Merge branch 'main' into mem0-1.0.0

Release beta version
Feat: Mem0 vector store backend integration for Neptune Analytics (#3453 )
2025-09-18 20:23:20 +05:30 · 2025-09-18 20:15:10 +05:30 · 2025-09-17 19:26:03 +05:30 · 2025-09-16 10:48:35 +05:30 · 2025-09-16 04:14:38 +05:30 · 2025-09-16 00:13:42 +05:30
276 changed files with 17185 additions and 4686 deletions
@@ -86,7 +86,6 @@ const memory = new Memory({
 const result = await memory.add('My name is John', { userId: 'john' });
 ```

-
 ## Core API Reference

 ### Memory Class (Self-Hosted)
@@ -305,7 +304,7 @@ config = MemoryConfig(
 - **langchain** - LangChain embeddings
 - **aws_bedrock** - AWS Bedrock embeddings

-#### Vector Store Providers (17 supported)
+#### Vector Store Providers (19 supported)
 - **qdrant** - Qdrant vector database (default)
 - **chroma** - ChromaDB
 - **pinecone** - Pinecone vector database
@@ -323,11 +322,14 @@ config = MemoryConfig(
 - **supabase** - Supabase vector
 - **baidu** - Baidu vector database
 - **langchain** - LangChain vector stores
+- **s3_vectors** - Amazon S3 Vectors
+- **databricks** - Databricks vector stores

-#### Graph Store Providers (3 supported)
+#### Graph Store Providers (4 supported)
 - **neo4j** - Neo4j graph database
 - **memgraph** - Memgraph
 - **neptune** - AWS Neptune Analytics
+- **kuzu** - Kuzu Graph database

 ### Configuration Examples

@@ -415,6 +417,54 @@ config = MemoryConfig(
 )
 ```

+#### LLM Providers
+- **OpenAI** - GPT-4, GPT-3.5-turbo, and structured outputs
+- **Anthropic** - Claude models with advanced reasoning
+- **Google AI** - Gemini models for multimodal applications
+- **AWS Bedrock** - Enterprise-grade AWS managed models
+- **Azure OpenAI** - Microsoft Azure hosted OpenAI models
+- **Groq** - High-performance LPU optimized models
+- **Together** - Open-source model inference platform
+- **Ollama** - Local model deployment for privacy
+- **vLLM** - High-performance inference framework
+- **LM Studio** - Local model management
+- **DeepSeek** - Advanced reasoning models
+- **Sarvam** - Indian language models
+- **XAI** - xAI models
+- **LiteLLM** - Unified LLM interface
+- **LangChain** - LangChain LLM integration
+
+#### Vector Store Providers
+- **Chroma** - AI-native open-source vector database
+- **Qdrant** - High-performance vector similarity search
+- **Pinecone** - Managed vector database with serverless options
+- **Weaviate** - Open-source vector search engine
+- **PGVector** - PostgreSQL extension for vector search
+- **Milvus** - Open-source vector database for scale
+- **Redis** - Real-time vector storage with Redis Stack
+- **Supabase** - Open-source Firebase alternative
+- **Upstash Vector** - Serverless vector database
+- **Elasticsearch** - Distributed search and analytics
+- **OpenSearch** - Open-source search and analytics
+- **FAISS** - Facebook AI Similarity Search
+- **MongoDB** - Document database with vector search
+- **Azure AI Search** - Microsoft's search service
+- **Vertex AI Vector Search** - Google Cloud vector search
+- **Databricks Vector Search** - Delta Lake integration
+- **Baidu** - Baidu vector database
+- **LangChain** - LangChain vector store integration
+
+#### Embedding Providers
+- **OpenAI** - High-quality text embeddings
+- **Azure OpenAI** - Enterprise Azure-hosted embeddings
+- **Google AI** - Gemini embedding models
+- **AWS Bedrock** - Amazon embedding models
+- **Hugging Face** - Open-source embedding models
+- **Vertex AI** - Google Cloud enterprise embeddings
+- **Ollama** - Local embedding models
+- **Together** - Open-source model embeddings
+- **LM Studio** - Local model embeddings
+- **LangChain** - LangChain embedder integration

 ## TypeScript/JavaScript SDK

@@ -569,6 +619,7 @@ print(result["relations"])   # Graph relationships
 - **Neo4j**: Full-featured graph database with Cypher queries
 - **Memgraph**: High-performance in-memory graph database
 - **Neptune**: AWS managed graph database service
+- **kuzu** - OSS Kuzu Graph database

 ### Multimodal Memory

@@ -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.
@@ -13,7 +13,7 @@ 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 langchain-aws rank-bm25 pymochow pymongo psycopg
+							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:
@@ -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
@@ -1,3 +0,0 @@
-<Note type="info">
-  🔐 Mem0 is now <strong>SOC 2</strong> and <strong>HIPAA</strong> compliant! We're committed to the highest standards of data security and privacy, enabling secure memory for enterprises, healthcare, and beyond. [Learn more](https://mem0.ai/security)
-</Note>
@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="async-memory-add.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
@@ -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>
@@ -1,4 +0,0 @@
---
-title: 'Delete Member'
-openapi: delete /api/v1/orgs/organizations/{org_id}/members/
---
@@ -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.
@@ -1,4 +0,0 @@
---
-title: 'Delete Member'
-openapi: delete /api/v1/orgs/organizations/{org_id}/projects/{project_id}/members/
---
@@ -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.
@@ -1,4 +0,0 @@
---
-title: 'Update Project'
-openapi: patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
---
@@ -3,11 +3,102 @@ title: "Product Updates"
 mode: "wide"
 ---

-<Snippet file="blank-notif.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:**
@@ -153,7 +244,7 @@ mode: "wide"
 <Update label="2025-06-11" description="v0.1.107">

 **Improvements:**
- **Documentation:** 
+- **Documentation:**
  - Updated Livekit documentation migration
  - Updated OpenMemory hosted version documentation
 - **Core:** Updated categorization flow
@@ -190,7 +281,7 @@ mode: "wide"
 - **LLM:** Added support for OpenAI compatible LLM providers with baseUrl configuration

 **Improvements:**
- **Documentation:** 
+- **Documentation:**
  - Fixed broken links
  - Improved Graph Memory features documentation clarity
  - Updated enable_graph documentation
@@ -207,14 +298,14 @@ mode: "wide"
 - **OpenMemory:** Added LLM and Embedding Providers support

 **Improvements:**
- **Documentation:** 
+- **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:** 
+- **Core:**
  - Removed support for passing string as input in client.add()
  - Added support for sarvam-m model
 - **TypeScript SDK:** Fixed types from message interface
@@ -231,7 +322,7 @@ mode: "wide"
 - **Neo4j:** Added base label configuration support

 **Improvements:**
- **Documentation:** 
+- **Documentation:**
  - Updated Healthcare example index
  - Enhanced collaborative task agent documentation clarity
  - Added criteria-based filtering documentation
@@ -327,7 +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
@@ -366,12 +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
@@ -386,7 +477,7 @@ mode: "wide"
 - **LLM Integrations:** Added Mistral AI as LLM provider

 **Improvements:**
- **Documentation:** 
+- **Documentation:**
  - Updated changelog
  - Fixed memory exclusion example
  - Updated xAI documentation
@@ -403,7 +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
@@ -520,6 +611,11 @@ 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
@@ -577,7 +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
@@ -992,6 +1087,16 @@ mode: "wide"

 <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.
@@ -1014,7 +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">
@@ -4,7 +4,6 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.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.

@@ -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:
@@ -6,7 +6,8 @@ To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variable

 ### Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

@@ -26,12 +27,37 @@ 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="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:
@@ -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>
@@ -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>
@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />
-
 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.

 ## Supported Embedders
@@ -4,8 +4,6 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />
-
 ## How to define configurations?

 <Tabs>
@@ -2,7 +2,6 @@
 title: Anthropic
 ---

-<Snippet file="blank-notif.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).

@@ -2,8 +2,6 @@
 title: AWS Bedrock
 ---

-<Snippet file="blank-notif.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)
@@ -2,12 +2,12 @@
 title: Azure OpenAI
 ---

-<Snippet file="blank-notif.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).
@@ -2,8 +2,6 @@
 title: DeepSeek
 ---

-<Snippet file="blank-notif.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
@@ -2,8 +2,6 @@
 title: Google AI
 ---

-<Snippet file="blank-notif.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).

 > **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`.
@@ -2,8 +2,6 @@
 title: Groq
 ---

-<Snippet file="blank-notif.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.
@@ -2,7 +2,6 @@
 title: LangChain
 ---

-<Snippet file="blank-notif.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>

@@ -1,5 +1,3 @@
-<Snippet file="blank-notif.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
@@ -2,8 +2,6 @@
 title: LM Studio
 ---

-<Snippet file="blank-notif.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
@@ -2,8 +2,6 @@
 title: Mistral AI
 ---

-<Snippet file="blank-notif.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
@@ -1,10 +1,9 @@
-<Snippet file="blank-notif.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).
@@ -2,8 +2,6 @@
 title: OpenAI
 ---

-<Snippet file="blank-notif.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`
@@ -2,8 +2,6 @@
 title: Sarvam AI
 ---

-<Snippet file="blank-notif.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/).
@@ -1,5 +1,3 @@
-<Snippet file="blank-notif.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
@@ -2,8 +2,6 @@
 title: vLLM
 ---

-<Snippet file="blank-notif.mdx" />
-
 [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
@@ -2,8 +2,6 @@
 title: xAI
 ---

-<Snippet file="blank-notif.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.
@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.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
@@ -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
+        }
+    }
+}
+```
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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
@@ -4,13 +4,11 @@ icon: "gear"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.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


@@ -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` |
@@ -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` |
@@ -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.
@@ -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

@@ -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` |
@@ -2,7 +2,8 @@

 ### Usage

-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory

@@ -24,12 +25,43 @@ 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:
@@ -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.
@@ -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` |
@@ -4,8 +4,6 @@ icon: "info"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.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
@@ -3,8 +3,6 @@ title: Development
 icon: "code"
 ---

-<Snippet file="blank-notif.mdx" />
-
 # Development Contributions

 We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
@@ -3,8 +3,6 @@ title: Documentation
 icon: "book"
 ---

-<Snippet file="blank-notif.mdx" />
-
 # Documentation Contributions

 ## 📌 Prerequisites
@@ -1,62 +0,0 @@
---
-title: Memory Operations
-description: Understanding the core operations for managing memories in AI applications
-icon: "gear"
-iconType: "solid"
---
-
-<Snippet file="blank-notif.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.
@@ -5,7 +5,6 @@ icon: "plus"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />

 ## Overview

@@ -5,8 +5,6 @@ icon: "trash"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />
-
 ## Overview

 Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
@@ -5,8 +5,6 @@ icon: "magnifying-glass"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />
-
 ## 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.
@@ -5,8 +5,6 @@ icon: "pencil"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.mdx" />
-
 ## 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.
@@ -5,8 +5,6 @@ icon: "memory"
 iconType: "solid"
 ---

-<Snippet file="blank-notif.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
@@ -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",
@@ -22,7 +22,7 @@
                "group": "Getting Started",
                "icon": "rocket",
                "pages": [
-                  "what-is-mem0",
+                  "introduction",
                  "quickstart",
                  "faqs"
                ]
@@ -46,10 +46,11 @@
              },
              {
                "group": "Platform",
-                "icon": "cogs",
+                "icon": "globe",
                "pages": [
                  "platform/overview",
                  "platform/quickstart",
+                  "platform/advanced-memory-operations",
                  {
                    "group": "Features",
                    "icon": "star",
@@ -57,6 +58,7 @@
                      "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/multimodal-support",
@@ -150,14 +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/baidu"
+                          "components/vectordbs/dbs/baidu",
+                          "components/vectordbs/dbs/s3_vectors",
+                          "components/vectordbs/dbs/databricks",
+                          "components/vectordbs/dbs/neptune_analytics"
                        ]
                      }
                    ]
@@ -216,6 +224,7 @@
                "pages": [
                  "examples",
                  "examples/aws_example",
+                  "examples/aws_neptune_analytics_hybrid_store",
                  "examples/mem0-demo",
                  "examples/ai_companion_js",
                  "examples/collaborative-task-agent",
@@ -375,7 +384,7 @@
  "background": {
    "color": {
      "light": "#fff",
-      "dark": "#0f1117"
+      "dark": "#09090b"
    }
  },
  "navbar": {
@@ -3,9 +3,6 @@ title: Overview
 description: How to use mem0 in your existing applications?
 ---

-<Snippet file="blank-notif.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
@@ -20,72 +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>  
-
-  <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 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.  
+<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="Personalized Search Assistant" icon="magnifying-glass" href="/examples/personalized-search-tavily-mem0">
-    Build a **Personalized Search Assistant** that tailors search according to user preferences.
+  <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 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="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 Learning System" icon="book-open" href="/examples/llama-index-mem0">
    Multi-agent learning system powered by memory.
-  </Card>  
+  </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="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">  
+  <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>
@@ -1,168 +0,0 @@
---
-title: AI Companion
---
-
-<Snippet file="blank-notif.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 companion'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 and previous_memories.get('results'):
-            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
-
-        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['memory']}")
-    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.
@@ -2,8 +2,6 @@
 title: AI Companion in Node.js
 ---

-<Snippet file="blank-notif.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
@@ -1,9 +1,7 @@
 ---
-title: Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics
+title: "Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics"
 ---

-<Snippet file="blank-notif.mdx" />
-
 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
@@ -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.
@@ -1,7 +1,5 @@
 # Mem0 Chrome Extension

-<Snippet file="blank-notif.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>
@@ -2,8 +2,6 @@
 title: Multi-User Collaboration with Mem0
 ---

-<Snippet file="blank-notif.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.
@@ -2,7 +2,6 @@
 title: Customer Support AI Agent
 ---

-<Snippet file="blank-notif.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.

@@ -2,8 +2,6 @@
 title: Eliza OS Character
 ---

-<Snippet file="blank-notif.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
@@ -2,8 +2,6 @@
 title: Email Processing with Mem0
 ---

-<Snippet file="blank-notif.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
@@ -1,7 +1,6 @@
 ---
 title: LlamaIndex ReAct Agent
 ---
-<Snippet file="blank-notif.mdx" />

 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.

@@ -2,7 +2,6 @@
 title: Mem0 as an Agentic Tool
 ---

-<Snippet file="blank-notif.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.
@@ -2,9 +2,6 @@
 title: Mem0 Demo
 ---

-
-<Snippet file="blank-notif.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
@@ -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="blank-notif.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
@@ -2,8 +2,6 @@
 title: Mem0 with Mastra
 ---

-<Snippet file="blank-notif.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).

@@ -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="blank-notif.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.
@@ -2,8 +2,6 @@
 title: Mem0 with Ollama
 ---

-<Snippet file="blank-notif.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.
@@ -1,7 +1,6 @@
 ---
 title: Memory-Guided Content Writing
 ---
-<Snippet file="blank-notif.mdx" />

 This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.

@@ -2,8 +2,6 @@
 title: Multimodal Demo with Mem0
 ---

-<Snippet file="blank-notif.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)
@@ -2,8 +2,6 @@
 title: OpenAI Inbuilt Tools
 ---

-<Snippet file="blank-notif.mdx" />
-
 Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.

 ## Getting Started
@@ -2,8 +2,6 @@
 title: Personalized AI Tutor
 ---

-<Snippet file="blank-notif.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
@@ -2,7 +2,6 @@
 title: Personal AI Travel Assistant
 ---

-<Snippet file="blank-notif.mdx" />

 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.

@@ -2,8 +2,6 @@
 title: Personalized Deep Research
 ---

-<Snippet file="blank-notif.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)
@@ -2,8 +2,6 @@
 title: YouTube Assistant Extension
 ---

-<Snippet file="blank-notif.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
@@ -4,8 +4,6 @@ icon: "question"
 iconType: "solid"
 ---

-<Snippet file="async-memory-add.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.
@@ -1,23 +0,0 @@
---
-title: Features
-icon: "wrench"
-iconType: "solid"
---
-
-<Snippet file="blank-notif.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 relevant 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" />
@@ -3,8 +3,6 @@ title: Overview
 description: How to integrate Mem0 into other frameworks
 ---

-<Snippet file="blank-notif.mdx" />
-
 Mem0 seamlessly integrates with popular AI frameworks and tools to enhance your LLM-based applications with persistent memory capabilities. By integrating Mem0, your applications benefit from:

 - Enhanced context management across multiple frameworks
@@ -300,8 +298,7 @@ Here are the available integrations for Mem0:
        <path d="M17.5 12c-.83 0-1.5-.67-1.5-1.5s.67-1.5 1.5-1.5 1.5.67 1.5 1.5-.67 1.5-1.5 1.5z" fill="currentColor"/>
        <path d="M6.5 12c-.83 0-1.5-.67-1.5-1.5S5.67 9 6.5 9s1.5.67 1.5 1.5S7.33 12 6.5 12z" fill="currentColor"/>
      </svg>
-    } 
-    href="/integrations/pipecat"
+    }     href="/integrations/pipecat"
  >
    Build conversational AI agents with memory using Pipecat.
  </Card>
@@ -1,7 +1,6 @@
 ---
 title: AgentOps
 ---
-<Snippet file="blank-notif.mdx" />

 Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.

@@ -18,7 +17,7 @@ Before setting up Mem0 with AgentOps, ensure you have:

 1. Installed the required packages:
 ```bash
-pip install mem0ai agentops
+pip install mem0ai agentops python-dotenv
 ```

 2. Valid API keys:
@@ -38,7 +37,9 @@ import asyncio
 import logging
 from dotenv import load_dotenv
 import agentops
+import openai

+load_dotenv()
 #Set up environment variables for API keys
 os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
@@ -1,8 +1,6 @@
 ---
 title: Agno
 ---
-<Snippet file="blank-notif.mdx" />
-

 This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno, enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.

@@ -20,7 +18,7 @@ Before setting up Mem0 with Agno, ensure you have:

 1. Installed the required packages:
 ```bash
-pip install agno mem0ai
+pip install agno mem0ai python-dotenv
 ```

 2. Valid API keys:
@@ -83,7 +81,7 @@ agent = Agent(

 def chat_user(
    user_input: Optional[str] = None,
-    user_id: str = "user_123",
+    user_id: str = "alex",
    image_path: Optional[str] = None
 ) -> str:
    """
@@ -122,13 +120,13 @@ def chat_user(
        })

        # Store messages in memory
-        client.add(messages, user_id=user_id)
+        client.add(messages, user_id=user_id, output_format='v1.1')
        print("✅ Image and text stored in memory.")

    if user_input:
        # Search for relevant memories
-        memories = client.search(user_input, user_id=user_id)
-        memory_context = "\n".join(f"- {m['memory']}" for m in memories.get('results', []))
+        memories = client.search(user_input, user_id=user_id, output_format='v1.1')
+        memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])

        # Construct the prompt
        prompt = f"""
@@ -152,7 +150,8 @@ User question:
            response = agent.run(prompt)

        # Store the interaction in memory
-        client.add(f"User: {user_input}\nAssistant: {response.content}", user_id=user_id)
+        interaction_message = [{"role": "user", "content": f"User: {user_input}\nAssistant: {response.content}"}]
+        client.add(interaction_message, user_id=user_id, output_format='v1.1')
        return response.content

    return "No user input or image provided."
@@ -161,9 +160,9 @@ User question:
 # Example Usage
 if __name__ == "__main__":
    response = chat_user(
-        "This is the picture of what I brought with me in the trip to Bahamas",
+        "I like to travel and my favorite destination is London",
        image_path="travel_items.jpeg",
-        user_id="user_123"
+        user_id="alex"
    )
    print(response)
 ```
@@ -1,6 +1,8 @@
-Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
+---
+title: AutoGen
+---

-<Snippet file="blank-notif.mdx" />
+Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.

 ## Overview

@@ -12,7 +14,7 @@ In this guide, we'll explore an example of creating a conversational AI system w
 Install necessary libraries:

 ```bash
-pip install pyautogen mem0ai openai
+pip install autogen mem0ai openai python-dotenv
 ```

 First, we'll import the necessary libraries and set up our configurations.
@@ -24,15 +26,18 @@ import os
 from autogen import ConversableAgent
 from mem0 import MemoryClient
 from openai import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()

 # Configuration
-OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
-USER_ID = "customer_service_bot"
+# OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
+USER_ID = "alice"

 # Set up OpenAI API key
-os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
-os.environ['MEM0_API_KEY'] = MEM0_API_KEY
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+# os.environ['MEM0_API_KEY'] = MEM0_API_KEY

 # Initialize Mem0 and AutoGen agents
 memory_client = MemoryClient()
@@ -57,7 +62,7 @@ conversation = [
    {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
 ]

-memory_client.add(messages=conversation, user_id=USER_ID)
+memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
 print("Conversation added to memory.")
 ```

@@ -67,7 +72,7 @@ Create a function to get context-aware responses based on user's question and pr

 ```python
 def get_context_aware_response(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])

    prompt = f"""Answer the user question considering the previous interactions:
@@ -99,7 +104,7 @@ manager = ConversableAgent(
 )

 def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])

    prompt = f"""
@@ -2,8 +2,6 @@
 title: AWS Bedrock
 ---

-<Snippet file="blank-notif.mdx" />
-
 This integration demonstrates how to use **Mem0** with **AWS Bedrock** and **Amazon OpenSearch Service (AOSS)** to enable persistent, semantic memory in intelligent agents.

 ## Overview
@@ -2,8 +2,6 @@
 title: CrewAI
 ---

-<Snippet file="blank-notif.mdx" />
-
 Build an AI system that combines CrewAI's agent-based architecture with Mem0's memory capabilities. This integration enables persistent memory across agent interactions and personalized task execution based on user history.

 ## Overview
@@ -2,8 +2,6 @@
 title: Dify
 ---

-<Snippet file="blank-notif.mdx" />
-
 # Integrating Mem0 with Dify AI

 Mem0 brings a robust memory layer to Dify AI, empowering your AI agents with persistent conversation storage and retrieval capabilities. With Mem0, your Dify applications gain the ability to recall past interactions and maintain context, ensuring more natural and insightful conversations.
@@ -2,8 +2,6 @@
 title: ElevenLabs
 ---

-<Snippet file="blank-notif.mdx" />
-
 Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables persistent, context-aware voice interactions that remember past conversations.

 ## Overview
@@ -18,7 +16,7 @@ In this guide, we'll build a voice agent that:
 Install necessary libraries:

 ```bash
-pip install elevenlabs mem0 python-dotenv
+pip install elevenlabs mem0ai python-dotenv
 ```

 Configure your environment variables:
@@ -2,8 +2,6 @@
 title: Flowise
 ---

-<Snippet file="blank-notif.mdx" />
-
 The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.

 ## Overview
@@ -2,8 +2,6 @@
 title: Google Agent Development Kit
 ---

-<Snippet file="blank-notif.mdx" />
-
 Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google Agent Development Kit (ADK)](https://github.com/google/adk-python), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.

 ## Overview
@@ -19,7 +17,7 @@ Before setting up Mem0 with Google ADK, ensure you have:

 1. Installed the required packages:
 ```bash
-pip install google-adk mem0ai
+pip install google-adk mem0ai python-dotenv
 ```

 2. Valid API keys:
@@ -32,15 +30,19 @@ The following example demonstrates how to create a Google ADK agent with Mem0 me

 ```python
 import os
+import asyncio
 from google.adk.agents import Agent
 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
+
+load_dotenv()

 # Set up environment variables
-os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"

 # Initialize Mem0 client
 mem0 = MemoryClient()
@@ -48,17 +50,18 @@ mem0 = MemoryClient()
 # Define memory function tools
 def search_memory(query: str, user_id: str) -> dict:
    """Search through past conversations and memories"""
-    memories = mem0.search(query, user_id=user_id)
+    memories = mem0.search(query, user_id=user_id, output_format='v1.1')
    if memories.get('results', []):
-        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories.get('results', [])])
+        memory_list = memories['results']
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
        return {"status": "success", "memories": memory_context}
    return {"status": "no_memories", "message": "No relevant memories found"}

 def save_memory(content: str, user_id: str) -> dict:
    """Save important information to memory"""
    try:
-        mem0.add([{"role": "user", "content": content}], user_id=user_id)
-        return {"status": "success", "message": "Information saved to memory"}
+        result = mem0.add([{"role": "user", "content": content}], user_id=user_id, output_format='v1.1')
+        return {"status": "success", "message": "Information saved to memory", "result": result}
    except Exception as e:
        return {"status": "error", "message": f"Failed to save memory: {str(e)}"}

@@ -74,7 +77,7 @@ personal_assistant = Agent(
    tools=[search_memory, save_memory]
 )

-def chat_with_agent(user_input: str, user_id: str) -> str:
+async def chat_with_agent(user_input: str, user_id: str) -> str:
    """
    Handle user input with automatic memory integration.

@@ -87,7 +90,7 @@ def chat_with_agent(user_input: str, user_id: str) -> str:
    """
    # Set up session and runner
    session_service = InMemorySessionService()
-    session = session_service.create_session(
+    session = await session_service.create_session(
        app_name="memory_assistant",
        user_id=user_id,
        session_id=f"session_{user_id}"
@@ -109,10 +112,10 @@ def chat_with_agent(user_input: str, user_id: str) -> str:

 # Example usage
 if __name__ == "__main__":
-    response = chat_with_agent(
+    response = asyncio.run(chat_with_agent(
        "I love Italian food and I'm planning a trip to Rome next month",
        user_id="alice"
-    )
+    ))
    print(response)
 ```

@@ -2,8 +2,6 @@
 title: Keywords AI
 ---

-<Snippet file="blank-notif.mdx" />
-
 Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Keywords AI.

 ## Overview
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
parshvadaftari	aa3206cafd	Merge branch 'main' into mem0-1.0.0	2025-09-18 20:23:20 +05:30
parshvadaftari	f98cd925a6	Release beta version	2025-09-18 20:15:10 +05:30
Andy KwokandGitHub	6b5582f474	Feat: Mem0 vector store backend integration for Neptune Analytics (#3453 ) Signed-off-by: Andy Kwok <andy.kwok@improving.com>	2025-09-17 19:26:03 +05:30
parshvadaftari	86a2b00c65	Fix reranker config and added huggingface reranker	2025-09-16 10:48:35 +05:30
parshvadaftari	c1ee71ad3f	Fixed json parsing acros differnet LLM providers	2025-09-16 04:14:38 +05:30
parshvadaftari	a93a7ea6cf	Add gcp auth for better supporton service account	2025-09-16 00:13:42 +05:30
parshvadaftari	2dd4e93af4	Merge branch 'main' into mem0-1.0.0	2025-09-16 00:01:42 +05:30
parshvadaftari	15fe2b978f	Added memgraph compatbility across different versions	2025-09-14 03:47:59 +05:30
parshvadaftari	c1b9da45aa	Add async_mode default value to True	2025-09-14 02:36:32 +05:30
parshvadaftari	78ea40c291	Added assisstant memory retrieval	2025-09-13 20:56:39 +05:30
Parshva DaftariandGitHub	d38e3f1962	Fix json parsing with new memories (#3456 )	2025-09-12 17:29:34 +05:30
◢ 徇 ◤andGitHub	a0685f3e8c	fix: correct typo in knowledge graph extraction guidelines (#3449 )	2025-09-12 15:07:37 +05:30
Parshva DaftariandGitHub	d48b1832c7	Fixes ollama and updates openai dependency (#3452 )	2025-09-12 01:39:39 +05:30
Saket AryanandGitHub	21d69307dc	docs: Update Search V2/Get All V2 Filters (#3450 )	2025-09-11 19:10:19 +05:30
Andrew CarbonettoandGitHub	9e5810dfb7	Fix bedrock anthropic models to use system field (#3438 ) Signed-off-by: Andrew Carbonetto <andrew.carbonetto@improving.com>	2025-09-11 02:55:45 +05:30
parshvadaftari	f0d98ec4cb	Enhanced reranker and updated corresponding docs	2025-09-11 02:33:55 +05:30
Swarnaprakash UdayakumarandGitHub	e3f0277cb9	feat(vector-store): Add Valkey vector store support (#3272 )	2025-09-10 04:01:53 +05:30
Prateek ChhikaraandGitHub	e64488b598	updates to the category docs (#3437 )	2025-09-09 11:47:09 -07:00
Parshva DaftariandGitHub	f5e0fb9e4b	Added support for chromadb cloud (#3436 )	2025-09-09 22:22:09 +05:30
Ranjith kumarandGitHub	77b4b6a2b9	fix: 🐛 replace hardcoded llm provider with provider from config (#3423 )	2025-09-05 22:38:04 +05:30
Josh HayesandGitHub	9477184582	databricks bug fixes (#3416 )	2025-09-05 16:22:09 +05:30
Gabe GoodhartandGitHub	b27879bfd4	fix: Use ConfigDict instead of class-based Config (#3409 ) Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>	2025-09-04 20:18:25 +05:30
Saket AryanandGitHub	f0e8c3f760	feat: Add metadata param to TS-SDK in client.update (#3415 )	2025-09-04 03:22:12 +05:30
Parshva DaftariandGitHub	5e8d5e4664	Release 0.1.117 (#3411 )	2025-09-03 23:09:00 +05:30
Shili CaoandGitHub	c8d864c1b6	fix: add missing provider for baidu vector db (#3405 )	2025-09-03 15:24:33 +05:30
Prateek ChhikaraandGitHub	617aabe5b3	[FIX] Graph Docs page was missing on the side bar (#3402 )	2025-09-02 13:32:23 -07:00
Prateek ChhikaraandGitHub	163dafb216	Add version param in search v2 API documentation (#3401 )	2025-09-02 13:24:22 -07:00
Srishti GurejaandGitHub	cc15a22bf9	support `store` for openai (#3399 )	2025-09-02 21:23:59 +05:30
Parshva DaftariandGitHub	64cbe84089	Updated favicon logo (#3398 )	2025-09-02 21:10:04 +05:30
Parshva DaftariandGitHub	c8f9f20dff	Updated integration docs (#3392 )	2025-09-02 04:26:43 +05:30
parshvadaftari	5182c8f311	Refactor reranking and output format for the OSS and platform	2025-09-02 02:20:24 +05:30
parshvadaftari	61e8668584	Added metadata filtering and reranker for the OSS	2025-09-02 01:15:17 +05:30
parshvadaftari	a9a1d57dfa	Merge branch 'main' into user/parshva/mem0_1.0.0	2025-09-01 23:14:37 +05:30
John LockwoodandGitHub	97fd320bbf	Fix/new mem mistaken for current fixing #2875 (#2876 )	2025-09-01 19:46:01 +05:30
Saket AryanandGitHub	748620f29b	fix(Vercel AI SDK): Streaming not working properly (#3386 )	2025-08-30 21:58:30 +05:30
Srishti GurejaandGitHub	0b2aa36e98	Bugfix: Pick AWS region from the environment variable correctly (#3384 )	2025-08-30 01:43:02 +05:30
Sheharyar AhmadandGitHub	3d0ece1bcf	Refactor PGVector to Use Internal Connection Pools and Context Managers (#3373 )	2025-08-29 19:06:39 +05:30
Rupam JanaandGitHub	458d7ab8a3	replace query_vector args in search method of mongodb vector_stores (#3379 )	2025-08-29 19:05:25 +05:30
Tushar ChandraandGitHub	84af5ad265	docs: fix typo in docs/platform/advanced-memory-operations.mdx (#3348 )	2025-08-27 17:35:45 -07:00
parshvadaftari	0cc26fdabf	Initial commit for 1.0.0	2025-08-27 20:38:44 +05:30
VikramIyer125 GitHub Vikram Iyer	6237a6acb9	Adding weaviate, faiss, pgvector, chroma, redis, elasticsearch, milvus vector store to openmemory (#3366 ) Co-authored-by: Vikram Iyer <vikramiyer@mac.local.meter>	2025-08-27 01:47:39 +05:30
VikramIyer125 GitHub Vikram Iyer	8c8368781d	Adding custom connection to weaviate connection client to enable client connection to local container (#3360 ) Co-authored-by: Vikram Iyer <vikramiyer@Vikrams-MacBook-Pro.local>	2025-08-25 23:54:02 +05:30
Padarn WilsonandGitHub	3b2d0ad0eb	Fix missing commas in Kuzu graph INSERT queries (#3358 )	2025-08-24 12:33:51 +05:30
Andy KwokandGitHub	9337a873ec	Fix: Missing app_id on Neptune Analytics client (#3278 ) Signed-off-by: Andy Kwok <andy.kwok@improving.com>	2025-08-23 21:06:19 +05:30
Srishti GurejaandGitHub	76411e2591	docs fix: remove `user_id` from `from_config` (#3320 )	2025-08-23 17:11:56 +05:30
Andy KwokandGitHub	d523070cbc	fix: Inconsistent `created` and `updated` properties on graph (#3220 ) Signed-off-by: Andy Kwok <andy.kwok@improving.com>	2025-08-23 16:51:13 +05:30
Enzo BiondoandGitHub	c72bfc3285	Add Amazon S3 Vectors Support (#3237 )	2025-08-23 16:44:42 +05:30
Parshva DaftariandGitHub	ee00bd5731	Fix typescript docs (#3357 )	2025-08-22 14:23:26 -07:00
VikramIyer125 GitHub Vikram Iyer	f914dca659	Add export_openmemory.sh migration script (#3352 ) Co-authored-by: Vikram Iyer <vikramiyer@Vikrams-MacBook-Pro.local>	2025-08-22 20:19:41 +05:30
VikramIyer125 GitHub Vikram Iyer	b64792590e	Add memory export / import feature (#3345 ) Co-authored-by: Vikram Iyer <vikramiyer@Vikrams-MacBook-Pro.local>	2025-08-21 23:27:28 +05:30
Parshva DaftariandGitHub	a7ac8bf13b	Updated discord and dashboard image (#3344 )	2025-08-20 14:11:26 -07:00
David A. TorresandGitHub	4487785cec	feature: add Azure Identity for Azure OpenAI and Azure AI Search authentication (#3262 )	2025-08-21 02:00:27 +05:30
NiLAy GitHub parshvadaftari	e4c5582808	fix-migration-collection-override (#3100 ) Co-authored-by: parshvadaftari <daftariparshva@gmail.com>	2025-08-21 00:11:28 +05:30
Vương Hữu Hưng (Hans)andGitHub	ff399e5528	Fix: Ollama checking model exists (#2682 )	2025-08-19 16:29:26 +05:30
Parshva DaftariandGitHub	e7013764f7	Update aws bedrock (#3334 )	2025-08-19 02:20:34 +05:30
Parshva DaftariandGitHub	c8ee17b884	Fix dependency and tests and updated docstring (#3337 )	2025-08-19 02:12:24 +05:30
AkisAyaandGitHub	346b913ace	feat: add es headers config (#3088 )	2025-08-19 01:15:23 +05:30
Parshva DaftariandGitHub	49ad64708b	Updated databricks docs (#3336 )	2025-08-18 13:24:32 -05:00
Josh HayesandGitHub	3a1eff425b	feat(vector-store): Add Databricks Mosaic AI vector store support (#3325 )	2025-08-18 22:20:33 +05:30
Parshva DaftariandGitHub	ebb411b11a	Refactor docs (#3335 )	2025-08-18 20:45:09 +05:30
Archie Sengupta GitHub Saket Aryan	8e8f13a48e	feat(Vercel AI SDK): add a param in config called `host` (#2634 ) Co-authored-by: Saket Aryan <94069182+whysosaket@users.noreply.github.com>	2025-08-17 16:21:06 +05:30
Siddhartha SahuandGitHub	a6a3928091	Add support for graph memory using Kuzu (#2934 )	2025-08-16 02:22:31 +05:30
Ankush MalakerandGitHub	a883b56aa8	AsyncMemory._add_to_vector_store bugfix when no facts found (#3313 )	2025-08-15 22:15:00 +05:30
Deshraj YadavandGitHub	246d9e8f69	Update llms.txt file (#3321 )	2025-08-14 14:51:03 -07:00
Deshraj YadavandGitHub	192db1844c	Update Docs (#3315 )	2025-08-13 21:37:15 -07:00