = memory_v1.add(messages)  # Returns raw list [{...}] (with warning)
+```
+
+## Migration Steps
+
+### Step 1: Update Dependencies
+
+```bash
+pip install mem0ai==1.0.0
+```
+
+### Step 2: Update Code
+
+#### Memory API Changes
+
+```python
+# Before
+from mem0 import Memory
+
+memory = Memory(config=MemoryConfig(version="v1.1"))
+
+# After - no changes needed, v1.1 is automatic
+from mem0 import Memory
+
+memory = Memory()  # Defaults to v1.1 format
+```
+
+#### Client API Changes
+
+```python
+# Before
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# Remove all version and output_format parameters
+result = client.add(messages, output_format="v1.1")
+memories = client.search(query, version="v2", output_format="v1.1")
+all_memories = client.get_all(version="v2", output_format="v1.1")
+
+# After
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-key")
+
+# Simplified API calls
+result = client.add(messages)
+memories = client.search(query)
+all_memories = client.get_all()
+```
+
+#### Response Handling
+
+```python
+# Before - inconsistent response formats
+result = memory.add(messages)
+if isinstance(result, list):
+    # Handle v1.0 format
+    for item in result:
+        print(item)
+else:
+    # Handle v1.1+ format
+    for item in result["results"]:
+        print(item)
+
+# After - consistent response format
+result = memory.add(messages)
+for item in result["results"]:
+    print(item)
+
+# Access graph relations if enabled
+if "relations" in result:
+    for relation in result["relations"]:
+        print(relation)
+```
+
+### Step 3: Remove Deprecated Code
+
+Remove any code that handled multiple API versions:
+
+```python
+# Remove these patterns
+if version == "v1.0":
+    # handle old format
+elif version == "v1.1":
+    # handle new format
+
+# Remove version-specific logic
+def handle_response(response, api_version):
+    if api_version == "v1.0":
+        return response  # list format
+    else:
+        return response["results"]  # dict format
+```
+
+### Step 4: Update Configuration
+
+#### Vector Store Configuration
+
+```python
+# Before - version in config
+config = MemoryConfig(
+    version="v1.1",
+    vector_store=VectorStoreConfig(...)
+)
+
+# After - no version needed
+config = MemoryConfig(
+    vector_store=VectorStoreConfig(...)
+)
+```
+
+#### Enhanced GCP Support
+
+```python
+# New: Enhanced Vertex AI configuration options
+from mem0.configs.vector_stores.vertex_ai_vector_search import GoogleMatchingEngineConfig
+
+# Option 1: Using credentials file (existing)
+config = GoogleMatchingEngineConfig(
+    project_id="your-project",
+    credentials_path="/path/to/service-account.json",
+    # ... other params
+)
+
+# Option 2: Using credentials dict (new in v1.0.0)
+service_account_info = {
+    "type": "service_account",
+    "project_id": "your-project",
+    # ... rest of service account JSON
+}
+
+config = GoogleMatchingEngineConfig(
+    project_id="your-project",
+    service_account_json=service_account_info,
+    # ... other params
+)
+```
+
+## Testing Your Migration
+
+### 1. Test Basic Functionality
+
+```python
+from mem0 import Memory
+
+# Test memory operations
+memory = Memory()
+
+# Test adding memories
+result = memory.add("I like pizza")
+assert "results" in result
+assert len(result["results"]) > 0
+
+# Test searching
+search_result = memory.search("food preferences", user_id="test_user")
+assert "results" in search_result
+
+# Test listing all
+all_memories = memory.get_all(user_id="test_user")
+assert "results" in all_memories
+```
+
+### 2. Test Client Operations
+
+```python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Test all client methods work without deprecated parameters
+messages = [{"role": "user", "content": "I love traveling"}]
+result = client.add(messages, user_id="test_user")
+assert "results" in result or isinstance(result, list)  # Platform may vary
+
+memories = client.search("travel", user_id="test_user")
+all_memories = client.get_all(user_id="test_user")
+```
+
+## New Features in v1.0.0
+
+### 1. Improved Vector Store Support
+
+- Fixed OpenSearch vector store integration
+- Enhanced error handling across all vector stores
+- Better performance and reliability
+
+### 2. Enhanced GCP Integration
+
+- Support for service account JSON dict (in addition to file path)
+- Improved Vertex AI Vector Search configuration
+
+### 3. Simplified API
+
+- Default API version is now v1.1 (v1.0 deprecated)
+- Removed deprecated parameters
+- Standardized response formats
+
+## Deprecation Warning for v1.0 Users
+
+If you're currently using `version="v1.0"`, you'll see a deprecation warning:
+
+```
+DeprecationWarning: The v1.0 API format is deprecated and will be removed in mem0ai 2.0.0. 
+Please upgrade to v1.1 format which returns a dict with 'results' key. 
+Set version='v1.1' in your MemoryConfig.
+```
+
+**To resolve this:**
+```python
+# Before (shows warning)
+memory = Memory(config=MemoryConfig(version="v1.0"))
+
+# After (no warning)
+memory = Memory()  # Uses v1.1 by default
+# OR explicitly set v1.1
+memory = Memory(config=MemoryConfig(version="v1.1"))
+```
+
+## Common Issues and Solutions
+
+### Issue 1: "KeyError: 'results'"
+
+**Problem:** Your code expects the old list format response.
+
+**Solution:** Update response handling:
+```python
+# Before
+for memory in response:  # Assuming response is a list
+    print(memory)
+
+# After  
+for memory in response["results"]:
+    print(memory)
+```
+
+### Issue 2: "TypeError: unexpected keyword argument 'output_format'"
+
+**Problem:** Code still passing deprecated parameters.
+
+**Solution:** Remove all deprecated parameters:
+```python
+# Before
+client.add(messages, output_format="v1.1", async_mode=True)
+
+# After
+client.add(messages)
+```
+
+### Issue 3: Vector Store Connection Issues
+
+**Problem:** Vector store tests failing after upgrade.
+
+**Solution:** The OpenSearch integration has been fixed. Update your test configurations and retry.
+
+## Support
+
+If you encounter issues during migration:
+
+1. Check the [GitHub Issues](https://github.com/mem0ai/mem0/issues) for similar problems
+2. Review the updated [API documentation](https://docs.mem0.ai/)  
+3. Create a new issue with your specific migration problem
+
+## Summary
+
+mem0ai 1.0.0 provides a cleaner, more consistent API while removing deprecated features. The migration primarily involves:
+
+1. Removing deprecated parameters (`output_format`, `version`, `async_mode`)
+2. Updating response handling to expect consistent `{"results": [...]}` format
+3. Updating dependencies to v1.0.0
+
+Most applications will require minimal changes, mainly removing deprecated parameters and updating response parsing logic.

Makefile

@@ -12,8 +12,8 @@ install:
 
 install_all:
 	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
-	                        google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
-							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j rank-bm25
+	            google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
+							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j langchain-aws rank-bm25 pymochow pymongo psycopg kuzu databricks-sdk valkey
 
 # Format code with ruff
 format:
@@ -50,3 +50,6 @@ test-py-3.10:
 
 test-py-3.11:
 	hatch run dev_py_3_11:test
+
+test-py-3.12:
+	hatch run dev_py_3_12:test

README.md

@@ -21,7 +21,7 @@
 
 <p align="center">
   <a href="https://mem0.dev/DiG">
-    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Mem0 Discord">
+    <img src="https://img.shields.io/badge/Discord-%235865F2.svg?&logo=discord&logoColor=white" alt="Mem0 Discord">
   </a>
   <a href="https://pepy.tech/project/mem0ai">
     <img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads">
@@ -47,6 +47,8 @@
   <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

docs/_snippets/async-memory-add.mdx

@@ -0,0 +1,5 @@
+<Note type="info">
+  📢 Heads up!
+  We're moving to async memory add for a faster experience.
+  If you signed up after July 1st, 2025, your add requests will work in the background and return right away.
+</Note> 

docs/api-reference.mdx

@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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.
 
 ## Key Features
@@ -60,6 +58,128 @@ const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR
   </Tab>
 </Tabs>
 
+### Project Management Methods
+
+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:

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

@@ -70,3 +70,39 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
   )
   ```
   </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/delete-org-member.mdx

@@ -1,4 +0,0 @@
----
-title: 'Delete Member'
-openapi: delete /api/v1/orgs/organizations/{org_id}/members/
----

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

@@ -1,9 +0,0 @@
----
-title: 'Update Member'
-openapi: put /api/v1/orgs/organizations/{org_id}/members/
----
-
-The API provides two roles for organization members:
-
-- `READER`: Allows viewing of organization resources.
-- `OWNER`: Grants full administrative access to manage the organization and its resources.

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

@@ -1,4 +0,0 @@
----
-title: 'Delete Member'
-openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
----

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

@@ -1,9 +0,0 @@
----
-title: 'Update Member'
-openapi: put /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
----
-
-The API provides two roles for project members:
-
-- `READER`: Allows viewing of project resources.
-- `OWNER`: Grants full administrative access to manage the project and its resources.

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

@@ -1,4 +0,0 @@
----
-title: 'Update Project'
-openapi: patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
----

docs/changelog.mdx

@@ -3,11 +3,334 @@ title: "Product Updates"
 mode: "wide"
 ---
 
-<Snippet file="paper-release.mdx" />
-
+ 
 <Tabs>
 <Tab title="Python">
 
+<Update label="2025-09-03" description="v0.1.117">
+
+**New Features & Updates:**
+- **OpenMemory:**
+  - Added memory export / import feature
+  - Added vector store integrations: Weaviate, FAISS, PGVector, Chroma, Redis, Elasticsearch, Milvus
+  - Added `export_openmemory.sh` migration script
+- **Vector Stores:**
+  - Added Amazon S3 Vectors support
+  - Added Databricks Mosaic AI vector store support
+  - Added support for OpenAI Store
+- **Graph Memory:** Added support for graph memory using Kuzu
+- **Azure:** Added Azure Identity for Azure OpenAI and Azure AI Search authentication
+- **Elasticsearch:** Added headers configuration support
+
+**Improvements:**
+  - Added custom connection client to enable connecting to local containers for Weaviate
+  - Updated configuration AWS Bedrock
+  - Fixed dependency issues and tests; updated docstrings
+- **Documentation:**
+  - Fixed Graph Docs page missing in sidebar
+  - Updated integration documentation
+  - Added version param in Search V2 API documentation
+  - Updated Databricks documentation and refactored docs
+  - Updated favicon logo
+  - Fixed typos and Typescript docs
+
+**Bug Fixes:**
+- Baidu: Added missing provider for Baidu vector DB
+- MongoDB: Replaced `query_vector` args in search method
+- Fixed new memory mistaken for current
+- AsyncMemory._add_to_vector_store: handled edge case when no facts found
+- Fixed missing commas in Kuzu graph INSERT queries
+- Fixed inconsistent created and updated properties for Graph
+- Fixed missing `app_id` on client for Neptune Analytics
+- Correctly pick AWS region from environment variable
+- Fixed Ollama model existence check
+
+**Refactoring:**
+- **PGVector:** Use internal connection pools and context managers
+
+</Update>
+
+<Update label="2025-08-14" description="v0.1.116">
+
+**New Features & Updates:**
+- **Pinecone:** Added namespace support and improved type safety
+- **Milvus:** Added db_name field to MilvusDBConfig
+- **Vector Stores:** Added multi-id filters support
+- **Vercel AI SDK:** Migration to AI SDK V5.0
+- **Python Support:** Added Python 3.12 support
+- **Graph Memory:** Added sanitizer methods for nodes and relationships
+- **LLM Monitoring:** Added monitoring callback support
+
+**Improvements:**
+- **Performance:**
+  - Improved async handling in AsyncMemory class
+- **Documentation:**
+  - Added async add announcement
+  - Added personalized search docs
+  - Added Neptune examples
+  - Added V5 migration docs
+- **Configuration:**
+  - Refactored base class config for LLMs
+  - Added sslmode for pgvector
+- **Dependencies:**
+  - Updated psycopg to version 3
+  - Updated Docker compose
+
+**Bug Fixes:**
+- **Tests:**
+  - Fixed failing tests
+  - Restricted package versions
+- **Memgraph:**
+  - Fixed async attribute errors
+  - Fixed n_embeddings usage
+  - Fixed indexing issues
+- **Vector Stores:**
+  - Fixed Qdrant cloud indexing
+  - Fixed Neo4j Cypher syntax
+  - Fixed LLM parameters
+- **Graph Store:**
+  - Fixed LM config prioritization
+- **Dependencies:**
+  - Fixed JSON import for psycopg
+
+**Refactoring:**
+- **Google AI:** Refactored from Gemini to Google AI
+- **Base Classes:** Refactored LLM base class configuration
+
+</Update>
+
+<Update label="2025-07-24" description="v0.1.115">
+
+**New Features & Updates:**
+- Enhanced project management via `client.project` and `AsyncMemoryClient.project` interfaces
+- Full support for project CRUD operations (create, read, update, delete)
+- Project member management: add, update, remove, and list members
+- Manage project settings including custom instructions, categories, retrieval criteria, and graph enablement
+- Both sync and async support for all project management operations
+
+**Improvements:**
+- **Documentation:**
+  - Added detailed API reference and usage examples for new project management methods.
+  - Updated all docs to use `client.project.get()` and `client.project.update()` instead of deprecated methods.
+  
+- **Deprecation:**
+  - Marked `get_project()` and `update_project()` as deprecated (these methods were already present); added warnings to guide users to the new API.
+
+**Bug Fixes:**
+- **Tests:**
+  - Fixed Gemini embedder and LLM test mocks for correct error handling and argument structure.
+- **vLLM:**
+  - Fixed duplicate import in vLLM module.
+
+</Update>
+
+<Update label="2025-07-05" description="v0.1.114">
+
+**New Features:**
+- **OpenAI Agents:** Added OpenAI agents SDK support
+- **Amazon Neptune:** Added Amazon Neptune Analytics graph_store configuration and integration
+- **vLLM:** Added vLLM support
+
+**Improvements:**
+- **Documentation:** 
+  - Added SOC2 and HIPAA compliance documentation
+  - Enhanced group chat feature documentation for platform
+  - Added Google AI ADK Integration documentation
+  - Fixed documentation images and links
+- **Setup:** Fixed Mem0 setup, logging, and documentation issues
+
+**Bug Fixes:**
+- **MongoDB:** Fixed MongoDB Vector Store misaligned strings and classes
+- **vLLM:** Fixed missing OpenAI import in vLLM module and call errors
+- **Dependencies:** Fixed CI issues related to missing dependencies
+- **Installation:** Reverted pip install changes
+
+</Update>
+
+<Update label="2025-06-30" description="v0.1.113">
+
+**Bug Fixes:**
+- **Gemini:** Fixed Gemini embedder configuration
+
+</Update>
+
+<Update label="2025-06-27" description="v0.1.112">
+
+**New Features:**
+- **Memory:** Added immutable parameter to add method
+- **OpenMemory:** Added async_mode parameter support
+
+**Improvements:**
+- **Documentation:** 
+  - Enhanced platform feature documentation
+  - Fixed documentation links
+  - Added async_mode documentation
+- **MongoDB:** Fixed MongoDB configuration name
+
+**Bug Fixes:**
+- **Bedrock:** Fixed Bedrock LLM, embeddings, tools, and temporary credentials
+- **Memory:** Fixed memory categorization by updating dependencies and correcting API usage
+- **Gemini:** Fixed Gemini Embeddings and LLM issues
+
+</Update>
+
+<Update label="2025-06-23" description="v0.1.111">
+
+**New Features:**
+- **OpenMemory:** 
+  - Added OpenMemory augment support
+  - Added OpenMemory Local Support using new library
+- **vLLM:** Added vLLM support integration
+
+**Improvements:**
+- **Documentation:** 
+  - Added MCP Client Integration Guide and updated installation commands
+  - Improved Agent Id documentation for Mem0 OSS Graph Memory
+- **Core:** Added JSON parsing to solve hallucination errors
+
+**Bug Fixes:**
+- **Gemini:** Fixed Gemini Embeddings migration
+
+</Update>
+
+<Update label="2025-06-20" description="v0.1.110">
+
+**New Features:**
+- **Baidu:** Added Baidu vector database integration
+
+**Improvements:**
+- **Documentation:** 
+  - Updated changelog
+  - Fixed example in quickstart page
+  - Updated client.update() method documentation in OpenAPI specification
+- **OpenSearch:** Updated logger warning
+
+**Bug Fixes:**
+- **CI:** Fixed failing CI pipeline
+
+</Update>
+
+<Update label="2025-06-19" description="v0.1.109">
+
+**New Features:**
+- **AgentOps:** Added AgentOps integration
+- **LM Studio:** Added response_format parameter for LM Studio configuration
+- **Examples:** Added Memory agent powered by voice (Cartesia + Agno)
+
+**Improvements:**
+- **AI SDK:** Added output_format parameter
+- **Client:** Enhanced update method to support metadata
+- **Google:** Added Google Genai library support
+
+**Bug Fixes:**
+- **Build:** Fixed Build CI failure
+- **Pinecone:** Fixed pinecone for async memory
+
+</Update>
+
+<Update label="2025-06-14" description="v0.1.108">
+
+**New Features:**
+- **MongoDB:** Added MongoDB Vector Store support
+- **Client:** Added client support for summary functionality
+
+**Improvements:**
+- **Pinecone:** Fixed pinecone version issues
+- **OpenSearch:** Added logger support
+- **Testing:** Added python version test environments
+
+</Update>
+
+<Update label="2025-06-11" description="v0.1.107">
+
+**Improvements:**
+- **Documentation:**
+  - Updated Livekit documentation migration
+  - Updated OpenMemory hosted version documentation
+- **Core:** Updated categorization flow
+- **Storage:** Fixed migration issues
+
+</Update>
+
+<Update label="2025-06-09" description="v0.1.106">
+
+**New Features:**
+- **Cloudflare:** Added Cloudflare vector store support
+- **Search:** Added threshold parameter to search functionality
+- **API:** Added wildcard character support for v2 Memory APIs
+
+**Improvements:**
+- **Documentation:** Updated README docs for OpenMemory environment setup
+- **Core:** Added support for unique user IDs
+
+**Bug Fixes:**
+- **Core:** Fixed error handling exceptions
+
+</Update>
+
+<Update label="2025-06-03" description="v0.1.104">
+
+**Bug Fixes:**
+- **Vector Stores:** Fixed GET_ALL functionality for FAISS and OpenSearch
+
+</Update>
+
+<Update label="2025-06-02" description="v0.1.103">
+
+**New Features:**
+- **LLM:** Added support for OpenAI compatible LLM providers with baseUrl configuration
+
+**Improvements:**
+- **Documentation:**
+  - Fixed broken links
+  - Improved Graph Memory features documentation clarity
+  - Updated enable_graph documentation
+- **TypeScript SDK:** Updated Google SDK peer dependency version
+- **Client:** Added async mode parameter
+
+</Update>
+
+<Update label="2025-05-26" description="v0.1.102">
+
+**New Features:**
+- **Examples:** Added Neo4j example
+- **AI SDK:** Added Google provider support
+- **OpenMemory:** Added LLM and Embedding Providers support
+
+**Improvements:**
+- **Documentation:**
+  - Updated memory export documentation
+  - Enhanced role-based memory attribution rules documentation
+  - Updated API reference and messages documentation
+  - Added Mastra and Raycast documentation
+  - Added NOT filter documentation for Search and GetAll V2
+  - Announced Claude 4 support
+- **Core:**
+  - Removed support for passing string as input in client.add()
+  - Added support for sarvam-m model
+- **TypeScript SDK:** Fixed types from message interface
+
+**Bug Fixes:**
+- **Memory:** Prevented saving prompt artifacts as memory when no new facts are present
+- **OpenMemory:** Fixed typos in MCP tool description
+
+</Update>
+
+<Update label="2025-05-15" description="v0.1.101">
+
+**New Features:**
+- **Neo4j:** Added base label configuration support
+
+**Improvements:**
+- **Documentation:**
+  - Updated Healthcare example index
+  - Enhanced collaborative task agent documentation clarity
+  - Added criteria-based filtering documentation
+- **OpenMemory:** Added cURL command for easy installation
+- **Build:** Migrated to Hatch build system
+
+</Update>
+
 <Update label="2025-05-10" description="v0.1.100">
 
 **New Features:**
@@ -95,7 +418,7 @@ mode: "wide"
 - **Vector Stores:** Added reset function for VectorDBs
 
 **Improvements:**
-- **Documentation:** 
+- **Documentation:**
   - Updated timestamp and expiration_date documentation
   - Fixed v2 search documentation
   - Added "memory" in EC "Custom config" section
@@ -134,12 +457,12 @@ mode: "wide"
 
 **New Features:**
 - **LLM Integrations:** Added Azure OpenAI Embedding Model
-- **Examples:** 
+- **Examples:**
   - Added movie recommendation using grok3
   - Added Voice Assistant using Elevenlabs
 
 **Improvements:**
-- **Documentation:** 
+- **Documentation:**
   - Added keywords AI
   - Reformatted navbar page URLs
   - Updated changelog
@@ -154,7 +477,7 @@ mode: "wide"
 - **LLM Integrations:** Added Mistral AI as LLM provider
 
 **Improvements:**
-- **Documentation:** 
+- **Documentation:**
   - Updated changelog
   - Fixed memory exclusion example
   - Updated xAI documentation
@@ -171,7 +494,7 @@ mode: "wide"
 
 **New Features:**
 - **Langchain Integration:** Added support for Langchain VectorStores
-- **Examples:** 
+- **Examples:**
   - Added personal assistant example
   - Added personal study buddy example
   - Added YouTube assistant Chrome extension example
@@ -288,6 +611,41 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
+<Update label="2025-09-04" description="v2.1.38">
+**New Features:**
+- **Client:** Added `metadata` param to `update` method.
+</Update>
+
+<Update label="2025-08-04" description="v2.1.37">
+**New Features:**
+- **OSS:** Added `RedisCloud` search module check
+</Update>
+
+<Update label="2025-07-08" description="v2.1.36">
+**New Features:**
+- **Client:** Added `structured_data_schema` param to `add` method.
+</Update>
+
+<Update label="2025-07-08" description="v2.1.35">
+**New Features:**
+- **Client:** Added `createMemoryExport` and `getMemoryExport` methods.
+</Update>
+
+<Update label="2025-07-03" description="v2.1.34">
+**New Features:**
+- **OSS:** Added Gemini support
+</Update>
+
+<Update label="2025-06-24" description="v2.1.33">
+**Improvement :**
+- **Client:** Added `immutable` param to `add` method.
+</Update>
+
+<Update label="2025-06-20" description="v2.1.32">
+**Improvement :**
+- **Client:** Made `api_version` V2 as default.
+</Update>
+
 <Update label="2025-06-17" description="v2.1.31">
 **Improvement :**
 - **Client:** Added param `filter_memories`.
@@ -315,7 +673,6 @@ mode: "wide"
 **Improvements:**
 - **OSS:** Added baseURL param in LLM Config.
 </Update>
-  
 <Update label="2025-05-23" description="v2.1.26">
 **Improvements:**
 - **Client:** Removed type `string` from `messages` interface
@@ -350,7 +707,7 @@ mode: "wide"
 
 <Update label="2025-04-28" description="v2.1.20">
 **Improvements:**
-- **Client:** Fixed `organizationId` and `projectId` being asssigned to default in `ping` method
+- **Client:** Fixed `organizationId` and `projectId` being assigned to default in `ping` method
 </Update>
 
 <Update label="2025-04-22" description="v2.1.19">
@@ -409,7 +766,7 @@ mode: "wide"
 
 <Update label="2025-03-29" description="v2.1.13">
 **Improvements:**
-- **Introuced `ping` method to check if API key is valid and populate org/project id**
+- **Introduced `ping` method to check if API key is valid and populate org/project id**
 </Update>
 
 <Update label="2025-03-29" description="AI SDK v1.0.0">
@@ -442,6 +799,283 @@ mode: "wide"
 
 <Tab title="Platform">
 
+<Update label="2025-07-23" description="">
+
+**Bug Fixes:**
+- **Memory:** Fixed ADD functionality
+
+</Update>
+
+<Update label="2025-07-19" description="">
+
+**New Features:**
+- **UI:** Added Settings UI and latency display
+- **Performance:** Neo4j query optimization
+
+**Bug Fixes:**
+- **OpenMemory:** Fixed OMM raising unnecessary exceptions
+
+</Update>
+
+<Update label="2025-07-18" description="">
+
+**Improvements:**
+- **UI:** Updated Event UI
+- **Performance:** Fixed N+1 query issue in semantic_search_v2 by optimizing MemorySerializer field selection
+
+**Bug Fixes:**
+- **Memory:** Fixed duplicate memory index sentry error
+
+</Update>
+
+<Update label="2025-07-17" description="">
+
+**New Features:**
+- **UI:** New Settings Page
+- **Memory:** Duplicate memories entities support
+
+**Improvements:**
+- **Performance:** Optimized semantic search and get_all APIs by eliminating N+1 queries
+
+</Update>
+
+<Update label="2025-07-16" description="">
+
+**New Features:**
+- **Database:** Implemented read replica routing with enhanced logging and app-specific DB routing
+
+**Improvements:**
+- **Performance:** Improved query performance in search v2 and get all v2 endpoints
+
+**Bug Fixes:**
+- **API:** Fixed pagination for get all API
+
+</Update>
+
+<Update label="2025-07-12" description="">
+
+**Bug Fixes:**
+- **Graph:** Fixed social graph bugs and connection issues
+
+</Update>
+
+<Update label="2025-07-11" description="">
+
+**Improvements:**
+- **Rate Limiting:** New rate limit for V2 Search
+
+**Bug Fixes:**
+- **Slack:** Fixed Slack rate limit error with backend improvements
+
+</Update>
+
+<Update label="2025-07-10" description="">
+
+**Improvements:**
+- **Performance:** 
+  - Changed connection pooling time to 5 minutes
+  - Separated graph lambdas for better performance
+
+</Update>
+
+<Update label="2025-07-09" description="">
+
+**Improvements:**
+- **Graph:** Graph Optimizations V2 and memory improvements
+
+</Update>
+
+<Update label="2025-07-08" description="">
+
+**New Features:**
+- **Database:** Added read replica support for improved database performance
+- **UI:** Implemented UI changes for Users Page
+- **Feedback:** Enabled feedback functionality
+
+**Bug Fixes:**
+- **Serializer:** Fixed GET ALL Serializer
+
+</Update>
+
+<Update label="2025-07-05" description="">
+
+**New Features:**
+- **UI:** User Page Revamp and New Users Page
+
+</Update>
+
+<Update label="2025-07-04" description="">
+
+**New Features:**
+- **Users:** New Users Page implementation
+- **Tools:** Added script to backfill memory categories
+
+**Bug Fixes:**
+- **Filters:** Fixed Filters Get All functionality
+
+</Update>
+
+<Update label="2025-07-03" description="">
+
+**Improvements:**
+- **Graph:** Graph Memory optimization
+- **Memory:** Fixed exact memories and semantically similar memories retrieval
+
+</Update>
+
+<Update label="2025-07-02" description="">
+
+**Improvements:**
+- **Categorization:** Refactored categorization logic to utilize Gemini 2.5 Flash and improve message handling
+
+</Update>
+
+<Update label="2025-07-01" description="">
+
+**Bug Fixes:**
+- **Memory:** Fixed old_memory issue in Async memory addition lambda
+- **Events:** Fixed missing events
+
+</Update>
+
+<Update label="2025-06-30" description="">
+
+**Improvements:**
+- **Graph:** Improvements to graph memory and added user to LTM-STM
+
+</Update>
+
+<Update label="2025-06-28" description="">
+
+**New Features:**
+- **Graph:** Added support for SQS in graph memory addition
+- **Testing:** Added Locust load testing script and Grafana Dashboard
+
+</Update>
+
+<Update label="2025-06-27" description="">
+
+**Improvements:**
+- **Rate Limiting:** Updated rate limiting for ADD API to 1000/min
+- **Performance:** Improved Neo4j performance
+
+</Update>
+
+<Update label="2025-06-26" description="">
+
+**New Features:**
+- **Memory:** Edit Memory From Drawer functionality
+- **API:** Added Topic Suggestions API Endpoint
+
+</Update>
+
+<Update label="2025-06-25" description="">
+
+**New Features:**
+- **Group Chat:** Group-Chat v2 with Actor-Aware Memories
+- **Memory:** Editable Metadata in Memories
+- **UI:** Memory Actions Badges
+
+</Update>
+
+<Update label="2025-06-19" description="">
+
+**New Features:**
+- **Rate Limiting:** Implemented comprehensive rate limiting system
+
+**Improvements:**
+- **Performance:** Added performance indexes for memory stats query
+
+**Bug Fixes:**
+- **Search:** Fixed search events not respecting top-k parameter
+
+</Update>
+
+<Update label="2025-06-18" description="">
+
+**New Features:**
+- **Memory Management:** Implemented OpenAI Batch API for Memory Cleaning with fallback
+- **Playground:** Added Claude 4 support on Playground
+
+**Improvements:**
+- **Memory:** Added ability to update memory metadata
+
+</Update>
+
+<Update label="2025-06-17" description="">
+
+**New Features:**
+- **UI:** New Memories Page UI design
+
+</Update>
+
+<Update label="2025-06-16" description="">
+
+**Improvements:**
+- **Infrastructure:** Migrated to Application Load Balancer (ALB)
+
+</Update>
+
+<Update label="2025-06-13" description="">
+
+**Improvements:**
+- **Memory Management:** Enhanced Memory Management with Cosine Similarity Fallback
+
+</Update>
+
+<Update label="2025-06-11" description="">
+
+**New Features:**
+- **OMM:** Added OMM Script and UI functionality
+
+**Improvements:**
+- **API:** Added filters validation to semantic_search_v2 endpoint
+
+</Update>
+
+<Update label="2025-06-09" description="">
+
+**New Features:**
+- **Intercom:** Set Intercom events for ADD and SEARCH operations
+- **OpenMemory:** Added Posthog integration and feedback functionality
+- **MCP:** New JavaScript MCP Server with feedback support
+
+**Improvements:**
+- **Structured Data:** Enhanced structured data handling in memory management
+
+</Update>
+
+<Update label="2025-06-06" description="">
+
+**New Features:**
+- **OAuth:** Added Mem0 OAuth integration
+- **OMM:** Added OMM-Mem0 sync for deleted memories
+
+</Update>
+
+<Update label="2025-06-05" description="">
+
+**New Features:**
+- **Filters:** Implemented Wildcard Filters and refactored filter logic in V2 Views
+
+</Update>
+
+<Update label="2025-06-02" description="">
+
+**New Features:**
+- **OpenMemory Cloud:** Added OpenMemory Cloud support
+- **Structured Data:** Added 'structured_attributes' field to Memory model
+
+</Update>
+
+<Update label="2025-05-30" description="">
+
+**New Features:**
+- **Projects:** Added version and enable_graph to project views
+- **OpenMemory:** Added Postgres support for OpenMemory
+
+</Update>
+
 <Update label="2025-05-19" description="">
 
 **Bug Fixes:**
@@ -449,254 +1083,25 @@ mode: "wide"
 
 </Update>
 
-<Update label="2025-05-17" description="">
-
-**New Features:**
-- **Graph:** Added Neo4J Graph Migration
-- **API:** Added API to set custom instructions
-
-</Update>
-
-<Update label="2025-05-16" description="">
-
-**New Features:**
-- **API:** Added Org-wide API Limit and Usage
-
-**Improvements:**
-- **Database:** Added migration for "is_deleted" column
-- **Graph:** Improved graph queries
-
-</Update>
-
-<Update label="2025-05-15" description="">
-
-**New Features:**
-- **Lambda:** Added actions to lambda
-- **Core:** Added background runs support
-- **Models:** Added o4-mini for pro users
-
-</Update>
-
-<Update label="2025-05-10" description="">
-
-**New Features:**
-- **Integrations:** Added Intercom Events integration
-- **Billing:** Added prefilled email for payments
-- **Organizations:** Added Pro organization marking
-
-**Improvements:**
-- **UI:** Fixed loading jitter for organization selection
-- **Infrastructure:** Improved production scaling
-
-</Update>
-
-<Update label="2025-05-09" description="">
-
-**Improvements:**
-- **Memory:** Fixed filters in Memory Page
-- **Deployment:** Added custom categories for on-premise
-
-</Update>
-
-<Update label="2025-05-08" description="">
-
-**Improvements:**
-- **Backend:** Updated Django settings for metrics
-- **Memory:** Added retries to memory filtering
-- **Search:** Added scoring mechanism
-
-</Update>
-
-<Update label="2025-05-07" description="">
-
-**Improvements:**
-- **Deployment:** Updated deployment scripts
-- **Testing:** Added code coverage tracking
-- **Memory:** Added background cron job for memory quality
-
-</Update>
-
-<Update label="2025-05-06" description="">
-
-**New Features:**
-- **Models:** Added support for 4.1-mini model
-
-**Improvements:**
-- **Infrastructure:** Increased instance count
-- **API:** Added V2 for Manage Entities
-
-</Update>
-
-<Update label="2025-05-04" description="">
-
-**New Features:**
-- **Testing:** Added code coverage tracking
-- **AI:** Added Keywords AI integration
-
-**Improvements:**
-- **UI:** Updated UI with tabs
-- **Database:** Added migrations for custom instructions
-- **Search:** Added criteria filtering
-
-</Update>
-
-<Update label="2025-04-26" description="">
-
-**Improvements:**
-- **Performance:** Parallelized embedding calls
-- **Monitoring:** Added timing for LLM calls
-- **Search:** Added category checking in Search V2
-- **Bug Fixes:** Fixed issues with ADD filters
-- **Graph:** Implemented new graph updates
-
-</Update>
-
-<Update label="2025-04-25" description="">
-
-**Improvements:**
-- **Memory:** Fixed memory export functionality
-- **Analytics:** Added logging for project
-
-</Update>
-
-<Update label="2025-04-24" description="">
-
-**Improvements:**
-- **Output:** Added memory_type display for ADD output
-
-</Update>
-
-<Update label="2025-04-23" description="">
-
-**New Features:**
-- **UI:** Added new Pricing Component
-- **Memory:** Implemented Long/Short term memory categorization
-- **Output:** Modified serializer to hide memory_type
-
-**Documentation:**
-- Updated README for deployment
-
-</Update>
-
-<Update label="2025-04-22" description="">
-
-**New Features:**
-- **Memory:** Added timestamp to ADD call
-
-**Bug Fixes:**
-- Fixed issues with coreV2
-
-</Update>
-
-<Update label="2025-04-21" description="">
-
-**New Features:**
-- **Memory:** Implemented backdating with migrations and backfilling script
-
-</Update>
-
-<Update label="2025-04-17" description="">
-
-**New Features:**
-- **Billing:** Integrated Stripe Billing Dashboard
-- **Admin:** Added webhook creation functionality
-
-**Bug Fixes:**
-- Fixed Users Page issues
-- Fixed Custom Categories
-- Fixed Table components
-- Updated Stripe configuration
-
-</Update>
-
-<Update label="2025-04-16" description="">
-
-**Improvements:**
-- **Performance:** Made Admin panel and Memory Page faster
-- **Security:** Implemented active session cancellation
-- **Analytics:** Added Stripe customer ID capture
-
-</Update>
-
-<Update label="2025-04-12" description="">
-
-**New Features:**
-- **Memory Management:** 
-  - Added ability to delete memories from Project level with filters
-  - Added delete memories capability on Memories Page
-- **Memory Visualization:** Released V1 Graph Memory Visualization
-- **Graph Playground:** Enabled for @mem0.ai users
-- **Notifications:** Added email alerts to organization owners when new members join
-- **Memory Export:** Added date support for filtering memory exports
-
-**Improvements:**
-- **Performance:** 
-  - Optimized graph for better performance
-  - Optimized database calls in ADD method
-- **Analytics:** Added flagging of paid users in Posthog
-- **CI/CD:** Improved CI pipeline and fixed lint issues
-
-</Update>
-
-<Update label="2025-04-10" description="">
-
-**New Features:**
-- **Notifications:** Implemented email notifications for organization owners when new members join
-
-**Improvements:**
-- **CI/CD:** Fixed Dockerfile for CI tests
-
-</Update>
-
-<Update label="2025-04-09" description="">
-
-**Improvements:**
-- **Integrations:** Updated chat model for Together Qwen
-- **Platform:** Removed older platforms
-- **Bug Fixes:** Fixed FILTER_MAPPING
-
-</Update>
-
-<Update label="2025-04-03" description="">
-
-**New Features:**
-- **Memory:** Added implicit memory capabilities
-- **API:** Improved implicit lambda and get_all v2 functionality
-
-</Update>
-
-<Update label="2025-04-02" description="">
-
-**New Features:**
-- **Integrations:** Added Clay integration
-
-**Improvements:**
-- **Integrations:** Removed deepseek coder from Together
-- **API:** Added custom instructions for add v2
-
-</Update>
-
-<Update label="2025-03-31" description="">
-
-**Security:**
-- **Validation:** Added key validation in messages
-
-</Update>
-
-<Update label="2025-03-28" description="">
-- **Updated Playground Prompt**
-- **Send Email on User Addition to Org/Proj**
-- **Fix Search Entity**
-</Update>
-
-<Update label="2025-03-19" description="">
-- **General Stability & Performance Improvements**
-</Update>
-
 </Tab>
 
 <Tab title="Vercel AI SDK">
 
+<Update label="2025-09-03" description="v2.0.2">
+**Bug Fix:**
+- **Vercel AI SDK:** Fixed streaming response in the AI SDK.
+</Update>
+
+<Update label="2025-08-05" description="v2.0.1">
+**New Features:**
+- **Vercel AI SDK:** Added a new param `host` to the config.
+</Update>
+
+<Update label="2025-08-05" description="v2.0.0">
+**New Features:**
+- **Vercel AI SDK:** Migration to AI SDK V5.
+</Update>
+
 <Update label="2025-06-15" description="v1.0.6">
 **New Features:**
 - **Vercel AI SDK:** Added param `filter_memories`.
@@ -714,7 +1119,7 @@ mode: "wide"
 
 <Update label="2025-05-08" description="v1.0.3">
 **Improvements:**
-- **Vercel AI SDK:** Added support for graceful failure in cases services are down. 
+- **Vercel AI SDK:** Added support for graceful failure in cases services are down.
 </Update>
 
 <Update label="2025-05-01" description="v1.0.1">

docs/components/embedders/config.mdx

@@ -4,7 +4,6 @@ icon: "gear"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
 
 Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
 

docs/components/embedders/models/azure_openai.mdx

@@ -77,6 +77,43 @@ await memory.add(messages, { userId: "john" });
 ```
 </CodeGroup>
 
+As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with [Azure OpenAI role-based security](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control). 
+
+<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
+
+Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:
+
+```python
+import os
+from mem0 import Memory
+# You can set the values directly in the config dictionary or use environment variables
+
+os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
+
+config = {
+    "llm": {
+        "provider": "azure_openai_structured",
+        "config": {
+            "model": "your-deployment-name",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+            "azure_kwargs": {
+                  "azure_deployment": "<your-deployment-name>",
+                  "api_version": "<version-to-use>",
+                  "azure_endpoint": "<your-api-base-url>",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+```
+
+Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues) for setting up an Azure Identity credential.
+
 ### Config
 
 Here are the parameters available for configuring Azure OpenAI embedder:

docs/components/embedders/models/gemini.mdx

@@ -1,43 +0,0 @@
----
-title: Gemini
----
-
-To use Gemini embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
-
-### Usage
-
-```python
-import os
-from mem0 import Memory
-
-os.environ["GOOGLE_API_KEY"] = "key"
-os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
-
-config = {
-    "embedder": {
-        "provider": "gemini",
-        "config": {
-            "model": "models/text-embedding-004",
-        }
-    }
-}
-
-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": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
-]
-m.add(messages, user_id="john")
-```
-
-### 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 | `768` |
-| `api_key` | The Gemini API key | `None` |

docs/components/embedders/models/google_AI.mdx

@@ -0,0 +1,69 @@
+---
+title: Google AI
+---
+
+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).
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["GOOGLE_API_KEY"] = "key"
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "gemini",
+        "config": {
+            "model": "models/text-embedding-004",
+        }
+    }
+}
+
+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": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+```typescript TypeScript
+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
+    },
+  },
+};
+
+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": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
+### 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` |

docs/components/embedders/models/langchain.mdx

@@ -44,29 +44,33 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from 'mem0ai/oss';
 import { OpenAIEmbeddings } from "@langchain/openai";
 
-const embeddings = new OpenAIEmbeddings();
+// Initialize a LangChain embeddings model directly
+const openaiEmbeddings = new OpenAIEmbeddings({
+    modelName: "text-embedding-3-small",
+    dimensions: 1536,
+    apiKey: process.env.OPENAI_API_KEY,
+});
+
 const config = {
-    "embedder": {
-        "provider": "langchain",
-        "config": {
-            "model": embeddings
-        }
-    }
-}
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: openaiEmbeddings,
+    },
+  },
+};
 
 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: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-    { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." }
+    {"role": "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": "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."}
 ]
-
-memory.add(messages, user_id="alice", metadata={"category": "movies"})
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
 ```
 </CodeGroup>
 
@@ -96,9 +100,10 @@ When using LangChain as an embedder provider, you'll need to:
 
 ### Examples with Different Providers
 
+<CodeGroup>
 #### HuggingFace Embeddings
 
-```python
+```python Python
 from langchain_huggingface import HuggingFaceEmbeddings
 
 # Initialize a HuggingFace embeddings model
@@ -117,9 +122,33 @@ config = {
 }
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+import { HuggingFaceEmbeddings } from "@langchain/community/embeddings/hf";
+
+// Initialize a HuggingFace embeddings model
+const hfEmbeddings = new HuggingFaceEmbeddings({
+    modelName: "BAAI/bge-small-en-v1.5",
+    encode: {
+        normalize_embeddings: true,
+    },
+});
+
+const config = {
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: hfEmbeddings,
+    },
+  },
+};
+```
+</CodeGroup>
+
+<CodeGroup>
 #### Ollama Embeddings
 
-```python
+```python Python
 from langchain_ollama import OllamaEmbeddings
 
 # Initialize an Ollama embeddings model
@@ -137,6 +166,27 @@ config = {
 }
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
+
+// Initialize an Ollama embeddings model
+const ollamaEmbeddings = new OllamaEmbeddings({
+    model: "nomic-embed-text",
+    baseUrl: "http://localhost:11434", // Ollama server URL
+});
+
+const config = {
+  embedder: {
+    provider: 'langchain',
+    config: {
+      model: ollamaEmbeddings,
+    },
+  },
+};
+```
+</CodeGroup>
+
 <Note>
   Make sure to install the necessary LangChain packages and any provider-specific dependencies.
 </Note>

docs/components/embedders/models/ollama.mdx

@@ -2,7 +2,8 @@ You can use embedding models from Ollama to run Mem0 locally.
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -21,18 +22,52 @@ 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": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="john")
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'ollama',
+    config: {
+      model: 'nomic-embed-text:latest', // or any other Ollama embedding model
+      url: 'http://localhost:11434', // Ollama server URL
+    },
+  },
+};
+
+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": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "john" });
+```
+</CodeGroup>
+
 ### Config
 
 Here are the parameters available for configuring Ollama embedder:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `model` | The name of the OpenAI model to use | `nomic-embed-text` |
+| `model` | The name of the Ollama model to use | `nomic-embed-text` |
 | `embedding_dims` | Dimensions of the embedding model | `512` |
-| `ollama_base_url` | Base URL for ollama connection | `None` |
+| `ollama_base_url` | Base URL for ollama connection | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
+| `url` | Base URL for Ollama server | `http://localhost:11434` |
+</Tab>
+</Tabs>

docs/components/embedders/overview.mdx

@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
 
 ## Supported Embedders
@@ -21,7 +19,7 @@ See the list of supported embedders below.
   <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
   <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
   <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
-  <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
+  <Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
   <Card title="Together" href="/components/embedders/models/together"></Card>
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>

docs/components/llms/config.mdx

@@ -4,8 +4,6 @@ icon: "gear"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 ## How to define configurations?
 
 <Tabs>
@@ -58,6 +56,7 @@ config = {
 
 m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
+
 ```
 
 ```typescript TypeScript
@@ -76,6 +75,7 @@ const config = {
 const memory = new Memory(config);
 await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
 ```
+
 </CodeGroup>
 
 ## Why is Config Needed?
@@ -117,6 +117,7 @@ Here's a comprehensive list of all parameters that can be used across different
     | `seed`               | Seed for deterministic sampling               | Sarvam            |
     | `stop`               | Stop sequences (max 4)                        | Sarvam            |
     | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
+    | `response_callback`  | LLM response callback function                | OpenAI            |
   </Tab>
   <Tab title="TypeScript">
     | Parameter            | Description                                   | Provider          |

docs/components/llms/models/anthropic.mdx

@@ -2,9 +2,8 @@
 title: Anthropic
 ---
 
-<Snippet file="paper-release.mdx" />
 
-To use anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
+To use Anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
 
 ## Usage
 

docs/components/llms/models/aws_bedrock.mdx

@@ -2,8 +2,6 @@
 title: AWS Bedrock
 ---
 
-<Snippet file="paper-release.mdx" />
-
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
 - You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)

docs/components/llms/models/azure_openai.mdx

@@ -2,12 +2,12 @@
 title: Azure OpenAI
 ---
 
-<Snippet file="paper-release.mdx" />
-
 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
 
 To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
 
+Optionally, you can use Azure Identity to authenticate with Azure OpenAI, which allows you to use managed identities or service principals for production and Azure CLI login for development instead of an API key. If an Azure Identity is to be used, ***do not*** set the `LLM_AZURE_OPENAI_API_KEY` environment variable or the api_key in the config dictionary.
+
 > **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
 
 
@@ -118,6 +118,44 @@ config = {
 }
 ```
 
+As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with [Azure OpenAI role-based security](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control). 
+
+<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
+
+Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:
+
+```python
+import os
+from mem0 import Memory
+# You can set the values directly in the config dictionary or use environment variables
+
+os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
+os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
+os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"
+
+config = {
+    "llm": {
+        "provider": "azure_openai_structured",
+        "config": {
+            "model": "your-deployment-name",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+            "azure_kwargs": {
+                  "azure_deployment": "<your-deployment-name>",
+                  "api_version": "<version-to-use>",
+                  "azure_endpoint": "<your-api-base-url>",
+                  "default_headers": {
+                    "CustomHeader": "your-custom-header",
+                  }
+              }
+        }
+    }
+}
+```
+
+Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues) for setting up an Azure Identity credential.
+
+
 ## Config
 
 All available parameters for the `azure_openai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/deepseek.mdx

@@ -2,8 +2,6 @@
 title: DeepSeek
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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").
 
 ## Usage

docs/components/llms/models/gemini.mdx

@@ -1,49 +0,0 @@
----
-title: Gemini
----
-
-<Snippet file="paper-release.mdx" />
-
-To use the Gemini model, set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
-
-> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
-
-> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
-
-## Usage
-
-```python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
-os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
-
-config = {
-    "llm": {
-        "provider": "gemini",
-        "config": {
-            "model": "gemini-2.0-flash-001",
-            "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 thrillers, but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
-]
-
-m.add(messages, user_id="alice", metadata={"category": "movies"})
-
-```
-
-## Config
-
-All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/google_AI.mdx

@@ -2,40 +2,73 @@
 title: Google AI
 ---
 
-<Snippet file="paper-release.mdx" />
+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).
 
-To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
+> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
+
+> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
-os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
-os.environ["GEMINI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
+os.environ["GOOGLE_API_KEY"] = "your-gemini-api-key"
 
 config = {
     "llm": {
-        "provider": "litellm",
+        "provider": "gemini",
         "config": {
-            "model": "gemini/gemini-pro",
+            "model": "gemini-2.0-flash-001",
             "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 a 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."}
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
 ]
+
 m.add(messages, user_id="alice", metadata={"category": "movies"})
+
 ```
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    llm: {
+        // You can also use "google" as provider ( for backward compatibility )
+        provider: "gemini",
+        config: {
+            model: "gemini-2.0-flash-001",
+            temperature: 0.1
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+    { role: "user", content: "I’m not a big fan of thrillers, but I love sci-fi movies." },
+    { role: "assistant", content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead." }
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
 
 ## Config
 
-All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/groq.mdx

@@ -2,8 +2,6 @@
 title: Groq
 ---
 
-<Snippet file="paper-release.mdx" />
-
 [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.
 
 In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/models/langchain.mdx

@@ -2,7 +2,6 @@
 title: LangChain
 ---
 
-<Snippet file="paper-release.mdx" />
 
 Mem0 supports LangChain as a provider to access a wide range of LLM models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various LLM providers through a consistent interface.
 
@@ -47,34 +46,34 @@ m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
 ```typescript TypeScript
-import { Memory } from "mem0ai";
+import { Memory } from 'mem0ai/oss';
 import { ChatOpenAI } from "@langchain/openai";
 
-const openai_model = new ChatOpenAI({
-    model: "gpt-4o",
+// Initialize a LangChain model directly
+const openaiModel = new ChatOpenAI({
+    modelName: "gpt-4",
     temperature: 0.2,
-    max_tokens: 2000
-})
+    maxTokens: 2000,
+    apiKey: process.env.OPENAI_API_KEY,
+});
 
 const config = {
-    "llm": {
-        "provider": "langchain",
-        "config": {
-            "model": openai_model
-        }
-    }
-}
+  llm: {
+    provider: 'langchain',
+    config: {
+      model: openaiModel,
+    },
+  },
+};
 
 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: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
-    { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." }
+    {"role": "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": "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."}
 ]
-
-memory.add(messages, user_id="alice", metadata={"category": "movies"})
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
 ```
 </CodeGroup>
 

docs/components/llms/models/litellm.mdx

@@ -1,5 +1,3 @@
-<Snippet file="paper-release.mdx" />
-
 [Litellm](https://litellm.vercel.app/docs/) is compatible with over 100 large language models (LLMs), all using a standardized input/output format. You can explore the [available models](https://litellm.vercel.app/docs/providers) to use with Litellm. Ensure you set the `API_KEY` for the model you choose to use.
 
 ## Usage

docs/components/llms/models/lmstudio.mdx

@@ -2,8 +2,6 @@
 title: LM Studio
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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.
 
 ## Usage

docs/components/llms/models/mistral_AI.mdx

@@ -2,8 +2,6 @@
 title: Mistral AI
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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.
 
 ## Usage

docs/components/llms/models/ollama.mdx

@@ -1,10 +1,9 @@
-<Snippet file="paper-release.mdx" />
-
 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -25,12 +24,37 @@ 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": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'ollama',
+    config: {
+      model: 'llama3.1:8b', // or any other Ollama model
+      url: 'http://localhost:11434', // Ollama server URL
+      temperature: 0.1,
+    },
+  },
+};
+
+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": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `ollama` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/openai.mdx

@@ -2,10 +2,10 @@
 title: OpenAI
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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).
 
+> **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
+
 ## Usage
 
 <CodeGroup>

docs/components/llms/models/sarvam.mdx

@@ -2,8 +2,6 @@
 title: Sarvam AI
 ---
 
-<Snippet file="paper-release.mdx" />
-
 **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.
 
 To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get from their [platform](https://dashboard.sarvam.ai/).

docs/components/llms/models/together.mdx

@@ -1,5 +1,3 @@
-<Snippet file="paper-release.mdx" />
-
 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).
 
 ## Usage

docs/components/llms/models/vllm.mdx

@@ -0,0 +1,107 @@
+---
+title: vLLM
+---
+
+[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.
+
+## Prerequisites
+
+1. **Install vLLM**:
+
+   ```bash
+   pip install vllm
+   ```
+
+2. **Start vLLM server**:
+
+   ```bash
+   # For testing with a small model
+   vllm serve microsoft/DialoGPT-medium --port 8000
+
+   # For production with a larger model (requires GPU)
+   vllm serve Qwen/Qwen2.5-32B-Instruct --port 8000
+   ```
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"  # used for embedding model
+
+config = {
+    "llm": {
+        "provider": "vllm",
+        "config": {
+            "model": "Qwen/Qwen2.5-32B-Instruct",
+            "vllm_base_url": "http://localhost:8000/v1",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+## Configuration Parameters
+
+| Parameter       | Description                       | Default                       | Environment Variable |
+| --------------- | --------------------------------- | ----------------------------- | -------------------- |
+| `model`         | Model name running on vLLM server | `"Qwen/Qwen2.5-32B-Instruct"` | -                    |
+| `vllm_base_url` | vLLM server URL                   | `"http://localhost:8000/v1"`  | `VLLM_BASE_URL`      |
+| `api_key`       | API key (dummy for local)         | `"vllm-api-key"`              | `VLLM_API_KEY`       |
+| `temperature`   | Sampling temperature              | `0.1`                         | -                    |
+| `max_tokens`    | Maximum tokens to generate        | `2000`                        | -                    |
+
+## Environment Variables
+
+You can set these environment variables instead of specifying them in config:
+
+```bash
+export VLLM_BASE_URL="http://localhost:8000/v1"
+export VLLM_API_KEY="your-vllm-api-key"
+export OPENAI_API_KEY="your-openai-api-key"  # for embeddings
+```
+
+## Benefits
+
+- **High Performance**: 2-24x faster inference than standard implementations
+- **Memory Efficient**: Optimized memory usage with PagedAttention
+- **Local Deployment**: Keep your data private and reduce API costs
+- **Easy Integration**: Drop-in replacement for other LLM providers
+- **Flexible**: Works with any model supported by vLLM
+
+## Troubleshooting
+
+1. **Server not responding**: Make sure vLLM server is running
+
+   ```bash
+   curl http://localhost:8000/health
+   ```
+
+2. **404 errors**: Ensure correct base URL format
+
+   ```python
+   "vllm_base_url": "http://localhost:8000/v1"  # Note the /v1
+   ```
+
+3. **Model not found**: Check model name matches server
+
+4. **Out of memory**: Try smaller models or reduce `max_model_len`
+
+   ```bash
+   vllm serve Qwen/Qwen2.5-32B-Instruct --max-model-len 4096
+   ```
+
+## Config
+
+All available parameters for the `vllm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/xAI.mdx

@@ -2,8 +2,6 @@
 title: xAI
 ---
 
-<Snippet file="paper-release.mdx" />
-
 [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.
 
 In order to use LLMs from xAI, go to their [platform](https://console.x.ai) and get the API key. Set the API key as `XAI_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/overview.mdx

@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 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.
 
 ## Usage
@@ -14,7 +12,9 @@ To use a llm, you must provide a configuration to customize its usage. If no con
 
 For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
 
-To view all supported llms, visit the [Supported LLMs](./models).
+## Supported LLMs
+
+See the list of supported LLMs below.
 
 <Note>
   All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
@@ -31,7 +31,6 @@ To view all supported llms, visit the [Supported LLMs](./models).
   <Card title="Mistral AI" href="/components/llms/models/mistral_ai" />
   <Card title="Google AI" href="/components/llms/models/google_ai" />
   <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
-  <Card title="Gemini" href="/components/llms/models/gemini" />
   <Card title="DeepSeek" href="/components/llms/models/deepseek" />
   <Card title="xAI" href="/components/llms/models/xAI" />
   <Card title="Sarvam AI" href="/components/llms/models/sarvam" />

docs/components/rerankers/config.mdx

@@ -0,0 +1,90 @@
+---
+title: Config
+description: 'Configuration options for rerankers in Mem0'
+icon: "gear"
+iconType: "solid"
+---
+
+## Common Configuration Parameters
+
+All rerankers share these common configuration parameters:
+
+| 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` |
+
+## Provider-Specific Configuration
+
+### Zero Entropy
+
+| 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
+
+| 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
+
+| 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` |
+
+### 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 |
+
+## Environment Variables
+
+You can set API keys using environment variables:
+
+- `ZERO_ENTROPY_API_KEY` - Zero Entropy API key
+- `COHERE_API_KEY` - Cohere API key
+- `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 = {
+    "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",
+            "top_k": 5
+        }
+    }
+}
+```

docs/components/rerankers/models/cohere.mdx

@@ -0,0 +1,147 @@
+---
+title: Cohere
+description: 'Enterprise-grade reranking with Cohere'
+icon: "building"
+iconType: "solid"
+---
+
+Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
+
+## Models
+
+Cohere offers several reranking models:
+
+- **`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
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "rerank": {
+        "provider": "cohere",
+        "config": {
+            "model": "rerank-english-v3.0",
+            "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)
+
+# 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"}
+]
+
+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()
+```
+
+## Multilingual Support
+
+For multilingual applications, use the multilingual model:
+
+```python Python
+config = {
+    "rerank": {
+        "provider": "cohere",
+        "config": {
+            "model": "rerank-multilingual-v3.0",
+            "top_k": 5
+        }
+    }
+}
+```
+
+## Configuration Parameters
+
+| 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` |
+
+## Features
+
+- **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
+
+## Best Practices
+
+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/llm.mdx

@@ -0,0 +1,214 @@
+---
+title: LLM-based
+description: 'Flexible reranking using any Large Language Model'
+icon: "robot"
+iconType: "solid"
+---
+
+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"
+        }
+    },
+    "rerank": {
+        "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["rerank"]["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"}},
+    "rerank": {
+        "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()
+```
+
+## 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 = {
+    "rerank": {
+        "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 = {
+    "rerank": {
+        "provider": "llm",
+        "config": {
+            "model": "claude-3-haiku-20240307",
+            "provider": "anthropic",
+            "temperature": 0.0
+        }
+    }
+}
+
+# Using local Ollama model
+ollama_config = {
+    "rerank": {
+        "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/sentence_transformer.mdx

@@ -0,0 +1,161 @@
+---
+title: Sentence Transformer
+description: 'Local reranking with HuggingFace cross-encoder models'
+icon: "server"
+iconType: "solid"
+---
+
+Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
+
+## Models
+
+Any HuggingFace cross-encoder model can be used. Popular choices include:
+
+- **`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 = {
+    "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",  # 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   # Larger batch size for GPU
+        }
+    }
+}
+```
+
+## 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)
+
+# 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"}
+]
+
+memory.add(messages, user_id="charlie")
+
+# Search with local reranking
+results = memory.search("What books does the user like?", user_id="charlie")
+
+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 HuggingFace cross-encoder model:
+
+```python Python
+# Using a different model
+config = {
+    "rerank": {
+        "provider": "sentence_transformer", 
+        "config": {
+            "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
+
+- **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 +1,119 @@
+---
+title: Zero Entropy
+description: 'State-of-the-art neural reranking with Zero Entropy'
+icon: "sparkles"
+iconType: "solid"
+---
+
+[Zero Entropy](https://www.zeroentropy.dev) provides state-of-the-art 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/overview.mdx

@@ -0,0 +1,47 @@
+---
+title: Overview
+icon: "arrow-up-arrow-down"
+iconType: "solid"
+---
+
+Mem0 includes built-in support for various reranking providers to improve the relevance of memory search results. Rerankers post-process initial vector search results by re-scoring and re-ordering them using more sophisticated relevance models.
+
+## Usage
+
+To use a reranker, you must provide a `rerank` configuration section in your memory config. If no reranker is configured, search results will rely on vector similarity scoring alone.
+
+For comprehensive configuration parameters for each reranker, please refer to [Config](./config).
+
+## How Reranking Works
+
+1. **Initial Search**: Vector similarity search retrieves candidate memories
+2. **Reranking**: Selected reranker re-scores candidates using advanced models
+3. **Final Results**: Re-ordered results with both vector and rerank scores
+
+<Note>
+Reranking operates as a post-processing step and can significantly improve search relevance at the cost of additional latency and API calls.
+</Note>
+
+## Supported Rerankers
+
+See the list of supported rerankers below.
+
+<CardGroup cols={2}>
+  <Card title="Zero Entropy" href="/components/rerankers/models/zero_entropy" />
+  <Card title="Cohere" href="/components/rerankers/models/cohere" />
+  <Card title="Sentence Transformer" href="/components/rerankers/models/sentence_transformer" />
+  <Card title="LLM-based" href="/components/rerankers/models/llm" />
+</CardGroup>
+
+## When to Use Reranking
+
+- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
+- **Domain-Specific Queries**: For specialized terminology or context that benefits from advanced models
+- **Quality vs Speed Trade-off**: When you can accept higher latency for better search quality
+- **Production Systems**: Where search quality directly impacts user experience
+
+Choose the reranker that best fits your use case:
+- **Zero Entropy**: Best balance of speed and quality for general use
+- **Cohere**: Enterprise-grade with excellent multilingual support  
+- **Sentence Transformer**: Local deployment for privacy-sensitive applications
+- **LLM-based**: Maximum customization with custom prompts and logic

docs/components/vectordbs/config.mdx

@@ -4,13 +4,11 @@ icon: "gear"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 ## How to define configurations?
 
 The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
   - `config`: A nested dictionary containing provider-specific settings
 
 

docs/components/vectordbs/dbs/azure.mdx

@@ -16,8 +16,8 @@ config = {
     "vector_store": {
         "provider": "azure_ai_search",
         "config": {
-            "service_name": "ai-search-test",
-            "api_key": "*****",
+            "service_name": "<your-azure-ai-search-service-name>",
+            "api_key": "<your-api-key>",
             "collection_name": "mem0", 
             "embedding_model_dims": 1536
         }
@@ -41,8 +41,8 @@ config = {
     "vector_store": {
         "provider": "azure_ai_search",
         "config": {
-            "service_name": "ai-search-test",
-            "api_key": "*****",
+            "service_name": "<your-azure-ai-search-service-name>",
+            "api_key": "<your-api-key>",
             "collection_name": "mem0", 
             "embedding_model_dims": 1536,
             "compression_type": "binary",
@@ -59,8 +59,8 @@ config = {
     "vector_store": {
         "provider": "azure_ai_search",
         "config": {
-            "service_name": "ai-search-test",
-            "api_key": "*****",
+            "service_name": "<your-azure-ai-search-service-name>",
+            "api_key": "<your-api-key>",
             "collection_name": "mem0", 
             "embedding_model_dims": 1536,
             "hybrid_search": True,
@@ -70,12 +70,92 @@ config = {
 }
 ```
 
+## Using Azure Identity for Authentication
+As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with Azure OpenAI. The list below shows the order of precedence for credential application:
+
+1. **Environment Credential:**
+Azure client ID, secret, tenant ID, or certificate in environment variables for service principal authentication.
+
+2. **Workload Identity Credential:**
+Utilizes Azure Workload Identity (relevant for Kubernetes and Azure workloads).
+
+3. **Managed Identity Credential:**
+Authenticates as a Managed Identity (for apps/services hosted in Azure with Managed Identity enabled), this is the most secure production credential.
+
+4. **Shared Token Cache Credential / Visual Studio Credential (Windows only):**
+Uses cached credentials from Visual Studio sign-ins (and sometimes VS Code if SSO is enabled).
+
+5. **Azure CLI Credential:**
+Uses the currently logged-in user from the Azure CLI (`az login`), this is the most common development credential.
+
+6. **Azure PowerShell Credential:**
+Uses the identity from Azure PowerShell (`Connect-AzAccount`).
+
+7. **Azure Developer CLI Credential:**
+Uses the session from Azure Developer CLI (`azd auth login`).
+
+<Note> If an API is provided, it will be used for authentication over an Azure Identity </Note>
+To enable Role-Based Access Control (RBAC) for Azure AI Search, follow these steps:
+
+1. In the Azure Portal, navigate to your **Azure AI Search** service.
+2. In the left menu, select **Settings** > **Keys**.
+3. Change the authentication setting to **Role-based access control**, or **Both** if you need API key compatibility. The default is “Key-based authentication”—you must switch it to use Azure roles.
+4. **Go to Access Control (IAM):**
+    - In the Azure Portal, select your Search service.
+    - Click **Access Control (IAM)** on the left.
+5. **Add a Role Assignment:**
+    - 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:
+        - 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`).
+8. **Complete the Assignment:**
+    - Click **Review + Assign**.
+
+If you are using Azure Identity, do not set the `api_key` in the configuration.
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "<your-azure-ai-search-service-name>",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "compression_type": "binary",
+            "use_float16": True  # Use half precision for storage efficiency
+        }
+    }
+}
+```
+
+### Environment Variables to set 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.
+  - `AZURE_CLIENT_SECRET`: The client secret of your service principal.
+* For a User-Assigned Managed Identity, you will need to set the following environment variable:
+  - `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:
+* 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`.
+
+Troubleshooting tips for [Azure Identity](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/identity/azure-identity/TROUBLESHOOTING.md#troubleshoot-environmentcredential-authentication-issues).
+
+
 ## Configuration Parameters
 
 | Parameter | Description | Default Value | Options |
 | --- | --- | --- | --- |
 | `service_name` | Azure AI Search service name | Required | - |
-| `api_key` | API key of the Azure AI Search service | Required | - |
+| `api_key` | API key of the Azure AI Search service | Optional | If not present, the [Azure Identity](#using-azure-identity-for-authentication) credential chain will be used |
 | `collection_name` | The name of the collection/index to store vectors | `mem0` | Any valid index name |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` | Any integer value |
 | `compression_type` | Type of vector compression to use | `none` | `none`, `scalar`, `binary` |

docs/components/vectordbs/dbs/baidu.mdx

@@ -0,0 +1,67 @@
+---
+title: Baidu VectorDB (Mochow)
+---
+
+[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.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "baidu",
+        "config": {
+            "endpoint": "http://your-mochow-endpoint:8287",
+            "account": "root",
+            "api_key": "your-api-key",
+            "database_name": "mem0",
+            "table_name": "mem0_table",
+            "embedding_model_dims": 1536,
+            "metric_type": "COSINE"
+        }
+    }
+}
+
+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 movie? 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"})
+```
+
+### Config
+
+Here are the available parameters for the `mochow` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |
+
+### Distance Metrics
+
+The following distance metrics are supported:
+
+- `L2`: Euclidean distance (default)
+- `IP`: Inner product
+- `COSINE`: Cosine similarity
+
+### Index Configuration
+
+The vector index is automatically configured with the following HNSW parameters:
+
+- `m`: 16 (number of connections per element)
+- `efconstruction`: 200 (size of the dynamic candidate list)
+- `auto_build`: true (automatically build index)
+- `auto_build_index_policy`: Incremental build with 10000 rows increment

docs/components/vectordbs/dbs/chroma.mdx

@@ -1,7 +1,9 @@
-[Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed.
+[Chroma](https://www.trychroma.com/) is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed. It supports both local deployment and cloud hosting through ChromaDB Cloud.
 
 ### Usage
 
+#### Local Installation
+
 ```python
 import os
 from mem0 import Memory
@@ -14,6 +16,9 @@ config = {
         "config": {
             "collection_name": "test",
             "path": "db",
+            # Optional: ChromaDB Cloud configuration
+            # "api_key": "your-chroma-cloud-api-key",
+            # "tenant": "your-chroma-cloud-tenant-id",
         }
     }
 }
@@ -38,4 +43,6 @@ Here are the parameters available for configuring Chroma:
 | `client` | Custom client for Chroma | `None` |
 | `path` | Path for the Chroma database | `db` |
 | `host` | The host where the Chroma server is running | `None` |
-| `port` | The port where the Chroma server is running | `None` |
+| `port` | The port where the Chroma server is running | `None` |
+| `api_key` | ChromaDB Cloud API key (for cloud usage) | `None` |
+| `tenant` | ChromaDB Cloud tenant ID (for cloud usage) | `None` |

docs/components/vectordbs/dbs/databricks.mdx

@@ -0,0 +1,130 @@
+[Databricks Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) is a serverless similarity search engine that allows you to store a vector representation of your data, including metadata, in a vector database. With Vector Search, you can create auto-updating vector search indexes from Delta tables managed by Unity Catalog and query them with a simple API to return the most similar vectors.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "databricks",
+        "config": {
+            "workspace_url": "https://your-workspace.databricks.com",
+            "access_token": "your-access-token",
+            "endpoint_name": "your-vector-search-endpoint",
+            "index_name": "catalog.schema.index_name",
+            "source_table_name": "catalog.schema.source_table",
+            "embedding_dimension": 1536
+        }
+    }
+}
+
+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"})
+```
+
+### Config
+
+Here are the parameters available for configuring Databricks Vector Search:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `workspace_url` | The URL of your Databricks workspace | **Required** |
+| `access_token` | Personal Access Token for authentication | `None` |
+| `service_principal_client_id` | Service principal client ID (alternative to access_token) | `None` |
+| `service_principal_client_secret` | Service principal client secret (required with client_id) | `None` |
+| `endpoint_name` | Name of the Vector Search endpoint | **Required** |
+| `index_name` | Name of the vector index (Unity Catalog format: catalog.schema.index) | **Required** |
+| `source_table_name` | Name of the source Delta table (Unity Catalog format: catalog.schema.table) | **Required** |
+| `embedding_dimension` | Dimension of self-managed embeddings | `1536` |
+| `embedding_source_column` | Column name for text when using Databricks-computed embeddings | `None` |
+| `embedding_model_endpoint_name` | Databricks serving endpoint for embeddings | `None` |
+| `embedding_vector_column` | Column name for self-managed embedding vectors | `embedding` |
+| `endpoint_type` | Type of endpoint (`STANDARD` or `STORAGE_OPTIMIZED`) | `STANDARD` |
+| `sync_computed_embeddings` | Whether to sync computed embeddings automatically | `True` |
+
+### Authentication
+
+Databricks Vector Search supports two authentication methods:
+
+#### Service Principal (Recommended for Production)
+```python
+config = {
+    "vector_store": {
+        "provider": "databricks",
+        "config": {
+            "workspace_url": "https://your-workspace.databricks.com",
+            "service_principal_client_id": "your-service-principal-id",
+            "service_principal_client_secret": "your-service-principal-secret",
+            "endpoint_name": "your-endpoint",
+            "index_name": "catalog.schema.index_name",
+            "source_table_name": "catalog.schema.source_table"
+        }
+    }
+}
+```
+
+#### Personal Access Token (for Development)
+```python
+config = {
+    "vector_store": {
+        "provider": "databricks",
+        "config": {
+            "workspace_url": "https://your-workspace.databricks.com",
+            "access_token": "your-personal-access-token",
+            "endpoint_name": "your-endpoint",
+            "index_name": "catalog.schema.index_name",
+            "source_table_name": "catalog.schema.source_table"
+        }
+    }
+}
+```
+
+### Embedding Options
+
+#### Self-Managed Embeddings (Default)
+Use your own embedding model and provide vectors directly:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "databricks",
+        "config": {
+            # ... authentication config ...
+            "embedding_dimension": 768,  # Match your embedding model
+            "embedding_vector_column": "embedding"
+        }
+    }
+}
+```
+
+#### Databricks-Computed Embeddings
+Let Databricks compute embeddings from text using a serving endpoint:
+
+```python
+config = {
+    "vector_store": {
+        "provider": "databricks",
+        "config": {
+            # ... authentication config ...
+            "embedding_source_column": "text",
+            "embedding_model_endpoint_name": "e5-small-v2"
+        }
+    }
+}
+```
+
+### Important Notes
+
+- **Delta Sync Index**: This implementation uses Delta Sync Index, which automatically syncs with your source Delta table. Direct vector insertion/deletion/update operations will log warnings as they're not supported with Delta Sync.
+- **Unity Catalog**: Both the source table and index must be in Unity Catalog format (`catalog.schema.table_name`).
+- **Endpoint Auto-Creation**: If the specified endpoint doesn't exist, it will be created automatically.
+- **Index Auto-Creation**: If the specified index doesn't exist, it will be created automatically with the provided configuration.
+- **Filter Support**: Supports filtering by metadata fields, with different syntax for STANDARD vs STORAGE_OPTIMIZED endpoints.

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -54,7 +54,8 @@ Let's see the available parameters for the `elasticsearch` config:
 | `password`             | Password for basic authentication                  | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
-| `custom_search_query`  | Function returning a custom search query            | `None`        |
+| `custom_search_query`  | Function returning a custom search query           | `None`        |
+| `headers`              | Custom headers to include in requests              | `None`        |
 
 ### Features
 

docs/components/vectordbs/dbs/milvus.mdx

@@ -14,6 +14,7 @@ config = {
             "embedding_model_dims": "123",
             "url": "127.0.0.1",
             "token": "8e4b8ca8cf2c67",
+            "db_name": "my_database",
         }
     }
 }
@@ -39,3 +40,4 @@ Here's the parameters available for configuring Milvus Database:
 | `collection_name` | The name of the collection | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `metric_type` | Metric type for similarity search | `L2` |
+| `db_name` | Name of the database | `""` |

docs/components/vectordbs/dbs/mongodb.mdx

@@ -16,8 +16,7 @@ config = {
         "config": {
             "db_name": "mem0-db",
             "collection_name": "mem0-collection",
-            "user": "my-user",
-            "password": "my-password",
+            "mongo_uri":"mongodb://username:password@localhost:27017"
         }
     }
 }
@@ -41,9 +40,6 @@ Here are the parameters available for configuring MongoDB:
 | db_name | Name of the MongoDB database | `"mem0_db"` |
 | collection_name | Name of the MongoDB collection | `"mem0_collection"` |
 | embedding_model_dims | Dimensions of the embedding vectors | `1536` |
-| user | MongoDB user for authentication | `None` |
-| password | Password for the MongoDB user | `None` |
-| host | MongoDB host | `"localhost"` |
-| port | MongoDB port | `27017` |
+| mongo_uri | The mongo URI connection string | mongodb://username:password@localhost:27017 |
 
-> **Note**: `user` and `password` must either be provided together or omitted together.
+> **Note**: If Mongo_uri is not provided it will default to mongodb://username:password@localhost:27017.

docs/components/vectordbs/dbs/neptune_analytics.mdx

@@ -0,0 +1,42 @@
+# Neptune Analytics Vector Store
+
+[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
+
+
+## Installation
+
+```bash
+pip install mem0ai[vector_stores]
+```
+
+## Usage
+
+```python
+config = {
+    "vector_store": {
+        "provider": "neptune",
+        "config": {
+            "collection_name": "mem0",
+            "endpoint": f"neptune-graph://my-graph-identifier",
+        },
+    },
+}
+
+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": "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"})
+```
+
+## Parameters
+
+Let's see the available parameters for the `neptune` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` |

docs/components/vectordbs/dbs/pgvector.mdx

@@ -2,7 +2,8 @@
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -24,19 +25,50 @@ 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": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
     {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
 ]
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'pgvector',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      user: 'test',
+      password: '123',
+      host: '127.0.0.1',
+      port: 5432,
+      dbname: 'vector_store', // Optional, defaults to 'postgres'
+      diskann: false, // Optional, requires pgvectorscale extension
+      hnsw: false, // Optional, for HNSW indexing
+    },
+  },
+};
+
+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": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ### Config
 
 Here's the parameters available for configuring pgvector:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `dbname` | The name of the  | `postgres` |
+| `dbname` | The name of the database | `postgres` |
 | `collection_name` | The name of the collection | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `user` | User name to connect to the database | `None` |
@@ -44,4 +76,12 @@ Here's the parameters available for configuring pgvector:
 | `host` | The host where the Postgres server is running | `None` |
 | `port` | The port where the Postgres server is running | `None` |
 | `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
-| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
+| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
+| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
+
+**Note**: The connection parameters have the following priority:
+1. `connection_pool` (highest priority)
+2. `connection_string`
+3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)

docs/components/vectordbs/dbs/pinecone.mdx

@@ -1,5 +1,7 @@
 [Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
 
+> **New**: Pinecone integration now supports custom namespaces! Use the `namespace` parameter to logically separate data within the same index. This is especially useful for multi-tenant or multi-user applications.
+
 > **Note**: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the `embedding_model_dims` in your config matches your chosen model's dimensions. For example, OpenAI's text-embedding-3-small uses 1536 dimensions.
 
 ### Usage
@@ -18,6 +20,7 @@ config = {
         "config": {
             "collection_name": "testing",
             "embedding_model_dims": 1536,  # Matches OpenAI's text-embedding-3-small
+            "namespace": "my-namespace", # Optional: specify a namespace for multi-tenancy
             "serverless_config": {
                 "cloud": "aws",  # Choose between 'aws' or 'gcp' or 'azure'
                 "region": "us-east-1"
@@ -53,6 +56,7 @@ Here are the parameters available for configuring Pinecone:
 | `hybrid_search` | Whether to enable hybrid search | `False` |
 | `metric` | Distance metric for vector similarity | `"cosine"` |
 | `batch_size` | Batch size for operations | `100` |
+| `namespace` | Namespace for the collection, useful for multi-tenancy. | `None` |
 
 > **Important**: You must choose either `serverless_config` or `pod_config` for your deployment, but not both.
 
@@ -64,6 +68,7 @@ config = {
         "config": {
             "collection_name": "memory_index",
             "embedding_model_dims": 1536,  # For OpenAI's text-embedding-3-small
+            "namespace": "my-namespace",  # Optional: custom namespace
             "serverless_config": {
                 "cloud": "aws",  # or "gcp" or "azure"
                 "region": "us-east-1"  # Choose appropriate region
@@ -81,6 +86,7 @@ config = {
         "config": {
             "collection_name": "memory_index",
             "embedding_model_dims": 1536,  # For OpenAI's text-embedding-ada-002
+            "namespace": "my-namespace",  # Optional: custom namespace
             "pod_config": {
                 "environment": "gcp-starter",
                 "replicas": 1,

docs/components/vectordbs/dbs/s3_vectors.mdx

@@ -0,0 +1,78 @@
+---
+title: Amazon S3 Vectors
+---
+
+[Amazon S3 Vectors](https://aws.amazon.com/s3/features/vectors/) is a purpose-built, cost-optimized vector storage and query service for semantic search and AI applications. It provides S3-level elasticity and durability with sub-second query performance.
+
+### Installation
+
+S3 Vectors support requires additional dependencies. Install them with:
+
+```bash
+pip install boto3
+```
+
+### Usage
+
+To use Amazon S3 Vectors with Mem0, you need to have an AWS account and the necessary IAM permissions (`s3vectors:*`). Ensure your environment is configured with AWS credentials (e.g., via `~/.aws/credentials` or environment variables).
+
+```python
+import os
+from mem0 import Memory
+
+# Ensure your AWS credentials are configured in your environment
+# e.g., by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_DEFAULT_REGION
+
+config = {
+    "vector_store": {
+        "provider": "s3_vectors",
+        "config": {
+            "vector_bucket_name": "my-mem0-vector-bucket",
+            "index_name": "my-memories-index",
+            "embedding_model_dims": 1536,
+            "distance_metric": "cosine",
+            "region_name": "us-east-1"
+        }
+    }
+}
+
+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 movie? 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"})
+```
+
+### Config
+
+Here are the available parameters for the `s3_vectors` config:
+
+| Parameter              | Description                                                          | Default Value |
+| ---------------------- | -------------------------------------------------------------------- | ------------- |
+| `vector_bucket_name`   | The name of the S3 Vector bucket to use. It will be created if it doesn't exist. | Required      |
+| `index_name`           | The name of the vector index within the bucket.                        | `mem0`        |
+| `embedding_model_dims` | Dimensions of the embedding model. Must match your embedder.         | `1536`        |
+| `distance_metric`      | Distance metric for similarity search. Options: `cosine`, `euclidean`. | `cosine`      |
+| `region_name`          | The AWS region where the bucket and index reside.                    | `None` (uses default from AWS config) |
+
+### IAM Permissions
+
+Your AWS identity (user or role) needs permissions to perform actions on S3 Vectors. A minimal policy would look like this:
+
+```json
+{
+    "Version": "2012-10-17",
+    "Statement": [
+        {
+            "Effect": "Allow",
+            "Action": "s3vectors:*",
+            "Resource": "*"
+        }
+    ]
+}
+```
+
+For production, it is recommended to scope down the resource ARN to your specific buckets and indexes.

docs/components/vectordbs/dbs/valkey.mdx

@@ -0,0 +1,49 @@
+# Valkey Vector Store
+
+[Valkey](https://valkey.io/) is an open source (BSD) high-performance key/value datastore that supports a variety of workloads and rich datastructures including vector search.
+
+## Installation
+
+```bash
+pip install mem0ai[vector_stores]
+```
+
+## Usage
+
+```python
+config = {
+    "vector_store": {
+        "provider": "valkey",
+        "config": {
+            "collection_name": "test",
+            "valkey_url": "valkey://localhost:6379",
+            "embedding_model_dims": 1536,
+            "index_type": "flat"
+        }
+    }
+}
+
+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": "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"})
+```
+
+## Parameters
+
+Let's see the available parameters for the `valkey` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `valkey_url` | Connection URL for the Valkey server | `valkey://localhost:6379` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `index_type` | Vector index algorithm (`hnsw` or `flat`) | `hnsw` |
+| `hnsw_m` | Number of bi-directional links for HNSW | `16` |
+| `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
+| `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
+| `distance_metric` | Distance metric for vector similarity | `cosine` |

docs/components/vectordbs/dbs/vertex_ai.mdx

@@ -12,7 +12,7 @@ To use Google Cloud Vertex AI Vector Search with `mem0`, you need to configure t
 import os
 from mem0 import Memory
 
-os.environ["GEMINI_API_KEY"] = = "sk-xx"
+os.environ["GOOGLE_API_KEY"] = "sk-xx"
 
 config = {
     "vector_store": {

docs/components/vectordbs/overview.mdx

@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
 
 ## Supported Vector Databases
@@ -13,7 +11,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis,Vectorize and in-memory vector database.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.
 </Note>
 
 <CardGroup cols={3}>
@@ -26,6 +24,7 @@ See the list of supported vector databases below.
   <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card>
   <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card>
   <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
+  <Card title="Valkey" href="/components/vectordbs/dbs/valkey"></Card>
   <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
   <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
   <Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card>
@@ -33,6 +32,8 @@ See the list of supported vector databases below.
   <Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card>
   <Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card>
   <Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card>
+  <Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card>
+  <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card>
 </CardGroup>
 
 ## Usage

docs/contributing/development.mdx

@@ -3,8 +3,6 @@ title: Development
 icon: "code"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 # Development Contributions
 
 We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
@@ -31,13 +29,18 @@ For detailed guidance on pull requests, refer to [GitHub's documentation](https:
 
 We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
-⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, run:
+⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, follow these steps in order:
 
 ```bash
-make install_all
+# 1. Install base dependencies
+make install
 
-# Activate virtual environment
-hatch shell
+# 2. Activate virtual environment (this will install deps.)
+hatch shell (for default env)
+hatch -e dev_py_3_11 shell (for dev_py_3_11) (differences are mentioned in pyproject.toml)
+
+# 3. Install all optional dependencies
+make install_all
 ```
 
 ---

docs/contributing/documentation.mdx

@@ -3,8 +3,6 @@ title: Documentation
 icon: "book"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 # Documentation Contributions
 
 ## 📌 Prerequisites

docs/core-concepts/memory-operations.mdx

@@ -1,62 +0,0 @@
----
-title: Memory Operations
-description: Understanding the core operations for managing memories in AI applications
-icon: "gear"
-iconType: "solid"
----
-
-<Snippet file="paper-release.mdx" />
-
-Mem0 provides two core operations for managing memories in AI applications: adding new memories and searching existing ones. This guide covers how these operations work and how to use them effectively in your application.
-
-
-## Core Operations
-
-Mem0 exposes two main endpoints for interacting with memories:
-- The `add` endpoint for ingesting conversations and storing them as memories
-- The `search` endpoint for retrieving relevant memories based on queries
-
-### Adding Memories
-
-<Frame caption="Architecture diagram illustrating the process of adding memories.">
-  <img src="../images/add_architecture.png" />
-</Frame>
-
-The add operation processes conversations through several steps:
-
-1. **Information Extraction**
-   * An LLM extracts relevant memories from the conversation
-   * It identifies important entities and their relationships
-
-2. **Conflict Resolution**
-   * The system compares new information with existing data
-   * It identifies and resolves any contradictions
-
-3. **Memory Storage**
-   * Vector database stores the actual memories
-   * Graph database maintains relationship information
-   * Information is continuously updated with each interaction
-
-### Searching Memories
-
-<Frame caption="Architecture diagram illustrating the memory search process.">
-  <img src="../images/search_architecture.png" />
-</Frame>
-
-The search operation retrieves memories through a multi-step process:
-
-1. **Query Processing**
-   * LLM processes and optimizes the search query
-   * System prepares filters for targeted search
-
-2. **Vector Search**
-   * Performs semantic search using the optimized query
-   * Ranks results by relevance to the query
-   * Applies specified filters (user, agent, metadata, etc.)
-
-3. **Result Processing**
-   * Combines and ranks the search results
-   * Returns memories with relevance scores
-   * Includes associated metadata and timestamps
-
-This semantic search approach ensures accurate memory retrieval, whether you're looking for specific information or exploring related concepts.

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

@@ -0,0 +1,153 @@
+---
+title: Add Memory
+description: Add memory into the Mem0 platform by storing user-assistant interactions and facts for later retrieval.
+icon: "plus"
+iconType: "solid"
+---
+
+
+## Overview
+
+The `add` operation is how you store memory into Mem0. Whether you're working with a chatbot, a voice assistant, or a multi-agent system, this is the entry point to create long-term memory.
+
+Memories typically come from a **user-assistant interaction** and Mem0 handles the extraction, transformation, and storage for you.
+
+Mem0 offers two implementation flows:
+
+- **Mem0 Platform** (Managed, scalable, with dashboard + API)
+- **Mem0 Open Source** (Lightweight, fully local, flexible SDKs)
+
+Each supports the same core memory operations, but with slightly different setup. Below, we walk through examples for both.
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the process of adding memories.">
+  <img src="../../images/add_architecture.png" />
+</Frame>
+
+When you call `add`, Mem0 performs the following steps under the hood:
+
+1. **Information Extraction**
+   The input messages are passed through an LLM that extracts key facts, decisions, preferences, or events worth remembering.
+
+2. **Conflict Resolution**
+   Mem0 compares the new memory against existing ones to detect duplication or contradiction and handles updates accordingly.
+
+3. **Memory Storage**
+   The result is stored in a vector database (for semantic search) and optionally in a graph structure (for relationship mapping).
+
+You don’t need to handle any of this manually, Mem0 takes care of it with a single API call or SDK method.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+messages = [
+    {"role": "user", "content": "I'm planning a trip to Tokyo next month."},
+    {"role": "assistant", "content": "Great! I’ll remember that for future suggestions."}
+]
+
+client.add(
+    messages=messages,
+    user_id="alice",
+    version="v2"
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const messages = [
+  { role: "user", content: "I'm planning a trip to Tokyo next month." },
+  { role: "assistant", content: "Great! I’ll remember that for future suggestions." }
+];
+
+await client.add({
+  messages,
+  user_id: "alice",
+  version: "v2"
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+m = Memory()
+
+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": "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."}
+]
+
+# Store inferred memories (default behavior)
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+
+# Optionally store raw messages without inference
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+
+const messages = [
+  {
+    role: "user",
+    content: "I like to drink coffee in the morning and go for a walk"
+  }
+];
+
+const result = memory.add(messages, {
+  userId: "alice",
+  metadata: { category: "preferences" }
+});
+```
+</CodeGroup>
+
+---
+
+## When Should You Add Memory?
+
+Add memory whenever your agent learns something useful:
+
+- A new user preference is shared
+- A decision or suggestion is made
+- A goal or task is completed
+- A new entity is introduced
+- A user gives feedback or clarification
+
+Storing this context allows the agent to reason better in future interactions.
+
+
+### More Details
+
+For full list of supported fields, required formats, and advanced options, see the
+[Add Memory API Reference](/api-reference/memory/add-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,141 @@
+---
+title: Delete Memory
+description: Remove memories from Mem0 either individually, in bulk, or via filters.
+icon: "trash"
+iconType: "solid"
+---
+
+## Overview
+
+Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
+
+1. **Delete a Single Memory**: Using a specific memory ID
+2. **Batch Delete**: Delete multiple known memory IDs (up to 1000)
+3. **Filtered Delete**: Delete memories matching a filter (e.g., `user_id`, `metadata`, `run_id`)
+
+This page walks through code example for each method.
+
+
+## Use Cases
+
+- Forget a user’s past preferences by request
+- Remove outdated or incorrect memory entries
+- Clean up memory after session expiration
+- Comply with data deletion requests (e.g., GDPR)
+
+---
+
+## 1. Delete a Single Memory by ID
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.delete(memory_id=memory_id)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.delete("your_memory_id")
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 2. Batch Delete Multiple Memories
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+delete_memories = [
+    {"memory_id": "id1"},
+    {"memory_id": "id2"}
+]
+
+response = client.batch_delete(delete_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const deleteMemories = [
+  { memory_id: "id1" },
+  { memory_id: "id2" }
+];
+
+client.batchDelete(deleteMemories)
+  .then(response => console.log('Batch delete response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 3. Delete Memories by Filter (e.g., user_id)
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Delete all memories for a specific user
+client.delete_all(user_id="alice")
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.deleteAll({ user_id: "alice" })
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+You can also filter by other parameters such as:
+- `agent_id`
+- `run_id`
+- `metadata` (as JSON string)
+
+---
+
+## Key Differences
+
+| Method                | Use When                                | IDs Needed | Filters |
+|----------------------|-------------------------------------------|------------|----------|
+| `delete(memory_id)`  | You know exactly which memory to remove   | ✔          | ✘        |
+| `batch_delete([...])`| You have a known list of memory IDs       | ✔          | ✘        |
+| `delete_all(...)`    | You want to delete by user/agent/run/etc | ✘          | ✔        |
+
+
+### More Details
+
+For request/response schema and additional filtering options, see:
+- [Delete Memory API Reference](/api-reference/memory/delete-memory)
+- [Batch Delete API Reference](/api-reference/memory/batch-delete)
+- [Delete Memories by Filter Reference](/api-reference/memory/delete-memories)
+
+You’ve now seen how to add, search, update, and delete memories in Mem0.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,124 @@
+---
+title: Search Memory
+description: Retrieve relevant memories from Mem0 using powerful semantic and filtered search capabilities.
+icon: "magnifying-glass"
+iconType: "solid"
+---
+
+## Overview
+
+The `search` operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior.
+
+Mem0 supports:
+- Semantic similarity search
+- Metadata filtering (with advanced logic)
+- Reranking and thresholds
+- Cross-agent, multi-session context resolution
+
+This applies to both:
+- **Mem0 Platform** (hosted API with full-scale features)
+- **Mem0 Open Source** (local-first with LLM inference and local vector DB)
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the memory search process.">
+  <img src="../../images/search_architecture.png" />
+</Frame>
+
+The search flow follows these steps:
+
+1. **Query Processing**
+   An LLM refines and optimizes your natural language query.
+
+2. **Vector Search**
+   Semantic embeddings are used to find the most relevant memories using cosine similarity.
+
+3. **Filtering & Ranking**
+   Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
+
+4. **Results Delivery**
+   Relevant memories are returned with associated metadata and timestamps.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+query = "What do you know about me?"
+filters = {
+   "OR": [
+      {"user_id": "alice"},
+      {"agent_id": {"in": ["travel-assistant", "customer-support"]}}
+   ]
+}
+
+results = client.search(query, version="v2", filters=filters)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const query = "I'm craving some pizza. Any recommendations?";
+const filters = {
+  AND: [
+    { user_id: "alice" }
+  ]
+};
+
+const results = await client.search(query, {
+  version: "v2",
+  filters
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+from mem0 import Memory
+
+m = Memory()
+related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
+```
+</CodeGroup>
+
+---
+
+## Tips for Better Search
+
+- Use descriptive natural queries (Mem0 can interpret intent)
+- Apply filters for scoped, faster lookup
+- Use `version: "v2"` for enhanced results
+- Consider wildcard filters (e.g., `run_id: "*"`) for broader matches
+- Tune with `top_k`, `threshold`, or `rerank` if needed
+
+
+### More Details
+
+For the full list of filter logic, comparison operators, and optional search parameters, see the
+[Search Memory API Reference](/api-reference/memory/v2-search-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,117 @@
+---
+title: Update Memory
+description: Modify an existing memory by updating its content or metadata.
+icon: "pencil"
+iconType: "solid"
+---
+
+## Overview
+
+User preferences, interests, and behaviors often evolve over time. The `update` operation lets you revise a stored memory, whether it's updating facts and memories, rephrasing a message, or enriching metadata.
+
+Mem0 supports both:
+- **Single Memory Update** for one specific memory using its ID
+- **Batch Update** for updating many memories at once (up to 1000)
+
+This guide includes usage for both single update and batch update of memories through **Mem0 Platform**
+
+
+## Use Cases
+
+- Refine a vague or incorrect memory after a correction
+- Add or edit memory with new metadata (e.g., categories, tags)
+- Evolve factual knowledge as the user’s profile changes
+- A user profile evolves: “I love spicy food” → later says “Actually, I can’t handle spicy food.”
+
+Updating memory ensures your agents remain accurate, adaptive, and personalized.
+
+---
+
+## Update Memory
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.update(
+    memory_id=memory_id,
+    text="Updated memory content about the user",
+    metadata={"category": "profile-update"}
+)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+const memory_id = "your_memory_id";
+
+client.update(memory_id, {
+  text: "Updated memory content about the user",
+  metadata: { category: "profile-update" }
+})
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Batch Update
+
+Update up to 1000 memories in one call.
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+update_memories = [
+    {"memory_id": "id1", "text": "Watches football"},
+    {"memory_id": "id2", "text": "Likes to travel"}
+]
+
+response = client.batch_update(update_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const updateMemories = [
+  { memoryId: "id1", text: "Watches football" },
+  { memoryId: "id2", text: "Likes to travel" }
+];
+
+client.batchUpdate(updateMemories)
+  .then(response => console.log('Batch update response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Tips
+
+- You can update both `text` and `metadata` in the same call.
+- Use `batchUpdate` when you're applying similar corrections at scale.
+- If memory is marked `immutable`, it must first be deleted and re-added.
+- Combine this with feedback mechanisms (e.g., user thumbs-up/down) to self-improve memory.
+
+
+### More Details
+
+Refer to the full [Update Memory API Reference](/api-reference/memory/update-memory) and [Batch Update Reference](/api-reference/memory/batch-update) for schema and advanced fields.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

docs/core-concepts/memory-types.mdx

@@ -5,8 +5,6 @@ icon: "memory"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 To build useful AI applications, we need to understand how different memory systems work together. This guide explores the fundamental types of memory in AI systems and shows how Mem0 implements these concepts.
 
 ## Why Memory Matters

docs/docs.json

@@ -1,8 +1,8 @@
 {
   "$schema": "https://mintlify.com/docs.json",
-  "theme": "maple",
   "name": "Mem0",
   "description": "Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users.",
+  "theme": "maple",
   "colors": {
     "primary": "#6c60f0",
     "light": "#E6FFA2",
@@ -19,10 +19,10 @@
             "tab": "Documentation",
             "groups": [
               {
-                "group": "Get Started",
+                "group": "Getting Started",
                 "icon": "rocket",
                 "pages": [
-                  "overview",
+                  "introduction",
                   "quickstart",
                   "faqs"
                 ]
@@ -32,35 +32,46 @@
                 "icon": "brain",
                 "pages": [
                   "core-concepts/memory-types",
-                  "core-concepts/memory-operations"
+                  {
+                    "group": "Memory Operations",
+                    "icon": "gear",
+                    "pages": [
+                      "core-concepts/memory-operations/add",
+                      "core-concepts/memory-operations/search",
+                      "core-concepts/memory-operations/update",
+                      "core-concepts/memory-operations/delete"
+                    ]
+                  }
                 ]
               },
               {
                 "group": "Platform",
-                "icon": "cogs",
+                "icon": "globe",
                 "pages": [
                   "platform/overview",
                   "platform/quickstart",
+                  "platform/advanced-memory-operations",
                   {
                     "group": "Features",
                     "icon": "star",
                     "pages": [
                       "platform/features/platform-overview",
+                      "platform/features/contextual-add",
+                      "platform/features/async-client",
+                      "platform/features/graph-memory",
                       "platform/features/advanced-retrieval",
                       "platform/features/criteria-retrieval",
-                      "platform/features/contextual-add",
                       "platform/features/multimodal-support",
-                      "platform/features/timestamp",
                       "platform/features/selective-memory",
                       "platform/features/custom-categories",
                       "platform/features/custom-instructions",
                       "platform/features/direct-import",
-                      "platform/features/async-client",
                       "platform/features/memory-export",
+                      "platform/features/timestamp",
+                      "platform/features/expiration-date",
                       "platform/features/webhooks",
-                      "platform/features/graph-memory",
                       "platform/features/feedback-mechanism",
-                      "platform/features/expiration-date"
+                      "platform/features/group-chat"
                     ]
                   }
                 ]
@@ -69,13 +80,14 @@
                 "group": "Open Source",
                 "icon": "code-branch",
                 "pages": [
-                  "open-source/quickstart",
+                  "open-source/overview",
                   "open-source/python-quickstart",
                   "open-source/node-quickstart",
                   {
                     "group": "Features",
-                    "icon": "wrench",
+                    "icon": "star",
                     "pages": [
+                      "open-source/features/overview",
                       "open-source/features/async-memory",
                       "open-source/features/openai_compatibility",
                       "open-source/features/custom-fact-extraction-prompt",
@@ -112,12 +124,12 @@
                           "components/llms/models/mistral_AI",
                           "components/llms/models/google_AI",
                           "components/llms/models/aws_bedrock",
-                          "components/llms/models/gemini",
                           "components/llms/models/deepseek",
                           "components/llms/models/xAI",
                           "components/llms/models/sarvam",
                           "components/llms/models/lmstudio",
-                          "components/llms/models/langchain"
+                          "components/llms/models/langchain",
+                          "components/llms/models/vllm"
                         ]
                       }
                     ]
@@ -140,13 +152,20 @@
                           "components/vectordbs/dbs/mongodb",
                           "components/vectordbs/dbs/azure",
                           "components/vectordbs/dbs/redis",
+                          "components/vectordbs/dbs/valkey",
                           "components/vectordbs/dbs/elasticsearch",
                           "components/vectordbs/dbs/opensearch",
                           "components/vectordbs/dbs/supabase",
+                          "components/vectordbs/dbs/upstash-vector",
+                          "components/vectordbs/dbs/vectorize",
                           "components/vectordbs/dbs/vertex_ai",
                           "components/vectordbs/dbs/weaviate",
                           "components/vectordbs/dbs/faiss",
-                          "components/vectordbs/dbs/langchain"
+                          "components/vectordbs/dbs/langchain",
+                          "components/vectordbs/dbs/baidu",
+                          "components/vectordbs/dbs/s3_vectors",
+                          "components/vectordbs/dbs/databricks",
+                          "components/vectordbs/dbs/neptune_analytics"
                         ]
                       }
                     ]
@@ -166,7 +185,7 @@
                           "components/embedders/models/ollama",
                           "components/embedders/models/huggingface",
                           "components/embedders/models/vertexai",
-                          "components/embedders/models/gemini",
+                          "components/embedders/models/google_AI",
                           "components/embedders/models/lmstudio",
                           "components/embedders/models/together",
                           "components/embedders/models/langchain",
@@ -192,7 +211,8 @@
             "icon": "square-terminal",
             "pages": [
               "openmemory/overview",
-              "openmemory/quickstart"
+              "openmemory/quickstart",
+              "openmemory/integrations"
             ]
           },
           {
@@ -204,9 +224,12 @@
                 "pages": [
                   "examples",
                   "examples/aws_example",
+                  "examples/aws_neptune_analytics_hybrid_store",
                   "examples/mem0-demo",
                   "examples/ai_companion_js",
                   "examples/collaborative-task-agent",
+                  "examples/llamaindex-multiagent-learning-system",
+                  "examples/personalized-search-tavily-mem0",
                   "examples/eliza_os",
                   "examples/mem0-mastra",
                   "examples/mem0-with-ollama",
@@ -215,7 +238,7 @@
                   "examples/personal-travel-assistant",
                   "examples/llama-index-mem0",
                   "examples/chrome-extension",
-                  "examples/document-writing",
+                  "examples/memory-guided-content-writing",
                   "examples/multimodal-demo",
                   "examples/personalized-deep-research",
                   "examples/mem0-agentic-tool",
@@ -236,24 +259,26 @@
                 "icon": "plug",
                 "pages": [
                   "integrations",
-                  "integrations/agentops",
-                  "integrations/vercel-ai-sdk",
-                  "integrations/flowise",
-                  "integrations/crewai",
-                  "integrations/autogen",
                   "integrations/langchain",
                   "integrations/langgraph",
                   "integrations/llama-index",
-                  "integrations/langchain-tools",
-                  "integrations/dify",
-                  "integrations/mcp-server",
-                  "integrations/livekit",
-                  "integrations/elevenlabs",
-                  "integrations/pipecat",
                   "integrations/agno",
+                  "integrations/autogen",
+                  "integrations/crewai",
+                  "integrations/openai-agents-sdk",
+                  "integrations/google-ai-adk",
+                  "integrations/mastra",
+                  "integrations/vercel-ai-sdk",
+                  "integrations/livekit",
+                  "integrations/pipecat",
+                  "integrations/elevenlabs",
+                  "integrations/aws-bedrock",
+                  "integrations/flowise",
+                  "integrations/langchain-tools",
+                  "integrations/agentops",
                   "integrations/keywords",
-                  "integrations/raycast",
-                  "integrations/mastra"
+                  "integrations/dify",
+                  "integrations/raycast"
                 ]
               }
             ]
@@ -348,31 +373,6 @@
             ]
           }
         ]
-      },
-      {
-        "anchor": "Your Dashboard",
-        "href": "https://app.mem0.ai",
-        "icon": "chart-simple"
-      },
-      {
-        "anchor": "Demo",
-        "href": "https://mem0.dev/demo",
-        "icon": "play"
-      },
-      {
-        "anchor": "Discord",
-        "href": "https://mem0.dev/DiD",
-        "icon": "discord"
-      },
-      {
-        "anchor": "GitHub",
-        "href": "https://github.com/mem0ai/mem0",
-        "icon": "github"
-      },
-      {
-        "anchor": "Support",
-        "href": "mailto:founders@mem0.ai",
-        "icon": "envelope"
       }
     ]
   },
@@ -384,7 +384,7 @@
   "background": {
     "color": {
       "light": "#fff",
-      "dark": "#0f1117"
+      "dark": "#09090b"
     }
   },
   "navbar": {

docs/examples.mdx

@@ -3,9 +3,6 @@ title: Overview
 description: How to use mem0 in your existing applications?
 ---
 
-<Snippet file="paper-release.mdx" />
-
-
 With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
 
 - More personalized
@@ -20,68 +17,72 @@ Here are some examples of how Mem0 can be integrated into various applications:
 
 Explore how **Mem0** can power real-world applications and bring personalized, intelligent experiences to life:
 
-<CardGroup cols={2}>  
-  <Card title="AI Companion in Node.js" icon="node" href="/examples/ai_companion_js">  
-    Build a personalized AI Companion in **Node.js** that remembers conversations and adapts over time using Mem0.  
-  </Card>  
+<CardGroup cols={2}>
+  <Card title="AI Companion in Node.js" icon="node" href="/examples/ai_companion_js">
+    Build a personalized AI Companion in **Node.js** that remembers conversations and adapts over time using Mem0.
+  </Card>
 
-  <Card title="Mem0 with Ollama" icon="server" href="/examples/mem0-with-ollama">  
-    Run **Mem0 locally** with **Ollama** to create private, stateful AI experiences without relying on cloud APIs.  
-  </Card>  
+  <Card title="Mem0 with Ollama" icon="server" href="/examples/mem0-with-ollama">
+    Run **Mem0 locally** with **Ollama** to create private, stateful AI experiences without relying on cloud APIs.
+  </Card>
 
-  <Card title="Personal AI Tutor" icon="graduation-cap" href="/examples/personal-ai-tutor">  
-    Create an **AI Tutor** that adapts to student progress, learning style, and history — for a truly customized learning experience.  
-  </Card>  
+  <Card title="Personal AI Tutor" icon="graduation-cap" href="/examples/personal-ai-tutor">
+    Create an **AI Tutor** that adapts to student progress, learning style, and history — for a truly customized learning experience.
+  </Card>
 
-  <Card title="Personal Travel Assistant" icon="plane" href="/examples/personal-travel-assistant">  
-    Develop a **Personal Travel Assistant** that remembers your preferences, past trips, and helps plan future adventures.  
-  </Card>  
+  <Card title="Personal Travel Assistant" icon="plane" href="/examples/personal-travel-assistant">
+    Develop a **Personal Travel Assistant** that remembers your preferences, past trips, and helps plan future adventures.
+  </Card>
 
-  <Card title="Customer Support Agent" icon="headset" href="/examples/customer-support-agent">  
-    Build a **Customer Support AI** that recalls user preferences, past chats, and provides context-aware, efficient help.  
-  </Card>  
+  <Card title="Customer Support Agent" icon="headset" href="/examples/customer-support-agent">
+    Build a **Customer Support AI** that recalls user preferences, past chats, and provides context-aware, efficient help.
+  </Card>
 
-  <Card title="LlamaIndex + Mem0" icon="book-open" href="/examples/llama-index-mem0">  
-    Combine **LlamaIndex** and Mem0 to create a powerful **ReAct Agent** with persistent memory for smarter interactions.  
-  </Card>  
+  <Card title="LlamaIndex + Mem0" icon="book-open" href="/examples/llama-index-mem0">
+    Combine **LlamaIndex** and Mem0 to create a powerful **ReAct Agent** with persistent memory for smarter interactions.
+  </Card>
 
-  <Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">  
-    Add **long-term memory** to ChatGPT, Claude, or Perplexity via the **Mem0 Chrome Extension** — personalize your AI chats anywhere.  
-  </Card>  
+  <Card title="LlamaIndex + Mem0 Learning System" icon="book-open" href="/examples/llama-index-mem0">
+    Multi-agent learning system powered by memory.
+  </Card>
 
-  <Card title="YouTube Assistant" icon="puzzle-piece" href="/examples/youtube-assistant">  
+  <Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">
+    Add **long-term memory** to ChatGPT, Claude, or Perplexity via the **Mem0 Chrome Extension** — personalize your AI chats anywhere.
+  </Card>
+
+  <Card title="YouTube Assistant" icon="puzzle-piece" href="/examples/youtube-assistant">
     Integrate **Mem0** into **YouTube's** native UI, providing personalized responses with video context. 
   </Card> 
 
-  <Card title="Document Writing Assistant" icon="pen" href="/examples/document-writing">  
-    Create a **Writing Assistant** that understands and adapts to your unique style, improving consistency and productivity.  
-  </Card>  
+  <Card title="Document Writing Assistant" icon="pen" href="/examples/document-writing">
+    Create a **Writing Assistant** that understands and adapts to your unique style, improving consistency and productivity.
+  </Card>
 
-  <Card title="Multimodal AI Demo" icon="image" href="/examples/multimodal-demo">  
-    Supercharge AI with **Mem0's multimodal memory** — blend text, images, and more for richer, context-aware interactions.  
-  </Card>  
+  <Card title="Multimodal AI Demo" icon="image" href="/examples/multimodal-demo">
+    Supercharge AI with **Mem0's multimodal memory** — blend text, images, and more for richer, context-aware interactions.
+  </Card>
 
-  <Card title="Personalized Research Agent" icon="magnifying-glass" href="/examples/personalized-deep-research">  
-    Build a **Deep Research AI** that remembers your research goals and compiles insights from vast information sources.  
-  </Card>  
+  <Card title="Personalized Research Agent" icon="magnifying-glass" href="/examples/personalized-deep-research">
+    Build a **Deep Research AI** that remembers your research goals and compiles insights from vast information sources.
+  </Card>
 
-  <Card title="Mem0 as an Agentic Tool" icon="robot" href="/examples/mem0-agentic-tool">  
-    Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.  
-  </Card>  
+  <Card title="Mem0 as an Agentic Tool" icon="robot" href="/examples/mem0-agentic-tool">
+    Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
+  </Card>
 
-  <Card title="OpenAI Inbuilt Tools" icon="robot" href="/examples/openai-inbuilt-tools">  
-    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.  
-  </Card>  
+  <Card title="OpenAI Inbuilt Tools" icon="robot" href="/examples/openai-inbuilt-tools">
+    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.
+  </Card>
 
-  <Card title="Mem0 OpenAI Voice Demo" icon="microphone" href="/examples/mem0-openai-voice-demo">  
-    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.  
+  <Card title="Mem0 OpenAI Voice Demo" icon="microphone" href="/examples/mem0-openai-voice-demo">
+    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.
   </Card>
 
   <Card title="Healthcare Assistant Google ADK" icon="microphone" href="/examples/mem0-google-adk-healthcare-assistant">
     Build a personalized healthcare assistant with persistent memory using Google's ADK and Mem0.
   </Card>
 
-  <Card title="Email Processing" icon="envelope" href="/examples/email_processing">  
-    Use Mem0's memory capabilities to process emails and create AI agents with persistent memory.  
-  </Card> 
+  <Card title="Email Processing" icon="envelope" href="/examples/email_processing">
+    Use Mem0's memory capabilities to process emails and create AI agents with persistent memory.
+  </Card>
 </CardGroup>

docs/examples/ai_companion.mdx

@@ -1,168 +0,0 @@
----
-title: AI Companion
----
-
-<Snippet file="paper-release.mdx" />
-
-You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
-
-## Overview
-
-The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates separate memories for both the user and the companion. By integrating with OpenAI's GPT-4 model, the companion can provide detailed and context-aware responses to user queries.
-
-## Setup
-Before you begin, ensure you have the required dependencies installed. You can install the necessary packages using pip:
-
-```bash
-pip install openai mem0ai
-```
-
-## Full Code Example
-
-Below is the complete code to create and interact with an AI Companion using Mem0:
-
-```python
-from openai import OpenAI
-from mem0 import Memory
-import os
-
-# Set the OpenAI API key
-os.environ['OPENAI_API_KEY'] = 'sk-xxx'
-
-# Initialize the OpenAI client
-client = OpenAI()
-
-class Companion:
-    def __init__(self, user_id, companion_id):
-        """
-        Initialize the Companion with memory configuration, OpenAI client, and user IDs.
-        :param user_id: ID for storing user-related memories
-        :param companion_id: ID for storing companion-related memories
-        """
-        config = {
-            "vector_store": {
-                "provider": "qdrant",
-                "config": {
-                    "host": "localhost",
-                    "port": 6333,
-                }
-            },
-        }
-        self.memory = Memory.from_config(config)
-        self.client = client
-        self.app_id = "app-1"
-        self.USER_ID = user_id
-        self.companion_id = companion_id
-
-    def analyze_question(self, question):
-        """
-        Analyze the question to determine whether it's about the user or the companion.
-        """
-        check_prompt = f"""
-        Analyze the given input and determine whether the user is primarily:
-        1) Talking about themselves or asking for personal advice. They may use words like "I" for this. 
-        2) Inquiring about the AI companions's capabilities or characteristics They may use words like "you" for this. 
-
-        Respond with a single word:
-        - 'user' if the input is focused on the user
-        - 'companion' if the input is focused on the AI companion
-
-        If the input is ambiguous or doesn't clearly fit either category, respond with 'user'.
-
-        Input: {question}
-        """
-        response = self.client.chat.completions.create(
-            model="gpt-4",
-            messages=[{"role": "user", "content": check_prompt}]
-        )
-        return response.choices[0].message.content
-
-    def ask(self, question):
-        """
-        Ask a question to the AI and store the relevant facts in memory
-        :param question: The question to ask the AI.
-        """
-        check_answer = self.analyze_question(question)
-        user_id_to_use = self.USER_ID if check_answer == "user" else self.companion_id
-
-        previous_memories = self.memory.search(question, user_id=user_id_to_use)
-        relevant_memories_text = ""
-        if previous_memories:
-            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
-
-        prompt = f"User input: {question}\nPrevious {check_answer} memories: {relevant_memories_text}"
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are the user's romantic companion. Use the user's input and previous memories to respond. Answer based on the context provided."
-            },
-            {
-                "role": "user",
-                "content": prompt
-            }
-        ]
-
-        stream = self.client.chat.completions.create(
-            model="gpt-4",
-            stream=True,
-            messages=messages
-        )
-
-        answer = ""
-        for chunk in stream:
-            if chunk.choices[0].delta.content is not None:
-                content = chunk.choices[0].delta.content
-                print(content, end="")
-                answer += content
-        # Store the question and answer in memory
-        self.memory.add(question, user_id=self.USER_ID, metadata={"app_id": self.app_id})
-        self.memory.add(answer, user_id=self.companion_id, metadata={"app_id": self.app_id})
-
-    def get_memories(self, user_id=None):
-        """
-        Retrieve all memories associated with the given user ID.
-        :param user_id: Optional user ID to filter memories.
-        :return: List of memories.
-        """
-        return self.memory.get_all(user_id=user_id)
-
-# Example usage:
-user_id = "user"
-companion_id = "companion"
-ai_companion = Companion(user_id, companion_id)
-
-# Ask a question
-ai_companion.ask("Ive been missing you. What have you been up to off late?")
-```
-
-### Fetching Memories
-
-You can fetch all the memories at any point in time using the following code:
-
-```python
-def print_memories(user_id, label):
-    print(f"\n{label} Memories:")
-    memories = ai_companion.get_memories(user_id=user_id)
-    if memories:
-        for m in memories:
-            print(f"- {m['text']}")
-    else:
-        print("No memories found.")
-
-# Print user memories
-print_memories(user_id, "User")
-
-# Print companion memories
-print_memories(companion_id, "Companion")
-```
-
-### Key Points
-
-- **Initialization**: The Companion class is initialized with the necessary memory configuration and OpenAI client setup.
-- **Asking Questions**: The ask method sends a question to the AI and stores the relevant information in memory.
-- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a user.
-
-### Conclusion
-
-As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized experience. This setup ensures that the AI Companion can offer contextually relevant and accurate responses, enhancing the user's experience.

docs/examples/ai_companion_js.mdx

@@ -2,8 +2,6 @@
 title: AI Companion in Node.js
 ---
 
-<Snippet file="paper-release.mdx" />
-
 You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
 ## Overview

docs/examples/aws_example.mdx

@@ -1,17 +1,15 @@
 ---
-title: AWS Bedrock and AOSS
+title: "Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics"
 ---
 
-<Snippet file="paper-release.mdx" />
-
-This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.
+This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock**, **OpenSearch Service (AOSS)**, and **AWS Neptune Analytics** for persistent memory capabilities in Python.
 
 ## Installation
 
-Install the required dependencies:
+Install the required dependencies to include the Amazon data stack, including **boto3**, **opensearch-py**, and **langchain-aws**:
 
 ```bash
-pip install mem0ai boto3 opensearch-py
+pip install "mem0ai[graph,extras]"
 ```
 
 ## Environment Setup
@@ -34,11 +32,15 @@ print(os.environ['AWS_SECRET_ACCESS_KEY'])
 
 ## Configuration and Usage
 
-This sets up Mem0 with AWS Bedrock for embeddings and LLM, and OpenSearch as the vector store.
+This sets up Mem0 with:
+- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
+- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
+- [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)
+- [Neptune Analytics as your graph store](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-neptune-analytics).
 
 ```python
 import boto3
-from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
+from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth
 from mem0.memory.main import Memory
 
 region = 'us-west-2'
@@ -56,7 +58,7 @@ config = {
     "llm": {
         "provider": "aws_bedrock",
         "config": {
-            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
+            "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
             "temperature": 0.1,
             "max_tokens": 2000
         }
@@ -68,21 +70,29 @@ config = {
             "host": "your-opensearch-domain.us-west-2.es.amazonaws.com",
             "port": 443,
             "http_auth": auth,
-            "embedding_model_dims": 1024,
             "connection_class": RequestsHttpConnection,
             "pool_maxsize": 20,
             "use_ssl": True,
-            "verify_certs": True
+            "verify_certs": True,
+            "embedding_model_dims": 1024,
         }
-    }
+    },
+    "graph_store": {
+        "provider": "neptune",
+        "config": {
+            "endpoint": f"neptune-graph://my-graph-identifier",
+        },
+    },
 }
 
-# Initialize memory system
+# Initialize the memory system
 m = Memory.from_config(config)
 ```
 
 ## Usage
 
+Reference [Notebook example](https://github.com/mem0ai/mem0/blob/main/examples/graph-db-demo/neptune-example.ipynb)
+
 #### Add a memory:
 
 ```python
@@ -117,4 +127,4 @@ memory = m.get(memory_id)
 
 ## Conclusion
 
-With Mem0 and AWS services like Bedrock and OpenSearch, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.
+With Mem0 and AWS services like Bedrock, OpenSearch, and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

docs/examples/aws_neptune_analytics_hybrid_store.mdx

@@ -0,0 +1,120 @@
+---
+title: "Amazon Stack - Neptune Analytics Hybrid Store: AWS Bedrock and Neptune Analytics"
+---
+
+This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **AWS Neptune Analytics** for persistent memory capabilities in Python.
+
+## Installation
+
+Install the required dependencies to include the Amazon data stack, including **boto3** and **langchain-aws**:
+
+```bash
+pip install "mem0ai[graph,extras]"
+```
+
+## Environment Setup
+
+Set your AWS environment variables:
+
+```python
+import os
+
+# Set these in your environment or notebook
+os.environ['AWS_REGION'] = 'us-west-2'
+os.environ['AWS_ACCESS_KEY_ID'] = 'AK00000000000000000'
+os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS00000000000000000'
+
+# Confirm they are set
+print(os.environ['AWS_REGION'])
+print(os.environ['AWS_ACCESS_KEY_ID'])
+print(os.environ['AWS_SECRET_ACCESS_KEY'])
+```
+
+## Configuration and Usage
+
+This sets up Mem0 with:
+- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
+- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
+- [Neptune Analytics as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/neptune_analytics)
+- [Neptune Analytics as the graph store](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-neptune-analytics).
+
+```python
+import boto3
+from mem0.memory.main import Memory
+
+region = 'us-west-2'
+neptune_analytics_endpoint = 'neptune-graph://my-graph-identifier'
+
+config = {
+    "embedder": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "amazon.titan-embed-text-v2:0"
+        }
+    },
+    "llm": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+            "temperature": 0.1,
+            "max_tokens": 2000
+        }
+    },
+    "vector_store": {
+        "provider": "neptune",
+        "config": {
+            "collection_name": "mem0",
+            "endpoint": neptune_analytics_endpoint,
+        },
+    },
+    "graph_store": {
+        "provider": "neptune",
+        "config": {
+            "endpoint": neptune_analytics_endpoint,
+        },
+    },
+}
+
+# Initialize the memory system
+m = Memory.from_config(config)
+```
+
+## Usage
+
+Reference [Notebook example](https://github.com/mem0ai/mem0/blob/main/examples/graph-db-demo/neptune-example.ipynb)
+
+#### Add a memory:
+
+```python
+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": "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."}
+]
+
+# Store inferred memories (default behavior)
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+```
+
+#### Search a memory:
+```python
+relevant_memories = m.search(query, user_id="alice")
+```
+
+#### Get all memories:
+```python
+all_memories = m.get_all(user_id="alice")
+```
+
+#### Get a specific memory:
+```python
+memory = m.get(memory_id)
+```
+
+
+---
+
+## Conclusion
+
+With Mem0 and AWS services like Bedrock and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

docs/examples/chrome-extension.mdx

@@ -1,7 +1,5 @@
 # Mem0 Chrome Extension
 
-<Snippet file="paper-release.mdx" />
-
 Enhance your AI interactions with **Mem0**, a Chrome extension that introduces a universal memory layer across platforms like `ChatGPT`, `Claude`, and `Perplexity`. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.
 
 <Note>

docs/examples/collaborative-task-agent.mdx

@@ -2,8 +2,6 @@
 title: Multi-User Collaboration with Mem0
 ---
 
-<Snippet file="paper-release.mdx" />
-
 ## Overview
 
 Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.

docs/examples/customer-support-agent.mdx

@@ -2,7 +2,6 @@
 title: Customer Support AI Agent
 ---
 
-<Snippet file="paper-release.mdx" />
 
 You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
@@ -23,6 +22,7 @@ pip install openai mem0ai
 Below is the simplified code to create and interact with a Customer Support AI Agent using Mem0:
 
 ```python
+import os
 from openai import OpenAI
 from mem0 import Memory
 

docs/examples/eliza_os.mdx

@@ -2,8 +2,6 @@
 title: Eliza OS Character
 ---
 
-<Snippet file="paper-release.mdx" />
-
 You can create a personalised Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
 ## Overview

docs/examples/email_processing.mdx

@@ -2,8 +2,6 @@
 title: Email Processing with Mem0
 ---
 
-<Snippet file="paper-release.mdx" />
-
 This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.
 
 ## Overview

docs/examples/llama-index-mem0.mdx

@@ -1,7 +1,6 @@
 ---
 title: LlamaIndex ReAct Agent
 ---
-<Snippet file="paper-release.mdx" />
 
 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
 
@@ -22,7 +21,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-4o")
 ```
 
-Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/quickstart).
+Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/api-keys). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
 
@@ -80,7 +79,7 @@ agent = FunctionCallingAgent.from_tools(
 ```
 
 Start the chat.
-<Note> The agent will use the Mem0 to store the relavant memories from the chat. </Note>
+<Note> The agent will use the Mem0 to store the relevant memories from the chat. </Note>
 
 Input
 ```python
@@ -139,7 +138,7 @@ Added user message to memory: I am feeling hungry, order me something and send m
 === LLM Response ===
 Please let me know your name and the dish you'd like to order, and I'll take care of it for you!
 ```
-<Note> The agent is not able to remember the past prefernces that user shared in previous chats. </Note>
+<Note> The agent is not able to remember the past preferences that user shared in previous chats. </Note>
 
 ### Using the agent WITH memory
 Input
@@ -171,4 +170,4 @@ Emailing... David
 === LLM Response ===
 I've ordered a pizza for you, and the bill has been sent to your email. Enjoy your meal! If there's anything else you need, feel free to let me know.
 ```
-<Note> The agent is able to remember the past prefernces that user shared and use them to perform actions. </Note>
+<Note> The agent is able to remember the past preferences that user shared and use them to perform actions. </Note>

docs/examples/llamaindex-multiagent-learning-system.mdx

@@ -0,0 +1,360 @@
+---
+title: LlamaIndex Multi-Agent Learning System
+---
+
+<Snippet file="blank-notif.mdx" />
+
+Build an intelligent multi-agent learning system that uses Mem0 to maintain persistent memory across multiple specialized agents. This example demonstrates how to create a tutoring system where different agents collaborate while sharing a unified memory layer.
+
+## Overview
+
+This example showcases a **Multi-Agent Personal Learning System** that combines:
+- **LlamaIndex AgentWorkflow** for multi-agent orchestration
+- **Mem0** for persistent, shared memory across agents
+- **Multi-agents** that collaborate on teaching tasks
+
+The system consists of two agents:
+- **TutorAgent**: Primary instructor for explanations and concept teaching
+- **PracticeAgent**: Generates exercises and tracks learning progress
+
+Both agents share the same memory context, enabling seamless collaboration and continuous learning from student interactions.
+
+## Key Features
+
+- **Persistent Memory**: Agents remember previous interactions across sessions
+- **Multi-Agent Collaboration**: Agents can hand off tasks to each other
+- **Personalized Learning**: Adapts to individual student needs and learning styles
+- **Progress Tracking**: Monitors learning patterns and skill development
+- **Memory-Driven Teaching**: References past struggles and successes
+
+## Prerequisites
+
+Install the required packages:
+
+```bash
+pip install llama-index-core llama-index-memory-mem0 openai python-dotenv
+```
+
+Set up your environment variables:
+- `MEM0_API_KEY`: Your Mem0 Platform API key
+- `OPENAI_API_KEY`: Your OpenAI API key
+
+You can obtain your Mem0 Platform API key from the [Mem0 Platform](https://app.mem0.ai).
+
+## Complete Implementation
+
+```python
+"""
+Multi-Agent Personal Learning System: Mem0 + LlamaIndex AgentWorkflow Example
+
+INSTALLATIONS:
+!pip install llama-index-core llama-index-memory-mem0 openai
+
+You need MEM0_API_KEY and OPENAI_API_KEY to run the example.
+"""
+
+import asyncio
+from datetime import datetime
+from dotenv import load_dotenv
+
+# LlamaIndex imports
+from llama_index.core.agent.workflow import AgentWorkflow, FunctionAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+
+# Memory integration
+from llama_index.memory.mem0 import Mem0Memory
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+load_dotenv()
+
+
+class MultiAgentLearningSystem:
+    """
+    Multi-Agent Architecture:
+    - TutorAgent: Main teaching and explanations
+    - PracticeAgent: Exercises and skill reinforcement
+    - Shared Memory: Both agents learn from student interactions
+    """
+
+    def __init__(self, student_id: str):
+        self.student_id = student_id
+        self.llm = OpenAI(model="gpt-4o", temperature=0.2)
+
+        # Memory context for this student
+        self.memory_context = {"user_id": student_id, "app": "learning_assistant"}
+        self.memory = Mem0Memory.from_client(
+            context=self.memory_context
+        )
+
+        self._setup_agents()
+
+    def _setup_agents(self):
+        """Setup two agents that work together and share memory"""
+
+        # TOOLS
+        async def assess_understanding(topic: str, student_response: str) -> str:
+            """Assess student's understanding of a topic and save insights"""
+            # Simulate assessment logic
+            if "confused" in student_response.lower() or "don't understand" in student_response.lower():
+                assessment = f"STRUGGLING with {topic}: {student_response}"
+                insight = f"Student needs more help with {topic}. Prefers step-by-step explanations."
+            elif "makes sense" in student_response.lower() or "got it" in student_response.lower():
+                assessment = f"UNDERSTANDS {topic}: {student_response}"
+                insight = f"Student grasped {topic} quickly. Can move to advanced concepts."
+            else:
+                assessment = f"PARTIAL understanding of {topic}: {student_response}"
+                insight = f"Student has basic understanding of {topic}. Needs reinforcement."
+
+            return f"Assessment: {assessment}\nInsight saved: {insight}"
+
+        async def track_progress(topic: str, success_rate: str) -> str:
+            """Track learning progress and identify patterns"""
+            progress_note = f"Progress on {topic}: {success_rate} - {datetime.now().strftime('%Y-%m-%d')}"
+            return f"Progress tracked: {progress_note}"
+
+        # Convert to FunctionTools
+        tools = [
+            FunctionTool.from_defaults(async_fn=assess_understanding),
+            FunctionTool.from_defaults(async_fn=track_progress)
+        ]
+
+        # AGENTS
+        # Tutor Agent - Main teaching and explanation
+        self.tutor_agent = FunctionAgent(
+            name="TutorAgent",
+            description="Primary instructor that explains concepts and adapts to student needs",
+            system_prompt="""
+            You are a patient, adaptive programming tutor. Your key strength is REMEMBERING and BUILDING on previous interactions.
+
+            Key Behaviors:
+            1. Always check what the student has learned before (use memory context)
+            2. Adapt explanations based on their preferred learning style
+            3. Reference previous struggles or successes
+            4. Build progressively on past lessons
+            5. Use assess_understanding to evaluate responses and save insights
+
+            MEMORY-DRIVEN TEACHING:
+            - "Last time you struggled with X, so let's approach Y differently..."
+            - "Since you prefer visual examples, here's a diagram..."
+            - "Building on the functions we covered yesterday..."
+
+            When student shows understanding, hand off to PracticeAgent for exercises.
+            """,
+            tools=tools,
+            llm=self.llm,
+            can_handoff_to=["PracticeAgent"]
+        )
+
+        # Practice Agent - Exercises and reinforcement
+        self.practice_agent = FunctionAgent(
+            name="PracticeAgent",
+            description="Creates practice exercises and tracks progress based on student's learning history",
+            system_prompt="""
+            You create personalized practice exercises based on the student's learning history and current level.
+
+            Key Behaviors:
+            1. Generate problems that match their skill level (from memory)
+            2. Focus on areas they've struggled with previously
+            3. Gradually increase difficulty based on their progress
+            4. Use track_progress to record their performance
+            5. Provide encouraging feedback that references their growth
+
+            MEMORY-DRIVEN PRACTICE:
+            - "Let's practice loops again since you wanted more examples..."
+            - "Here's a harder version of the problem you solved yesterday..."
+            - "You've improved a lot in functions, ready for the next level?"
+
+            After practice, can hand back to TutorAgent for concept review if needed.
+            """,
+            tools=tools,
+            llm=self.llm,
+            can_handoff_to=["TutorAgent"]
+        )
+
+        # Create the multi-agent workflow
+        self.workflow = AgentWorkflow(
+            agents=[self.tutor_agent, self.practice_agent],
+            root_agent=self.tutor_agent.name,
+            initial_state={
+                "current_topic": "",
+                "student_level": "beginner",
+                "learning_style": "unknown",
+                "session_goals": []
+            }
+        )
+
+    async def start_learning_session(self, topic: str, student_message: str = "") -> str:
+        """
+        Start a learning session with multi-agent memory-aware teaching
+        """
+
+        if student_message:
+            request = f"I want to learn about {topic}. {student_message}"
+        else:
+            request = f"I want to learn about {topic}."
+
+        # The magic happens here - multi-agent memory is automatically shared!
+        response = await self.workflow.run(
+            user_msg=request,
+            memory=self.memory
+        )
+
+        return str(response)
+
+    async def get_learning_history(self) -> str:
+        """Show what the system remembers about this student"""
+        try:
+            # Search memory for learning patterns
+            memories = self.memory.search(
+                user_id=self.student_id,
+                query="learning machine learning"
+            )
+
+            if memories and memories.get('results'):
+                history = "\n".join(f"- {m['memory']}" for m in memories['results'])
+                return history
+            else:
+                return "No learning history found yet. Let's start building your profile!"
+
+        except Exception as e:
+            return f"Memory retrieval error: {str(e)}"
+
+
+async def run_learning_agent():
+
+    learning_system = MultiAgentLearningSystem(student_id="Alexander")
+
+    # First session
+    print("Session 1:")
+    response = await learning_system.start_learning_session(
+        "Vision Language Models",
+        "I'm new to machine learning but I have good hold on Python and have 4 years of work experience.")
+    print(response)
+
+    # Second session - multi-agent memory will remember the first
+    print("\nSession 2:")
+    response2 = await learning_system.start_learning_session(
+        "Machine Learning", "what all did I cover so far?")
+    print(response2)
+
+    # Show what the multi-agent system remembers
+    print("\nLearning History:")
+    history = await learning_system.get_learning_history()
+    print(history)
+
+
+if __name__ == "__main__":
+    """Run the example"""
+    print("Multi-agent Learning System powered by LlamaIndex and Mem0")
+
+    async def main():
+        await run_learning_agent()
+
+    asyncio.run(main())
+```
+
+## How It Works
+
+### 1. Memory Context Setup
+
+```python
+# Memory context for this student
+self.memory_context = {"user_id": student_id, "app": "learning_assistant"}
+self.memory = Mem0Memory.from_client(context=self.memory_context)
+```
+
+The memory context identifies the specific student and application, ensuring memory isolation and proper retrieval.
+
+### 2. Agent Collaboration
+
+```python
+# Agents can hand off to each other
+can_handoff_to=["PracticeAgent"]  # TutorAgent can hand off to PracticeAgent
+can_handoff_to=["TutorAgent"]     # PracticeAgent can hand off back
+```
+
+Agents collaborate seamlessly, with the TutorAgent handling explanations and the PracticeAgent managing exercises.
+
+### 3. Shared Memory
+
+```python
+# Both agents share the same memory instance
+response = await self.workflow.run(
+    user_msg=request,
+    memory=self.memory  # Shared across all agents
+)
+```
+
+All agents in the workflow share the same memory context, enabling true collaborative learning.
+
+### 4. Memory-Driven Interactions
+
+The system prompts guide agents to:
+- Reference previous learning sessions
+- Adapt to discovered learning styles
+- Build progressively on past lessons
+- Track and respond to learning patterns
+
+## Running the Example
+
+```python
+# Initialize the learning system
+learning_system = MultiAgentLearningSystem(student_id="Alexander")
+
+# Start a learning session
+response = await learning_system.start_learning_session(
+    "Vision Language Models",
+    "I'm new to machine learning but I have good hold on Python and have 4 years of work experience."
+)
+
+# Continue learning in a new session (memory persists)
+response2 = await learning_system.start_learning_session(
+    "Machine Learning",
+    "what all did I cover so far?"
+)
+
+# Check learning history
+history = await learning_system.get_learning_history()
+```
+
+## Expected Output
+
+The system will demonstrate memory-aware interactions:
+
+```
+Session 1:
+I understand you want to learn about Vision Language Models and you mentioned you're new to machine learning but have a strong Python background with 4 years of experience. That's a great foundation to build on!
+
+Let me start with an explanation tailored to your programming background...
+[Agent provides explanation and may hand off to PracticeAgent for exercises]
+
+Session 2:
+Based on our previous session, I remember we covered Vision Language Models and I noted that you have a strong Python background with 4 years of experience. You mentioned being new to machine learning, so we started with foundational concepts...
+[Agent references previous session and builds upon it]
+```
+
+## Key Benefits
+
+1. **Persistent Learning**: Agents remember across sessions, creating continuity
+2. **Collaborative Teaching**: Multiple specialized agents work together seamlessly
+3. **Personalized Adaptation**: System learns and adapts to individual learning styles
+4. **Scalable Architecture**: Easy to add more specialized agents
+5. **Memory Efficiency**: Shared memory prevents duplication and ensures consistency
+
+
+## Best Practices
+
+1. **Clear Agent Roles**: Define specific responsibilities for each agent
+2. **Memory Context**: Use descriptive context for memory isolation
+3. **Handoff Strategy**: Design clear handoff criteria between agents
+5. **Memory Hygiene**: Regularly review and clean memory for optimal performance
+
+## Help & Resources
+
+- [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/examples/mem0-agentic-tool.mdx

@@ -2,7 +2,6 @@
 title: Mem0 as an Agentic Tool
 ---
 
-<Snippet file="paper-release.mdx" />
 
 Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
 You can create agents that remember past conversations and use that context to provide better responses.

docs/examples/mem0-demo.mdx

@@ -2,9 +2,6 @@
 title: Mem0 Demo
 ---
 
-
-<Snippet file="paper-release.mdx" />
-
 You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.
 
 <video

docs/examples/mem0-google-adk-healthcare-assistant.mdx

@@ -3,7 +3,6 @@ title: 'Healthcare Assistant with Mem0 and Google ADK'
 description: 'Build a personalized healthcare agent that remembers patient information across conversations using Mem0 and Google ADK'
 ---
 
-<Snippet file="paper-release.mdx" />
 
 # Healthcare Assistant with Memory
 
@@ -25,8 +24,7 @@ Before you begin, make sure you have:
 
 Installed Google ADK and Mem0 SDK:
 ```bash
-pip install google-adk
-pip install mem0ai
+pip install google-adk mem0ai python-dotenv
 ```
 
 ## Code Breakdown
@@ -36,21 +34,25 @@ Let's get started and understand the different components required in building a
 ```python
 # Import dependencies
 import os
+import asyncio
 from google.adk.agents import Agent
-from google.adk.sessions import InMemorySessionService
 from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
 from google.genai import types
 from mem0 import MemoryClient
+from dotenv import load_dotenv
 
-# Set up API keys (replace with your actual keys)
-os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+load_dotenv()
+
+# Set up environment variables
+# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Define a global user ID for simplicity
 USER_ID = "Alex"
 
 # Initialize Mem0 client
-mem0_client = MemoryClient()
+mem0 = MemoryClient()
 ```
 
 ## Define Memory Tools

docs/examples/mem0-mastra.mdx

@@ -2,8 +2,6 @@
 title: Mem0 with Mastra
 ---
 
-<Snippet file="paper-release.mdx" />
-
 In this example you'll learn how to use the Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use.
 This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).
 

docs/examples/mem0-openai-voice-demo.mdx

@@ -3,8 +3,6 @@ title: 'Mem0 with OpenAI Agents SDK for Voice'
 description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
 ---
 
-<Snippet file="paper-release.mdx" />
-
 # Building Voice Agents with Memory using Mem0 and OpenAI Agents SDK
 
 This guide demonstrates how to combine OpenAI's Agents SDK for voice applications with Mem0's memory capabilities to create a voice assistant that remembers user preferences and past interactions.

docs/examples/mem0-with-ollama.mdx

@@ -2,8 +2,6 @@
 title: Mem0 with Ollama
 ---
 
-<Snippet file="paper-release.mdx" />
-
 ## Running Mem0 Locally with Ollama
 
 Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.
@@ -21,7 +19,6 @@ Before you begin, ensure you have Mem0 and Ollama installed and properly configu
 Below is the complete code to set up and use Mem0 locally with Ollama:
 
 ```python
-import os
 from mem0 import Memory
 
 config = {

docs/examples/memory-guided-content-writing.mdx

@@ -1,103 +1,132 @@
 ---
-title: Document Editing with Mem0
+title: Memory-Guided Content Writing
 ---
-<Snippet file="paper-release.mdx" />
 
-This guide demonstrates how to leverage **Mem0** to edit documents efficiently, ensuring they align with your unique writing style and preferences.
+This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
 
-## **Why Use Mem0?**
+## Why Use Mem0?
 
-By integrating Mem0 into your workflow, you can streamline your document editing process with:
+Integrating Mem0 into your writing workflow helps you:
 
-1. **Persistent Writing Preferences**: Mem0 stores and recalls your style preferences, ensuring consistency across all documents.
-2. **Automated Enhancements**: Your stored preferences guide document refinements, making edits seamless and efficient.
-3. **Scalability & Reusability**: Your writing style can be applied to multiple documents, saving time and effort.
+1. **Store persistent writing preferences** ensuring consistent tone, formatting, and structure.
+2. **Automate content refinement** by retrieving preferences when rewriting or reviewing content.
+3. **Scale your writing style** so it applies consistently across multiple documents or sessions.
 
----
-## **Setup**
+## Setup
 
 ```python
 import os
+from openai import OpenAI
 from mem0 import MemoryClient
 
-# Set up Mem0 client
 os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+
+# Set up Mem0 and OpenAI client
 client = MemoryClient()
+openai = OpenAI()
 
-# Define constants
 USER_ID = "content_writer"
 RUN_ID = "smart_editing_session"
 ```
 
----
 ## **Storing Your Writing Preferences in Mem0**
 
 ```python
 def store_writing_preferences():
     """Store your writing preferences in Mem0."""
     
-    # Define writing preferences
     preferences = """My writing preferences:
 1. Use headings and sub-headings for structure.
-2. Keep paragraphs concise (8-10 sentences max).
+2. Keep paragraphs concise (8–10 sentences max).
 3. Incorporate specific numbers and statistics.
 4. Provide concrete examples.
 5. Use bullet points for clarity.
 6. Avoid jargon and buzzwords."""
 
-    # Store preferences in Mem0
-    preference_message = [
-        {"role": "user", "content": "Here are my writing style preferences"},
+    messages = [
+        {"role": "user", "content": "Here are my writing style preferences."},
         {"role": "assistant", "content": preferences}
     ]
-    
-    response = client.add(preference_message, user_id=USER_ID, run_id=RUN_ID, metadata={"type": "preferences", "category": "writing_style"})
-    
-    print("Writing preferences stored successfully.")
+
+    response = client.add(
+        messages,
+        user_id=USER_ID,
+        run_id=RUN_ID,
+        metadata={"type": "preferences", "category": "writing_style"}
+    )
+
     return response
 ```
 
----
-## **Editing Documents with Mem0**
+## **Editing Content Using Stored Preferences**
 
 ```python
-def edit_document_based_on_preferences(original_content):
-    """Edit a document using Mem0-based stored preferences."""
-    
-    # Retrieve stored preferences
-    query = "What are my writing style preferences?"
-    preferences_results = client.search(query, user_id=USER_ID, run_id=RUN_ID)
-    
-    if not preferences_results:
-        print("No writing preferences found.")
+def apply_writing_style(original_content):
+    """Use preferences stored in Mem0 to guide content rewriting."""
+
+    results = client.search(
+        query="What are my writing style preferences?",
+        version="v2",
+        filters={
+            "AND": [
+                {
+                    "user_id": USER_ID
+                },
+                {
+                    "run_id": RUN_ID
+                }
+            ]
+        },
+    )
+
+    if not results:
+        print("No preferences found.")
         return None
-    
-    # Extract preferences
-    preferences = ' '.join(memory["memory"] for memory in preferences_results)
-    
-    # Apply stored preferences to refine the document
-    edited_content = f"Applying stored preferences:\n{preferences}\n\nEdited Document:\n{original_content}"
-    
-    return edited_content
+
+    preferences = "\n".join(r["memory"] for r in results.get('results', []))
+
+    system_prompt = f"""
+You are a writing assistant.
+
+Apply the following writing style preferences to improve the user's content:
+
+Preferences:
+{preferences}
+"""
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"""Original Content:
+    {original_content}"""}
+    ]
+
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages
+    )
+    clean_response = response.choices[0].message.content.strip()
+
+    return clean_response
 ```
 
----
-## **Complete Workflow: Document Editing**
+## **Complete Workflow: Content Editing**
 
 ```python
-def document_editing_workflow(content):
+def content_writing_workflow(content):
     """Automated workflow for editing a document based on writing preferences."""
     
-    # Step 1: Store writing preferences (if not already stored)
-    store_writing_preferences()
+    # Store writing preferences (if not already stored)
+    store_writing_preferences()  # Ideally done once, or with a conditional check
     
-    # Step 2: Edit the document with Mem0 preferences
-    edited_content = edit_document_based_on_preferences(content)
+    # Edit the document with Mem0 preferences
+    edited_content = apply_writing_style(content)
     
     if not edited_content:
         return "Failed to edit document."
     
-    # Step 3: Display results
+    # Display results
     print("\n=== ORIGINAL DOCUMENT ===\n")
     print(content)
     
@@ -107,7 +136,6 @@ def document_editing_workflow(content):
     return edited_content
 ```
 
----
 ## **Example Usage**
 
 ```python
@@ -125,10 +153,9 @@ We plan to launch the campaign in July and continue through September.
 """
 
 # Run the workflow
-result = document_editing_workflow(original_content)
+result = content_writing_workflow(original_content)
 ```
 
----
 ## **Expected Output**
 
 Your document will be transformed into a structured, well-formatted version based on your preferences.
@@ -182,4 +209,10 @@ This proposal outlines our strategy for the Q3 marketing campaign. We aim to sig
 We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let’s work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
 ```
 
-Mem0 creates a seamless, intelligent document editing experience—perfect for content creators, technical writers, and businesses alike!
+Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.
+
+## Help & Resources
+
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/examples/multimodal-demo.mdx

@@ -2,32 +2,30 @@
 title: Multimodal Demo with Mem0
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Enhance your AI interactions with **Mem0**'s multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.
 
-> 🎉 Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
+> Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
 
-## 🚀 Features
+## Features
 
-- **🖼️ Image Understanding**: Share and discuss images with AI assistants while maintaining context.
-- **🔍 Smart Visual Context**: Automatically capture and reference visual elements in conversations.
-- **🔗 Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
-- **📌 Cross-Session Recall**: Reference previously discussed visual content across different conversations.
-- **⚡ Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
+- **Image Understanding**: Share and discuss images with AI assistants while maintaining context.
+- **Smart Visual Context**: Automatically capture and reference visual elements in conversations.
+- **Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
+- **Cross-Session Recall**: Reference previously discussed visual content across different conversations.
+- **Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
 
-## 📖 How It Works
+## How It Works
 
-1. **📂 Upload Visual Content**: Simply drag and drop or paste images into your conversations.
-2. **💬 Natural Interaction**: Discuss the visual content naturally with AI assistants.
-3. **📚 Memory Integration**: Visual context is automatically stored and linked with your conversation history.
-4. **🔄 Persistent Recall**: Retrieve and reference past visual content effortlessly.
+1. **Upload Visual Content**: Simply drag and drop or paste images into your conversations.
+2. **Natural Interaction**: Discuss the visual content naturally with AI assistants.
+3. **Memory Integration**: Visual context is automatically stored and linked with your conversation history.
+4. **Persistent Recall**: Retrieve and reference past visual content effortlessly.
 
 ## Demo Video
 
 <iframe width="700" height="400" src="https://www.youtube.com/embed/2Md5AEFVpmg?si=rXXupn6CiDUPJsi3" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
 
-## 🔥 Try It Out
+## Try It Out
 
 Visit [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai) to experience Mem0's multimodal capabilities firsthand. Upload images and see how Mem0 understands and remembers visual context across your conversations.
 

docs/examples/openai-inbuilt-tools.mdx

@@ -2,8 +2,6 @@
 title: OpenAI Inbuilt Tools
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
 
 ## Getting Started
@@ -225,7 +223,7 @@ async function addSampleMemories() {
 
 const getMemoryString = (memories) => {
     const MEMORY_STRING_PREFIX = "These are the memories I have stored. Give more weightage to the question by users and try to answer that first. You have to modify your answer based on the memories I have provided. If the memories are irrelevant you can ignore them. Also don't reply to this section of the prompt, or the memories, they are only for your reference. The MEMORIES of the USER are: \n\n";
-    const memoryString = memories.map((mem) => `${mem.memory}`).join("\n") ?? "";
+    const memoryString = (memories?.results || memories).map((mem) => `${mem.memory}`).join("\n") ?? "";
     return memoryString.length > 0 ? `${MEMORY_STRING_PREFIX}${memoryString}` : "";
 };
 

docs/examples/personal-ai-tutor.mdx

@@ -2,8 +2,6 @@
 title: Personalized AI Tutor
 ---
 
-<Snippet file="paper-release.mdx" />
-
 You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
 ## Overview

docs/examples/personal-travel-assistant.mdx

@@ -2,7 +2,6 @@
 title: Personal AI Travel Assistant
 ---
 
-<Snippet file="paper-release.mdx" />
 
 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
 
@@ -155,11 +154,11 @@ class PersonalTravelAssistant:
 
     def get_memories(self, user_id):
         memories = self.memory.get_all(user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
     def search_memories(self, query, user_id):
         memories = self.memory.search(query, user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
 # Usage example
 user_id = "traveler_123"

docs/examples/personalized-deep-research.mdx

@@ -2,10 +2,10 @@
 title: Personalized Deep Research
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.
 
+You can checkout GitHub repositry here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
+
 ## Overview
 
 Deep Research leverages Mem0's memory capabilities to:
@@ -30,13 +30,6 @@ Watch Deep Research in action:
   allowfullscreen
 ></iframe>
 
-## Getting Started
-
-1. Visit [deep-research.mem0.ai](https://deep-research.mem0.ai/)
-2. Upload your resume (PDF or text) or manually enter information about yourself
-3. Enter your research topic
-4. Click "Start Research" to begin
-
 ## Features
 
 ### 1. Personalized Research
@@ -69,4 +62,6 @@ Watch Deep Research in action:
 
 ## Try It Out
 
-Experience AI-powered research personalization at [deep-research.mem0.ai](https://deep-research.mem0.ai/)
+> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
+
+- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)

docs/examples/personalized-search-tavily-mem0.mdx

@@ -0,0 +1,190 @@
+---
+title: 'Personalized Search with Mem0 and Tavily'
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great wifi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a **7-year-old daughter** and recommends **peanut-free options** that align with her allergy.
+
+That's what we are going to build today, a **Personalized Search Assistant** powered by **Mem0** for memory and [Tavily](https://tavily.com) for real-time search.
+
+
+## Why Personalized Search
+
+Most assistants treat every query like they’ve never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
+
+- With **Mem0**, your assistant builds a memory of the user’s world.
+- With **Tavily**, it fetches fresh and accurate results in real time.
+
+Together, they make every interaction **smarter, faster, and more personal**.
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+1. Installed the dependencies:
+```bash
+pip install langchain mem0ai langchain-tavily langchain-openai
+```
+
+2. Set up your API keys in a .env file:
+```bash
+OPENAI_API_KEY=your-openai-key
+TAVILY_API_KEY=your-tavily-key
+MEM0_API_KEY=your-mem0-key
+```
+
+## Code Walkthrough
+Let’s break down the main components.
+
+### 1: Initialize Mem0 with Custom Instructions
+
+We configure Mem0 with custom instructions that guide it to infer user memories tailored specifically for our usecase.
+
+```python
+from mem0 import MemoryClient
+
+mem0_client = MemoryClient()
+
+mem0_client.project.update(
+    custom_instructions='''
+INFER THE MEMORIES FROM USER QUERIES EVEN IF IT'S A QUESTION.
+
+We are building personalized search for which we need to understand about user's preferences and life
+and extract facts and memories accordingly.
+'''
+)
+```
+Now, if a user casually mentions "I need to pick up my daughter", or "What's the weather at Los Angeles", Mem0 remembers they have a daughter or user is somewhat interested/connected with Los Angeles in terms of location, those will be referred for future searches.
+
+### 2. Simulating User History
+To test personalization, we preload some sample conversation history for a user:
+
+```python
+def setup_user_history(user_id):
+    conversations = [
+        [{"role": "user", "content": "What will be the weather today at Los Angeles? I need to pick up my daughter from office."},
+         {"role": "assistant", "content": "I'll check the weather in LA for you."}],
+        [{"role": "user", "content": "I'm looking for vegan restaurants in Santa Monica"},
+         {"role": "assistant", "content": "I'll find great vegan options in Santa Monica."}],
+        [{"role": "user", "content": "My 7-year-old daughter is allergic to peanuts"},
+         {"role": "assistant", "content": "I'll remember to check for peanut-free options."}],
+        [{"role": "user", "content": "I work remotely and need coffee shops with good wifi"},
+         {"role": "assistant", "content": "I'll find remote-work-friendly coffee shops."}],
+        [{"role": "user", "content": "We love hiking and outdoor activities on weekends"},
+         {"role": "assistant", "content": "Great! I'll keep your outdoor activity preferences in mind."}],
+    ]
+
+    for conversation in conversations:
+        mem0_client.add(conversation, user_id=user_id, output_format="v1.1")
+```
+This gives the agent a baseline understanding of the user’s lifestyle and needs.
+
+### 3. Retrieving User Context from Memory
+When a user makes a new search query, we retrieve relevant memories to enhance the search query:
+
+```python
+def get_user_context(user_id, query):
+    filters = {"AND": [{"user_id": user_id}]}
+    user_memories = mem0_client.search(query=query, version="v2", filters=filters)
+
+    if user_memories:
+        context = "\n".join([f"- {memory['memory']}" for memory in user_memories])
+        return context
+    else:
+        return "No previous user context available."
+```
+This context is injected into the search agent so results are personalized.
+
+### 4. Creating the Personalized Search Agent
+The agent uses Tavily search, but always augments search queries with user context:
+
+```python
+def create_personalized_search_agent(user_context):
+    tavily_search = TavilySearch(
+        max_results=10,
+        search_depth="advanced",
+        include_answer=True,
+        topic="general"
+    )
+
+    tools = [tavily_search]
+
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", f"""You are a personalized search assistant.
+
+USER CONTEXT AND PREFERENCES:
+{user_context}
+
+YOUR ROLE:
+1. Analyze the user's query and context.
+2. Enhance the query with relevant personal memories.
+3. Always use tavily_search for results.
+4. Explain which memories influenced personalization.
+"""),
+        MessagesPlaceholder(variable_name="messages"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ])
+
+    agent = create_openai_tools_agent(llm=llm, tools=tools, prompt=prompt)
+    return AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
+```
+
+### 5. Run a Personalized Search
+The workflow ties everything together:
+
+```python
+def conduct_personalized_search(user_id, query):
+    user_context = get_user_context(user_id, query)
+    agent_executor = create_personalized_search_agent(user_context)
+
+    response = agent_executor.invoke({"messages": [HumanMessage(content=query)]})
+    return {"agent_response": response['output']}
+```
+
+### 6. Store New Interactions
+Every new query/response pair is stored for future personalization:
+
+```python
+def store_search_interaction(user_id, original_query, agent_response):
+    interaction = [
+        {"role": "user", "content": f"Searched for: {original_query}"},
+        {"role": "assistant", "content": f"Results based on preferences: {agent_response}"}
+    ]
+    mem0_client.add(messages=interaction, user_id=user_id, output_format="v1.1")
+```
+
+### Full Example Run
+
+```python
+if __name__ == "__main__":
+    user_id = "john"
+    setup_user_history(user_id)
+
+    queries = [
+        "good coffee shops nearby for working",
+        "what can I make for my kid in lunch?"
+    ]
+
+    for q in queries:
+        results = conduct_personalized_search(user_id, q)
+        print(f"\nQuery: {q}")
+        print(f"Personalized Response: {results['agent_response']}")
+```
+
+## How It Works in Practice
+Here’s how personalization plays out:
+
+- Context Gathering: User previously mentioned living in Los Angeles, being vegan, and having a 7-year-old daughter allergic to peanuts.
+- Enhanced Search Query:
+Query -> "good coffee shops nearby for working"
+Enhanced Query -> "good coffee shops in Los Angeles with strong wifi, remote-work-friendly"
+- Personalized Results: The assistant only returns wifi-friendly, work-friendly cafes near Los Angeles.
+- Memory Update: Interaction is saved for better future recommendations.
+
+## Conclusion
+With Mem0 + Tavily, you can build a search assistant that doesn’t just fetch results but it understands the person behind the query.
+
+Whether for shopping, travel, or daily life, this approach turns a generic search into a truly personalized experience.
+
+Full Code: [Personalized Search GitHub](https://github.com/mem0ai/mem0/blob/main/examples/misc/personalized_search.py)

docs/examples/youtube-assistant.mdx

@@ -2,8 +2,6 @@
 title: YouTube Assistant Extension
 ---
 
-<Snippet file="paper-release.mdx" />
-
 Enhance your YouTube experience with Mem0's **YouTube Assistant**, a Chrome extension that brings AI-powered chat directly to your YouTube videos. Get instant, personalized answers about video content while leveraging your own knowledge and memories - all without leaving the page.
 
 ## Features

docs/faqs.mdx

@@ -4,8 +4,6 @@ icon: "question"
 iconType: "solid"
 ---
 
-<Snippet file="paper-release.mdx" />
-
 <AccordionGroup>
     <Accordion title="How does Mem0 work?">
         Mem0 utilizes a sophisticated hybrid database system to efficiently manage and retrieve memories for AI agents and assistants. Each memory is linked to a unique identifier, such as a user ID or agent ID, enabling Mem0 to organize and access memories tailored to specific individuals or contexts.
@@ -14,7 +12,7 @@ iconType: "solid"
 
         When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
 
-        The retrieved memories can be seamlessly integrated into the LLM's prompt as required, enhancing the personalization and relevance of responses.
+        The retrieved memories can be seamlessly integrated into the system prompt as required, enhancing the personalization and relevance of responses.
   </Accordion>
 
     <Accordion title="What are the key features of Mem0?">
@@ -23,7 +21,7 @@ iconType: "solid"
         - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
         - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
         - **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
-        - **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
+        - **Save Costs**: Saves costs by adding relevant memories instead of complete transcripts to context window
     </Accordion>
 
     <Accordion title="How Mem0 is different from traditional RAG?">

docs/features.mdx

@@ -1,23 +0,0 @@
----
-title: Features
-icon: "wrench"
-iconType: "solid"
----
-
-<Snippet file="paper-release.mdx" />
-
-## Core features
-
-- **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
-- **Adaptive Personalization**: Continuously updates memories based on user interactions and feedback.
-- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
-- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
-- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
-- **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
-
-
-
-
-If you have any questions, please feel free to reach out to us using one of the following methods:
-
-<Snippet file="get-help.mdx" />