docs: Update for new Project API and deprecation notices (#3212)

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Saket Aryan <94069182+whysosaket@users.noreply.github.com>

.github/workflows/cd.yml

@@ -18,29 +18,27 @@ jobs:
         with:
           python-version: '3.11'
 
-      - name: Install Poetry
+      - name: Install Hatch
         run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
+          pip install hatch
 
       - name: Install dependencies
         run: |
-          cd embedchain
-          poetry install
+          hatch env create
 
       - name: Build a binary wheel and a source tarball
         run: |
-          cd embedchain
-          poetry build
+          hatch build --clean
 
-      - name: Publish distribution 📦 to Test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository_url: https://test.pypi.org/legacy/
-          packages_dir: embedchain/dist/
+      # TODO: Needs to setup mem0 repo on Test PyPI
+      # - name: Publish distribution 📦 to Test PyPI
+      #   uses: pypa/gh-action-pypi-publish@release/v1
+      #   with:
+      #     repository_url: https://test.pypi.org/legacy/
+      #     packages_dir: dist/
 
       - name: Publish distribution 📦 to PyPI
         if: startsWith(github.ref, 'refs/tags')
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          packages_dir: embedchain/dist/
+          packages_dir: dist/

.github/workflows/ci.yml

@@ -44,21 +44,24 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: 1.4.2
-          virtualenvs-create: true
-          virtualenvs-in-project: true
+      - name: Install Hatch
+        run: pip install hatch
       - name: Load cached venv
-        id: cached-poetry-dependencies
+        id: cached-hatch-dependencies
         uses: actions/cache@v3
         with:
           path: .venv
-          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+          key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
+      - name: Install GEOS Libraries
+        run: sudo apt-get update && sudo apt-get install -y libgeos-dev
       - name: Install dependencies
-        run: make install_all
-        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        run: |
+          pip install --upgrade pip
+          pip install -e ".[test,graph,vector_stores,llms,extras]"
+          pip install ruff
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
+      - name: Run Linting
+        run: make lint
       - name: Run tests and generate coverage report
         run: make test
 
@@ -75,21 +78,21 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: Install poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: 1.4.2
-          virtualenvs-create: true
-          virtualenvs-in-project: true
+      - name: Install Hatch
+        run: pip install hatch
       - name: Load cached venv
-        id: cached-poetry-dependencies
+        id: cached-hatch-dependencies
         uses: actions/cache@v3
         with:
           path: .venv
-          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+          key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/pyproject.toml') }}
       - name: Install dependencies
         run: cd embedchain && make install_all
-        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        if: steps.cached-hatch-dependencies.outputs.cache-hit != 'true'
+      - name: Run Formatting
+        run: |
+          mkdir -p embedchain/.ruff_cache && chmod -R 777 embedchain/.ruff_cache
+          cd embedchain && hatch run format
       - name: Lint with ruff
         run: cd embedchain && make lint
       - name: Run tests and generate coverage report
@@ -99,4 +102,4 @@ jobs:
         with:
           file: coverage.xml
         env:
-          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

CONTRIBUTING.md

@@ -16,18 +16,19 @@ To make a contribution, follow these steps:
 For more details about pull requests, please read [GitHub's guides](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
 
 
-### 📦 Package manager
+### 📦 Development Environment
 
-We use `poetry` as our package manager. You can install poetry by following the instructions [here](https://python-poetry.org/docs/#installation).
-
-Please DO NOT use pip or conda to install the dependencies. Instead, use poetry:
+We use `hatch` for managing development environments. To set up:
 
 ```bash
-make install_all
+# Activate environment for specific Python version:
+hatch shell dev_py_3_9   # Python 3.9
+hatch shell dev_py_3_10  # Python 3.10  
+hatch shell dev_py_3_11  # Python 3.11
 
-#activate
-
-poetry shell
+# The environment will automatically install all dev dependencies
+# Run tests within the activated shell:
+make test
 ```
 
 ### 📌 Pre-commit
@@ -40,16 +41,21 @@ pre-commit install
 
 ### 🧪 Testing
 
-We use `pytest` to test our code. You can run the tests by running the following command:
+We use `pytest` to test our code across multiple Python versions. You can run tests using:
 
 ```bash
-poetry run pytest tests
-
-# or
-
+# Run tests with default Python version
 make test
+
+# Test specific Python versions:
+make test-py-3.9   # Python 3.9 environment
+make test-py-3.10  # Python 3.10 environment
+make test-py-3.11  # Python 3.11 environment
+
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
 ```
 
-Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass. Make sure that all tests pass before submitting a pull request.
+Make sure that all tests pass across all supported Python versions before submitting a pull request.
 
-We look forward to your pull requests and can't wait to see your contributions!
+We look forward to your pull requests and can't wait to see your contributions!

Makefile

@@ -8,36 +8,45 @@ PROJECT_NAME := mem0ai
 all: format sort lint
 
 install:
-	poetry install
+	hatch env create
 
 install_all:
-	poetry install
-	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
-	                        google-generativeai elasticsearch opensearch-py vecs
+	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
 
 # Format code with ruff
 format:
-	poetry run ruff format mem0/
+	hatch run format
 
 # Sort imports with isort
 sort:
-	poetry run isort mem0/
+	hatch run isort mem0/
 
 # Lint code with ruff
 lint:
-	poetry run ruff check mem0/
+	hatch run lint
 
 docs:
 	cd docs && mintlify dev
 
 build:
-	poetry build
+	hatch build
 
 publish:
-	poetry publish
+	hatch publish
 
 clean:
-	poetry run rm -rf dist
+	rm -rf dist
 
 test:
-	poetry run pytest tests
+	hatch run test
+
+test-py-3.9:
+	hatch run dev_py_3_9:test
+
+test-py-3.10:
+	hatch run dev_py_3_10:test
+
+test-py-3.11:
+	hatch run dev_py_3_11:test

README.md

@@ -1,22 +1,22 @@
 <p align="center">
   <a href="https://github.com/mem0ai/mem0">
-  <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
+    <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
   </a>
+</p>
 <p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
-  <a href="https://trendshift.io/repositories/11194" target="_blank">
-    <img src="https://trendshift.io/api/badge/repositories/11194" alt="mem0ai%2Fmem0 | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
-  </a>
-  <a href="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps" target="_blank">
-    <img alt="Launch YC: Mem0 - Open Source Memory Layer for AI Apps" src="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps/upvote_embed.svg"/>
+  <a href="https://trendshift.io/repositories/11194" target="blank">
+    <img src="https://trendshift.io/api/badge/repositories/11194" alt="mem0ai%2Fmem0 | Trendshift" width="250" height="55"/>
   </a>
 </p>
 
-
-  <p align="center">
-    <a href="https://mem0.ai">Learn more</a>
-    ·
-    <a href="https://mem0.dev/DiG">Join Discord</a>
-  </p>
+<p align="center">
+  <a href="https://mem0.ai">Learn more</a>
+  ·
+  <a href="https://mem0.dev/DiG">Join Discord</a>
+  ·
+  <a href="https://mem0.dev/demo">Demo</a>
+  ·
+  <a href="https://mem0.dev/openmemory">OpenMemory</a>
 </p>
 
 <p align="center">
@@ -24,62 +24,78 @@
     <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Mem0 Discord">
   </a>
   <a href="https://pepy.tech/project/mem0ai">
-    <img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads" >
+    <img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads">
   </a>
   <a href="https://github.com/mem0ai/mem0">
     <img src="https://img.shields.io/github/commit-activity/m/mem0ai/mem0?style=flat-square" alt="GitHub commit activity">
   </a>
-  <a href="https://pypi.org/project/mem0ai" target="_blank">
-        <img src="https://img.shields.io/pypi/v/mem0ai?color=%2334D058&label=pypi%20package" alt="Package version">
-    </a>
-    <a href="https://www.npmjs.com/package/mem0ai" target="_blank">
-        <img src="https://img.shields.io/npm/v/mem0ai" alt="Npm package">
-    </a>
+  <a href="https://pypi.org/project/mem0ai" target="blank">
+    <img src="https://img.shields.io/pypi/v/mem0ai?color=%2334D058&label=pypi%20package" alt="Package version">
+  </a>
+  <a href="https://www.npmjs.com/package/mem0ai" target="blank">
+    <img src="https://img.shields.io/npm/v/mem0ai" alt="Npm package">
+  </a>
   <a href="https://www.ycombinator.com/companies/mem0">
     <img src="https://img.shields.io/badge/Y%20Combinator-S24-orange?style=flat-square" alt="Y Combinator S24">
   </a>
 </p>
 
+<p align="center">
+  <a href="https://mem0.ai/research"><strong>📄 Building Production-Ready AI Agents with Scalable Long-Term Memory →</strong></a>
+</p>
+<p align="center">
+  <strong>⚡ +26% Accuracy vs. OpenAI Memory • 🚀 91% Faster • 💰 90% Fewer Tokens</strong>
+</p>
+
+##  🔥 Research Highlights
+- **+26% Accuracy** over OpenAI Memory on the LOCOMO benchmark
+- **91% Faster Responses** than full-context, ensuring low-latency at scale
+- **90% Lower Token Usage** than full-context, cutting costs without compromise
+- [Read the full paper](https://mem0.ai/research)
 
 # Introduction
 
-[Mem0](https://mem0.ai) (pronounced as "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.
+[Mem0](https://mem0.ai) ("mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time—ideal for customer support chatbots, AI assistants, and autonomous systems.
 
-### Features & Use Cases
+### Key Features & Use Cases
 
-Core Capabilities:
-- **Multi-Level Memory**: User, Session, and AI Agent memory retention with adaptive personalization
-- **Developer-Friendly**: Simple API integration, cross-platform consistency, and hassle-free managed service
+**Core Capabilities:**
+- **Multi-Level Memory**: Seamlessly retains User, Session, and Agent state with adaptive personalization
+- **Developer-Friendly**: Intuitive API, cross-platform SDKs, and a fully managed service option
 
-Applications:
-- **AI Assistants**: Seamless conversations with context and personalization
-- **Learning & Support**: Tailored content recommendations and context-aware customer assistance
-- **Healthcare & Companions**: Patient history tracking and deeper relationship building
-- **Productivity & Gaming**: Streamlined workflows and adaptive environments based on user behavior
+**Applications:**
+- **AI Assistants**: Consistent, context-rich conversations
+- **Customer Support**: Recall past tickets and user history for tailored help
+- **Healthcare**: Track patient preferences and history for personalized care
+- **Productivity & Gaming**: Adaptive workflows and environments based on user behavior
 
-## Get Started
+## 🚀 Quickstart Guide <a name="quickstart"></a>
 
-Get started quickly with [Mem0 Platform](https://app.mem0.ai) - our fully managed solution that provides automatic updates, advanced analytics, enterprise security, and dedicated support. [Create a free account](https://app.mem0.ai) to begin.
+Choose between our hosted platform or self-hosted package:
 
-For complete control, you can self-host Mem0 using our open-source package. See the [Quickstart guide](#quickstart) below to set up your own instance.
+### Hosted Platform
 
-## Quickstart Guide <a name="quickstart"></a>
+Get up and running in minutes with automatic updates, analytics, and enterprise security.
 
-Install the Mem0 package via pip:
+1. Sign up on [Mem0 Platform](https://app.mem0.ai)
+2. Embed the memory layer via SDK or API keys
+
+### Self-Hosted (Open Source)
+
+Install the sdk via pip:
 
 ```bash
 pip install mem0ai
 ```
 
-Install the Mem0 package via npm:
-
+Install sdk via npm:
 ```bash
 npm install mem0ai
 ```
 
 ### Basic Usage
 
-Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/llms).
+Mem0 requires an LLM to function, with `gpt-4o-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview).
 
 First step is to instantiate the memory:
 
@@ -93,8 +109,8 @@ memory = Memory()
 def chat_with_memories(message: str, user_id: str = "default_user") -> str:
     # Retrieve relevant memories
     relevant_memories = memory.search(query=message, user_id=user_id, limit=3)
-    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories)
-    
+    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])
+
     # Generate Assistant response
     system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
     messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
@@ -120,66 +136,34 @@ if __name__ == "__main__":
     main()
 ```
 
-See the example for [Node.js](https://docs.mem0.ai/examples/ai_companion_js).
+For detailed integration steps, see the [Quickstart](https://docs.mem0.ai/quickstart) and [API Reference](https://docs.mem0.ai/api-reference).
 
-For more advanced usage and API documentation, visit our [documentation](https://docs.mem0.ai).
+## 🔗 Integrations & Demos
 
-> [!TIP]
-> For a hassle-free experience, try our [hosted platform](https://app.mem0.ai) with automatic updates and enterprise features.
+- **ChatGPT with Memory**: Personalized chat powered by Mem0 ([Live Demo](https://mem0.dev/demo))
+- **Browser Extension**: Store memories across ChatGPT, Perplexity, and Claude ([Chrome Extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb))
+- **Langgraph Support**: Build a customer bot with Langgraph + Mem0 ([Guide](https://docs.mem0.ai/integrations/langgraph))
+- **CrewAI Integration**: Tailor CrewAI outputs with Mem0 ([Example](https://docs.mem0.ai/integrations/crewai))
 
-## Demos
+## 📚 Documentation & Support
 
-- AI Companion: Experience personalized conversations with an AI that remembers your preferences and past interactions
+- Full docs: https://docs.mem0.ai
+- Community: [Discord](https://mem0.dev/DiG) · [Twitter](https://x.com/mem0ai)
+- Contact: founders@mem0.ai
 
-[AI Companion Demo](https://github.com/user-attachments/assets/3fc72023-a72c-4593-8be0-3cee3ba744da)
+## Citation
 
-<br/><br/>
+We now have a paper you can cite:
 
-- Mem0 Demo: A personalized AI chat app powered by Mem0 that remembers your preferences, facts, and memories.
+```bibtex
+@article{mem0,
+  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
+  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
+  journal={arXiv preprint arXiv:2504.19413},
+  year={2025}
+}
+```
 
-[Mem0 Demo](https://github.com/user-attachments/assets/cebc4f8e-bdb9-4837-868d-13c5ab7bb433)
+## ⚖️ License
 
-<br/><br/>
-
-- Enhance your AI interactions by storing memories across ChatGPT, Perplexity, and Claude using our browser extension. Get [chrome extension](https://chromewebstore.google.com/detail/mem0/onihkkbipkfeijkadecaafbgagkhglop?hl=en).
-
-
-[Chrome Extension Demo](https://github.com/user-attachments/assets/ca92e40b-c453-4ff6-b25e-739fb18a8650)
-
-<br/><br/>
-
-- Customer support bot using <strong>Langgraph and Mem0</strong>. Get the complete code from [here](https://docs.mem0.ai/integrations/langgraph)
-
-
-[Langgraph: Customer Bot](https://github.com/user-attachments/assets/ca6b482e-7f46-42c8-aa08-f88d1d93a5f4)
-
-<br/><br/>
-
-- Use Mem0 with CrewAI to get personalized results. Full example [here](https://docs.mem0.ai/integrations/crewai)
-
-[CrewAI Demo](https://github.com/user-attachments/assets/69172a79-ccb9-4340-91f1-caa7d2dd4213)
-
-
-
-## Documentation
-
-For detailed usage instructions and API reference, visit our [documentation](https://docs.mem0.ai). You'll find:
-- Complete API reference
-- Integration guides
-- Advanced configuration options
-- Best practices and examples
-- More details about:
-  - Open-source version
-  - [Hosted Mem0 Platform](https://app.mem0.ai)
-
-## Support
-
-Join our community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods:
-
-- [Join our Discord](https://mem0.dev/DiG)
-- [Follow us on Twitter](https://x.com/mem0ai)
-- [Email founders](mailto:founders@mem0.ai)
-
-## License
-
-This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
+Apache 2.0 — see the [LICENSE](LICENSE) file for details.

cookbooks/customer-support-chatbot.ipynb

@@ -13,7 +13,7 @@
     "import anthropic\n",
     "\n",
     "# Set up environment variables\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\" # needed for embedding model\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your_openai_api_key\"  # needed for embedding model\n",
     "os.environ[\"ANTHROPIC_API_KEY\"] = \"your_anthropic_api_key\""
    ]
   },
@@ -33,7 +33,7 @@
     "                    \"model\": \"claude-3-5-sonnet-latest\",\n",
     "                    \"temperature\": 0.1,\n",
     "                    \"max_tokens\": 2000,\n",
-    "                }\n",
+    "                },\n",
     "            }\n",
     "        }\n",
     "        self.client = anthropic.Client(api_key=os.environ[\"ANTHROPIC_API_KEY\"])\n",
@@ -50,11 +50,7 @@
     "        - Keep track of open issues and follow-ups\n",
     "        \"\"\"\n",
     "\n",
-    "    def store_customer_interaction(self,\n",
-    "                                 user_id: str,\n",
-    "                                 message: str,\n",
-    "                                 response: str,\n",
-    "                                 metadata: Dict = None):\n",
+    "    def store_customer_interaction(self, user_id: str, message: str, response: str, metadata: Dict = None):\n",
     "        \"\"\"Store customer interaction in memory.\"\"\"\n",
     "        if metadata is None:\n",
     "            metadata = {}\n",
@@ -63,24 +59,17 @@
     "        metadata[\"timestamp\"] = datetime.now().isoformat()\n",
     "\n",
     "        # Format conversation for storage\n",
-    "        conversation = [\n",
-    "            {\"role\": \"user\", \"content\": message},\n",
-    "            {\"role\": \"assistant\", \"content\": response}\n",
-    "        ]\n",
+    "        conversation = [{\"role\": \"user\", \"content\": message}, {\"role\": \"assistant\", \"content\": response}]\n",
     "\n",
     "        # Store in Mem0\n",
-    "        self.memory.add(\n",
-    "            conversation,\n",
-    "            user_id=user_id,\n",
-    "            metadata=metadata\n",
-    "        )\n",
+    "        self.memory.add(conversation, user_id=user_id, metadata=metadata)\n",
     "\n",
     "    def get_relevant_history(self, user_id: str, query: str) -> List[Dict]:\n",
     "        \"\"\"Retrieve relevant past interactions.\"\"\"\n",
     "        return self.memory.search(\n",
     "            query=query,\n",
     "            user_id=user_id,\n",
-    "            limit=5  # Adjust based on needs\n",
+    "            limit=5,  # Adjust based on needs\n",
     "        )\n",
     "\n",
     "    def handle_customer_query(self, user_id: str, query: str) -> str:\n",
@@ -112,15 +101,12 @@
     "            model=\"claude-3-5-sonnet-latest\",\n",
     "            messages=[{\"role\": \"user\", \"content\": prompt}],\n",
     "            max_tokens=2000,\n",
-    "            temperature=0.1\n",
+    "            temperature=0.1,\n",
     "        )\n",
     "\n",
     "        # Store interaction\n",
     "        self.store_customer_interaction(\n",
-    "            user_id=user_id,\n",
-    "            message=query,\n",
-    "            response=response,\n",
-    "            metadata={\"type\": \"support_query\"}\n",
+    "            user_id=user_id, message=query, response=response, metadata={\"type\": \"support_query\"}\n",
     "        )\n",
     "\n",
     "        return response.content[0].text"
@@ -203,12 +189,12 @@
     "    # Get user input\n",
     "    query = input()\n",
     "    print(\"Customer:\", query)\n",
-    "    \n",
+    "\n",
     "    # Check if user wants to exit\n",
-    "    if query.lower() == 'exit':\n",
+    "    if query.lower() == \"exit\":\n",
     "        print(\"Thank you for using our support service. Goodbye!\")\n",
     "        break\n",
-    "    \n",
+    "\n",
     "    # Handle the query and print the response\n",
     "    response = chatbot.handle_customer_query(user_id, query)\n",
     "    print(\"Support:\", response, \"\\n\\n\")"

cookbooks/helper/mem0_teachability.py

@@ -7,10 +7,12 @@
 # forked from autogen.agentchat.contrib.capabilities.teachability.Teachability
 
 from typing import Dict, Optional, Union
+
 from autogen.agentchat.assistant_agent import ConversableAgent
 from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
 from termcolor import colored
+
 from mem0 import Memory
 
 

docs/_snippets/paper-release.mdx

@@ -0,0 +1,3 @@
+<Note type="info">
+  📢 Announcing our research paper: Mem0 achieves <strong>26%</strong> higher accuracy than OpenAI Memory, <strong>91%</strong> lower latency, and <strong>90%</strong> token savings! [Read the paper](https://mem0.ai/research) to learn how we're revolutionizing AI agent memory.
+</Note>

docs/_snippets/security-compliance.mdx

@@ -0,0 +1,3 @@
+<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>

docs/api-reference.mdx

@@ -0,0 +1,193 @@
+---
+title: Overview
+icon: "info"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.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
+
+- **Memory Management**: Add, retrieve, update, and delete memories with ease.
+- **Entity-based Operations**: Perform operations on memories associated with specific users, agents, apps, or runs.
+- **Advanced Search**: Utilize our search API to find relevant memories based on various criteria.
+- **History Tracking**: Access the history of memory interactions for comprehensive analysis.
+- **User Management**: Manage user entities and their associated memories.
+
+## API Structure
+
+Our API is organized into several main categories:
+
+1. **Memory APIs**: Core operations for managing individual memories and collections.
+2. **Entities APIs**: Manage different entity types (users, agents, etc.) and their associated memories.
+3. **Search API**: Advanced search functionality to retrieve relevant memories.
+4. **History API**: Track and retrieve the history of memory interactions.
+
+## Authentication
+
+All API requests require authentication using HTTP Basic Auth. Ensure you include your API key in the Authorization header of each request.
+
+## Organizations and projects (optional)
+
+Organizations and projects provide the following capabilities:
+
+- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
+- **Member Management**: Control access to data through organization and project membership
+- **Access Control**: Only members can access memories and data within their organization/project scope
+- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
+
+Example with the mem0 Python package:
+
+<Tabs>
+  <Tab title="Python">
+
+```python
+from mem0 import MemoryClient
+client = MemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+```
+
+  </Tab>
+
+  <Tab title="Node.js">
+
+```javascript
+import { MemoryClient } from "mem0ai";
+const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR_PROJECT_ID"});
+```
+
+  </Tab>
+</Tabs>
+
+### Project Management Methods
+
+The Mem0 client provides comprehensive project management capabilities through the `client.project` interface:
+
+#### Get Project Details
+
+Retrieve information about the current project:
+
+```python
+# Get all project details
+project_info = client.project.get()
+
+# Get specific fields only
+project_info = client.project.get(fields=["name", "description", "custom_categories"])
+```
+
+#### Create a New Project
+
+Create a new project within your organization:
+
+```python
+# Create a project with name and description
+new_project = client.project.create(
+    name="My New Project",
+    description="A project for managing customer support memories"
+)
+```
+
+#### Update Project Settings
+
+Modify project configuration including custom instructions, categories, and graph settings:
+
+```python
+# Update project with custom categories
+client.project.update(
+    custom_categories=[
+        {"customer_preferences": "Customer likes, dislikes, and preferences"},
+        {"support_history": "Previous support interactions and resolutions"}
+    ]
+)
+
+# Update project with custom instructions
+client.project.update(
+    custom_instructions="..."
+)
+
+# Enable graph memory for the project
+client.project.update(enable_graph=True)
+
+# Update multiple settings at once
+client.project.update(
+    custom_instructions="...",
+    custom_categories=[
+        {"personal_info": "User personal information and preferences"},
+        {"work_context": "Professional context and work-related information"}
+    ],
+    enable_graph=True
+)
+```
+
+#### Delete Project
+
+<Note>
+This action will remove all memories, messages, and other related data in the project. This operation is irreversible.
+</Note>
+
+Remove a project and all its associated data:
+
+```python
+# Delete the current project (irreversible)
+result = client.project.delete()
+```
+
+#### Member Management
+
+Manage project members and their access levels:
+
+```python
+# Get all project members
+members = client.project.get_members()
+
+# Add a new member as a reader
+client.project.add_member(
+    email="colleague@company.com",
+    role="READER"  # or "OWNER"
+)
+
+# Update a member's role
+client.project.update_member(
+    email="colleague@company.com",
+    role="OWNER"
+)
+
+# Remove a member from the project
+client.project.remove_member(email="colleague@company.com")
+```
+
+#### Member Roles
+
+- **READER**: Can view and search memories, but cannot modify project settings or manage members
+- **OWNER**: Full access including project modification, member management, and all reader permissions
+
+#### Async Support
+
+All project methods are also available in async mode:
+
+```python
+from mem0 import AsyncMemoryClient
+
+async def manage_project():
+    client = AsyncMemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+
+    # All methods support async/await
+    project_info = await client.project.get()
+    await client.project.update(enable_graph=True)
+    members = await client.project.get_members()
+
+# To call the async function properly
+import asyncio
+asyncio.run(manage_project())
+```
+
+## Getting Started
+
+To begin using the Mem0 API, you'll need to:
+
+1. Sign up for a [Mem0 account](https://app.mem0.ai) and obtain your API key.
+2. Familiarize yourself with the API endpoints and their functionalities.
+3. Make your first API call to add or retrieve a memory.
+
+Explore the detailed documentation for each API endpoint to learn more about request/response formats, parameters, and example usage.

docs/api-reference/memory/feedback.mdx

@@ -0,0 +1,4 @@
+---
+title: 'Feedback'
+openapi: post /v1/feedback/
+---

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

@@ -1,6 +1,6 @@
 ---
 title: 'Get Memory Export'
-openapi: get /v1/exports/
+openapi: post /v1/exports/get
 ---
 
 Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.

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

@@ -3,12 +3,15 @@ title: 'Get Memories (v2)'
 openapi: post /v2/memories/
 ---
 
-The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
 - `gt`: Greater than
 - `lt`: Less than
+- `ne`: Not equal to
+- `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
 
 <CodeGroup>
 ```python Code
@@ -41,3 +44,22 @@ memories = m.get_all(
 ]
 ```
 </CodeGroup>
+
+<CodeGroup>
+```python Wildcard Example
+# Using wildcard to get all memories for a specific user across all run_ids
+memories = m.get_all(
+    filters={
+        "AND": [
+            {
+                "user_id": "alex"
+            },
+            {
+                "run_id": "*"
+            }
+        ]
+    },
+    version="v2"
+)
+```
+</CodeGroup>

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

@@ -3,7 +3,7 @@ title: 'Search Memories (v2)'
 openapi: post /v2/memories/search/
 ---
 
-The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR, NOT) and comparison operators for advanced filtering capabilities. The comparison operators include:
 - `in`: Matches any of the values specified
 - `gte`: Greater than or equal to
 - `lte`: Less than or equal to
@@ -11,14 +11,15 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
 - `lt`: Less than
 - `ne`: Not equal to
 - `icontains`: Case-insensitive containment check
+- `*`: Wildcard character that matches everything
 
   <CodeGroup>
   ```python Code
-  related_memories = m.vsearch(
+  related_memories = m.search(
       query="What are Alice's hobbies?",
       version="v2",
       filters={
-          "AND": [
+          "OR": [
               {
                 "user_id": "alice"
               },
@@ -49,3 +50,23 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
   }
   ```
   </CodeGroup>
+
+  <CodeGroup>
+  ```python Wildcard Example
+  # Using wildcard to match all run_ids for a specific user
+  all_memories = m.search(
+      query="What are Alice's hobbies?",
+      version="v2",
+      filters={
+          "AND": [
+              {
+                  "user_id": "alice"
+              },
+              {
+                  "run_id": "*"
+              }
+          ]
+      },
+  )
+  ```
+  </CodeGroup>

docs/api-reference/overview.mdx

@@ -1,69 +0,0 @@
----
-title: Overview
-icon: "info"
-iconType: "solid"
----
-
-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
-
-- **Memory Management**: Add, retrieve, update, and delete memories with ease.
-- **Entity-based Operations**: Perform operations on memories associated with specific users, agents, apps, or runs.
-- **Advanced Search**: Utilize our search API to find relevant memories based on various criteria.
-- **History Tracking**: Access the history of memory interactions for comprehensive analysis.
-- **User Management**: Manage user entities and their associated memories.
-
-## API Structure
-
-Our API is organized into several main categories:
-
-1. **Memory APIs**: Core operations for managing individual memories and collections.
-2. **Entities APIs**: Manage different entity types (users, agents, etc.) and their associated memories.
-3. **Search API**: Advanced search functionality to retrieve relevant memories.
-4. **History API**: Track and retrieve the history of memory interactions.
-
-## Authentication
-
-All API requests require authentication using HTTP Basic Auth. Ensure you include your API key in the Authorization header of each request.
-
-## Organizations and projects (optional)
-
-Organizations and projects provide the following capabilities:
-
-- **Multi-org/project Support**: Specify organization and project when initializing the Mem0 client to attribute API usage appropriately
-- **Member Management**: Control access to data through organization and project membership
-- **Access Control**: Only members can access memories and data within their organization/project scope
-- **Team Isolation**: Maintain data separation between different teams and projects for secure collaboration
-
-Example with the mem0 Python package:
-
-<Tabs>
-  <Tab title="Python">
-
-```python
-from mem0 import MemoryClient
-client = MemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
-```
-
-  </Tab>
-
-  <Tab title="Node.js">
-
-```javascript
-import { MemoryClient } from "mem0ai";
-const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR_PROJECT_ID"});
-```
-
-  </Tab>
-</Tabs>
-
-## Getting Started
-
-To begin using the Mem0 API, you'll need to:
-
-1. Sign up for a [Mem0 account](https://app.mem0.ai) and obtain your API key.
-2. Familiarize yourself with the API endpoints and their functionalities.
-3. Make your first API call to add or retrieve a memory.
-
-Explore the detailed documentation for each API endpoint to learn more about request/response formats, parameters, and example usage.

docs/components/embedders/config.mdx

@@ -4,6 +4,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.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.
 
 ## How to define configurations?
@@ -84,6 +86,7 @@ Here's a comprehensive list of all parameters that can be used across different
 | `memory_add_embedding_type` | The type of embedding to use for the add memory action                       | VertexAI            |
 | `memory_update_embedding_type` | The type of embedding to use for the update memory action                       | VertexAI            |
 | `memory_search_embedding_type` | The type of embedding to use for the search memory action                       | VertexAI            |
+| `lmstudio_base_url` | Base URL for LM Studio API                    | LM Studio         |
 </Tab>
 <Tab title="TypeScript">
 | Parameter | Description | Provider |

docs/components/embedders/models/aws_bedrock.mdx

@@ -0,0 +1,62 @@
+---
+title: AWS Bedrock
+---
+
+To use AWS Bedrock embedding models, you need to have the appropriate AWS credentials and permissions. The embeddings implementation relies on the `boto3` library.
+
+### Setup
+- Ensure you have model access from the [AWS Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess)
+- Authenticate the boto3 client using a method described in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
+- Set up environment variables for authentication:
+  ```bash
+  export AWS_REGION=us-east-1
+  export AWS_ACCESS_KEY_ID=your-access-key
+  export AWS_SECRET_ACCESS_KEY=your-secret-key
+  ```
+
+### Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+# For LLM if needed
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+# AWS credentials
+os.environ["AWS_REGION"] = "us-west-2"
+os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
+os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"
+
+config = {
+    "embedder": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "amazon.titan-embed-text-v2:0"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice")
+```
+</CodeGroup>
+
+### Config
+
+Here are the parameters available for configuring AWS Bedrock embedder:
+
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `amazon.titan-embed-text-v1` |
+</Tab>
+</Tabs>

docs/components/embedders/models/azure_openai.mdx

@@ -6,7 +6,8 @@ To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`,
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -46,6 +47,36 @@ messages = [
 m.add(messages, user_id="john")
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+    embedder: {
+        provider: "azure_openai",
+        config: {
+            model: "text-embedding-3-large",
+            modelProperties: {
+                endpoint: "your-api-base-url",
+                deployment: "your-deployment-name",
+                apiVersion: "version-to-use",
+            }
+        }
+    }
+}
+
+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 Azure OpenAI embedder:

docs/components/embedders/models/gemini.mdx

@@ -39,5 +39,5 @@ Here are the parameters available for configuring Gemini embedder:
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `models/text-embedding-004` |
-| `embedding_dims` | Dimensions of the embedding model | `768` |
+| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
 | `api_key` | The Gemini API key | `None` |

docs/components/embedders/models/huggingface.mdx

@@ -25,12 +25,44 @@ 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")
 ```
 
+### Using Text Embeddings Inference (TEI)
+
+You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+# Using HuggingFace Text Embeddings Inference API
+config = {
+    "embedder": {
+        "provider": "huggingface",
+        "config": {
+            "huggingface_base_url": "http://localhost:3000/v1"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("This text will be embedded using the TEI service.", user_id="john")
+```
+
+To run the TEI service, you can use Docker:
+
+```bash
+docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \
+    ghcr.io/huggingface/text-embeddings-inference:cpu-1.6 \
+    --model-id BAAI/bge-small-en-v1.5
+```
+
 ### Config
 
 Here are the parameters available for configuring Huggingface embedder:
@@ -39,4 +71,5 @@ Here are the parameters available for configuring Huggingface embedder:
 | --- | --- | --- |
 | `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` |
 | `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
-| `model_kwargs` | Additional arguments for the model | `None` |
+| `model_kwargs` | Additional arguments for the model | `None` |
+| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |

docs/components/embedders/models/langchain.mdx

@@ -0,0 +1,146 @@
+---
+title: LangChain
+---
+
+Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
+
+For a complete list of available embedding models supported by LangChain, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+from langchain_openai import OpenAIEmbeddings
+
+# Set necessary environment variables for your chosen LangChain provider
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize a LangChain embeddings model directly
+openai_embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    dimensions=1536
+)
+
+# Pass the initialized model to the config
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": openai_embeddings
+        }
+    }
+}
+
+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"})
+```
+
+```typescript TypeScript
+import { Memory } from "mem0ai";
+import { OpenAIEmbeddings } from "@langchain/openai";
+
+const embeddings = new OpenAIEmbeddings();
+const config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": 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." }
+]
+
+memory.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+</CodeGroup>
+
+## Supported LangChain Embedding Providers
+
+LangChain supports a wide range of embedding providers, including:
+
+- OpenAI (`OpenAIEmbeddings`)
+- Cohere (`CohereEmbeddings`)
+- Google (`VertexAIEmbeddings`)
+- Hugging Face (`HuggingFaceEmbeddings`)
+- Sentence Transformers (`HuggingFaceEmbeddings`)
+- Azure OpenAI (`AzureOpenAIEmbeddings`)
+- Ollama (`OllamaEmbeddings`)
+- Together (`TogetherEmbeddings`)
+- And many more
+
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available embedding providers, refer to the [LangChain Text Embedding documentation](https://python.langchain.com/docs/integrations/text_embedding/).
+
+## Provider-Specific Configuration
+
+When using LangChain as an embedder provider, you'll need to:
+
+1. Set the appropriate environment variables for your chosen embedding provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
+
+### Examples with Different Providers
+
+#### HuggingFace Embeddings
+
+```python
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Initialize a HuggingFace embeddings model
+hf_embeddings = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-small-en-v1.5",
+    encode_kwargs={"normalize_embeddings": True}
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": hf_embeddings
+        }
+    }
+}
+```
+
+#### Ollama Embeddings
+
+```python
+from langchain_ollama import OllamaEmbeddings
+
+# Initialize an Ollama embeddings model
+ollama_embeddings = OllamaEmbeddings(
+    model="nomic-embed-text"
+)
+
+config = {
+    "embedder": {
+        "provider": "langchain",
+        "config": {
+            "model": ollama_embeddings
+        }
+    }
+}
+```
+
+<Note>
+  Make sure to install the necessary LangChain packages and any provider-specific dependencies.
+</Note>
+
+## Config
+
+All available parameters for the `langchain` embedder config are present in [Master List of All Params in Config](../config).

docs/components/embedders/models/lmstudio.mdx

@@ -0,0 +1,38 @@
+You can use embedding models from LM Studio to run Mem0 locally.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
+
+config = {
+    "embedder": {
+        "provider": "lmstudio",
+        "config": {
+            "model": "nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
+```
+
+### Config
+
+Here are the parameters available for configuring Ollama embedder:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the OpenAI model to use | `nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf` |
+| `embedding_dims` | Dimensions of the embedding model | `1536` |
+| `lmstudio_base_url` | Base URL for LM Studio connection | `http://localhost:1234/v1` |

docs/components/embedders/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
 
 ## Supported Embedders
@@ -22,6 +24,9 @@ See the list of supported embedders below.
   <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
   <Card title="Together" href="/components/embedders/models/together"></Card>
+  <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
+  <Card title="Langchain" href="/components/embedders/models/langchain"></Card>
+  <Card title="AWS Bedrock" href="/components/embedders/models/aws_bedrock"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -4,6 +4,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 ## How to define configurations?
 
 <Tabs>
@@ -29,7 +31,7 @@ iconType: "solid"
 Config values are applied in the following order of precedence (from highest to lowest):
 
 1. Values explicitly set in the `config` object/dictionary
-2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
+2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_BASE_URL`)
 3. Default values defined in the LLM implementation
 
 This means that values specified in the `config` will override corresponding environment variables, which in turn override default values.
@@ -56,6 +58,7 @@ config = {
 
 m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
+
 ```
 
 ```typescript TypeScript
@@ -74,6 +77,7 @@ const config = {
 const memory = new Memory(config);
 await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
 ```
+
 </CodeGroup>
 
 ## Why is Config Needed?
@@ -108,6 +112,13 @@ Here's a comprehensive list of all parameters that can be used across different
     | `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
+    | `sarvam_base_url`    | Base URL for Sarvam API                       | Sarvam            |
+    | `reasoning_effort`   | Reasoning level (low, medium, high)           | Sarvam            |
+    | `frequency_penalty`  | Penalize frequent tokens (-2.0 to 2.0)        | Sarvam            |
+    | `presence_penalty`   | Penalize existing tokens (-2.0 to 2.0)        | Sarvam            |
+    | `seed`               | Seed for deterministic sampling               | Sarvam            |
+    | `stop`               | Stop sequences (max 4)                        | Sarvam            |
+    | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
   </Tab>
   <Tab title="TypeScript">
     | Parameter            | Description                                   | Provider          |

docs/components/llms/models/anthropic.mdx

@@ -2,7 +2,9 @@
 title: Anthropic
 ---
 
-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).
+<Snippet file="security-compliance.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).
 
 ## Usage
 
@@ -18,7 +20,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-3-7-sonnet-latest",
+            "model": "claude-sonnet-4-20250514",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -43,7 +45,7 @@ const config = {
     provider: 'anthropic',
     config: {
       apiKey: process.env.ANTHROPIC_API_KEY || '',
-      model: 'claude-3-7-sonnet-latest',
+      model: 'claude-sonnet-4-20250514',
       temperature: 0.1,
       maxTokens: 2000,
     },

docs/components/llms/models/aws_bedrock.mdx

@@ -2,6 +2,8 @@
 title: AWS Bedrock
 ---
 
+<Snippet file="security-compliance.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)
@@ -13,16 +15,15 @@ title: AWS Bedrock
 import os
 from mem0 import Memory
 
-os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
-os.environ['AWS_REGION'] = 'us-east-1'
-os.environ["AWS_ACCESS_KEY"] = "xx"
+os.environ['AWS_REGION'] = 'us-west-2'
+os.environ["AWS_ACCESS_KEY_ID"] = "xx"
 os.environ["AWS_SECRET_ACCESS_KEY"] = "xx"
 
 config = {
     "llm": {
         "provider": "aws_bedrock",
         "config": {
-            "model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
+            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
             "temperature": 0.2,
             "max_tokens": 2000,
         }

docs/components/llms/models/azure_openai.mdx

@@ -2,14 +2,24 @@
 title: Azure OpenAI
 ---
 
+<Snippet file="security-compliance.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/).
 
+> **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
+
+
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+
 os.environ["LLM_AZURE_OPENAI_API_KEY"] = "your-api-key"
 os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
 os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
@@ -45,7 +55,38 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
-We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'azure_openai',
+    config: {
+      apiKey: process.env.AZURE_OPENAI_API_KEY || '',
+      modelProperties: {
+        endpoint: 'https://your-api-base-url',
+        deployment: 'your-deployment-name',
+        modelName: 'your-model-name',
+        apiVersion: 'version-to-use',
+        // Any other parameters you want to pass to the Azure OpenAI API
+      },
+    },
+  },
+};
+
+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>
+
+
+We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model. Typescript SDK does not support the `azure_openai_structured` model yet.
 
 ```python
 import os

docs/components/llms/models/deepseek.mdx

@@ -2,6 +2,8 @@
 title: DeepSeek
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").
 
 ## Usage

docs/components/llms/models/gemini.mdx

@@ -2,37 +2,74 @@
 title: Gemini
 ---
 
-To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
+<Snippet file="security-compliance.mdx" />
+
+To use the Gemini model, set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
+
+> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
+
+> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
-os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
-os.environ["GEMINI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
+os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
 
 config = {
     "llm": {
         "provider": "gemini",
         "config": {
-            "model": "gemini-1.5-flash-latest",
+            "model": "gemini-2.0-flash-001",
             "temperature": 0.2,
             "max_tokens": 2000,
+            "top_p": 1.0
         }
     }
 }
 
 m = Memory.from_config(config)
+
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
 ]
+
 m.add(messages, user_id="alice", metadata={"category": "movies"})
+
 ```
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    llm: {
+        // You can also use "google" as provider ( for backward compatibility )
+        provider: "gemini",
+        config: {
+            model: "gemini-2.0-flash-001",
+            temperature: 0.1
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+    { role: "user", content: "I’m not a big fan of thrillers, but I love sci-fi movies." },
+    { role: "assistant", content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead." }
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
 
 ## Config
 

docs/components/llms/models/google_AI.mdx

@@ -2,6 +2,8 @@
 title: Google AI
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
 
 ## Usage

docs/components/llms/models/groq.mdx

@@ -2,6 +2,8 @@
 title: Groq
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 [Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
 
 In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key. Set the API key as `GROQ_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/models/langchain.mdx

@@ -0,0 +1,110 @@
+---
+title: LangChain
+---
+
+<Snippet file="security-compliance.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.
+
+For a complete list of available chat models supported by LangChain, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+from langchain_openai import ChatOpenAI
+
+# Set necessary environment variables for your chosen LangChain provider
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Initialize a LangChain model directly
+openai_model = ChatOpenAI(
+    model="gpt-4o",
+    temperature=0.2,
+    max_tokens=2000
+)
+
+# Pass the initialized model to the config
+config = {
+    "llm": {
+        "provider": "langchain",
+        "config": {
+            "model": openai_model
+        }
+    }
+}
+
+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"})
+```
+
+```typescript TypeScript
+import { Memory } from "mem0ai";
+import { ChatOpenAI } from "@langchain/openai";
+
+const openai_model = new ChatOpenAI({
+    model: "gpt-4o",
+    temperature: 0.2,
+    max_tokens: 2000
+})
+
+const config = {
+    "llm": {
+        "provider": "langchain",
+        "config": {
+            "model": openai_model
+        }
+    }
+}
+
+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." }
+]
+
+memory.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+</CodeGroup>
+
+## Supported LangChain Providers
+
+LangChain supports a wide range of LLM providers, including:
+
+- OpenAI (`ChatOpenAI`)
+- Anthropic (`ChatAnthropic`)
+- Google (`ChatGoogleGenerativeAI`, `ChatGooglePalm`)
+- Mistral (`ChatMistralAI`)
+- Ollama (`ChatOllama`)
+- Azure OpenAI (`AzureChatOpenAI`)
+- HuggingFace (`HuggingFaceChatEndpoint`)
+- And many more
+
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available providers, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
+
+## Provider-Specific Configuration
+
+When using LangChain as a provider, you'll need to:
+
+1. Set the appropriate environment variables for your chosen LLM provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
+
+<Note>
+  Make sure to install the necessary LangChain packages and any provider-specific dependencies.
+</Note>
+
+## Config
+
+All available parameters for the `langchain` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/litellm.mdx

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

docs/components/llms/models/lmstudio.mdx

@@ -0,0 +1,85 @@
+---
+title: LM Studio
+---
+
+<Snippet file="security-compliance.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
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+
+config = {
+    "llm": {
+        "provider": "lmstudio",
+        "config": {
+            "model": "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf",
+            "temperature": 0.2,
+            "max_tokens": 2000,
+            "lmstudio_base_url": "http://localhost:1234/v1", # default LM Studio API URL
+            "lmstudio_response_format": {"type": "json_schema", "json_schema": {"type": "object", "schema": {}}},
+        }
+    }
+}
+
+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"})
+```
+</CodeGroup>
+
+### Running Completely Locally
+
+You can also use LM Studio for both LLM and embedding to run Mem0 entirely locally:
+
+```python
+from mem0 import Memory
+
+# No external API keys needed!
+config = {
+    "llm": {
+        "provider": "lmstudio"
+    },
+    "embedder": {
+        "provider": "lmstudio"
+    }
+}
+
+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="alice123", metadata={"category": "movies"})
+```
+
+<Note>
+  When using LM Studio for both LLM and embedding, make sure you have:
+  1. An LLM model loaded for generating responses
+  2. An embedding model loaded for vector embeddings
+  3. The server enabled with the correct endpoints accessible
+</Note>
+
+<Note>
+  To use LM Studio, you need to:
+  1. Download and install [LM Studio](https://lmstudio.ai/)
+  2. Start a local server from the "Server" tab
+  3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually http://localhost:1234/v1)
+</Note>
+
+## Config
+
+All available parameters for the `lmstudio` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/mistral_AI.mdx

@@ -2,11 +2,14 @@
 title: Mistral AI
 ---
 
-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.
+<Snippet file="security-compliance.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
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -34,6 +37,32 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'mistral',
+    config: {
+      apiKey: process.env.MISTRAL_API_KEY || '',
+      model: 'mistral-tiny-latest', // Or 'mistral-small-latest', 'mistral-medium-latest', etc.
+      temperature: 0.1,
+      maxTokens: 2000,
+    },
+  },
+};
+
+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 `litellm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/ollama.mdx

@@ -1,3 +1,5 @@
+<Snippet file="security-compliance.mdx" />
+
 You can use LLMs from Ollama to run Mem0 locally. These [models](https://ollama.com/search?c=tools) support tool support.
 
 ## Usage

docs/components/llms/models/openai.mdx

@@ -2,6 +2,8 @@
 title: OpenAI
 ---
 
+<Snippet file="security-compliance.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).
 
 ## Usage
@@ -92,10 +94,6 @@ config = {
 m = Memory.from_config(config)
 ```
 
-<Note>
-  OpenAI structured-outputs is currently only available in the Python implementation.
-</Note>
-
 ## Config
 
-All available parameters for the `openai` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `openai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/sarvam.mdx

@@ -0,0 +1,75 @@
+---
+title: Sarvam AI
+---
+
+<Snippet file="security-compliance.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/).
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["SARVAM_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "sarvam",
+        "config": {
+            "model": "sarvam-m",
+            "temperature": 0.7,
+        }
+    }
+}
+
+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="alex")
+```
+
+## Advanced Usage with Sarvam-Specific Features
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "llm": {
+        "provider": "sarvam",
+        "config": {
+            "model": {
+                "name": "sarvam-m",
+                "reasoning_effort": "high",  # Enable advanced reasoning
+                "frequency_penalty": 0.1,    # Reduce repetition
+                "seed": 42                   # For deterministic outputs
+            },
+            "temperature": 0.3,
+            "max_tokens": 2000,
+            "api_key": "your-sarvam-api-key"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+
+# Example with Hindi conversation
+messages = [
+    {"role": "user", "content": "मैं SBI में joint account खोलना चाहता हूँ।"},
+    {"role": "assistant", "content": "SBI में joint account खोलने के लिए आपको कुछ documents की जरूरत होगी। क्या आप जानना चाहते हैं कि कौन से documents चाहिए?"}
+]
+m.add(messages, user_id="rajesh", metadata={"language": "hindi", "topic": "banking"})
+```
+
+## Config
+
+All available parameters for the `sarvam` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/together.mdx

@@ -1,3 +1,5 @@
+<Snippet file="security-compliance.mdx" />
+
 To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
 
 ## Usage

docs/components/llms/models/vllm.mdx

@@ -0,0 +1,109 @@
+---
+title: vLLM
+---
+
+<Snippet file="security-compliance.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
+
+1. **Install vLLM**:
+
+   ```bash
+   pip install vllm
+   ```
+
+2. **Start vLLM server**:
+
+   ```bash
+   # For testing with a small model
+   vllm serve microsoft/DialoGPT-medium --port 8000
+
+   # For production with a larger model (requires GPU)
+   vllm serve Qwen/Qwen2.5-32B-Instruct --port 8000
+   ```
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"  # used for embedding model
+
+config = {
+    "llm": {
+        "provider": "vllm",
+        "config": {
+            "model": "Qwen/Qwen2.5-32B-Instruct",
+            "vllm_base_url": "http://localhost:8000/v1",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+## Configuration Parameters
+
+| Parameter       | Description                       | Default                       | Environment Variable |
+| --------------- | --------------------------------- | ----------------------------- | -------------------- |
+| `model`         | Model name running on vLLM server | `"Qwen/Qwen2.5-32B-Instruct"` | -                    |
+| `vllm_base_url` | vLLM server URL                   | `"http://localhost:8000/v1"`  | `VLLM_BASE_URL`      |
+| `api_key`       | API key (dummy for local)         | `"vllm-api-key"`              | `VLLM_API_KEY`       |
+| `temperature`   | Sampling temperature              | `0.1`                         | -                    |
+| `max_tokens`    | Maximum tokens to generate        | `2000`                        | -                    |
+
+## Environment Variables
+
+You can set these environment variables instead of specifying them in config:
+
+```bash
+export VLLM_BASE_URL="http://localhost:8000/v1"
+export VLLM_API_KEY="your-vllm-api-key"
+export OPENAI_API_KEY="your-openai-api-key"  # for embeddings
+```
+
+## Benefits
+
+- **High Performance**: 2-24x faster inference than standard implementations
+- **Memory Efficient**: Optimized memory usage with PagedAttention
+- **Local Deployment**: Keep your data private and reduce API costs
+- **Easy Integration**: Drop-in replacement for other LLM providers
+- **Flexible**: Works with any model supported by vLLM
+
+## Troubleshooting
+
+1. **Server not responding**: Make sure vLLM server is running
+
+   ```bash
+   curl http://localhost:8000/health
+   ```
+
+2. **404 errors**: Ensure correct base URL format
+
+   ```python
+   "vllm_base_url": "http://localhost:8000/v1"  # Note the /v1
+   ```
+
+3. **Model not found**: Check model name matches server
+
+4. **Out of memory**: Try smaller models or reduce `max_model_len`
+
+   ```bash
+   vllm serve Qwen/Qwen2.5-32B-Instruct --max-model-len 4096
+   ```
+
+## Config
+
+All available parameters for the `vllm` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/xAI.mdx

@@ -2,6 +2,8 @@
 title: xAI
 ---
 
+<Snippet file="security-compliance.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.
@@ -19,7 +21,7 @@ config = {
     "llm": {
         "provider": "xai",
         "config": {
-            "model": "grok-2-latest",
+            "model": "grok-3-beta",
             "temperature": 0.1,
             "max_tokens": 2000,
         }

docs/components/llms/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.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
@@ -12,7 +14,9 @@ To use a llm, you must provide a configuration to customize its usage. If no con
 
 For a comprehensive list of available parameters for llm configuration, please refer to [Config](./config).
 
-To view all supported llms, visit the [Supported LLMs](./models).
+## Supported LLMs
+
+See the list of supported LLMs below.
 
 <Note>
   All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
@@ -32,6 +36,9 @@ To view all supported llms, visit the [Supported LLMs](./models).
   <Card title="Gemini" href="/components/llms/models/gemini" />
   <Card title="DeepSeek" href="/components/llms/models/deepseek" />
   <Card title="xAI" href="/components/llms/models/xAI" />
+  <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
+  <Card title="LM Studio" href="/components/llms/models/lmstudio" />
+  <Card title="Langchain" href="/components/llms/models/langchain" />
 </CardGroup>
 
 ## Structured vs Unstructured Outputs

docs/components/vectordbs/config.mdx

@@ -4,12 +4,15 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.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", "azure_ai_search")
-  - `config`: A nested object containing provider-specific settings
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search")
+  - `config`: A nested dictionary containing provider-specific settings
+
 
 ## How to Use Config
 
@@ -86,6 +89,12 @@ Here's a comprehensive list of all parameters that can be used across different
 | `url` | Full URL for the server |
 | `api_key` | API key for the server |
 | `on_disk` | Enable persistent storage |
+| `endpoint_id` | Endpoint ID (vertex_ai_vector_search) |
+| `index_id` | Index ID (vertex_ai_vector_search) |
+| `deployment_index_id` | Deployment index ID (vertex_ai_vector_search) |
+| `project_id` | Project ID (vertex_ai_vector_search) |
+| `project_number` | Project number (vertex_ai_vector_search) |
+| `vector_search_api_endpoint` | Vector search API endpoint (vertex_ai_vector_search) |
 | `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
 | `index_method` | Vector index method (for Supabase) |
 | `index_measure` | Distance measure for similarity search (for Supabase) |

docs/components/vectordbs/dbs/azure.mdx

@@ -0,0 +1,99 @@
+---
+title: Azure AI Search
+---
+
+[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"   # This key is used for embedding purpose
+
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 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 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"})
+```
+
+## Using binary compression for large vector collections
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "compression_type": "binary",
+            "use_float16": True  # Use half precision for storage efficiency
+        }
+    }
+}
+```
+
+## Using hybrid search
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "hybrid_search": True,
+            "vector_filter_mode": "postFilter"
+        }
+    }
+}
+```
+
+## 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 | - |
+| `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` |
+| `use_float16` | Store vectors in half precision (Edm.Half) | `False` | `True`, `False` |
+| `vector_filter_mode` | Vector filter mode to use | `preFilter` | `postFilter`, `preFilter` |
+| `hybrid_search` | Use hybrid search | `False` | `True`, `False` |
+
+## Notes on Configuration Options
+
+- **compression_type**: 
+  - `none`: No compression, uses full vector precision
+  - `scalar`: Scalar quantization with reasonable balance of speed and accuracy
+  - `binary`: Binary quantization for maximum compression with some accuracy trade-off
+
+- **vector_filter_mode**:
+  - `preFilter`: Applies filters before vector search (faster)
+  - `postFilter`: Applies filters after vector search (may provide better relevance)
+
+- **use_float16**: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
+
+- **Filterable Fields**: The implementation automatically extracts `user_id`, `run_id`, and `agent_id` fields from payloads for filtering.

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -1,44 +0,0 @@
-[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
-
-### Usage
-
-```python
-import os
-from mem0 import Memory
-
-os.environ["OPENAI_API_KEY"] = "sk-xx"   #this key is used for embedding purpose
-
-config = {
-    "vector_store": {
-        "provider": "azure_ai_search",
-        "config": {
-            "service_name": "ai-search-test",
-            "api_key": "*****",
-            "collection_name": "mem0", 
-            "embedding_model_dims": 1536 ,
-            "use_compression": False
-        }
-    }
-}
-
-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"})
-```
-
-### Config
-
-Let's see the available parameters for the `qdrant` config:
-service_name (str): Azure Cognitive Search service name.  
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `service_name` | Azure AI Search service name | `None` |
-| `api_key` | API key of the Azure AI Search service | `None` |
-| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `use_compression` | Use scalar quantization vector compression | False |

docs/components/vectordbs/dbs/baidu.mdx

@@ -0,0 +1,67 @@
+---
+title: Baidu VectorDB (Mochow)
+---
+
+[Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "baidu",
+        "config": {
+            "endpoint": "http://your-mochow-endpoint:8287",
+            "account": "root",
+            "api_key": "your-api-key",
+            "database_name": "mem0",
+            "table_name": "mem0_table",
+            "embedding_model_dims": 1536,
+            "metric_type": "COSINE"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+### Config
+
+Here are the available parameters for the `mochow` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
+| `account` | Baidu VectorDB account name | `root` |
+| `api_key` | API key for accessing Baidu VectorDB | Required |
+| `database_name` | Name of the database | `mem0` |
+| `table_name` | Name of the table | `mem0_table` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `metric_type` | Distance metric for similarity search | `L2` |
+
+### Distance Metrics
+
+The following distance metrics are supported:
+
+- `L2`: Euclidean distance (default)
+- `IP`: Inner product
+- `COSINE`: Cosine similarity
+
+### Index Configuration
+
+The vector index is automatically configured with the following HNSW parameters:
+
+- `m`: 16 (number of connections per element)
+- `efconstruction`: 200 (size of the dynamic candidate list)
+- `auto_build`: true (automatically build index)
+- `auto_build_index_policy`: Incremental build with 10000 rows increment

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -54,6 +54,7 @@ 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`        |
 
 ### Features
 
@@ -62,3 +63,46 @@ Let's see the available parameters for the `elasticsearch` config:
 - Multiple authentication methods (Basic Auth, API Key)
 - Automatic index creation with optimized mappings for vector search
 - Memory isolation through payload filtering
+- Custom search query function to customize the search query
+
+### Custom Search Query
+
+The `custom_search_query` parameter allows you to customize the search query when `Memory.search` is called.  
+  
+__Example__  
+```python
+import os
+from typing import List, Optional, Dict
+from mem0 import Memory
+
+def custom_search_query(query: List[float], limit: int, filters: Optional[Dict]) -> Dict:
+    return {
+        "knn": {
+            "field": "vector", 
+            "query_vector": query, 
+            "k": limit, 
+            "num_candidates": limit * 2
+        }
+    }
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536,
+            "custom_search_query": custom_search_query
+        }
+    }
+}
+```
+It should be a function that takes the following parameters:
+- `query`: a query vector used in `Memory.search`
+- `limit`: a number of results used in `Memory.search`
+- `filters`: a dictionary of key-value pairs used in `Memory.search`. You can add custom pairs for the custom search query.  
+  
+The function should return a query body for the Elasticsearch search API.

docs/components/vectordbs/dbs/faiss.mdx

@@ -0,0 +1,72 @@
+[FAISS](https://github.com/facebookresearch/faiss) is a library for efficient similarity search and clustering of dense vectors. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data. FAISS is optimized for memory usage and search speed, making it an excellent choice for production environments.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "faiss",
+        "config": {
+            "collection_name": "test",
+            "path": "/tmp/faiss_memories",
+            "distance_strategy": "euclidean"
+        }
+    }
+}
+
+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"})
+```
+
+### Installation
+
+To use FAISS in your mem0 project, you need to install the appropriate FAISS package for your environment:
+
+```bash
+# For CPU version
+pip install faiss-cpu
+
+# For GPU version (requires CUDA)
+pip install faiss-gpu
+```
+
+### Config
+
+Here are the parameters available for configuring FAISS:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection | `mem0` |
+| `path` | Path to store FAISS index and metadata | `/tmp/faiss/<collection_name>` |
+| `distance_strategy` | Distance metric strategy to use (options: 'euclidean', 'inner_product', 'cosine') | `euclidean` |
+| `normalize_L2` | Whether to normalize L2 vectors (only applicable for euclidean distance) | `False` |
+
+### Performance Considerations
+
+FAISS offers several advantages for vector search:
+
+1. **Efficiency**: FAISS is optimized for memory usage and speed, making it suitable for large-scale applications.
+2. **Offline Support**: FAISS works entirely locally, with no need for external servers or API calls.
+3. **Storage Options**: Vectors can be stored in-memory for maximum speed or persisted to disk.
+4. **Multiple Index Types**: FAISS supports different index types optimized for various use cases (though mem0 currently uses the basic flat index).
+
+### Distance Strategies
+
+FAISS in mem0 supports three distance strategies:
+
+- **euclidean**: L2 distance, suitable for most embedding models
+- **inner_product**: Dot product similarity, useful for some specialized embeddings
+- **cosine**: Cosine similarity, best for comparing semantic similarity regardless of vector magnitude
+
+When using `cosine` or `inner_product` with normalized vectors, you may want to set `normalize_L2=True` for better results.

docs/components/vectordbs/dbs/langchain.mdx

@@ -0,0 +1,112 @@
+---
+title: LangChain
+---
+
+Mem0 supports LangChain as a provider for vector store integration. LangChain provides a unified interface to various vector databases, making it easy to integrate different vector store providers through a consistent API.
+
+<Note>
+  When using LangChain as your vector store provider, you must set the collection name to "mem0". This is a required configuration for proper integration with Mem0.
+</Note>
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+from langchain_community.vectorstores import Chroma
+from langchain_openai import OpenAIEmbeddings
+
+# Initialize a LangChain vector store
+embeddings = OpenAIEmbeddings()
+vector_store = Chroma(
+    persist_directory="./chroma_db",
+    embedding_function=embeddings,
+    collection_name="mem0"  # Required collection name
+)
+
+# Pass the initialized vector store to the config
+config = {
+    "vector_store": {
+        "provider": "langchain",
+        "config": {
+            "client": vector_store
+        }
+    }
+}
+
+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"})
+```
+
+```typescript TypeScript
+import { Memory } from "mem0ai";
+import { OpenAIEmbeddings } from "@langchain/openai";
+import { MemoryVectorStore as LangchainMemoryStore } from "langchain/vectorstores/memory";
+
+const embeddings = new OpenAIEmbeddings();
+const vectorStore = new LangchainVectorStore(embeddings);
+
+const config = {
+    "vector_store": {
+        "provider": "langchain",
+        "config": { "client": vectorStore }
+    }
+}
+
+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." }
+]
+
+memory.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+</CodeGroup>
+
+## Supported LangChain Vector Stores
+
+LangChain supports a wide range of vector store providers, including:
+
+- Chroma
+- FAISS
+- Pinecone
+- Weaviate
+- Milvus
+- Qdrant
+- And many more
+
+You can use any of these vector store instances directly in your configuration. For a complete and up-to-date list of available providers, refer to the [LangChain Vector Stores documentation](https://python.langchain.com/docs/integrations/vectorstores).
+
+## Limitations
+
+When using LangChain as a vector store provider, there are some limitations to be aware of:
+
+1. **Bulk Operations**: The `get_all` and `delete_all` operations are not supported when using LangChain as the vector store provider. This is because LangChain's vector store interface doesn't provide standardized methods for these bulk operations across all providers.
+
+2. **Provider-Specific Features**: Some advanced features may not be available depending on the specific vector store implementation you're using through LangChain.
+
+## Provider-Specific Configuration
+
+When using LangChain as a vector store provider, you'll need to:
+
+1. Set the appropriate environment variables for your chosen vector store provider
+2. Import and initialize the specific vector store class you want to use
+3. Pass the initialized vector store instance to the config
+
+<Note>
+  Make sure to install the necessary LangChain packages and any provider-specific dependencies.
+</Note>
+
+## Config
+
+All available parameters for the `langchain` vector store config are present in [Master List of All Params in Config](../config).

docs/components/vectordbs/dbs/mongodb.mdx

@@ -0,0 +1,45 @@
+# MongoDB
+
+[MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "mongodb",
+        "config": {
+            "db_name": "mem0-db",
+            "collection_name": "mem0-collection",
+            "mongo_uri":"mongodb://username:password@localhost:27017"
+        }
+    }
+}
+
+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"})
+```
+
+## Config
+
+Here are the parameters available for configuring MongoDB:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| db_name | Name of the MongoDB database | `"mem0_db"` |
+| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
+| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
+| mongo_uri | The mongo URI connection string | mongodb://username:password@localhost:27017 |
+
+> **Note**: If Mongo_uri is not provided it will default to mongodb://username:password@localhost:27017.

docs/components/vectordbs/dbs/opensearch.mdx

@@ -1,59 +1,75 @@
-[OpenSearch](https://opensearch.org/) is an open-source, enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
+[OpenSearch](https://opensearch.org/) is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
 
 ### Installation
 
 OpenSearch support requires additional dependencies. Install them with:
 
 ```bash
-pip install opensearch>=2.8.0
+pip install opensearch-py
 ```
 
+### Prerequisites
+
+Before using OpenSearch with Mem0, you need to set up a collection in AWS OpenSearch Service.
+
+#### AWS OpenSearch Service
+You can create a collection through the AWS Console:
+- Navigate to [OpenSearch Service Console](https://console.aws.amazon.com/aos/home)
+- Click "Create collection"
+- Select "Serverless collection" and then enable "Vector search" capabilities
+- Once created, note the endpoint URL (host) for your configuration
+
+
 ### Usage
 
 ```python
 import os
 from mem0 import Memory
+import boto3
+from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
 
-os.environ["OPENAI_API_KEY"] = "sk-xx"
+# For AWS OpenSearch Service with IAM authentication
+region = 'us-west-2'
+service = 'aoss'
+credentials = boto3.Session().get_credentials()
+auth = AWSV4SignerAuth(credentials, region, service)
 
 config = {
     "vector_store": {
         "provider": "opensearch",
         "config": {
             "collection_name": "mem0",
-            "host": "localhost",
-            "port": 9200,
-            "embedding_model_dims": 1536
+            "host": "your-domain.us-west-2.aoss.amazonaws.com",
+            "port": 443,
+            "http_auth": auth,
+            "embedding_model_dims": 1024,
+            "connection_class": RequestsHttpConnection,
+            "pool_maxsize": 20,
+            "use_ssl": True,
+            "verify_certs": True
         }
     }
 }
+```
 
+### Add Memories
+
+```python
 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"})
 ```
 
-### Config
+### Search Memories
 
-Let's see the available parameters for the `opensearch` config:
-
-| Parameter              | Description                                        | Default Value |
-| ---------------------- | -------------------------------------------------- | ------------- |
-| `collection_name`      | The name of the index to store the vectors         | `mem0`        |
-| `embedding_model_dims` | Dimensions of the embedding model                  | `1536`        |
-| `host`                 | The host where the OpenSearch server is running    | `localhost`   |
-| `port`                 | The port where the OpenSearch server is running    | `9200`        |
-| `api_key`              | API key for authentication                         | `None`        |
-| `user`                 | Username for basic authentication                  | `None`        |
-| `password`             | Password for basic authentication                  | `None`        |
-| `verify_certs`         | Whether to verify SSL certificates                 | `False`        |
-| `auto_create_index`    | Whether to automatically create the index          | `True`        |
-| `use_ssl`              | Whether to use SSL for connection                  | `False`        |
+```python
+results = m.search("What kind of movies does Alice like?", user_id="alice")
+```
 
 ### Features
 

docs/components/vectordbs/dbs/pinecone.mdx

@@ -0,0 +1,92 @@
+[Pinecone](https://www.pinecone.io/) is a fully managed vector database designed for machine learning applications, offering high performance vector search with low latency at scale. It's particularly well-suited for semantic search, recommendation systems, and other AI-powered applications.
+
+> **Note**: Before configuring Pinecone, you need to select an embedding model (e.g., OpenAI, Cohere, or custom models) and ensure the `embedding_model_dims` in your config matches your chosen model's dimensions. For example, OpenAI's text-embedding-3-small uses 1536 dimensions.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+os.environ["PINECONE_API_KEY"] = "your-api-key"
+
+# Example using serverless configuration
+config = {
+    "vector_store": {
+        "provider": "pinecone",
+        "config": {
+            "collection_name": "testing",
+            "embedding_model_dims": 1536,  # Matches OpenAI's text-embedding-3-small
+            "serverless_config": {
+                "cloud": "aws",  # Choose between 'aws' or 'gcp' or 'azure'
+                "region": "us-east-1"
+            },
+            "metric": "cosine"
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller 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 Pinecone:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | Name of the index/collection | Required |
+| `embedding_model_dims` | Dimensions of the embedding model (must match your chosen embedding model) | Required |
+| `client` | Existing Pinecone client instance | `None` |
+| `api_key` | API key for Pinecone | Environment variable: `PINECONE_API_KEY` |
+| `environment` | Pinecone environment | `None` |
+| `serverless_config` | Configuration for serverless deployment (AWS or GCP or Azure) | `None` |
+| `pod_config` | Configuration for pod-based deployment | `None` |
+| `hybrid_search` | Whether to enable hybrid search | `False` |
+| `metric` | Distance metric for vector similarity | `"cosine"` |
+| `batch_size` | Batch size for operations | `100` |
+
+> **Important**: You must choose either `serverless_config` or `pod_config` for your deployment, but not both.
+
+#### Serverless Config Example
+```python
+config = {
+    "vector_store": {
+        "provider": "pinecone",
+        "config": {
+            "collection_name": "memory_index",
+            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-3-small
+            "serverless_config": {
+                "cloud": "aws",  # or "gcp" or "azure"
+                "region": "us-east-1"  # Choose appropriate region
+            }
+        }
+    }
+}
+```
+
+#### Pod Config Example
+```python
+config = {
+    "vector_store": {
+        "provider": "pinecone",
+        "config": {
+            "collection_name": "memory_index",
+            "embedding_model_dims": 1536,  # For OpenAI's text-embedding-ada-002
+            "pod_config": {
+                "environment": "gcp-starter",
+                "replicas": 1,
+                "pod_type": "starter"
+            }
+        }
+    }
+}
+```

docs/components/vectordbs/dbs/supabase.mdx

@@ -4,7 +4,8 @@ Create a [Supabase](https://supabase.com/dashboard/projects) account and project
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -32,10 +33,90 @@ messages = [
 m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript Typescript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    vectorStore: {
+      provider: "supabase",
+      config: {
+        collectionName: "memories",
+        embeddingModelDims: 1536,
+        supabaseUrl: process.env.SUPABASE_URL || "",
+        supabaseKey: process.env.SUPABASE_KEY || "",
+        tableName: "memories",
+      },
+    },
+}
+
+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>
+
+### SQL Migrations for TypeScript Implementation
+
+The following SQL migrations are required to enable the vector extension and create the memories table:
+
+```sql
+-- Enable the vector extension
+create extension if not exists vector;
+
+-- Create the memories table
+create table if not exists memories (
+  id text primary key,
+  embedding vector(1536),
+  metadata jsonb,
+  created_at timestamp with time zone default timezone('utc', now()),
+  updated_at timestamp with time zone default timezone('utc', now())
+);
+
+-- Create the vector similarity search function
+create or replace function match_vectors(
+  query_embedding vector(1536),
+  match_count int,
+  filter jsonb default '{}'::jsonb
+)
+returns table (
+  id text,
+  similarity float,
+  metadata jsonb
+)
+language plpgsql
+as $$
+begin
+  return query
+  select
+    t.id::text,
+    1 - (t.embedding <=> query_embedding) as similarity,
+    t.metadata
+  from memories t
+  where case
+    when filter::text = '{}'::text then true
+    else t.metadata @> filter
+  end
+  order by t.embedding <=> query_embedding
+  limit match_count;
+end;
+$$;
+```
+
+Goto [Supabase](https://supabase.com/dashboard/projects) and run the above SQL migrations inside the SQL Editor.
+
 ### Config
 
 Here are the parameters available for configuring Supabase:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `connection_string` | PostgreSQL connection string (required) | None |
@@ -43,6 +124,17 @@ Here are the parameters available for configuring Supabase:
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `index_method` | Vector index method to use | `auto` |
 | `index_measure` | Distance measure for similarity search | `cosine_distance` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | Name for the vector collection | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `supabaseUrl` | Supabase URL | None |
+| `supabaseKey` | Supabase key | None |
+| `tableName` | Name for the vector table | `memories` |
+</Tab>
+</Tabs>
 
 ### Index Methods
 

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

@@ -0,0 +1,70 @@
+[Upstash Vector](https://upstash.com/docs/vector) is a serverless vector database with built-in embedding models.
+
+### Usage with Upstash embeddings
+
+You can enable the built-in embedding models by setting `enable_embeddings` to `True`. This allows you to use Upstash's embedding models for vectorization.
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
+os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
+
+config = {
+    "vector_store": {
+        "provider": "upstash_vector",
+        "enable_embeddings": True,
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+<Note>
+    Setting `enable_embeddings` to `True` will bypass any external embedding provider you have configured.
+</Note>
+
+### Usage with external embedding providers
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "..."
+os.environ["UPSTASH_VECTOR_REST_URL"] = "..."
+os.environ["UPSTASH_VECTOR_REST_TOKEN"] = "..."
+
+config = {
+    "vector_store": {
+        "provider": "upstash_vector",
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        },
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+### Config
+
+Here are the parameters available for configuring Upstash Vector:
+
+| Parameter           | Description                        | Default Value |
+| ------------------- | ---------------------------------- | ------------- |
+| `url`               | URL for the Upstash Vector index   | `None`        |
+| `token`             | Token for the Upstash Vector index | `None`        |
+| `client`            | An `upstash_vector.Index` instance | `None`        |
+| `collection_name`   | The default namespace used         | `""`          |
+| `enable_embeddings` | Whether to use Upstash embeddings  | `False`       |
+
+<Note>
+  When `url` and `token` are not provided, the `UPSTASH_VECTOR_REST_URL` and
+  `UPSTASH_VECTOR_REST_TOKEN` environment variables are used.
+</Note>

docs/components/vectordbs/dbs/vectorize.mdx

@@ -0,0 +1,45 @@
+[Cloudflare Vectorize](https://developers.cloudflare.com/vectorize/) is a vector database offering from Cloudflare, allowing you to build AI-powered applications with vector embeddings.
+
+### Usage
+
+<CodeGroup>
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'vectorize',
+    config: {
+      indexName: 'my-memory-index',
+      accountId: 'your-cloudflare-account-id',
+      apiKey: 'your-cloudflare-api-key',
+      dimension: 1536, // Optional: defaults to 1536
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm looking for a good book to read."},
+    {"role": "assistant", "content": "Sure, what genre are you interested in?"},
+    {"role": "user", "content": "I enjoy fantasy novels with strong world-building."},
+    {"role": "assistant", "content": "Great! I'll keep that in mind for future recommendations."}
+]
+await memory.add(messages, { userId: "bob", metadata: { interest: "books" } });
+```
+</CodeGroup>
+
+### Config
+
+Let's see the available parameters for the `vectorize` config:
+
+<Tabs>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `indexName` | The name of the Vectorize index | `None` (Required) |
+| `accountId` | Your Cloudflare account ID | `None` (Required) |
+| `apiKey` | Your Cloudflare API token | `None` (Required) |
+| `dimension` | Dimensions of the embedding model | `1536` |
+</Tab>
+</Tabs>

docs/components/vectordbs/dbs/vertex_ai.mdx

@@ -0,0 +1,48 @@
+---
+title: Vertex AI Vector Search
+---
+
+
+### Usage
+
+To use Google Cloud Vertex AI Vector Search with `mem0`, you need to configure the `vector_store` in your `mem0` config:
+
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["GEMINI_API_KEY"] = = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "vertex_ai_vector_search",
+        "config": {
+            "endpoint_id": "YOUR_ENDPOINT_ID",            # Required: Vector Search endpoint ID
+            "index_id": "YOUR_INDEX_ID",                  # Required: Vector Search index ID 
+            "deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID",  # Required: Deployment-specific ID
+            "project_id": "YOUR_PROJECT_ID",              # Required: Google Cloud project ID
+            "project_number": "YOUR_PROJECT_NUMBER",      # Required: Google Cloud project number
+            "region": "YOUR_REGION",                      # Optional: Defaults to GOOGLE_CLOUD_REGION
+            "credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
+            "vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
+        }
+    }
+}
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+
+### Required Parameters
+
+| Parameter | Description | Required |
+|-----------|-------------|----------|
+| `endpoint_id` | Vector Search endpoint ID | Yes |
+| `index_id` | Vector Search index ID | Yes |
+| `deployment_index_id` | Deployment-specific index ID | Yes |
+| `project_id` | Google Cloud project ID | Yes |
+| `project_number` | Google Cloud project number | Yes |
+| `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
+| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
+| `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |

docs/components/vectordbs/dbs/weaviate.mdx

@@ -0,0 +1,47 @@
+[Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.
+
+
+### Installation
+```bash
+pip install weaviate weaviate-client
+```
+
+### Usage
+
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "weaviate",
+        "config": {
+            "collection_name": "test",
+            "cluster_url": "http://localhost:8080",
+            "auth_client_secret": None,
+        }
+    }
+}
+
+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
+
+Let's see the available parameters for the `weaviate` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `cluster_url` | URL for the Weaviate server | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |

docs/components/vectordbs/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.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
@@ -11,19 +13,26 @@ 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 and in-memory vector database.
+  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis,Vectorize and in-memory vector database.
 </Note>
 
 <CardGroup cols={3}>
   <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
   <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
   <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
+  <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
   <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
-  <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
+  <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
+  <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="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>
+  <Card title="Vertex AI" href="/components/vectordbs/dbs/vertex_ai"></Card>
+  <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>
 </CardGroup>
 
 ## Usage

docs/contributing/development.mdx

@@ -3,6 +3,8 @@ title: Development
 icon: "code"
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 # Development Contributions
 
 We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
@@ -27,15 +29,20 @@ For detailed guidance on pull requests, refer to [GitHub's documentation](https:
 
 ## 📦 Dependency Management
 
-We use `poetry` as our package manager. Install it by following the [official instructions](https://python-poetry.org/docs/#installation).
+We use `hatch` as our package manager. Install it by following the [official instructions](https://hatch.pypa.io/latest/install/).
 
-⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, run:
+⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, follow these steps in order:
 
 ```bash
-make install_all
+# 1. Install base dependencies
+make install
 
-# Activate virtual environment
-poetry shell
+# 2. Activate virtual environment (this will install deps.)
+hatch shell (for default env)
+hatch -e dev_py_3_11 shell (for dev_py_3_11) (differences are mentioned in pyproject.toml)
+
+# 3. Install all optional dependencies
+make install_all
 ```
 
 ---
@@ -58,9 +65,9 @@ Run the linter and fix any reported issues before submitting your PR:
 make lint
 ```
 
-### 🎨 Code Formatting with `black`
+### 🎨 Code Formatting
 
-To maintain a consistent code style, format your code using `black`:
+To maintain a consistent code style, format your code:
 
 ```bash
 make format
@@ -74,7 +81,7 @@ Run tests to verify functionality before submitting your PR:
 make test
 ```
 
-💡 **Note:** Some dependencies have been removed from Poetry to reduce package size. Run `make install_all` to install necessary dependencies before running tests.
+💡 **Note:** Some dependencies have been removed from the main dependencies to reduce package size. Run `make install_all` to install necessary dependencies before running tests.
 
 ---
 

docs/contributing/documentation.mdx

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

docs/core-concepts/memory-operations.mdx

@@ -5,6 +5,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.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.
 
 

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

@@ -0,0 +1,154 @@
+---
+title: Add Memory
+description: Add memory into the Mem0 platform by storing user-assistant interactions and facts for later retrieval.
+icon: "plus"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+The `add` operation is how you store memory into Mem0. Whether you're working with a chatbot, a voice assistant, or a multi-agent system, this is the entry point to create long-term memory.
+
+Memories typically come from a **user-assistant interaction** and Mem0 handles the extraction, transformation, and storage for you.
+
+Mem0 offers two implementation flows:
+
+- **Mem0 Platform** (Managed, scalable, with dashboard + API)
+- **Mem0 Open Source** (Lightweight, fully local, flexible SDKs)
+
+Each supports the same core memory operations, but with slightly different setup. Below, we walk through examples for both.
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the process of adding memories.">
+  <img src="../../images/add_architecture.png" />
+</Frame>
+
+When you call `add`, Mem0 performs the following steps under the hood:
+
+1. **Information Extraction**
+   The input messages are passed through an LLM that extracts key facts, decisions, preferences, or events worth remembering.
+
+2. **Conflict Resolution**
+   Mem0 compares the new memory against existing ones to detect duplication or contradiction and handles updates accordingly.
+
+3. **Memory Storage**
+   The result is stored in a vector database (for semantic search) and optionally in a graph structure (for relationship mapping).
+
+You don’t need to handle any of this manually, Mem0 takes care of it with a single API call or SDK method.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+messages = [
+    {"role": "user", "content": "I'm planning a trip to Tokyo next month."},
+    {"role": "assistant", "content": "Great! I’ll remember that for future suggestions."}
+]
+
+client.add(
+    messages=messages,
+    user_id="alice",
+    version="v2"
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const messages = [
+  { role: "user", content: "I'm planning a trip to Tokyo next month." },
+  { role: "assistant", content: "Great! I’ll remember that for future suggestions." }
+];
+
+await client.add({
+  messages,
+  user_id: "alice",
+  version: "v2"
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+m = Memory()
+
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+# Store inferred memories (default behavior)
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+
+# Optionally store raw messages without inference
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+
+const messages = [
+  {
+    role: "user",
+    content: "I like to drink coffee in the morning and go for a walk"
+  }
+];
+
+const result = memory.add(messages, {
+  userId: "alice",
+  metadata: { category: "preferences" }
+});
+```
+</CodeGroup>
+
+---
+
+## When Should You Add Memory?
+
+Add memory whenever your agent learns something useful:
+
+- A new user preference is shared
+- A decision or suggestion is made
+- A goal or task is completed
+- A new entity is introduced
+- A user gives feedback or clarification
+
+Storing this context allows the agent to reason better in future interactions.
+
+
+### More Details
+
+For full list of supported fields, required formats, and advanced options, see the
+[Add Memory API Reference](/api-reference/memory/add-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,143 @@
+---
+title: Delete Memory
+description: Remove memories from Mem0 either individually, in bulk, or via filters.
+icon: "trash"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.mdx" />
+
+## Overview
+
+Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
+
+1. **Delete a Single Memory**: Using a specific memory ID
+2. **Batch Delete**: Delete multiple known memory IDs (up to 1000)
+3. **Filtered Delete**: Delete memories matching a filter (e.g., `user_id`, `metadata`, `run_id`)
+
+This page walks through code example for each method.
+
+
+## Use Cases
+
+- Forget a user’s past preferences by request
+- Remove outdated or incorrect memory entries
+- Clean up memory after session expiration
+- Comply with data deletion requests (e.g., GDPR)
+
+---
+
+## 1. Delete a Single Memory by ID
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.delete(memory_id=memory_id)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.delete("your_memory_id")
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 2. Batch Delete Multiple Memories
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+delete_memories = [
+    {"memory_id": "id1"},
+    {"memory_id": "id2"}
+]
+
+response = client.batch_delete(delete_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const deleteMemories = [
+  { memory_id: "id1" },
+  { memory_id: "id2" }
+];
+
+client.batchDelete(deleteMemories)
+  .then(response => console.log('Batch delete response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## 3. Delete Memories by Filter (e.g., user_id)
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+# Delete all memories for a specific user
+client.delete_all(user_id="alice")
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+client.deleteAll({ user_id: "alice" })
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+You can also filter by other parameters such as:
+- `agent_id`
+- `run_id`
+- `metadata` (as JSON string)
+
+---
+
+## Key Differences
+
+| Method                | Use When                                | IDs Needed | Filters |
+|----------------------|-------------------------------------------|------------|----------|
+| `delete(memory_id)`  | You know exactly which memory to remove   | ✔          | ✘        |
+| `batch_delete([...])`| You have a known list of memory IDs       | ✔          | ✘        |
+| `delete_all(...)`    | You want to delete by user/agent/run/etc | ✘          | ✔        |
+
+
+### More Details
+
+For request/response schema and additional filtering options, see:
+- [Delete Memory API Reference](/api-reference/memory/delete-memory)
+- [Batch Delete API Reference](/api-reference/memory/batch-delete)
+- [Delete Memories by Filter Reference](/api-reference/memory/delete-memories)
+
+You’ve now seen how to add, search, update, and delete memories in Mem0.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,126 @@
+---
+title: Search Memory
+description: Retrieve relevant memories from Mem0 using powerful semantic and filtered search capabilities.
+icon: "magnifying-glass"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.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.
+
+Mem0 supports:
+- Semantic similarity search
+- Metadata filtering (with advanced logic)
+- Reranking and thresholds
+- Cross-agent, multi-session context resolution
+
+This applies to both:
+- **Mem0 Platform** (hosted API with full-scale features)
+- **Mem0 Open Source** (local-first with LLM inference and local vector DB)
+
+
+## Architecture
+
+<Frame caption="Architecture diagram illustrating the memory search process.">
+  <img src="../../images/search_architecture.png" />
+</Frame>
+
+The search flow follows these steps:
+
+1. **Query Processing**
+   An LLM refines and optimizes your natural language query.
+
+2. **Vector Search**
+   Semantic embeddings are used to find the most relevant memories using cosine similarity.
+
+3. **Filtering & Ranking**
+   Logical and comparison-based filters are applied. Memories are scored, filtered, and optionally reranked.
+
+4. **Results Delivery**
+   Relevant memories are returned with associated metadata and timestamps.
+
+---
+
+## Example: Mem0 Platform
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+query = "What do you know about me?"
+filters = {
+   "OR": [
+      {"user_id": "alice"},
+      {"agent_id": {"in": ["travel-assistant", "customer-support"]}}
+   ]
+}
+
+results = client.search(query, version="v2", filters=filters)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0ai";
+
+const client = new MemoryClient({apiKey: "your-api-key"});
+
+const query = "I'm craving some pizza. Any recommendations?";
+const filters = {
+  AND: [
+    { user_id: "alice" }
+  ]
+};
+
+const results = await client.search(query, {
+  version: "v2",
+  filters
+});
+```
+</CodeGroup>
+
+---
+
+## Example: Mem0 Open Source
+
+<CodeGroup>
+```python Python
+from mem0 import Memory
+
+m = Memory()
+related_memories = m.search("Should I drink coffee or tea?", user_id="alice")
+```
+
+```javascript JavaScript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+const relatedMemories = memory.search("Should I drink coffee or tea?", { userId: "alice" });
+```
+</CodeGroup>
+
+---
+
+## Tips for Better Search
+
+- Use descriptive natural queries (Mem0 can interpret intent)
+- Apply filters for scoped, faster lookup
+- Use `version: "v2"` for enhanced results
+- Consider wildcard filters (e.g., `run_id: "*"`) for broader matches
+- Tune with `top_k`, `threshold`, or `rerank` if needed
+
+
+### More Details
+
+For the full list of filter logic, comparison operators, and optional search parameters, see the
+[Search Memory API Reference](/api-reference/memory/v2-search-memories).
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

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

@@ -0,0 +1,119 @@
+---
+title: Update Memory
+description: Modify an existing memory by updating its content or metadata.
+icon: "pencil"
+iconType: "solid"
+---
+
+<Snippet file="security-compliance.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.
+
+Mem0 supports both:
+- **Single Memory Update** for one specific memory using its ID
+- **Batch Update** for updating many memories at once (up to 1000)
+
+This guide includes usage for both single update and batch update of memories through **Mem0 Platform**
+
+
+## Use Cases
+
+- Refine a vague or incorrect memory after a correction
+- Add or edit memory with new metadata (e.g., categories, tags)
+- Evolve factual knowledge as the user’s profile changes
+- A user profile evolves: “I love spicy food” → later says “Actually, I can’t handle spicy food.”
+
+Updating memory ensures your agents remain accurate, adaptive, and personalized.
+
+---
+
+## Update Memory
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+memory_id = "your_memory_id"
+client.update(
+    memory_id=memory_id,
+    text="Updated memory content about the user",
+    metadata={"category": "profile-update"}
+)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+const memory_id = "your_memory_id";
+
+client.update(memory_id, {
+  text: "Updated memory content about the user",
+  metadata: { category: "profile-update" }
+})
+  .then(result => console.log(result))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Batch Update
+
+Update up to 1000 memories in one call.
+
+<CodeGroup>
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(api_key="your-api-key")
+
+update_memories = [
+    {"memory_id": "id1", "text": "Watches football"},
+    {"memory_id": "id2", "text": "Likes to travel"}
+]
+
+response = client.batch_update(update_memories)
+print(response)
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: "your-api-key" });
+
+const updateMemories = [
+  { memoryId: "id1", text: "Watches football" },
+  { memoryId: "id2", text: "Likes to travel" }
+];
+
+client.batchUpdate(updateMemories)
+  .then(response => console.log('Batch update response:', response))
+  .catch(error => console.error(error));
+```
+</CodeGroup>
+
+---
+
+## Tips
+
+- You can update both `text` and `metadata` in the same call.
+- Use `batchUpdate` when you're applying similar corrections at scale.
+- If memory is marked `immutable`, it must first be deleted and re-added.
+- Combine this with feedback mechanisms (e.g., user thumbs-up/down) to self-improve memory.
+
+
+### More Details
+
+Refer to the full [Update Memory API Reference](/api-reference/memory/update-memory) and [Batch Update Reference](/api-reference/memory/batch-update) for schema and advanced fields.
+
+---
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>

docs/core-concepts/memory-types.mdx

@@ -4,6 +4,9 @@ description: Understanding different types of memory in AI Applications
 icon: "memory"
 iconType: "solid"
 ---
+
+<Snippet file="security-compliance.mdx" />
+
 To build useful AI applications, we need to understand how different memory systems work together. This guide explores the fundamental types of memory in AI systems and shows how Mem0 implements these concepts.
 
 ## Why Memory Matters

docs/docs.json

@@ -19,10 +19,10 @@
             "tab": "Documentation",
             "groups": [
               {
-                "group": "Get Started",
+                "group": "Getting Started",
                 "icon": "rocket",
                 "pages": [
-                  "overview",
+                  "what-is-mem0",
                   "quickstart",
                   "faqs"
                 ]
@@ -32,7 +32,16 @@
                 "icon": "brain",
                 "pages": [
                   "core-concepts/memory-types",
-                  "core-concepts/memory-operations"
+                  {
+                    "group": "Memory Operations",
+                    "icon": "gear",
+                    "pages": [
+                      "core-concepts/memory-operations/add",
+                      "core-concepts/memory-operations/search",
+                      "core-concepts/memory-operations/update",
+                      "core-concepts/memory-operations/delete"
+                    ]
+                  }
                 ]
               },
               {
@@ -45,16 +54,21 @@
                     "group": "Features",
                     "icon": "star",
                     "pages": [
-                      "features/platform-overview",
-                      "features/advanced-retrieval",
-                      "features/multimodal-support",
-                      "features/selective-memory",
-                      "features/custom-categories",
-                      "features/custom-instructions",
-                      "features/direct-import",
-                      "features/async-client",
-                      "features/memory-export",
-                      "features/webhooks"
+                      "platform/features/platform-overview",
+                      "platform/features/contextual-add",
+                      "platform/features/async-client",
+                      "platform/features/advanced-retrieval",
+                      "platform/features/criteria-retrieval",
+                      "platform/features/selective-memory",
+                      "platform/features/custom-categories",
+                      "platform/features/custom-instructions",
+                      "platform/features/direct-import",
+                      "platform/features/memory-export",
+                      "platform/features/timestamp",
+                      "platform/features/expiration-date",
+                      "platform/features/webhooks",
+                      "platform/features/feedback-mechanism",
+                      "platform/features/group-chat"
                     ]
                   }
                 ]
@@ -63,16 +77,18 @@
                 "group": "Open Source",
                 "icon": "code-branch",
                 "pages": [
-                  "open-source/quickstart",
+                  "open-source/overview",
                   "open-source/python-quickstart",
                   "open-source/node-quickstart",
                   {
                     "group": "Features",
-                    "icon": "wrench",
+                    "icon": "star",
                     "pages": [
-                      "features/openai_compatibility",
-                      "features/custom-prompts",
-                      "open-source/multimodal-support",
+                      "open-source/features/async-memory",
+                      "open-source/features/openai_compatibility",
+                      "open-source/features/custom-fact-extraction-prompt",
+                      "open-source/features/custom-update-memory-prompt",
+                      "open-source/features/multimodal-support",
                       "open-source/features/rest-api"
                     ]
                   },
@@ -106,7 +122,11 @@
                           "components/llms/models/aws_bedrock",
                           "components/llms/models/gemini",
                           "components/llms/models/deepseek",
-                          "components/llms/models/xAI"
+                          "components/llms/models/xAI",
+                          "components/llms/models/sarvam",
+                          "components/llms/models/lmstudio",
+                          "components/llms/models/langchain",
+                          "components/llms/models/vllm"
                         ]
                       }
                     ]
@@ -125,11 +145,18 @@
                           "components/vectordbs/dbs/chroma",
                           "components/vectordbs/dbs/pgvector",
                           "components/vectordbs/dbs/milvus",
-                          "components/vectordbs/dbs/azure_ai_search",
+                          "components/vectordbs/dbs/pinecone",
+                          "components/vectordbs/dbs/mongodb",
+                          "components/vectordbs/dbs/azure",
                           "components/vectordbs/dbs/redis",
                           "components/vectordbs/dbs/elasticsearch",
                           "components/vectordbs/dbs/opensearch",
-                          "components/vectordbs/dbs/supabase"
+                          "components/vectordbs/dbs/supabase",
+                          "components/vectordbs/dbs/vertex_ai",
+                          "components/vectordbs/dbs/weaviate",
+                          "components/vectordbs/dbs/faiss",
+                          "components/vectordbs/dbs/langchain",
+                          "components/vectordbs/dbs/baidu"
                         ]
                       }
                     ]
@@ -149,7 +176,11 @@
                           "components/embedders/models/ollama",
                           "components/embedders/models/huggingface",
                           "components/embedders/models/vertexai",
-                          "components/embedders/models/gemini"
+                          "components/embedders/models/gemini",
+                          "components/embedders/models/lmstudio",
+                          "components/embedders/models/together",
+                          "components/embedders/models/langchain",
+                          "components/embedders/models/aws_bedrock"
                         ]
                       }
                     ]
@@ -166,6 +197,15 @@
               }
             ]
           },
+          {
+            "tab": "OpenMemory",
+            "icon": "square-terminal",
+            "pages": [
+              "openmemory/overview",
+              "openmemory/quickstart",
+              "openmemory/integrations"
+            ]
+          },
           {
             "tab": "Examples",
             "groups": [
@@ -173,14 +213,29 @@
                 "group": "💡 Examples",
                 "icon": "lightbulb",
                 "pages": [
-                  "examples/overview",
+                  "examples",
+                  "examples/aws_example",
                   "examples/mem0-demo",
                   "examples/ai_companion_js",
+                  "examples/collaborative-task-agent",
+                  "examples/llamaindex-multiagent-learning-system",
+                  "examples/eliza_os",
+                  "examples/mem0-mastra",
                   "examples/mem0-with-ollama",
                   "examples/personal-ai-tutor",
                   "examples/customer-support-agent",
                   "examples/personal-travel-assistant",
-                  "examples/llama-index-mem0"
+                  "examples/llama-index-mem0",
+                  "examples/chrome-extension",
+                  "examples/memory-guided-content-writing",
+                  "examples/multimodal-demo",
+                  "examples/personalized-deep-research",
+                  "examples/mem0-agentic-tool",
+                  "examples/openai-inbuilt-tools",
+                  "examples/mem0-openai-voice-demo",
+                  "examples/mem0-google-adk-healthcare-assistant",
+                  "examples/email_processing",
+                  "examples/youtube-assistant"
                 ]
               }
             ]
@@ -192,15 +247,27 @@
                 "group": "Integrations",
                 "icon": "plug",
                 "pages": [
-                  "integrations/overview",
-                  "integrations/vercel-ai-sdk",
-                  "integrations/crewai",
-                  "integrations/autogen",
+                  "integrations",
                   "integrations/langchain",
                   "integrations/langgraph",
                   "integrations/llama-index",
+                  "integrations/agno",
+                  "integrations/autogen",
+                  "integrations/crewai",
+                  "integrations/openai-agents-sdk",
+                  "integrations/google-ai-adk",
+                  "integrations/mastra",
+                  "integrations/vercel-ai-sdk",
+                  "integrations/livekit",
+                  "integrations/pipecat",
+                  "integrations/elevenlabs",
+                  "integrations/aws-bedrock",
+                  "integrations/flowise",
                   "integrations/langchain-tools",
-                  "integrations/dify"
+                  "integrations/agentops",
+                  "integrations/keywords",
+                  "integrations/dify",
+                  "integrations/raycast"
                 ]
               }
             ]
@@ -213,7 +280,7 @@
                 "group": "API Reference",
                 "icon": "terminal",
                 "pages": [
-                  "api-reference/overview",
+                  "api-reference",
                   {
                     "group": "Memory APIs",
                     "icon": "microchip",
@@ -231,7 +298,8 @@
                       "api-reference/memory/batch-delete",
                       "api-reference/memory/delete-memories",
                       "api-reference/memory/create-memory-export",
-                      "api-reference/memory/get-memory-export"
+                      "api-reference/memory/get-memory-export",
+                      "api-reference/memory/feedback"
                     ]
                   },
                   {
@@ -254,6 +322,18 @@
                       "api-reference/organization/delete-org"
                     ]
                   },
+                  {
+                    "group": "Project APIs",
+                    "icon": "folder",
+                    "pages": [
+                      "api-reference/project/create-project",
+                      "api-reference/project/get-projects",
+                      "api-reference/project/get-project",
+                      "api-reference/project/get-project-members",
+                      "api-reference/project/add-project-member",
+                      "api-reference/project/delete-project"
+                    ]
+                  },
                   {
                     "group": "Webhook APIs",
                     "icon": "webhook",
@@ -267,33 +347,21 @@
                 ]
               }
             ]
+          },
+          {
+            "tab": "Changelog",
+            "icon": "clock",
+            "groups": [
+              {
+                "group": "Product Updates",
+                "icon": "rocket",
+                "pages": [
+                  "changelog"
+                ]
+              }
+            ]
           }
         ]
-      },
-      {
-        "anchor": "Your Dashboard",
-        "href": "https://app.mem0.ai",
-        "icon": "chart-simple"
-      },
-      {
-        "anchor": "Demo",
-        "href": "https://demo.mem0.ai",
-        "icon": "play"
-      },
-      {
-        "anchor": "Discord",
-        "href": "https://mem0.dev/DiD",
-        "icon": "discord"
-      },
-      {
-        "anchor": "GitHub",
-        "href": "https://github.com/mem0ai/mem0",
-        "icon": "github"
-      },
-      {
-        "anchor": "Support",
-        "href": "mailto:founders@mem0.ai",
-        "icon": "envelope"
       }
     ]
   },
@@ -327,6 +395,9 @@
     "posthog": {
       "apiKey": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX",
       "apiHost": "https://mango.mem0.ai"
+    },
+    "intercom": {
+      "appId": "jjv2r0tt"
     }
   }
 }

docs/examples.mdx

@@ -0,0 +1,87 @@
+---
+title: Overview
+description: How to use mem0 in your existing applications?
+---
+
+<Snippet file="security-compliance.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
+- More reliable
+- Cost-effective by reducing the number of LLM interactions
+- More engaging
+- Enables long-term memory
+
+Here are some examples of how Mem0 can be integrated into various applications:
+
+## Examples
+
+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.  
+  </Card>  
+
+  <Card title="LlamaIndex + Mem0" icon="book-open" href="/examples/llama-index-mem0">  
+    Combine **LlamaIndex** and Mem0 to create a powerful **ReAct Agent** with persistent memory for smarter interactions.  
+  </Card>  
+
+  <Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">  
+    Add **long-term memory** to ChatGPT, Claude, or Perplexity via the **Mem0 Chrome Extension** — personalize your AI chats anywhere.  
+  </Card>  
+
+  <Card title="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="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="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="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> 
+</CardGroup>

docs/examples/ai_companion.mdx

@@ -2,6 +2,8 @@
 title: AI Companion
 ---
 
+<Snippet file="security-compliance.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
@@ -59,7 +61,7 @@ class Companion:
         check_prompt = f"""
         Analyze the given input and determine whether the user is primarily:
         1) Talking about themselves or asking for personal advice. They may use words like "I" for this. 
-        2) Inquiring about the AI companions's capabilities or characteristics They may use words like "you" for this. 
+        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

docs/examples/ai_companion_js.mdx

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

docs/examples/aws_example.mdx

@@ -0,0 +1,120 @@
+---
+title: AWS Bedrock and AOSS
+---
+
+<Snippet file="security-compliance.mdx" />
+
+This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.
+
+## Installation
+
+Install the required dependencies:
+
+```bash
+pip install mem0ai boto3 opensearch-py
+```
+
+## 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 embeddings and LLM, and OpenSearch as the vector store.
+
+```python
+import boto3
+from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
+from mem0.memory.main import Memory
+
+region = 'us-west-2'
+service = 'aoss'
+credentials = boto3.Session().get_credentials()
+auth = AWSV4SignerAuth(credentials, region, service)
+
+config = {
+    "embedder": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "amazon.titan-embed-text-v2:0"
+        }
+    },
+    "llm": {
+        "provider": "aws_bedrock",
+        "config": {
+            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
+            "temperature": 0.1,
+            "max_tokens": 2000
+        }
+    },
+    "vector_store": {
+        "provider": "opensearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "your-opensearch-domain.us-west-2.es.amazonaws.com",
+            "port": 443,
+            "http_auth": auth,
+            "embedding_model_dims": 1024,
+            "connection_class": RequestsHttpConnection,
+            "pool_maxsize": 20,
+            "use_ssl": True,
+            "verify_certs": True
+        }
+    }
+}
+
+# Initialize memory system
+m = Memory.from_config(config)
+```
+
+## Usage
+
+#### 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 OpenSearch, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

docs/examples/chrome-extension.mdx

@@ -0,0 +1,57 @@
+# Mem0 Chrome Extension
+
+<Snippet file="security-compliance.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>
+  🎉 We now support Grok! The Mem0 Chrome Extension has been updated to work with Grok, bringing the same powerful memory capabilities to your Grok conversations.
+</Note>
+
+
+## Features
+
+- **Universal Memory Layer**: Share context seamlessly across ChatGPT, Claude, Perplexity, and Grok.
+- **Smart Context Detection**: Automatically captures relevant information from your conversations.
+- **Intelligent Memory Retrieval**: Surfaces pertinent memories at the right time.
+- **One-Click Sync**: Easily synchronize with existing ChatGPT memories.
+- **Memory Dashboard**: Manage all your memories in one centralized location.
+
+## Installation
+
+You can install the Mem0 Chrome Extension using one of the following methods:
+
+### Method 1: Chrome Web Store Installation
+
+1. **Download the Extension**: Open Google Chrome and navigate to the [Mem0 Chrome Extension page](https://chromewebstore.google.com/detail/mem0/onihkkbipkfeijkadecaafbgagkhglop?hl=en).
+2. **Add to Chrome**: Click on the "Add to Chrome" button.
+3. **Confirm Installation**: In the pop-up dialog, click "Add extension" to confirm. The Mem0 icon should now appear in your Chrome toolbar.
+
+### Method 2: Manual Installation
+
+1. **Download the Extension**: Clone or download the extension files from the [Mem0 Chrome Extension GitHub repository](https://github.com/mem0ai/mem0-chrome-extension).
+2. **Access Chrome Extensions**: Open Google Chrome and navigate to `chrome://extensions`.
+3. **Enable Developer Mode**: Toggle the "Developer mode" switch in the top right corner.
+4. **Load Unpacked Extension**: Click "Load unpacked" and select the directory containing the extension files.
+5. **Confirm Installation**: The Mem0 Chrome Extension should now appear in your Chrome toolbar.
+
+## Usage
+
+1. **Locate the Mem0 Icon**: After installation, find the Mem0 icon in your Chrome toolbar.
+2. **Sign In**: Click the icon and sign in with your Google account.
+3. **Interact with AI Assistants**:
+   - **ChatGPT and Perplexity**: Continue your conversations as usual; Mem0 operates seamlessly in the background.
+   - **Claude**: Click the Mem0 button or use the shortcut `Ctrl + M` to activate memory functions.
+
+## Configuration
+
+- **API Key**: Obtain your API key from the Mem0 Dashboard to connect the extension to the Mem0 API.
+- **User ID**: This is your unique identifier in the Mem0 system. If not provided, it defaults to 'chrome-extension-user'.
+
+## Demo Video
+
+<iframe width="700" height="400" src="https://www.youtube.com/embed/dqenCMMlfwQ?si=zhGVrkq6IS_0Jwyj" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## Privacy and Data Security
+
+Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.

docs/examples/collaborative-task-agent.mdx

@@ -0,0 +1,125 @@
+---
+title: Multi-User Collaboration with Mem0
+---
+
+<Snippet file="security-compliance.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.
+
+## Setup
+
+Install the required packages:
+
+```bash
+pip install openai mem0ai
+```
+
+## Full Code Example
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+import os
+from datetime import datetime
+from collections import defaultdict
+
+# Set your OpenAI API key
+os.environ["OPENAI_API_KEY"] = "sk-your-key"
+
+# Shared project context
+RUN_ID = "project-demo"
+
+# Initialize Mem0
+mem = Memory()
+
+class CollaborativeAgent:
+    def __init__(self, run_id):
+        self.run_id = run_id
+        self.mem = mem
+
+    def add_message(self, role, name, content):
+        msg = {"role": role, "name": name, "content": content}
+        self.mem.add([msg], run_id=self.run_id, infer=False)
+
+    def brainstorm(self, prompt):
+        # Get recent messages for context
+        memories = self.mem.search(prompt, run_id=self.run_id, limit=5)["results"]
+        context = "\n".join(f"- {m['memory']} (by {m.get('actor_id', 'Unknown')})" for m in memories)
+        client = OpenAI()
+        messages = [
+            {"role": "system", "content": "You are a helpful project assistant."},
+            {"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}
+        ]
+        reply = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=messages
+        ).choices[0].message.content.strip()
+        self.add_message("assistant", "assistant", reply)
+        return reply
+
+    def get_all_messages(self):
+        return self.mem.get_all(run_id=self.run_id)["results"]
+
+    def print_sorted_by_time(self):
+        messages = self.get_all_messages()
+        messages.sort(key=lambda m: m.get('created_at', ''))
+        print("\n--- Messages (sorted by time) ---")
+        for m in messages:
+            who = m.get("actor_id") or "Unknown"
+            ts = m.get('created_at', 'Timestamp N/A')
+            try:
+                dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
+                ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
+            except Exception:
+                ts_fmt = ts
+            print(f"[{ts_fmt}] [{who}] {m['memory']}")
+
+    def print_grouped_by_actor(self):
+        messages = self.get_all_messages()
+        grouped = defaultdict(list)
+        for m in messages:
+            grouped[m.get("actor_id") or "Unknown"].append(m)
+        print("\n--- Messages (grouped by actor) ---")
+        for actor, mems in grouped.items():
+            print(f"\n=== {actor} ===")
+            for m in mems:
+                ts = m.get('created_at', 'Timestamp N/A')
+                try:
+                    dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
+                    ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
+                except Exception:
+                    ts_fmt = ts
+                print(f"[{ts_fmt}] {m['memory']}")
+```
+
+## Usage
+
+```python
+# Example usage
+agent = CollaborativeAgent(RUN_ID)
+agent.add_message("user", "alice", "Let's list tasks for the new landing page.")
+agent.add_message("user", "bob", "I'll own the hero section copy.")
+agent.add_message("user", "carol", "I'll choose product screenshots.")
+
+# Brainstorm with context
+print("\nAssistant reply:\n", agent.brainstorm("What are the current open tasks?"))
+
+# Print all messages sorted by time
+agent.print_sorted_by_time()
+
+# Print all messages grouped by actor
+agent.print_grouped_by_actor()
+```
+
+## Key Points
+
+- Each message is attributed to a user or agent (actor)
+- All messages are stored in a shared project space (`run_id`)
+- You can sort messages by time, group by actor, and format timestamps for clarity
+- Mem0 makes it easy to build collaborative, attributed chat/task systems
+
+## Conclusion
+
+Mem0 enables fast, transparent collaboration for teams and agents, with full attribution, flexible memory search, and easy message organization.

docs/examples/customer-support-agent.mdx

@@ -2,6 +2,8 @@
 title: Customer Support AI Agent
 ---
 
+<Snippet file="security-compliance.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.
 
 ## Overview
@@ -94,8 +96,8 @@ You can fetch all the memories at any point in time using the following code:
 
 ```python
 memories = support_agent.get_memories(user_id=customer_id)
-for m in memories:
-    print(m['text'])
+for m in memories['results']:
+    print(m['memory'])
 ```
 
 ### Key Points

docs/examples/eliza_os.mdx

@@ -0,0 +1,75 @@
+---
+title: Eliza OS Character
+---
+
+<Snippet file="security-compliance.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
+
+ElizaOS is a powerful AI agent framework for autonomy & personality. It is a collection of tools that help you create a personalised AI agent.
+
+## Setup
+You can start by cloning the eliza-os repository:
+
+```bash
+git clone https://github.com/elizaOS/eliza.git
+```
+
+Change the directory to the eliza-os repository:
+
+```bash
+cd eliza
+```
+
+Install the dependencies:
+
+```bash
+pnpm install
+```
+
+Build the project:
+
+```bash
+pnpm build
+```
+
+## Setup ENVs
+
+Create a `.env` file in the root of the project and add the following ( You can use the `.env.example` file as a reference):
+
+```bash
+# Mem0 Configuration
+MEM0_API_KEY= # Mem0 API Key ( Get from https://app.mem0.ai/dashboard/api-keys )
+MEM0_USER_ID= # Default: eliza-os-user
+MEM0_PROVIDER= # Default: openai
+MEM0_PROVIDER_API_KEY= # API Key for the provider (openai, anthropic, etc.)
+SMALL_MEM0_MODEL= # Default: gpt-4o-mini
+MEDIUM_MEM0_MODEL= # Default: gpt-4o
+LARGE_MEM0_MODEL= # Default: gpt-4o
+```
+
+## Make the default character use Mem0
+
+By default, there is a character called `eliza` that uses the `ollama` model. You can make this character use Mem0 by changing the config in the `agent/src/defaultCharacter.ts` file.
+
+```ts
+modelProvider: ModelProviderName.MEM0,
+```
+
+This will make the character use Mem0 to generate responses.
+
+## Run the project
+
+```bash
+pnpm start
+```
+
+## Conclusion
+
+You have now created a personalised Eliza OS Character using Mem0. You can now start interacting with the character by running the project and talking to the character.
+
+This is a simple example of how to use Mem0 to create a personalised AI agent. You can use this as a starting point to create your own AI agent.
+
+

docs/examples/email_processing.mdx

@@ -0,0 +1,188 @@
+---
+title: Email Processing with Mem0
+---
+
+<Snippet file="security-compliance.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
+
+Email overload is a common challenge for many professionals. By leveraging Mem0's memory capabilities, you can build an intelligent system that:
+
+- Stores emails as searchable memories
+- Categorizes emails automatically
+- Retrieves relevant past conversations
+- Prioritizes messages based on importance
+- Generates summaries and action items
+
+## Setup
+
+Before you begin, ensure you have the required dependencies installed:
+
+```bash
+pip install mem0ai openai
+```
+
+## Implementation
+
+### Basic Email Memory System
+
+The following example shows how to create a basic email processing system with Mem0:
+
+```python
+import os
+from mem0 import MemoryClient
+from email.parser import Parser
+
+# Configure API keys
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Initialize Mem0 client
+client = MemoryClient()
+
+class EmailProcessor:
+    def __init__(self):
+        """Initialize the Email Processor with Mem0 memory client"""
+        self.client = client
+        
+    def process_email(self, email_content, user_id):
+        """
+        Process an email and store it in Mem0 memory
+        
+        Args:
+            email_content (str): Raw email content
+            user_id (str): User identifier for memory association
+        """
+        # Parse email
+        parser = Parser()
+        email = parser.parsestr(email_content)
+        
+        # Extract email details
+        sender = email['from']
+        recipient = email['to']
+        subject = email['subject']
+        date = email['date']
+        body = self._get_email_body(email)
+        
+        # Create message object for Mem0
+        message = {
+            "role": "user",
+            "content": f"Email from {sender}: {subject}\n\n{body}"
+        }
+        
+        # Create metadata for better retrieval
+        metadata = {
+            "email_type": "incoming",
+            "sender": sender,
+            "recipient": recipient,
+            "subject": subject,
+            "date": date
+        }
+        
+        # Store in Mem0 with appropriate categories
+        response = self.client.add(
+            messages=[message],
+            user_id=user_id,
+            metadata=metadata,
+            categories=["email", "correspondence"],
+            version="v2"
+        )
+        
+        return response
+    
+    def _get_email_body(self, email):
+        """Extract the body content from an email"""
+        # Simplified extraction - in real-world, handle multipart emails
+        if email.is_multipart():
+            for part in email.walk():
+                if part.get_content_type() == "text/plain":
+                    return part.get_payload(decode=True).decode()
+        else:
+            return email.get_payload(decode=True).decode()
+    
+    def search_emails(self, query, user_id):
+        """
+        Search through stored emails
+        
+        Args:
+            query (str): Search query
+            user_id (str): User identifier
+        """
+        # Search Mem0 for relevant emails
+        results = self.client.search(
+            query=query,
+            user_id=user_id,
+            categories=["email"],
+            output_format="v1.1",
+            version="v2"
+        )
+        
+        return results
+        
+    def get_email_thread(self, subject, user_id):
+        """
+        Retrieve all emails in a thread based on subject
+        
+        Args:
+            subject (str): Email subject to match
+            user_id (str): User identifier
+        """
+        filters = {
+            "AND": [
+                {"user_id": user_id},
+                {"categories": {"contains": "email"}},
+                {"metadata": {"subject": {"contains": subject}}}
+            ]
+        }
+        
+        thread = self.client.get_all(
+            version="v2",
+            filters=filters,
+            output_format="v1.1"
+        )
+        
+        return thread
+
+# Initialize the processor
+processor = EmailProcessor()
+
+# Example raw email
+sample_email = """From: alice@example.com
+To: bob@example.com
+Subject: Meeting Schedule Update
+Date: Mon, 15 Jul 2024 14:22:05 -0700
+
+Hi Bob,
+
+I wanted to update you on the schedule for our upcoming project meeting.
+We'll be meeting this Thursday at 2pm instead of Friday.
+
+Could you please prepare your section of the presentation?
+
+Thanks,
+Alice
+"""
+
+# Process and store the email
+user_id = "bob@example.com"
+processor.process_email(sample_email, user_id)
+
+# Later, search for emails about meetings
+meeting_emails = processor.search_emails("meeting schedule", user_id)
+print(f"Found {len(meeting_emails['results'])} relevant emails")
+```
+
+## Key Features and Benefits
+
+- **Long-term Email Memory**: Store and retrieve email conversations across long periods
+- **Semantic Search**: Find relevant emails even if they don't contain exact keywords
+- **Intelligent Categorization**: Automatically sort emails into meaningful categories
+- **Action Item Extraction**: Identify and track tasks mentioned in emails
+- **Priority Management**: Focus on important emails based on AI-determined priority
+- **Context Awareness**: Maintain thread context for more relevant interactions
+
+## Conclusion
+
+By combining Mem0's memory capabilities with email processing, you can create intelligent email management systems that help users organize, prioritize, and act on their inbox effectively. The advanced capabilities like automatic categorization, action item extraction, and priority management can significantly reduce the time spent on email management, allowing users to focus on more important tasks.
+

docs/examples/llama-index-mem0.mdx

@@ -1,6 +1,8 @@
 ---
 title: LlamaIndex ReAct Agent
 ---
+<Snippet file="security-compliance.mdx" />
+
 Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
 
 ### Overview
@@ -20,7 +22,7 @@ os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
 llm = OpenAI(model="gpt-4o")
 ```
 
-Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/quickstart).
+Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/api-keys). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).
 ```python
 os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
 
@@ -78,7 +80,7 @@ agent = FunctionCallingAgent.from_tools(
 ```
 
 Start the chat.
-<Note> The agent will use the Mem0 to store the relavant memories from the chat. </Note>
+<Note> The agent will use the Mem0 to store the relevant memories from the chat. </Note>
 
 Input
 ```python
@@ -137,7 +139,7 @@ Added user message to memory: I am feeling hungry, order me something and send m
 === LLM Response ===
 Please let me know your name and the dish you'd like to order, and I'll take care of it for you!
 ```
-<Note> The agent is not able to remember the past prefernces that user shared in previous chats. </Note>
+<Note> The agent is not able to remember the past preferences that user shared in previous chats. </Note>
 
 ### Using the agent WITH memory
 Input
@@ -169,4 +171,4 @@ Emailing... David
 === LLM Response ===
 I've ordered a pizza for you, and the bill has been sent to your email. Enjoy your meal! If there's anything else you need, feel free to let me know.
 ```
-<Note> The agent is able to remember the past prefernces that user shared and use them to perform actions. </Note>
+<Note> The agent is able to remember the past preferences that user shared and use them to perform actions. </Note>

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

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

docs/examples/mem0-agentic-tool.mdx

@@ -0,0 +1,228 @@
+---
+title: Mem0 as an Agentic Tool
+---
+
+<Snippet file="security-compliance.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.
+
+## Installation
+
+First, install the required packages:
+```bash
+pip install mem0ai pydantic openai-agents
+```
+
+You'll also need a custom agents framework for this implementation.
+
+## Setting Up Environment Variables
+
+Store your Mem0 API key as an environment variable:
+
+```bash
+export MEM0_API_KEY="your_mem0_api_key"
+```
+
+Or in your Python script:
+
+```python
+import os
+os.environ["MEM0_API_KEY"] = "your_mem0_api_key"
+```
+
+## Code Structure
+
+The integration consists of three main components:
+
+1. **Context Manager**: Defines user context for memory operations
+2. **Memory Tools**: Functions to add, search, and retrieve memories
+3. **Memory Agent**: An agent configured to use these memory tools
+
+## Step-by-Step Implementation
+
+### 1. Import Dependencies
+
+```python
+from __future__ import annotations
+import os
+import asyncio
+from pydantic import BaseModel
+try:
+    from mem0 import AsyncMemoryClient
+except ImportError:
+    raise ImportError("mem0 is not installed. Please install it using 'pip install mem0ai'.")
+from agents import (
+    Agent,
+    ItemHelpers,
+    MessageOutputItem,
+    RunContextWrapper,
+    Runner,
+    ToolCallItem,
+    ToolCallOutputItem,
+    TResponseInputItem,
+    function_tool,
+)
+```
+
+### 2. Define Memory Context
+
+```python
+class Mem0Context(BaseModel):
+    user_id: str | None = None
+```
+
+### 3. Initialize the Mem0 Client
+
+```python
+client = AsyncMemoryClient(api_key=os.getenv("MEM0_API_KEY"))
+```
+
+### 4. Create Memory Tools
+
+#### Add to Memory
+
+```python
+@function_tool
+async def add_to_memory(
+    context: RunContextWrapper[Mem0Context],
+    content: str,
+) -> str:
+    """
+    Add a message to Mem0
+    Args:
+        content: The content to store in memory.
+    """
+    messages = [{"role": "user", "content": content}]
+    user_id = context.context.user_id or "default_user"
+    await client.add(messages, user_id=user_id)
+    return f"Stored message: {content}"
+```
+
+#### Search Memory
+
+```python
+@function_tool
+async def search_memory(
+    context: RunContextWrapper[Mem0Context],
+    query: str,
+) -> str:
+    """
+    Search for memories in Mem0
+    Args:
+        query: The search query.
+    """
+    user_id = context.context.user_id or "default_user"
+    memories = await client.search(query, user_id=user_id, output_format="v1.1")
+    results = '\n'.join([result["memory"] for result in memories["results"]])
+    return str(results)
+```
+
+#### Get All Memories
+
+```python
+@function_tool
+async def get_all_memory(
+    context: RunContextWrapper[Mem0Context],
+) -> str:
+    """Retrieve all memories from Mem0"""
+    user_id = context.context.user_id or "default_user"
+    memories = await client.get_all(user_id=user_id, output_format="v1.1")
+    results = '\n'.join([result["memory"] for result in memories["results"]])
+    return str(results)
+```
+
+### 5. Configure the Memory Agent
+
+```python
+memory_agent = Agent[Mem0Context](
+    name="Memory Assistant",
+    instructions="""You are a helpful assistant with memory capabilities. You can:
+    1. Store new information using add_to_memory
+    2. Search existing information using search_memory
+    3. Retrieve all stored information using get_all_memory
+    When users ask questions:
+    - If they want to store information, use add_to_memory
+    - If they're searching for specific information, use search_memory
+    - If they want to see everything stored, use get_all_memory""",
+    tools=[add_to_memory, search_memory, get_all_memory],
+)
+```
+
+### 6. Implement the Main Runtime Loop
+
+```python
+async def main():
+    current_agent: Agent[Mem0Context] = memory_agent
+    input_items: list[TResponseInputItem] = []
+    context = Mem0Context()
+    while True:
+        user_input = input("Enter your message (or 'quit' to exit): ")
+        if user_input.lower() == 'quit':
+            break
+        input_items.append({"content": user_input, "role": "user"})
+        result = await Runner.run(current_agent, input_items, context=context)
+        for new_item in result.new_items:
+            agent_name = new_item.agent.name
+            if isinstance(new_item, MessageOutputItem):
+                print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}")
+            elif isinstance(new_item, ToolCallItem):
+                print(f"{agent_name}: Calling a tool")
+            elif isinstance(new_item, ToolCallOutputItem):
+                print(f"{agent_name}: Tool call output: {new_item.output}")
+            else:
+                print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}")
+        input_items = result.to_input_list()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Usage Examples
+
+### Storing Information
+
+```
+User: Remember that my favorite color is blue
+Agent: Calling a tool
+Agent: Tool call output: Stored message: my favorite color is blue
+Agent: I've stored that your favorite color is blue in my memory. I'll remember that for future conversations.
+```
+
+### Searching Memory
+
+```
+User: What's my favorite color?
+Agent: Calling a tool
+Agent: Tool call output: my favorite color is blue
+Agent: Your favorite color is blue, based on what you've told me earlier.
+```
+
+### Retrieving All Memories
+
+```
+User: What do you know about me?
+Agent: Calling a tool
+Agent: Tool call output: favorite color is blue
+my birthday is on March 15
+Agent: Based on our previous conversations, I know that:
+1. Your favorite color is blue
+2. Your birthday is on March 15
+```
+
+## Advanced Configuration
+
+### Custom User IDs
+
+You can specify different user IDs to maintain separate memory stores for multiple users:
+
+```python
+context = Mem0Context(user_id="user123")
+```
+
+
+## Resources
+
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
+- [API Reference](https://docs.mem0.ai/api-reference)

docs/examples/mem0-demo.mdx

@@ -2,6 +2,9 @@
 title: Mem0 Demo
 ---
 
+
+<Snippet file="security-compliance.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
@@ -13,6 +16,8 @@ You can create a personalized AI Companion using Mem0. This guide will walk you
   src="https://github.com/user-attachments/assets/cebc4f8e-bdb9-4837-868d-13c5ab7bb433"
 ></video>
 
+You can try the [Mem0 Demo](https://mem0-4vmi.vercel.app) live here.
+
 ## Overview
 
 The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates memories for each user interaction and integrates with OpenAI's GPT models to provide detailed and context-aware responses to user queries.

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

@@ -0,0 +1,291 @@
+---
+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="security-compliance.mdx" />
+
+# Healthcare Assistant with Memory
+
+This example demonstrates how to build a healthcare assistant that remembers patient information across conversations using Google ADK and Mem0.
+
+## Overview
+
+The Healthcare Assistant helps patients by:
+- Remembering their medical history and symptoms
+- Providing general health information
+- Scheduling appointment reminders
+- Maintaining a personalized experience across conversations
+
+By integrating Mem0's memory layer with Google ADK, the assistant maintains context about the patient without requiring them to repeat information.
+
+## Setup
+
+Before you begin, make sure you have:
+
+Installed Google ADK and Mem0 SDK:
+```bash
+pip install google-adk
+pip install mem0ai
+```
+
+## Code Breakdown
+
+Let's get started and understand the different components required in building a healthcare assistant powered by memory
+
+```python
+# Import dependencies
+import os
+from google.adk.agents import Agent
+from google.adk.sessions import InMemorySessionService
+from google.adk.runners import Runner
+from google.genai import types
+from mem0 import MemoryClient
+
+# 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"
+
+# Define a global user ID for simplicity
+USER_ID = "Alex"
+
+# Initialize Mem0 client
+mem0_client = MemoryClient()
+```
+
+## Define Memory Tools
+
+First, we'll create tools that allow our agent to store and retrieve information using Mem0:
+
+```python
+def save_patient_info(information: str) -> dict:
+    """Saves important patient information to memory."""
+
+    # Store in Mem0
+    response = mem0_client.add(
+        [{"role": "user", "content": information}],
+        user_id=USER_ID,
+        run_id="healthcare_session",
+        metadata={"type": "patient_information"}
+    )
+
+
+def retrieve_patient_info(query: str) -> dict:
+    """Retrieves relevant patient information from memory."""
+
+    # Search Mem0
+    results = mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+
+    # Format and return the results
+    if results and len(results) > 0:
+        memories = [memory["memory"] for memory in results.get('results', [])]
+        return {
+            "status": "success",
+            "memories": memories,
+            "count": len(memories)
+        }
+    else:
+        return {
+            "status": "no_results",
+            "memories": [],
+            "count": 0
+        }
+```
+
+## Define Healthcare Tools
+
+Next, we'll add tools specific to healthcare assistance:
+
+```python
+def schedule_appointment(date: str, time: str, reason: str) -> dict:
+    """Schedules a doctor's appointment."""
+    # In a real app, this would connect to a scheduling system
+    appointment_id = f"APT-{hash(date + time) % 10000}"
+
+    return {
+        "status": "success",
+        "appointment_id": appointment_id,
+        "confirmation": f"Appointment scheduled for {date} at {time} for {reason}",
+        "message": "Please arrive 15 minutes early to complete paperwork."
+    }
+```
+
+## Create the Healthcare Assistant Agent
+
+Now we'll create our main agent with all the tools:
+
+```python
+# Create the agent
+healthcare_agent = Agent(
+    name="healthcare_assistant",
+    model="gemini-1.5-flash",  # Using Gemini for healthcare assistant
+    description="Healthcare assistant that helps patients with health information and appointment scheduling.",
+    instruction="""You are a helpful Healthcare Assistant with memory capabilities.
+
+Your primary responsibilities are to:
+1. Remember patient information using the 'save_patient_info' tool when they share symptoms, conditions, or preferences.
+2. Retrieve past patient information using the 'retrieve_patient_info' tool when relevant to the current conversation.
+3. Help schedule appointments using the 'schedule_appointment' tool.
+
+IMPORTANT GUIDELINES:
+- Always be empathetic, professional, and helpful.
+- Save important patient information like symptoms, conditions, allergies, and preferences.
+- Check if you have relevant patient information before asking for details they may have shared previously.
+- Make it clear you are not a doctor and cannot provide medical diagnosis or treatment.
+- For serious symptoms, always recommend consulting a healthcare professional.
+- Keep all patient information confidential.
+""",
+    tools=[save_patient_info, retrieve_patient_info, schedule_appointment]
+)
+```
+
+## Set Up Session and Runner
+
+```python
+# Set up Session Service and Runner
+session_service = InMemorySessionService()
+
+# Define constants for the conversation
+APP_NAME = "healthcare_assistant_app"
+USER_ID = "Alex"
+SESSION_ID = "session_001"
+
+# Create a session
+session = session_service.create_session(
+    app_name=APP_NAME,
+    user_id=USER_ID,
+    session_id=SESSION_ID
+)
+
+# Create the runner
+runner = Runner(
+    agent=healthcare_agent,
+    app_name=APP_NAME,
+    session_service=session_service
+)
+```
+
+## Interact with the Healthcare Assistant
+
+```python
+# Function to interact with the agent
+async def call_agent_async(query, runner, user_id, session_id):
+    """Sends a query to the agent and returns the final response."""
+    print(f"\n>>> Patient: {query}")
+
+    # Format the user's message
+    content = types.Content(
+        role='user',
+        parts=[types.Part(text=query)]
+    )
+
+    # Set user_id for tools to access
+    save_patient_info.user_id = user_id
+    retrieve_patient_info.user_id = user_id
+
+    # Run the agent
+    async for event in runner.run_async(
+        user_id=user_id,
+        session_id=session_id,
+        new_message=content
+    ):
+        if event.is_final_response():
+            if event.content and event.content.parts:
+                response = event.content.parts[0].text
+                print(f"<<< Assistant: {response}")
+                return response
+
+    return "No response received."
+
+# Example conversation flow
+async def run_conversation():
+    # First interaction - patient introduces themselves with key information
+    await call_agent_async(
+        "Hi, I'm Alex. I've been having headaches for the past week, and I have a penicillin allergy.",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Request for health information
+    await call_agent_async(
+        "Can you tell me more about what might be causing my headaches?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Schedule an appointment
+    await call_agent_async(
+        "I think I should see a doctor. Can you help me schedule an appointment for next Monday at 2pm?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+    # Test memory - should remember patient name, symptoms, and allergy
+    await call_agent_async(
+        "What medications should I avoid for my headaches?",
+        runner=runner,
+        user_id=USER_ID,
+        session_id=SESSION_ID
+    )
+
+# Run the conversation example
+if __name__ == "__main__":
+    asyncio.run(run_conversation())
+```
+
+## How It Works
+
+This healthcare assistant demonstrates several key capabilities:
+
+1. **Memory Storage**: When Alex mentions her headaches and penicillin allergy, the agent stores this information in Mem0 using the `save_patient_info` tool.
+
+2. **Contextual Retrieval**: When Alex asks about headache causes, the agent uses the `retrieve_patient_info` tool to recall her specific situation.
+
+3. **Memory Application**: When discussing medications, the agent remembers Alex's penicillin allergy without her needing to repeat it, providing safer and more personalized advice.
+
+4. **Conversation Continuity**: The agent maintains context across the entire conversation session, creating a more natural and efficient interaction.
+
+## Key Implementation Details
+
+### User ID Management
+
+Instead of passing the user ID as a parameter to the memory tools (which would require modifying the ADK's tool calling system), we attach it directly to the function object:
+
+```python
+# Set user_id for tools to access
+save_patient_info.user_id = user_id
+retrieve_patient_info.user_id = user_id
+```
+
+Inside the tool functions, we retrieve this attribute:
+
+```python
+# Get user_id from session state or use default
+user_id = getattr(save_patient_info, 'user_id', 'default_user')
+```
+
+This approach allows our tools to maintain user context without complicating their parameter signatures.
+
+### Mem0 Integration
+
+The integration with Mem0 happens through two primary functions:
+
+1. `mem0_client.add()` - Stores new information with appropriate metadata
+2. `mem0_client.search()` - Retrieves relevant memories using semantic search
+
+The `threshold` parameter in the search function ensures that only highly relevant memories are returned.
+
+## Conclusion
+
+This example demonstrates how to build a healthcare assistant with persistent memory using Google ADK and Mem0. The integration allows for a more personalized patient experience by maintaining context across conversation turns, which is particularly valuable in healthcare scenarios where continuity of information is crucial.
+
+By storing and retrieving patient information intelligently, the assistant provides more relevant responses without requiring the patient to repeat their medical history, symptoms, or preferences.

docs/examples/mem0-mastra.mdx

@@ -0,0 +1,128 @@
+---
+title: Mem0 with Mastra
+---
+
+<Snippet file="security-compliance.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).
+
+You can find the complete example code in the [Mastra repository](https://github.com/mastra-ai/mastra/tree/main/examples/memory-with-mem0).
+
+## Overview
+
+This guide will show you how to integrate Mem0 with Mastra to add long-term memory capabilities to your agents. We'll create tools that allow agents to save and retrieve memories using Mem0's API.
+
+### Installation
+
+1. **Install the Integration Package**
+
+To install the Mem0 integration, run:
+
+```bash
+npm install @mastra/mem0
+```
+
+2. **Add the Integration to Your Project**
+
+Create a new file for your integrations and import the integration:
+
+```typescript integrations/index.ts
+import { Mem0Integration } from "@mastra/mem0";
+
+export const mem0 = new Mem0Integration({
+  config: {
+    apiKey: process.env.MEM0_API_KEY!,
+    userId: "alice",
+  },
+});
+```
+
+3. **Use the Integration in Tools or Workflows**
+
+You can now use the integration when defining tools for your agents or in workflows.
+
+```typescript tools/index.ts
+import { createTool } from "@mastra/core";
+import { z } from "zod";
+import { mem0 } from "../integrations";
+
+export const mem0RememberTool = createTool({
+  id: "Mem0-remember",
+  description:
+    "Remember your agent memories that you've previously saved using the Mem0-memorize tool.",
+  inputSchema: z.object({
+    question: z
+      .string()
+      .describe("Question used to look up the answer in saved memories."),
+  }),
+  outputSchema: z.object({
+    answer: z.string().describe("Remembered answer"),
+  }),
+  execute: async ({ context }) => {
+    console.log(`Searching memory "${context.question}"`);
+    const memory = await mem0.searchMemory(context.question);
+    console.log(`\nFound memory "${memory}"\n`);
+
+    return {
+      answer: memory,
+    };
+  },
+});
+
+export const mem0MemorizeTool = createTool({
+  id: "Mem0-memorize",
+  description:
+    "Save information to mem0 so you can remember it later using the Mem0-remember tool.",
+  inputSchema: z.object({
+    statement: z.string().describe("A statement to save into memory"),
+  }),
+  execute: async ({ context }) => {
+    console.log(`\nCreating memory "${context.statement}"\n`);
+    // to reduce latency memories can be saved async without blocking tool execution
+    void mem0.createMemory(context.statement).then(() => {
+      console.log(`\nMemory "${context.statement}" saved.\n`);
+    });
+    return { success: true };
+  },
+});
+```
+
+4. **Create a new agent**
+
+```typescript agents/index.ts
+import { openai } from '@ai-sdk/openai';
+import { Agent } from '@mastra/core/agent';
+import { mem0MemorizeTool, mem0RememberTool } from '../tools';
+
+export const mem0Agent = new Agent({
+  name: 'Mem0 Agent',
+  instructions: `
+    You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
+  `,
+  model: openai('gpt-4o'),
+  tools: { mem0RememberTool, mem0MemorizeTool },
+});
+```
+
+5. **Run the agent**
+
+```typescript index.ts
+import { Mastra } from '@mastra/core/mastra';
+import { createLogger } from '@mastra/core/logger';
+
+import { mem0Agent } from './agents';
+
+export const mastra = new Mastra({
+  agents: { mem0Agent },
+  logger: createLogger({
+    name: 'Mastra',
+    level: 'error',
+  }),
+});
+```
+
+In the example above:
+- We import the `@mastra/mem0` integration.
+- We define two tools that uses the Mem0 API client to create new memories and recall previously saved memories.
+- The tool accepts `question` as an input and returns the memory as a string.

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

@@ -0,0 +1,540 @@
+---
+title: 'Mem0 with OpenAI Agents SDK for Voice'
+description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
+---
+
+<Snippet file="security-compliance.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.
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+1. Installed OpenAI Agents SDK with voice dependencies:
+```bash
+pip install 'openai-agents[voice]'
+```
+
+2. Installed Mem0 SDK:
+```bash
+pip install mem0ai
+```
+
+3. Installed other required dependencies:
+```bash
+pip install numpy sounddevice pydantic
+```
+
+4. Set up your API keys:
+   - OpenAI API key for the Agents SDK
+   - Mem0 API key from the Mem0 Platform
+
+## Code Breakdown
+
+Let's break down the key components of this implementation:
+
+### 1. Setting Up Dependencies and Environment
+
+```python
+# OpenAI Agents SDK imports
+from agents import (
+    Agent,
+    function_tool
+)
+from agents.voice import (
+    AudioInput,
+    SingleAgentVoiceWorkflow,
+    VoicePipeline
+)
+from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
+
+# Mem0 imports
+from mem0 import AsyncMemoryClient
+
+# Set up API keys (replace with your actual keys)
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Define a global user ID for simplicity
+USER_ID = "voice_user"
+
+# Initialize Mem0 client
+mem0_client = AsyncMemoryClient()
+```
+
+This section handles:
+- Importing required modules from OpenAI Agents SDK and Mem0
+- Setting up environment variables for API keys
+- Defining a simple user identification system (using a global variable)
+- Initializing the Mem0 client that will handle memory operations
+
+### 2. Memory Tools with Function Decorators
+
+The `@function_tool` decorator transforms Python functions into callable tools for the OpenAI agent. Here are the key memory tools:
+
+#### Storing User Memories
+
+```python
+import logging
+
+# Set up logging at the top of your file
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    force=True
+)
+logger = logging.getLogger("memory_voice_agent")
+
+# Then use logger in your function tools
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """Store a user memory in memory."""
+    # This will be visible in your console
+    logger.debug(f"Saving memory: {memory} for user {USER_ID}")
+    
+    # Store the preference in Mem0
+    memory_content = f"User memory - {memory}"
+    await mem0_client.add(
+        memory_content,
+        user_id=USER_ID,
+    )
+
+    return f"I've saved your memory: {memory}"
+```
+
+This function:
+- Takes a memory string
+- Creates a formatted memory string
+- Stores it in Mem0 using the `add()` method
+- Includes metadata to categorize the memory for easier retrieval
+- Returns a confirmation message that the agent will speak
+
+#### Finding Relevant Memories
+
+```python
+@function_tool
+async def search_memories(
+    query: str
+) -> str:
+    """
+    Find memories relevant to the current conversation.
+    Args:
+        query: The search query to find relevant memories
+    """
+    print(f"Finding memories related to: {query}")
+    results = await mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+    
+    # Format and return the results
+    if not results.get('results', []):
+        return "I don't have any relevant memories about this topic."
+    
+    memories = [f"• {result['memory']}" for result in results.get('results', [])]
+    return "Here's what I remember that might be relevant:\n" + "\n".join(memories)
+```
+
+This tool:
+- Takes a search query string
+- Passes it to Mem0's semantic search to find related memories
+- Sets a threshold for relevance to ensure quality results
+- Returns a formatted list of relevant memories or a default message
+
+### 3. Creating the Voice Agent
+
+```python
+def create_memory_voice_agent():
+    # Create the agent with memory-enabled tools
+    agent = Agent(
+        name="Memory Assistant",
+        instructions=prompt_with_handoff_instructions(
+            """You're speaking to a human, so be polite and concise.
+            Always respond in clear, natural English.
+            You have the ability to remember information about the user.
+            Use the save_memories tool when the user shares an important information worth remembering.
+            Use the search_memories tool when you need context from past conversations or user asks you to recall something.
+            """,
+        ),
+        model="gpt-4o",
+        tools=[save_memories, search_memories],
+    )
+    
+    return agent
+```
+
+This function:
+- Creates an OpenAI Agent with specific instructions
+- Configures it to use gpt-4o (you can use other models)
+- Registers the memory-related tools with the agent
+- Uses `prompt_with_handoff_instructions` to include standard voice agent behaviors
+
+### 4. Microphone Recording Functionality
+
+```python
+async def record_from_microphone(duration=5, samplerate=24000):
+    """Record audio from the microphone for a specified duration."""
+    print(f"Recording for {duration} seconds...")
+    
+    # Create a buffer to store the recorded audio
+    frames = []
+    
+    # Callback function to store audio data
+    def callback(indata, frames_count, time_info, status):
+        frames.append(indata.copy())
+    
+    # Start recording
+    with sd.InputStream(samplerate=samplerate, channels=1, callback=callback, dtype=np.int16):
+        await asyncio.sleep(duration)
+    
+    # Combine all frames into a single numpy array
+    audio_data = np.concatenate(frames)
+    return audio_data
+```
+
+This function:
+- Creates a simple asynchronous microphone recording function
+- Uses the sounddevice library to capture audio input
+- Stores frames in a buffer during recording
+- Combines frames into a single numpy array when complete
+- Returns the audio data for processing
+
+### 5. Main Loop and Voice Processing
+
+```python
+async def main():
+    # Create the agent
+    agent = create_memory_voice_agent()
+    
+    # Set up the voice pipeline
+    pipeline = VoicePipeline(
+        workflow=SingleAgentVoiceWorkflow(agent)
+    )
+    
+    # Configure TTS settings
+    pipeline.config.tts_settings.voice = "alloy"
+    pipeline.config.tts_settings.speed = 1.0
+    
+    try:
+        while True:
+            # Get user input
+            print("\nPress Enter to start recording (or 'q' to quit)...")
+            user_input = input()
+            if user_input.lower() == 'q':
+                break
+            
+            # Record and process audio
+            audio_data = await record_from_microphone(duration=5)
+            audio_input = AudioInput(buffer=audio_data)
+            result = await pipeline.run(audio_input)
+            
+            # Play response and handle events
+            player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
+            player.start()
+            
+            agent_response = ""
+            print("\nAgent response:")
+            
+            async for event in result.stream():
+                if event.type == "voice_stream_event_audio":
+                    player.write(event.data)
+                elif event.type == "voice_stream_event_content":
+                    content = event.data
+                    agent_response += content
+                    print(content, end="", flush=True)
+            
+            # Save the agent's response to memory
+            if agent_response:
+                try:
+                    await mem0_client.add(
+                        f"Agent response: {agent_response}", 
+                        user_id=USER_ID,
+                        metadata={"type": "agent_response"}
+                    )
+                except Exception as e:
+                    print(f"Failed to store memory: {e}")
+    
+    except KeyboardInterrupt:
+        print("\nExiting...")
+```
+
+This main function orchestrates the entire process:
+1. Creates the memory-enabled voice agent
+2. Sets up the voice pipeline with TTS settings
+3. Implements an interactive loop for recording and processing voice input
+4. Handles streaming of response events (both audio and text)
+5. Automatically saves the agent's responses to memory
+6. Includes proper error handling and exit mechanisms
+
+## Create a Memory-Enabled Voice Agent
+
+Now that we've explained each component, here's the complete implementation that combines OpenAI Agents SDK for voice with Mem0's memory capabilities:
+
+```python
+import asyncio
+import os
+import logging
+from typing import Optional, List, Dict, Any
+import numpy as np
+import sounddevice as sd
+from pydantic import BaseModel
+
+# OpenAI Agents SDK imports
+from agents import (
+    Agent,
+    function_tool
+)
+from agents.voice import (
+    AudioInput,
+    SingleAgentVoiceWorkflow,
+    VoicePipeline
+)
+from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
+
+# Mem0 imports
+from mem0 import AsyncMemoryClient
+
+# Set up API keys (replace with your actual keys)
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Define a global user ID for simplicity
+USER_ID = "voice_user"
+
+# Initialize Mem0 client
+mem0_client = AsyncMemoryClient()
+
+# Create tools that utilize Mem0's memory
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """
+    Store a user memory in memory.
+    Args:
+        memory: The memory to save
+    """
+    print(f"Saving memory: {memory} for user {USER_ID}")
+
+    # Store the preference in Mem0
+    memory_content = f"User memory - {memory}"
+    await mem0_client.add(
+        memory_content,
+        user_id=USER_ID,
+    )
+
+    return f"I've saved your memory: {memory}"
+
+@function_tool
+async def search_memories(
+    query: str
+) -> str:
+    """
+    Find memories relevant to the current conversation.
+    Args:
+        query: The search query to find relevant memories
+    """
+    print(f"Finding memories related to: {query}")
+    results = await mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+    
+    # Format and return the results
+    if not results.get('results', []):
+        return "I don't have any relevant memories about this topic."
+    
+    memories = [f"• {result['memory']}" for result in results.get('results', [])]
+    return "Here's what I remember that might be relevant:\n" + "\n".join(memories)
+
+# Create the agent with memory-enabled tools
+def create_memory_voice_agent():
+    # Create the agent with memory-enabled tools
+    agent = Agent(
+        name="Memory Assistant",
+        instructions=prompt_with_handoff_instructions(
+            """You're speaking to a human, so be polite and concise.
+            Always respond in clear, natural English.
+            You have the ability to remember information about the user.
+            Use the save_memories tool when the user shares an important information worth remembering.
+            Use the search_memories tool when you need context from past conversations or user asks you to recall something.
+            """,
+        ),
+        model="gpt-4o",
+        tools=[save_memories, search_memories],
+    )
+    
+    return agent
+
+async def record_from_microphone(duration=5, samplerate=24000):
+    """Record audio from the microphone for a specified duration."""
+    print(f"Recording for {duration} seconds...")
+    
+    # Create a buffer to store the recorded audio
+    frames = []
+    
+    # Callback function to store audio data
+    def callback(indata, frames_count, time_info, status):
+        frames.append(indata.copy())
+    
+    # Start recording
+    with sd.InputStream(samplerate=samplerate, channels=1, callback=callback, dtype=np.int16):
+        await asyncio.sleep(duration)
+    
+    # Combine all frames into a single numpy array
+    audio_data = np.concatenate(frames)
+    return audio_data
+
+async def main():
+    print("Starting Memory Voice Agent")
+    
+    # Create the agent and context
+    agent = create_memory_voice_agent()
+    
+    # Set up the voice pipeline
+    pipeline = VoicePipeline(
+        workflow=SingleAgentVoiceWorkflow(agent)
+    )
+    
+    # Configure TTS settings
+    pipeline.config.tts_settings.voice = "alloy"
+    pipeline.config.tts_settings.speed = 1.0
+    
+    try:
+        while True:
+            # Get user input
+            print("\nPress Enter to start recording (or 'q' to quit)...")
+            user_input = input()
+            if user_input.lower() == 'q':
+                break
+            
+            # Record and process audio
+            audio_data = await record_from_microphone(duration=5)
+            audio_input = AudioInput(buffer=audio_data)
+            
+            print("Processing your request...")
+            
+            # Process the audio input
+            result = await pipeline.run(audio_input)
+            
+            # Create an audio player
+            player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
+            player.start()
+            
+            # Store the agent's response for adding to memory
+            agent_response = ""
+            
+            print("\nAgent response:")
+            # Play the audio stream as it comes in
+            async for event in result.stream():
+                if event.type == "voice_stream_event_audio":
+                    player.write(event.data)
+                elif event.type == "voice_stream_event_content":
+                    # Accumulate and print the text response
+                    content = event.data
+                    agent_response += content
+                    print(content, end="", flush=True)
+            
+            print("\n")
+            
+            # Example of saving the conversation to Mem0 after completion
+            if agent_response:
+                try:
+                    await mem0_client.add(
+                        f"Agent response: {agent_response}", 
+                        user_id=USER_ID,
+                        metadata={"type": "agent_response"}
+                    )
+                except Exception as e:
+                    print(f"Failed to store memory: {e}")
+    
+    except KeyboardInterrupt:
+        print("\nExiting...")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Key Features of This Implementation
+
+This implementation offers several key features:
+
+1. **Simplified User Management**: Uses a global `USER_ID` variable for simplicity, but can be extended to manage multiple users.
+
+2. **Real Microphone Input**: Includes a `record_from_microphone()` function that captures actual voice input from your microphone.
+
+3. **Interactive Voice Loop**: Implements a continuous interaction loop, allowing for multiple back-and-forth exchanges.
+
+4. **Memory Management Tools**:
+   - `save_memories`: Stores user memories in Mem0
+   - `search_memories`: Searches for relevant past information
+
+5. **Voice Configuration**: Demonstrates how to configure TTS settings for the voice response.
+
+## Running the Example
+
+To run this example:
+
+1. Replace the placeholder API keys with your actual keys
+2. Make sure your microphone is properly connected
+3. Run the script with Python 3.8 or newer
+4. Press Enter to start recording, then speak your request
+5. Press 'q' to quit the application
+
+The agent will listen to your request, process it through the OpenAI model, utilize Mem0 for memory operations as needed, and respond both through text output and voice speech.
+
+## Best Practices for Voice Agents with Memory
+
+1. **Optimizing Memory for Voice**: Keep memories concise and relevant for voice responses.
+
+2. **Forgetting Mechanism**: Implement a way to delete or expire memories that are no longer relevant.
+
+3. **Context Preservation**: Store enough context with each memory to make retrieval effective.
+
+4. **Error Handling**: Implement robust error handling for memory operations, as voice interactions should continue smoothly even if memory operations fail.
+
+## Conclusion
+
+By combining OpenAI's Agents SDK with Mem0's memory capabilities, you can create voice agents that maintain persistent memory of user preferences and past interactions. This significantly enhances the user experience by making conversations more natural and personalized.
+
+As you build your voice application, experiment with different memory strategies and filtering approaches to find the optimal balance between comprehensive memory and efficient retrieval for your specific use case.
+
+## Debugging Function Tools
+
+When working with the OpenAI Agents SDK, you might notice that regular `print()` statements inside `@function_tool` decorated functions don't appear in your console output. This is because the Agents SDK captures and redirects standard output when executing these functions.
+
+To effectively debug your function tools, use Python's `logging` module instead:
+
+```python
+import logging
+
+# Set up logging at the top of your file
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    force=True
+)
+logger = logging.getLogger("memory_voice_agent")
+
+# Then use logger in your function tools
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """Store a user memory in memory."""
+    # This will be visible in your console
+    logger.debug(f"Saving memory: {memory} for user {USER_ID}")
+    
+    # Rest of your function...
+```

docs/examples/mem0-with-ollama.mdx

@@ -2,6 +2,8 @@
 title: Mem0 with Ollama
 ---
 
+<Snippet file="security-compliance.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.

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

@@ -0,0 +1,219 @@
+---
+title: Memory-Guided Content Writing
+---
+<Snippet file="security-compliance.mdx" />
+
+This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
+
+## Why Use Mem0?
+
+Integrating Mem0 into your writing workflow helps you:
+
+1. **Store persistent writing preferences** ensuring consistent tone, formatting, and structure.
+2. **Automate content refinement** by retrieving preferences when rewriting or reviewing content.
+3. **Scale your writing style** so it applies consistently across multiple documents or sessions.
+
+## Setup
+
+```python
+import os
+from openai import OpenAI
+from mem0 import MemoryClient
+
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+
+# Set up Mem0 and OpenAI client
+client = MemoryClient()
+openai = OpenAI()
+
+USER_ID = "content_writer"
+RUN_ID = "smart_editing_session"
+```
+
+## **Storing Your Writing Preferences in Mem0**
+
+```python
+def store_writing_preferences():
+    """Store your writing preferences in Mem0."""
+    
+    preferences = """My writing preferences:
+1. Use headings and sub-headings for structure.
+2. Keep paragraphs concise (8–10 sentences max).
+3. Incorporate specific numbers and statistics.
+4. Provide concrete examples.
+5. Use bullet points for clarity.
+6. Avoid jargon and buzzwords."""
+
+    messages = [
+        {"role": "user", "content": "Here are my writing style preferences."},
+        {"role": "assistant", "content": preferences}
+    ]
+
+    response = client.add(
+        messages,
+        user_id=USER_ID,
+        run_id=RUN_ID,
+        metadata={"type": "preferences", "category": "writing_style"}
+    )
+
+    return response
+```
+
+## **Editing Content Using Stored Preferences**
+
+```python
+def apply_writing_style(original_content):
+    """Use preferences stored in Mem0 to guide content rewriting."""
+
+    results = client.search(
+        query="What are my writing style preferences?",
+        version="v2",
+        filters={
+            "AND": [
+                {
+                    "user_id": USER_ID
+                },
+                {
+                    "run_id": RUN_ID
+                }
+            ]
+        },
+    )
+
+    if not results:
+        print("No preferences found.")
+        return None
+
+    preferences = "\n".join(r["memory"] for r in results)
+
+    system_prompt = f"""
+You are a writing assistant.
+
+Apply the following writing style preferences to improve the user's content:
+
+Preferences:
+{preferences}
+"""
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"""Original Content:
+    {original_content}"""}
+    ]
+
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages
+    )
+    clean_response = response.choices[0].message.content.strip()
+
+    return clean_response
+```
+
+## **Complete Workflow: Content Editing**
+
+```python
+def content_writing_workflow(content):
+    """Automated workflow for editing a document based on writing preferences."""
+    
+    # Store writing preferences (if not already stored)
+    store_writing_preferences()  # Ideally done once, or with a conditional check
+    
+    # Edit the document with Mem0 preferences
+    edited_content = apply_writing_style(content)
+    
+    if not edited_content:
+        return "Failed to edit document."
+    
+    # Display results
+    print("\n=== ORIGINAL DOCUMENT ===\n")
+    print(content)
+    
+    print("\n=== EDITED DOCUMENT ===\n")
+    print(edited_content)
+    
+    return edited_content
+```
+
+## **Example Usage**
+
+```python
+# Define your document
+original_content = """Project Proposal
+    
+The following proposal outlines our strategy for the Q3 marketing campaign. 
+We believe this approach will significantly increase our market share.
+
+Increase brand awareness
+Boost sales by 15%
+Expand our social media following
+
+We plan to launch the campaign in July and continue through September.
+"""
+
+# Run the workflow
+result = content_writing_workflow(original_content)
+```
+
+## **Expected Output**
+
+Your document will be transformed into a structured, well-formatted version based on your preferences.
+
+### **Original Document**
+```
+Project Proposal
+    
+The following proposal outlines our strategy for the Q3 marketing campaign. 
+We believe this approach will significantly increase our market share.
+
+Increase brand awareness
+Boost sales by 15%
+Expand our social media following
+
+We plan to launch the campaign in July and continue through September.
+```
+
+### **Edited Document**
+```
+# **Project Proposal**
+
+## **Q3 Marketing Campaign Strategy**
+
+This proposal outlines our strategy for the Q3 marketing campaign. We aim to significantly increase our market share with this approach.
+
+### **Objectives**
+
+- **Increase Brand Awareness**: Implement targeted advertising and community engagement to enhance visibility.
+- **Boost Sales by 15%**: Increase sales by 15% compared to Q2 figures.
+- **Expand Social Media Following**: Grow our social media audience by 20%.
+
+### **Timeline**
+
+- **Launch Date**: July
+- **Duration**: July – September
+
+### **Key Actions**
+
+- **Targeted Advertising**: Utilize platforms like Google Ads and Facebook to reach specific demographics.
+- **Community Engagement**: Host webinars and live Q&A sessions.
+- **Content Creation**: Produce engaging videos and infographics.
+
+### **Supporting Data**
+
+- **Previous Campaign Success**: Our Q2 campaign increased sales by 12%. We will refine similar strategies for Q3.
+- **Social Media Growth**: Last year, our Instagram followers grew by 25% during a similar campaign.
+
+### **Conclusion**
+
+We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let’s work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
+```
+
+Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.
+
+## Help & Resources
+
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/examples/multimodal-demo.mdx

@@ -0,0 +1,33 @@
+---
+title: Multimodal Demo with Mem0
+---
+
+<Snippet file="security-compliance.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)
+
+## Features
+
+- **Image Understanding**: Share and discuss images with AI assistants while maintaining context.
+- **Smart Visual Context**: Automatically capture and reference visual elements in conversations.
+- **Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
+- **Cross-Session Recall**: Reference previously discussed visual content across different conversations.
+- **Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
+
+## How It Works
+
+1. **Upload Visual Content**: Simply drag and drop or paste images into your conversations.
+2. **Natural Interaction**: Discuss the visual content naturally with AI assistants.
+3. **Memory Integration**: Visual context is automatically stored and linked with your conversation history.
+4. **Persistent Recall**: Retrieve and reference past visual content effortlessly.
+
+## Demo Video
+
+<iframe width="700" height="400" src="https://www.youtube.com/embed/2Md5AEFVpmg?si=rXXupn6CiDUPJsi3" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## Try It Out
+
+Visit [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai) to experience Mem0's multimodal capabilities firsthand. Upload images and see how Mem0 understands and remembers visual context across your conversations.
+

docs/examples/openai-inbuilt-tools.mdx

@@ -0,0 +1,314 @@
+---
+title: OpenAI Inbuilt Tools
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
+
+## Getting Started
+
+### Installation
+
+```bash
+npm install mem0ai openai zod
+```
+
+## Environment Setup
+
+Save your Mem0 and OpenAI API keys in a `.env` file:
+
+```
+MEM0_API_KEY=your_mem0_api_key
+OPENAI_API_KEY=your_openai_api_key
+```
+
+Get your Mem0 API key from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-keys).
+
+### Configuration
+
+```javascript
+const mem0Config = {
+    apiKey: process.env.MEM0_API_KEY,
+    user_id: "sample-user",
+};
+
+const openAIClient = new OpenAI();
+const mem0Client = new MemoryClient(mem0Config);
+```
+
+### Adding Memories
+
+Store user preferences, past interactions, or any relevant information:
+<CodeGroup>
+```javascript JavaScript
+async function addUserPreferences() {
+    const mem0Client = new MemoryClient(mem0Config);
+    
+    const userPreferences = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
+    
+    await mem0Client.add([{
+        role: "user",
+        content: userPreferences,
+    }], mem0Config);
+}
+
+await addUserPreferences();
+```
+
+```json Output (Memories)
+ [
+  {
+    "id": "ff9f3367-9e83-415d-b9c5-dc8befd9a4b4",
+    "data": { "memory": "Loves BMW, Audi, and Porsche" },
+    "event": "ADD"
+  },
+  {
+    "id": "04172ce6-3d7b-45a3-b4a1-ee9798593cb4",
+    "data": { "memory": "Hates Mercedes" },
+    "event": "ADD"
+  },
+  {
+    "id": "db363a5d-d258-4953-9e4c-777c120de34d",
+    "data": { "memory": "Loves red cars and maroon cars" },
+    "event": "ADD"
+  },
+  {
+    "id": "5519aaad-a2ac-4c0d-81d7-0d55c6ecdba8",
+    "data": { "memory": "Has a budget of 120K to 150K USD" },
+    "event": "ADD"
+  },
+  {
+    "id": "523b7693-7344-4563-922f-5db08edc8634",
+    "data": { "memory": "Likes Audi the most" },
+    "event": "ADD"
+  }
+]
+```
+</CodeGroup>
+### Retrieving Memories
+
+Search for relevant memories based on the current user input:
+
+```javascript
+const relevantMemories = await mem0Client.search(userInput, mem0Config);
+```
+
+### Structured Responses with Zod
+
+Define structured response schemas to get consistent output formats:
+
+```javascript
+// Define the schema for a car recommendation
+const CarSchema = z.object({
+  car_name: z.string(),
+  car_price: z.string(),
+  car_url: z.string(),
+  car_image: z.string(),
+  car_description: z.string(),
+});
+
+// Schema for a list of car recommendations
+const Cars = z.object({
+  cars: z.array(CarSchema),
+});
+
+// Create a function tool based on the schema
+const carRecommendationTool = zodResponsesFunction({ 
+    name: "carRecommendations", 
+    parameters: Cars 
+});
+
+// Use the tool in your OpenAI request
+const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, carRecommendationTool],
+    input: `${getMemoryString(relevantMemories)}\n${userInput}`,
+});
+```
+
+### Using Web Search
+
+Combine memory with web search for up-to-date recommendations:
+
+```javascript
+const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, carRecommendationTool],
+    input: `${getMemoryString(relevantMemories)}\n${userInput}`,
+});
+```
+
+## Examples
+
+### Complete Car Recommendation System
+
+```javascript
+import MemoryClient from "mem0ai";
+import { OpenAI } from "openai";
+import { zodResponsesFunction } from "openai/helpers/zod";
+import { z } from "zod";
+import dotenv from 'dotenv';
+
+dotenv.config();
+
+const mem0Config = {
+    apiKey: process.env.MEM0_API_KEY,
+    user_id: "sample-user",
+};
+
+async function run() {
+    // Responses without memories
+    console.log("\n\nRESPONSES WITHOUT MEMORIES\n\n");
+    await main();
+
+    // Adding sample memories
+    await addSampleMemories();
+
+    // Responses with memories
+    console.log("\n\nRESPONSES WITH MEMORIES\n\n");
+    await main(true);
+}
+
+// OpenAI Response Schema
+const CarSchema = z.object({
+  car_name: z.string(),
+  car_price: z.string(),
+  car_url: z.string(),
+  car_image: z.string(),
+  car_description: z.string(),
+});
+
+const Cars = z.object({
+  cars: z.array(CarSchema),
+});
+
+async function main(memory = false) {
+  const openAIClient = new OpenAI();
+  const mem0Client = new MemoryClient(mem0Config);
+
+  const input = "Suggest me some cars that I can buy today.";
+
+  const tool = zodResponsesFunction({ name: "carRecommendations", parameters: Cars });
+
+  // Store the user input as a memory
+  await mem0Client.add([{
+    role: "user",
+    content: input,
+  }], mem0Config);
+
+  // Search for relevant memories
+  let relevantMemories = []
+  if (memory) {
+    relevantMemories = await mem0Client.search(input, mem0Config);
+  }
+
+  const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, tool],
+    input: `${getMemoryString(relevantMemories)}\n${input}`,
+  });
+
+  console.log(response.output);
+}
+
+async function addSampleMemories() {
+  const mem0Client = new MemoryClient(mem0Config);
+
+  const myInterests = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
+  
+  await mem0Client.add([{
+    role: "user",
+    content: myInterests,
+  }], mem0Config);
+}
+
+const getMemoryString = (memories) => {
+    const MEMORY_STRING_PREFIX = "These are the memories I have stored. Give more weightage to the question by users and try to answer that first. You have to modify your answer based on the memories I have provided. If the memories are irrelevant you can ignore them. Also don't reply to this section of the prompt, or the memories, they are only for your reference. The MEMORIES of the USER are: \n\n";
+    const memoryString = memories.map((mem) => `${mem.memory}`).join("\n") ?? "";
+    return memoryString.length > 0 ? `${MEMORY_STRING_PREFIX}${memoryString}` : "";
+};
+
+run().catch(console.error);
+```
+
+### Responses
+
+<CodeGroup>
+    ```json Without Memories
+    {
+      "cars": [
+        {
+          "car_name": "Toyota Camry",
+          "car_price": "$25,000",
+          "car_url": "https://www.toyota.com/camry/",
+          "car_image": "https://link-to-toyota-camry-image.com",
+          "car_description": "Reliable mid-size sedan with great fuel efficiency."
+        },
+        {
+          "car_name": "Honda Accord",
+          "car_price": "$26,000",
+          "car_url": "https://www.honda.com/accord/",
+          "car_image": "https://link-to-honda-accord-image.com",
+          "car_description": "Comfortable and spacious with advanced safety features."
+        },
+        {
+          "car_name": "Ford Mustang",
+          "car_price": "$28,000",
+          "car_url": "https://www.ford.com/mustang/",
+          "car_image": "https://link-to-ford-mustang-image.com",
+          "car_description": "Iconic sports car with powerful engine options."
+        },
+        {
+          "car_name": "Tesla Model 3",
+          "car_price": "$38,000",
+          "car_url": "https://www.tesla.com/model3",
+          "car_image": "https://link-to-tesla-model3-image.com",
+          "car_description": "Electric vehicle with advanced technology and long range."
+        },
+        {
+          "car_name": "Chevrolet Equinox",
+          "car_price": "$24,000",
+          "car_url": "https://www.chevrolet.com/equinox/",
+          "car_image": "https://link-to-chevron-equinox-image.com",
+          "car_description": "Compact SUV with a spacious interior and user-friendly technology."
+        }
+      ]
+    }
+    ```
+  
+    ```json With Memories
+    {
+      "cars": [
+        {
+          "car_name": "Audi RS7",
+          "car_price": "$118,500",
+          "car_url": "https://www.audiusa.com/us/web/en/models/rs7/2023/overview.html",
+          "car_image": "https://www.audiusa.com/content/dam/nemo/us/models/rs7/my23/gallery/1920x1080_AOZ_A717_191004.jpg",
+          "car_description": "The Audi RS7 is a high-performance hatchback with a sleek design, powerful 591-hp twin-turbo V8, and luxurious interior. It's available in various colors including red."
+        },
+        {
+          "car_name": "Porsche Panamera GTS",
+          "car_price": "$129,300",
+          "car_url": "https://www.porsche.com/usa/models/panamera/panamera-models/panamera-gts/",
+          "car_image": "https://files.porsche.com/filestore/image/multimedia/noneporsche-panamera-gts-sample-m02-high/normal/8a6327c3-6c7f-4c6f-a9a8-fb9f58b21795;sP;twebp/porsche-normal.webp",
+          "car_description": "The Porsche Panamera GTS is a luxury sports sedan with a 473-hp V8 engine, exquisite handling, and available in stunning red. Balances sportiness and comfort."
+        },
+        {
+          "car_name": "BMW M5",
+          "car_price": "$105,500",
+          "car_url": "https://www.bmwusa.com/vehicles/m-models/m5/sedan/overview.html",
+          "car_image": "https://www.bmwusa.com/content/dam/bmwusa/M/m5/2023/bmw-my23-m5-sapphire-black-twilight-purple-exterior-02.jpg",
+          "car_description": "The BMW M5 is a powerhouse sedan with a 600-hp V8 engine, known for its great handling and luxury. It comes in several distinctive colors including maroon."
+        }
+      ]
+    }
+    ```
+</CodeGroup>
+
+## Resources
+
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
+- [API Reference](https://docs.mem0.ai/api-reference)
+- [OpenAI Documentation](https://platform.openai.com/docs)

docs/examples/overview.mdx

@@ -1,38 +0,0 @@
----
-title: Overview
-description: How to use mem0 in your existing applications?
----
-
-
-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
-- More reliable
-- Cost-effective by reducing the number of LLM interactions
-- More engaging
-- Enables long-term memory
-
-Here are some examples of how Mem0 can be integrated into various applications:
-
-## Examples
-
-<CardGroup cols={2}>
-  <Card title="AI Companion in Node.js" icon="square-6" href="/examples/ai_companion_js">
-    Create a Personalized AI Companion using Mem0 in Node.js.
-  </Card>
-  <Card title="Mem0 with Ollama" icon="square-1" href="/examples/mem0-with-ollama">
-    Run Mem0 locally with Ollama.
-  </Card>
-  <Card title="Personal AI Tutor" icon="square-2" href="/examples/personal-ai-tutor">
-    Create a Personalized AI Tutor that adapts to student progress and learning preferences.
-  </Card>
-  <Card title="Personal Travel Assistant" icon="square-3" href="/examples/personal-travel-assistant">
-    Build a Personalized AI Travel Assistant that understands your travel preferences and past itineraries.
-  </Card>
-  <Card title="Customer Support Agent" icon="square-4" href="/examples/customer-support-agent">
-    Develop a Personal AI Assistant that remembers user preferences, past interactions, and context to provide personalized and efficient assistance.
-  </Card>
-  <Card title="LlamaIndex Mem0" icon="square-5" href="/examples/llama-index-mem0">
-    Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
-  </Card>
-</CardGroup>

docs/examples/personal-ai-tutor.mdx

@@ -2,6 +2,8 @@
 title: Personalized AI Tutor
 ---
 
+<Snippet file="security-compliance.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
@@ -20,6 +22,7 @@ pip install openai mem0ai
 Below is the complete code to create and interact with a Personalized AI Tutor using Mem0:
 
 ```python
+import os 
 from openai import OpenAI
 from mem0 import Memory
 
@@ -54,22 +57,21 @@ class PersonalAITutor:
         :param question: The question to ask the AI.
         :param user_id: Optional user ID to associate with the memory.
         """
-        # Start a streaming chat completion request to the AI
-        stream = self.client.chat.completions.create(
-            model="gpt-4",
-            stream=True,
-            messages=[
-                {"role": "system", "content": "You are a personal AI Tutor."},
-                {"role": "user", "content": question}
-            ]
+        # Start a streaming response request to the AI
+        response = self.client.responses.create(
+            model="gpt-4o",
+            instructions="You are a personal AI Tutor.",
+            input=question,
+            stream=True
         )
+
         # Store the question in memory
         self.memory.add(question, user_id=user_id, metadata={"app_id": self.app_id})
 
         # Print the response from the AI in real-time
-        for chunk in stream:
-            if chunk.choices[0].delta.content is not None:
-                print(chunk.choices[0].delta.content, end="")
+        for event in response:
+            if event.type == "response.output_text.delta":
+                print(event.delta, end="")
 
     def get_memories(self, user_id=None):
         """
@@ -96,8 +98,8 @@ You can fetch all the memories at any point in time using the following code:
 
 ```python
 memories = ai_tutor.get_memories(user_id=user_id)
-for m in memories:
-    print(m['text'])
+for m in memories['results']:
+    print(m['memory'])
 ```
 
 ### Key Points

docs/examples/personal-travel-assistant.mdx

@@ -1,6 +1,9 @@
 ---
 title: Personal AI Travel Assistant
 ---
+
+<Snippet file="security-compliance.mdx" />
+
 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
 
 ## Overview
@@ -63,18 +66,23 @@ class PersonalTravelAssistant:
     def ask_question(self, question, user_id):
         # Fetch previous related memories
         previous_memories = self.search_memories(question, user_id=user_id)
-        prompt = question
-        if previous_memories:
-            prompt = f"User input: {question}\n Previous memories: {previous_memories}"
-        self.messages.append({"role": "user", "content": prompt})
 
-        # Generate response using GPT-4o
-        response = self.client.chat.completions.create(
+        # Build the prompt
+        system_message = "You are a personal AI Assistant."
+
+        if previous_memories:
+            prompt = f"{system_message}\n\nUser input: {question}\nPrevious memories: {', '.join(previous_memories)}"
+        else:
+            prompt = f"{system_message}\n\nUser input: {question}"
+
+        # Generate response using Responses API
+        response = self.client.responses.create(
             model="gpt-4o",
-            messages=self.messages
+            input=prompt
         )
-        answer = response.choices[0].message.content
-        self.messages.append({"role": "assistant", "content": answer})
+
+        # Extract answer from the response
+        answer = response.output[0].content[0].text
 
         # Store the question in memory
         self.memory.add(question, user_id=user_id)
@@ -82,11 +90,11 @@ class PersonalTravelAssistant:
 
     def get_memories(self, user_id):
         memories = self.memory.get_all(user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories['results']]
 
     def search_memories(self, query, user_id):
         memories = self.memory.search(query, user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories['results']]
 
 # Usage example
 user_id = "traveler_123"

docs/examples/personalized-deep-research.mdx

@@ -0,0 +1,69 @@
+---
+title: Personalized Deep Research
+---
+
+<Snippet file="security-compliance.mdx" />
+
+Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.
+
+You can checkout GitHub repositry here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
+
+## Overview
+
+Deep Research leverages Mem0's memory capabilities to:
+- Synthesize large amounts of online data
+- Complete complex research tasks
+- Customize results to your preferences
+- Store and utilize personal insights
+- Maintain context across research sessions
+
+## Demo
+
+Watch Deep Research in action:
+
+<iframe 
+  width="700" 
+  height="400" 
+  src="https://www.youtube.com/embed/8vQlCtXzF60?si=b8iTOgummAVzR7ia" 
+  title="YouTube video player" 
+  frameborder="0" 
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" 
+  referrerpolicy="strict-origin-when-cross-origin" 
+  allowfullscreen
+></iframe>
+
+## Features
+
+### 1. Personalized Research
+- Analyzes your background and expertise
+- Tailors research depth and complexity to your level
+- Incorporates your previous research context
+
+### 2. Comprehensive Data Synthesis
+- Processes multiple online sources
+- Extracts relevant information
+- Provides coherent summaries
+
+### 3. Memory Integration
+- Stores research findings for future reference
+- Maintains context across sessions
+- Links related research topics
+
+### 4. Interactive Exploration
+- Allows real-time query refinement
+- Supports follow-up questions
+- Enables deep-diving into specific areas
+
+## Use Cases
+
+- **Academic Research**: Literature reviews, thesis research, paper writing
+- **Market Research**: Industry analysis, competitor research, trend identification
+- **Technical Research**: Technology evaluation, solution comparison
+- **Business Research**: Strategic planning, opportunity analysis
+
+
+## Try It Out
+
+> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
+
+- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)

docs/examples/youtube-assistant.mdx

@@ -0,0 +1,58 @@
+---
+title: YouTube Assistant Extension
+---
+
+<Snippet file="security-compliance.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
+
+- **Contextual AI Chat**: Ask questions about videos you're watching
+- **Seamless Integration**: Chat interface sits alongside YouTube's native UI
+- **Memory Integration**: Personalized responses based on your knowledge through Mem0
+- **Real-Time Memory**: Memories are updated in real-time based on your interactions
+
+## Demo Video
+
+<video
+  autoPlay
+  muted
+  loop
+  playsInline
+  width="700"
+  height="400"
+  src="https://github.com/user-attachments/assets/c0334ccd-311b-4dd7-8034-ef88204fc751"
+></video>
+
+## Installation
+
+This extension is not available on the Chrome Web Store yet. You can install it manually using below method:
+
+### Manual Installation (Developer Mode)
+
+1. **Download the Extension**: Clone or download the extension files from the [Mem0 GitHub repository](https://github.com/mem0ai/mem0/tree/main/examples).
+2. **Build**: Run `npm install` followed by `npm run build` to install the dependencies and build the extension.
+3. **Access Chrome Extensions**: Open Google Chrome and navigate to `chrome://extensions`.
+4. **Enable Developer Mode**: Toggle the "Developer mode" switch in the top right corner.
+5. **Load Unpacked Extension**: Click "Load unpacked" and select the directory containing the extension files.
+6. **Confirm Installation**: The Mem0 YouTube Assistant Extension should now appear in your Chrome toolbar.
+
+## Setup
+
+1. **Configure API Settings**: Click the extension icon and enter your OpenAI API key (required to use the extension)
+2. **Customize Settings**: Configure additional settings such as model, temperature, and memory settings
+3. **Navigate to YouTube**: Start using the assistant on any YouTube video
+4. **Memories**: Enter your Mem0 API key to enable personalized responses, and feed initial memories from settings
+
+## Example Prompts
+
+- "Can you summarize the main points of this video?"
+- "Explain the concept they just mentioned"
+- "How does this relate to what I already know?"
+- "What are some practical applications of this topic related to my work?"
+
+
+## Privacy and Data Security
+
+Your API keys are stored locally in your browser. Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.

docs/faqs.mdx

@@ -4,6 +4,7 @@ icon: "question"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.mdx" />
 
 <AccordionGroup>
     <Accordion title="How does Mem0 work?">
@@ -13,7 +14,7 @@ iconType: "solid"
 
         When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
 
-        The retrieved memories can be seamlessly integrated into the LLM's prompt as required, enhancing the personalization and relevance of responses.
+        The retrieved memories can be seamlessly integrated into the system prompt as required, enhancing the personalization and relevance of responses.
   </Accordion>
 
     <Accordion title="What are the key features of Mem0?">
@@ -22,7 +23,7 @@ iconType: "solid"
         - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
         - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
         - **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
-        - **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
+        - **Save Costs**: Saves costs by adding relevant memories instead of complete transcripts to context window
     </Accordion>
 
     <Accordion title="How Mem0 is different from traditional RAG?">
@@ -84,9 +85,65 @@ iconType: "solid"
         - Include specific examples or cases rather than general definitions
     </Accordion>
 
+    <Accordion title="How do I configure Mem0 for AWS Lambda?">
+        When deploying Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.
+
+        To configure Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:
+
+        ```bash
+        MEM0_DIR=/tmp/.mem0
+        ```
+
+        If you're not using environment variables, you'll need to modify the storage path in your code:
+
+        ```python
+        # Change from
+        home_dir = os.path.expanduser("~")
+        mem0_dir = os.environ.get("MEM0_DIR") or os.path.join(home_dir, ".mem0")
+
+        # To
+        mem0_dir = os.environ.get("MEM0_DIR", "/tmp/.mem0")
+        ```
+
+        Note that the `/tmp` directory in Lambda has a size limit of 512MB and its contents are not persistent between function invocations.
+    </Accordion>
+
+    <Accordion title="How can I use metadata with Mem0?">
+        Metadata is the recommended approach for incorporating additional information with Mem0. You can store any type of structured data as metadata during the `add` method, such as location, timestamp, weather conditions, user state, or application context. This enriches your memories with valuable contextual information that can be used for more precise retrieval and filtering.
+
+        During retrieval, you have two main approaches for using metadata:
+
+        1. **Pre-filtering**: Include metadata parameters in your initial search query to narrow down the memory pool
+        2. **Post-processing**: Retrieve a broader set of memories based on query, then apply metadata filters to refine the results
+
+        Examples of useful metadata you might store:
+
+        - **Contextual information**: Location, time, device type, application state
+        - **User attributes**: Preferences, skill levels, demographic information
+        - **Interaction details**: Conversation topics, sentiment, urgency levels
+        - **Custom tags**: Any domain-specific categorization relevant to your application
+
+        This flexibility allows you to create highly contextually aware AI applications that can adapt to specific user needs and situations. Metadata provides an additional dimension for memory retrieval, enabling more precise and relevant responses.
+    </Accordion>
+
+    <Accordion title="How do I disable telemetry in Mem0?">
+        To disable telemetry in Mem0, you can set the `MEM0_TELEMETRY` environment variable to `False`:
+
+        ```bash
+        MEM0_TELEMETRY=False
+        ```
+
+        You can also disable telemetry programmatically in your code:
+
+        ```python
+        import os
+        os.environ["MEM0_TELEMETRY"] = "False"
+        ```
+
+        Setting this environment variable will prevent Mem0 from collecting and sending any usage data, ensuring complete privacy for your application.
+    </Accordion>
+
 </AccordionGroup>
 
 
 
-
-

docs/features.mdx

@@ -4,6 +4,8 @@ icon: "wrench"
 iconType: "solid"
 ---
 
+<Snippet file="security-compliance.mdx" />
+
 ## Core features
 
 - **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
@@ -11,7 +13,7 @@ iconType: "solid"
 - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
 - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
 - **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
-- **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
+- **Save Costs**: Saves costs by adding relevant memories instead of complete transcripts to context window
 
 
 

docs/features/advanced-retrieval.mdx

@@ -1,49 +0,0 @@
----
-title: Advanced Retrieval
-icon: "magnifying-glass"
-iconType: "solid"
----
-
-Mem0's **Advanced Retrieval** feature delivers superior search results by leveraging state-of-the-art search algorithms. Beyond the default search functionality, Mem0 offers the following advanced retrieval modes:
-
-1. **Keyword Search**
-
-   This mode emphasizes keywords within the query, returning memories that contain the most relevant keywords alongside those from the default search. By default, this parameter is set to `false`. Enabling it enhances search recall, though it may slightly impact precision.
-
-   ```python
-   client.search(query, keyword_search=True, user_id='alex')
-   ```
-
-2. **Reranking**
-
-   Reranking allows you to reorder the memories returned by the default search based on relevance. This parameter is set to `false` by default. When enabled, it reorders the memories based on the relevance score.
-
-   ```python
-   client.search(query, rerank=True, user_id='alex')
-   ```
-
-3. **Filtering**
-
-   Filtering enables you to narrow down the search results by applying specific criteria. This parameter is set to `false` by default. Activating it enhances search precision, potentially reducing recall by a small margin.
-
-   ```python
-   client.search(query, filter_memories=True, user_id='alex')
-   ```
-
-**Note:** You can enable or disable these search modes by passing the respective parameters to the `search` method. There is no required sequence for these modes, and any combination can be used based on your needs.
-
-
-### Latency Numbers
-
-Here are the typical latency ranges for each search mode:
-
-| **Mode**            | **Latency**      |
-|---------------------|------------------|
-| **Keyword Search**  | **<10ms**        |
-| **Reranking**       | **150-200ms**    |
-| **Filtering**       | **200-300ms**    |
-
-
-If you have any questions, please feel free to reach out to us using one of the following methods:
-
-<Snippet file="get-help.mdx" />

docs/features/multimodal-support.mdx

@@ -1,120 +0,0 @@
----
-title: Multimodal Support
-description: Integrate images into your interactions with Mem0
-icon: "image"
-iconType: "solid"
----
-
-Mem0 extends its capabilities beyond text by supporting multimodal data, including images. With this feature, users can seamlessly integrate images into their interactions—allowing Mem0 to extract relevant information from visual content and enrich the memory system.
-
-## How It Works
-
-When a user submits an image, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall visual inputs.
-
-<CodeGroup>
-```python Code
-import os
-from mem0 import MemoryClient
-
-os.environ["MEM0_API_KEY"] = "your-api-key"
-
-client = MemoryClient()
-
-messages = [
-    {
-        "role": "user",
-        "content": "Hi, my name is Alice."
-    },
-    {
-        "role": "assistant",
-        "content": "Nice to meet you, Alice! What do you like to eat?"
-    },
-    {
-        "role": "user",
-        "content": {
-            "type": "image_url",
-            "image_url": {
-                "url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
-            }
-        }
-    },
-]
-
-# Calling the add method to ingest messages into the memory system
-client.add(messages, user_id="alice")
-```
-
-```json Output
-{
-  "results": [
-    {
-      "memory": "Name is Alice",
-      "event": "ADD",
-      "id": "7ae113a3-3cb5-46e9-b6f7-486c36391847"
-    },
-    {
-      "memory": "Likes large pizza with toppings including cherry tomatoes, black olives, green spinach, yellow bell peppers, diced ham, and sliced mushrooms",
-      "event": "ADD",
-      "id": "56545065-7dee-4acf-8bf2-a5b2535aabb3"
-    }
-  ]
-}
-```
-</CodeGroup>
-
-## Image Integration Methods
-
-Mem0 supports incorporating images into user interactions using two primary methods: by providing an image URL or by using a Base64-encoded image. The examples below demonstrate both approaches.
-
-## 1. Using an Image URL (Recommended)
-
-You can include an image by providing its direct URL. This method is simple and efficient for online images.
-
-```python {2, 5-13}
-# Define the image URL
-image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
-
-# Create the message dictionary with the image URL
-image_message = {
-    "role": "user",
-    "content": {
-        "type": "image_url",
-        "image_url": {
-            "url": image_url
-        }
-    }
-}
-client.add([image_message], user_id="alice")
-```
-
-## 2. Using Base64 Image Encoding for Local Files
-
-For local images—or when embedding the image directly is preferable—you can use a Base64-encoded string.
-```python
-import base64
-
-# Path to the image file
-image_path = "path/to/your/image.jpg"
-
-# Encode the image in Base64
-with open(image_path, "rb") as image_file:
-    base64_image = base64.b64encode(image_file.read()).decode("utf-8")
-
-# Create the message dictionary with the Base64-encoded image
-image_message = {
-    "role": "user",
-    "content": {
-        "type": "image_url",
-        "image_url": {
-            "url": f"data:image/jpeg;base64,{base64_image}"
-        }
-    }
-}
-client.add([image_message], user_id="alice")
-```
-
-Using these methods, you can seamlessly incorporate images into your interactions, further enhancing Mem0's multimodal capabilities.
-
-If you have any questions, please feel free to reach out to us using one of the following methods:
-
-<Snippet file="get-help.mdx" />