Update version to 0.1.116 (#3312)

Signed-off-by: Andrew Carbonetto <andrew.carbonetto@improving.com>

.github/workflows/cd.yml

@@ -18,20 +18,17 @@ 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 mem0
-          poetry install
+          hatch env create
 
       - name: Build a binary wheel and a source tarball
         run: |
-          cd mem0
-          poetry build
+          hatch build --clean
 
       # TODO: Needs to setup mem0 repo on Test PyPI
       # - name: Publish distribution 📦 to Test PyPI

.github/workflows/ci.yml

@@ -37,28 +37,31 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
         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
 
@@ -68,28 +71,28 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python-version }}
         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 +16,20 @@ 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
+hatch shell dev_py_3_12  # Python 3.12
 
-#activate
-
-poetry shell
+# The environment will automatically install all dev dependencies
+# Run tests within the activated shell:
+make test
 ```
 
 ### 📌 Pre-commit
@@ -40,16 +42,22 @@ 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
+make test-py-3.12  # Python 3.12 environment
+
+# When using hatch shells, run tests with:
+make test  # After activating a shell with hatch shell test_XX
 ```
 
-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

@@ -8,37 +8,48 @@ 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 weaviate weaviate-client sentence_transformers vertexai \
-	                        google-generativeai elasticsearch opensearch-py vecs pinecone pinecone-text faiss-cpu langchain-community \
-							upstash-vector azure-search-documents
+	pip install ruff==0.6.9 groq together boto3 litellm ollama chromadb weaviate weaviate-client sentence_transformers vertexai \
+	            google-generativeai elasticsearch opensearch-py vecs "pinecone<7.0.0" pinecone-text faiss-cpu langchain-community \
+							upstash-vector azure-search-documents langchain-memgraph langchain-neo4j langchain-aws rank-bm25 pymochow pymongo psycopg
 
 # 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
+
+test-py-3.12:
+	hatch run dev_py_3_12:test

README.md

@@ -1,24 +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>
-    ·
-    <a href="https://mem0.dev/demo">Demo</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">
@@ -26,62 +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-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/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:
 
@@ -96,7 +110,7 @@ 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["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}]
@@ -122,68 +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
 
-- Mem0 - ChatGPT with Memory: A personalized AI chat app powered by Mem0 that remembers your preferences, facts, and memories.
+- Full docs: https://docs.mem0.ai
+- Community: [Discord](https://mem0.dev/DiG) · [Twitter](https://x.com/mem0ai)
+- Contact: founders@mem0.ai
 
-[Mem0 - ChatGPT with Memory](https://github.com/user-attachments/assets/cebc4f8e-bdb9-4837-868d-13c5ab7bb433)
+## Citation
 
-Try live [demo](https://mem0.dev/demo/)
+We now have a paper you can cite:
 
-<br/><br/>
+```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}
+}
+```
 
-- AI Companion: Experience personalized conversations with an AI that remembers your preferences and past interactions
+## ⚖️ License
 
-[AI Companion Demo](https://github.com/user-attachments/assets/3fc72023-a72c-4593-8be0-3cee3ba744da)
-
-<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/async-memory-add.mdx

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

docs/_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

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="async-memory-add.mdx" />
+
 Mem0 provides a powerful set of APIs that allow you to integrate advanced memory management capabilities into your applications. Our APIs are designed to be intuitive, efficient, and scalable, enabling you to create, retrieve, update, and delete memories across various entities such as users, agents, apps, and runs.
 
 ## Key Features
@@ -58,6 +60,128 @@ const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR
   </Tab>
 </Tabs>
 
+### Project Management Methods
+
+The Mem0 client provides comprehensive project management capabilities through the `client.project` interface:
+
+#### Get Project Details
+
+Retrieve information about the current project:
+
+```python
+# Get all project details
+project_info = client.project.get()
+
+# Get specific fields only
+project_info = client.project.get(fields=["name", "description", "custom_categories"])
+```
+
+#### Create a New Project
+
+Create a new project within your organization:
+
+```python
+# Create a project with name and description
+new_project = client.project.create(
+    name="My New Project",
+    description="A project for managing customer support memories"
+)
+```
+
+#### Update Project Settings
+
+Modify project configuration including custom instructions, categories, and graph settings:
+
+```python
+# Update project with custom categories
+client.project.update(
+    custom_categories=[
+        {"customer_preferences": "Customer likes, dislikes, and preferences"},
+        {"support_history": "Previous support interactions and resolutions"}
+    ]
+)
+
+# Update project with custom instructions
+client.project.update(
+    custom_instructions="..."
+)
+
+# Enable graph memory for the project
+client.project.update(enable_graph=True)
+
+# Update multiple settings at once
+client.project.update(
+    custom_instructions="...",
+    custom_categories=[
+        {"personal_info": "User personal information and preferences"},
+        {"work_context": "Professional context and work-related information"}
+    ],
+    enable_graph=True
+)
+```
+
+#### Delete Project
+
+<Note>
+This action will remove all memories, messages, and other related data in the project. This operation is irreversible.
+</Note>
+
+Remove a project and all its associated data:
+
+```python
+# Delete the current project (irreversible)
+result = client.project.delete()
+```
+
+#### Member Management
+
+Manage project members and their access levels:
+
+```python
+# Get all project members
+members = client.project.get_members()
+
+# Add a new member as a reader
+client.project.add_member(
+    email="colleague@company.com",
+    role="READER"  # or "OWNER"
+)
+
+# Update a member's role
+client.project.update_member(
+    email="colleague@company.com",
+    role="OWNER"
+)
+
+# Remove a member from the project
+client.project.remove_member(email="colleague@company.com")
+```
+
+#### Member Roles
+
+- **READER**: Can view and search memories, but cannot modify project settings or manage members
+- **OWNER**: Full access including project modification, member management, and all reader permissions
+
+#### Async Support
+
+All project methods are also available in async mode:
+
+```python
+from mem0 import AsyncMemoryClient
+
+async def manage_project():
+    client = AsyncMemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
+
+    # All methods support async/await
+    project_info = await client.project.get()
+    await client.project.update(enable_graph=True)
+    members = await client.project.get_members()
+
+# To call the async function properly
+import asyncio
+asyncio.run(manage_project())
+```
+
 ## Getting Started
 
 To begin using the Mem0 API, you'll need to:

docs/api-reference/memory/v2-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/changelog.mdx

@@ -3,9 +3,402 @@ title: "Product Updates"
 mode: "wide"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 <Tabs>
 <Tab title="Python">
 
+<Update label="2025-07-24" description="v0.1.115">
+
+**New Features & Updates:**
+- Enhanced project management via `client.project` and `AsyncMemoryClient.project` interfaces
+- Full support for project CRUD operations (create, read, update, delete)
+- Project member management: add, update, remove, and list members
+- Manage project settings including custom instructions, categories, retrieval criteria, and graph enablement
+- Both sync and async support for all project management operations
+
+**Improvements:**
+- **Documentation:**
+  - Added detailed API reference and usage examples for new project management methods.
+  - Updated all docs to use `client.project.get()` and `client.project.update()` instead of deprecated methods.
+  
+- **Deprecation:**
+  - Marked `get_project()` and `update_project()` as deprecated (these methods were already present); added warnings to guide users to the new API.
+
+**Bug Fixes:**
+- **Tests:**
+  - Fixed Gemini embedder and LLM test mocks for correct error handling and argument structure.
+- **vLLM:**
+  - Fixed duplicate import in vLLM module.
+
+</Update>
+
+<Update label="2025-07-05" description="v0.1.114">
+
+**New Features:**
+- **OpenAI Agents:** Added OpenAI agents SDK support
+- **Amazon Neptune:** Added Amazon Neptune Analytics graph_store configuration and integration
+- **vLLM:** Added vLLM support
+
+**Improvements:**
+- **Documentation:** 
+  - Added SOC2 and HIPAA compliance documentation
+  - Enhanced group chat feature documentation for platform
+  - Added Google AI ADK Integration documentation
+  - Fixed documentation images and links
+- **Setup:** Fixed Mem0 setup, logging, and documentation issues
+
+**Bug Fixes:**
+- **MongoDB:** Fixed MongoDB Vector Store misaligned strings and classes
+- **vLLM:** Fixed missing OpenAI import in vLLM module and call errors
+- **Dependencies:** Fixed CI issues related to missing dependencies
+- **Installation:** Reverted pip install changes
+
+</Update>
+
+<Update label="2025-06-30" description="v0.1.113">
+
+**Bug Fixes:**
+- **Gemini:** Fixed Gemini embedder configuration
+
+</Update>
+
+<Update label="2025-06-27" description="v0.1.112">
+
+**New Features:**
+- **Memory:** Added immutable parameter to add method
+- **OpenMemory:** Added async_mode parameter support
+
+**Improvements:**
+- **Documentation:** 
+  - Enhanced platform feature documentation
+  - Fixed documentation links
+  - Added async_mode documentation
+- **MongoDB:** Fixed MongoDB configuration name
+
+**Bug Fixes:**
+- **Bedrock:** Fixed Bedrock LLM, embeddings, tools, and temporary credentials
+- **Memory:** Fixed memory categorization by updating dependencies and correcting API usage
+- **Gemini:** Fixed Gemini Embeddings and LLM issues
+
+</Update>
+
+<Update label="2025-06-23" description="v0.1.111">
+
+**New Features:**
+- **OpenMemory:** 
+  - Added OpenMemory augment support
+  - Added OpenMemory Local Support using new library
+- **vLLM:** Added vLLM support integration
+
+**Improvements:**
+- **Documentation:** 
+  - Added MCP Client Integration Guide and updated installation commands
+  - Improved Agent Id documentation for Mem0 OSS Graph Memory
+- **Core:** Added JSON parsing to solve hallucination errors
+
+**Bug Fixes:**
+- **Gemini:** Fixed Gemini Embeddings migration
+
+</Update>
+
+<Update label="2025-06-20" description="v0.1.110">
+
+**New Features:**
+- **Baidu:** Added Baidu vector database integration
+
+**Improvements:**
+- **Documentation:** 
+  - Updated changelog
+  - Fixed example in quickstart page
+  - Updated client.update() method documentation in OpenAPI specification
+- **OpenSearch:** Updated logger warning
+
+**Bug Fixes:**
+- **CI:** Fixed failing CI pipeline
+
+</Update>
+
+<Update label="2025-06-19" description="v0.1.109">
+
+**New Features:**
+- **AgentOps:** Added AgentOps integration
+- **LM Studio:** Added response_format parameter for LM Studio configuration
+- **Examples:** Added Memory agent powered by voice (Cartesia + Agno)
+
+**Improvements:**
+- **AI SDK:** Added output_format parameter
+- **Client:** Enhanced update method to support metadata
+- **Google:** Added Google Genai library support
+
+**Bug Fixes:**
+- **Build:** Fixed Build CI failure
+- **Pinecone:** Fixed pinecone for async memory
+
+</Update>
+
+<Update label="2025-06-14" description="v0.1.108">
+
+**New Features:**
+- **MongoDB:** Added MongoDB Vector Store support
+- **Client:** Added client support for summary functionality
+
+**Improvements:**
+- **Pinecone:** Fixed pinecone version issues
+- **OpenSearch:** Added logger support
+- **Testing:** Added python version test environments
+
+</Update>
+
+<Update label="2025-06-11" description="v0.1.107">
+
+**Improvements:**
+- **Documentation:** 
+  - Updated Livekit documentation migration
+  - Updated OpenMemory hosted version documentation
+- **Core:** Updated categorization flow
+- **Storage:** Fixed migration issues
+
+</Update>
+
+<Update label="2025-06-09" description="v0.1.106">
+
+**New Features:**
+- **Cloudflare:** Added Cloudflare vector store support
+- **Search:** Added threshold parameter to search functionality
+- **API:** Added wildcard character support for v2 Memory APIs
+
+**Improvements:**
+- **Documentation:** Updated README docs for OpenMemory environment setup
+- **Core:** Added support for unique user IDs
+
+**Bug Fixes:**
+- **Core:** Fixed error handling exceptions
+
+</Update>
+
+<Update label="2025-06-03" description="v0.1.104">
+
+**Bug Fixes:**
+- **Vector Stores:** Fixed GET_ALL functionality for FAISS and OpenSearch
+
+</Update>
+
+<Update label="2025-06-02" description="v0.1.103">
+
+**New Features:**
+- **LLM:** Added support for OpenAI compatible LLM providers with baseUrl configuration
+
+**Improvements:**
+- **Documentation:** 
+  - Fixed broken links
+  - Improved Graph Memory features documentation clarity
+  - Updated enable_graph documentation
+- **TypeScript SDK:** Updated Google SDK peer dependency version
+- **Client:** Added async mode parameter
+
+</Update>
+
+<Update label="2025-05-26" description="v0.1.102">
+
+**New Features:**
+- **Examples:** Added Neo4j example
+- **AI SDK:** Added Google provider support
+- **OpenMemory:** Added LLM and Embedding Providers support
+
+**Improvements:**
+- **Documentation:** 
+  - Updated memory export documentation
+  - Enhanced role-based memory attribution rules documentation
+  - Updated API reference and messages documentation
+  - Added Mastra and Raycast documentation
+  - Added NOT filter documentation for Search and GetAll V2
+  - Announced Claude 4 support
+- **Core:** 
+  - Removed support for passing string as input in client.add()
+  - Added support for sarvam-m model
+- **TypeScript SDK:** Fixed types from message interface
+
+**Bug Fixes:**
+- **Memory:** Prevented saving prompt artifacts as memory when no new facts are present
+- **OpenMemory:** Fixed typos in MCP tool description
+
+</Update>
+
+<Update label="2025-05-15" description="v0.1.101">
+
+**New Features:**
+- **Neo4j:** Added base label configuration support
+
+**Improvements:**
+- **Documentation:** 
+  - Updated Healthcare example index
+  - Enhanced collaborative task agent documentation clarity
+  - Added criteria-based filtering documentation
+- **OpenMemory:** Added cURL command for easy installation
+- **Build:** Migrated to Hatch build system
+
+</Update>
+
+<Update label="2025-05-10" description="v0.1.100">
+
+**New Features:**
+- **Memory:** Added Group Chat Memory Feature support
+- **Examples:** Added Healthcare assistant using Mem0 and Google ADK
+
+**Bug Fixes:**
+- **SSE:** Fixed SSE connection issues
+- **MCP:** Fixed memories not appearing in MCP clients added from Dashboard
+
+</Update>
+
+<Update label="2025-05-07" description="v0.1.99">
+
+**New Features:**
+- **OpenMemory:** Added OpenMemory support
+- **Neo4j:** Added weights to Neo4j model
+- **AWS:** Added support for Opsearch Serverless
+- **Examples:** Added ElizaOS Example
+
+**Improvements:**
+- **Documentation:** Updated Azure AI documentation
+- **AI SDK:** Added missing parameters and updated demo application
+- **OSS:** Fixed AOSS and AWS BedRock LLM
+
+</Update>
+
+<Update label="2025-04-30" description="v0.1.98">
+
+**New Features:**
+- **Neo4j:** Added support for Neo4j database
+- **AWS:** Added support for AWS Bedrock Embeddings
+
+**Improvements:**
+- **Client:** Updated delete_users() to use V2 API endpoints
+- **Documentation:** Updated timestamp and dual-identity memory management docs
+- **Neo4j:** Improved Neo4j queries and removed warnings
+- **AI SDK:** Added support for graceful failure when services are down
+
+**Bug Fixes:**
+- Fixed AI SDK filters
+- Fixed new memories wrong type
+- Fixed duplicated metadata issue while adding/updating memories
+
+</Update>
+
+<Update label="2025-04-23" description="v0.1.97">
+
+**New Features:**
+- **HuggingFace:** Added support for HF Inference
+
+**Bug Fixes:**
+- Fixed proxy for Mem0
+
+</Update>
+
+<Update label="2025-04-16" description="v0.1.96">
+
+**New Features:**
+- **Vercel AI SDK:** Added Graph Memory support
+
+**Improvements:**
+- **Documentation:** Fixed timestamp and README links
+- **Client:** Updated TS client to use proper types for deleteUsers
+- **Dependencies:** Removed unnecessary dependencies from base package
+
+</Update>
+
+<Update label="2025-04-09" description="v0.1.95">
+
+**Improvements:**
+- **Client:** Fixed Ping Method for using default org_id and project_id
+- **Documentation:** Updated documentation
+
+**Bug Fixes:**
+- Fixed mem0-migrations issue
+
+</Update>
+
+<Update label="2025-04-26" description="v0.1.94">
+
+**New Features:**
+- **Integrations:** Added Memgraph integration
+- **Memory:** Added timestamp support
+- **Vector Stores:** Added reset function for VectorDBs
+
+**Improvements:**
+- **Documentation:** 
+  - Updated timestamp and expiration_date documentation
+  - Fixed v2 search documentation
+  - Added "memory" in EC "Custom config" section
+  - Fixed typos in the json config sample
+
+</Update>
+
+<Update label="2025-04-21" description="v0.1.93">
+
+**Improvements:**
+- **Vector Stores:** Initialized embedding_model_dims in all vectordbs
+
+**Bug Fixes:**
+- **Documentation:** Fixed agno link
+
+</Update>
+
+<Update label="2025-04-18" description="v0.1.92">
+
+**New Features:**
+- **Memory:** Added Memory Reset functionality
+- **Client:** Added support for Custom Instructions
+- **Examples:** Added Fitness Checker powered by memory
+
+**Improvements:**
+- **Core:** Updated capture_event
+- **Documentation:** Fixed curl for v2 get_all
+
+**Bug Fixes:**
+- **Vector Store:** Fixed user_id functionality
+- **Client:** Various client improvements
+
+</Update>
+
+<Update label="2025-04-16" description="v0.1.91">
+
+**New Features:**
+- **LLM Integrations:** Added Azure OpenAI Embedding Model
+- **Examples:** 
+  - Added movie recommendation using grok3
+  - Added Voice Assistant using Elevenlabs
+
+**Improvements:**
+- **Documentation:** 
+  - Added keywords AI
+  - Reformatted navbar page URLs
+  - Updated changelog
+  - Updated openai.mdx
+- **FAISS:** Silenced FAISS info logs
+
+</Update>
+
+<Update label="2025-04-11" description="v0.1.90">
+
+**New Features:**
+- **LLM Integrations:** Added Mistral AI as LLM provider
+
+**Improvements:**
+- **Documentation:** 
+  - Updated changelog
+  - Fixed memory exclusion example
+  - Updated xAI documentation
+  - Updated YouTube Chrome extension example documentation
+
+**Bug Fixes:**
+- **Core:** Fixed EmbedderFactory.create() in GraphMemory
+- **Azure OpenAI:** Added patch to fix Azure OpenAI
+- **Telemetry:** Fixed telemetry issue
+
+</Update>
+
 <Update label="2025-04-11" description="v0.1.89">
 
 **New Features:**
@@ -127,6 +520,110 @@ mode: "wide"
 
 <Tab title="TypeScript">
 
+<Update label="2025-08-04" description="v2.1.37">
+**New Features:**
+- **OSS:** Added `RedisCloud` search module check
+</Update>
+
+<Update label="2025-07-08" description="v2.1.36">
+**New Features:**
+- **Client:** Added `structured_data_schema` param to `add` method.
+</Update>
+
+<Update label="2025-07-08" description="v2.1.35">
+**New Features:**
+- **Client:** Added `createMemoryExport` and `getMemoryExport` methods.
+</Update>
+
+<Update label="2025-07-03" description="v2.1.34">
+**New Features:**
+- **OSS:** Added Gemini support
+</Update>
+
+<Update label="2025-06-24" description="v2.1.33">
+**Improvement :**
+- **Client:** Added `immutable` param to `add` method.
+</Update>
+
+<Update label="2025-06-20" description="v2.1.32">
+**Improvement :**
+- **Client:** Made `api_version` V2 as default.
+</Update>
+
+<Update label="2025-06-17" description="v2.1.31">
+**Improvement :**
+- **Client:** Added param `filter_memories`.
+</Update>
+
+<Update label="2025-06-06" description="v2.1.30">
+**New Features:**
+- **OSS:** Added Cloudflare support
+
+**Improvements:**
+- **OSS:** Fixed baseURL param in LLM Config.
+</Update>
+
+<Update label="2025-05-30" description="v2.1.29">
+**Improvements:**
+- **Client:** Added Async Mode Param for `add` method.
+</Update>
+
+<Update label="2025-05-30" description="v2.1.28">
+**Improvements:**
+- **SDK:** Update Google SDK Peer Dependency Version.
+</Update>
+
+<Update label="2025-05-27" description="v2.1.27">
+**Improvements:**
+- **OSS:** Added baseURL param in LLM Config.
+</Update>
+  
+<Update label="2025-05-23" description="v2.1.26">
+**Improvements:**
+- **Client:** Removed type `string` from `messages` interface
+</Update>
+
+<Update label="2025-05-08" description="v2.1.25">
+**Improvements:**
+- **Client:** Improved error handling in client.
+</Update>
+
+<Update label="2025-05-06" description="v2.1.24">
+**New Features:**
+- **Client:** Added new param `output_format` to match Python SDK.
+- **Client:** Added new enum `OutputFormat` for `v1.0` and `v1.1`
+</Update>
+
+<Update label="2025-05-05" description="v2.1.23">
+**New Features:**
+- **Client:** Updated `deleteUsers` to use `v2` API.
+- **Client:** Deprecated `deleteUser` and added deprecation warning.
+</Update>
+
+<Update label="2025-05-02" description="v2.1.22">
+**New Features:**
+- **Client:** Updated `deleteUser` to use `entity_id` and `entity_type`
+</Update>
+
+<Update label="2025-05-01" description="v2.1.21">
+**Improvements:**
+- **OSS SDK:** Bumped version of `@anthropic-ai/sdk` to `0.40.1`
+</Update>
+
+<Update label="2025-04-28" description="v2.1.20">
+**Improvements:**
+- **Client:** Fixed `organizationId` and `projectId` being assigned to default in `ping` method
+</Update>
+
+<Update label="2025-04-22" description="v2.1.19">
+**Improvements:**
+- **Client:** Added support for `timestamps`
+</Update>
+
+<Update label="2025-04-17" description="v2.1.18">
+**Improvements:**
+- **Client:** Added support for custom instructions
+</Update>
 
 <Update label="2025-04-15" description="v2.1.17">
 **New Features:**
@@ -174,7 +671,7 @@ mode: "wide"
 
 <Update label="2025-03-29" description="v2.1.13">
 **Improvements:**
-- **Introuced `ping` method to check if API key is valid and populate org/project id**
+- **Introduced `ping` method to check if API key is valid and populate org/project id**
 </Update>
 
 <Update label="2025-03-29" description="AI SDK v1.0.0">
@@ -207,81 +704,325 @@ mode: "wide"
 
 <Tab title="Platform">
 
-<Update label="2025-04-12" description="">
+<Update label="2025-07-23" description="">
+
+**Bug Fixes:**
+- **Memory:** Fixed ADD functionality
+
+</Update>
+
+<Update label="2025-07-19" description="">
 
 **New Features:**
-- **Memory Management:** 
-  - Added ability to delete memories from Project level with filters
-  - Added delete memories capability on Memories Page
-- **Memory Visualization:** Released V1 Graph Memory Visualization
-- **Graph Playground:** Enabled for @mem0.ai users
-- **Notifications:** Added email alerts to organization owners when new members join
-- **Memory Export:** Added date support for filtering memory exports
+- **UI:** Added Settings UI and latency display
+- **Performance:** Neo4j query optimization
+
+**Bug Fixes:**
+- **OpenMemory:** Fixed OMM raising unnecessary exceptions
+
+</Update>
+
+<Update label="2025-07-18" description="">
+
+**Improvements:**
+- **UI:** Updated Event UI
+- **Performance:** Fixed N+1 query issue in semantic_search_v2 by optimizing MemorySerializer field selection
+
+**Bug Fixes:**
+- **Memory:** Fixed duplicate memory index sentry error
+
+</Update>
+
+<Update label="2025-07-17" description="">
+
+**New Features:**
+- **UI:** New Settings Page
+- **Memory:** Duplicate memories entities support
+
+**Improvements:**
+- **Performance:** Optimized semantic search and get_all APIs by eliminating N+1 queries
+
+</Update>
+
+<Update label="2025-07-16" description="">
+
+**New Features:**
+- **Database:** Implemented read replica routing with enhanced logging and app-specific DB routing
+
+**Improvements:**
+- **Performance:** Improved query performance in search v2 and get all v2 endpoints
+
+**Bug Fixes:**
+- **API:** Fixed pagination for get all API
+
+</Update>
+
+<Update label="2025-07-12" description="">
+
+**Bug Fixes:**
+- **Graph:** Fixed social graph bugs and connection issues
+
+</Update>
+
+<Update label="2025-07-11" description="">
+
+**Improvements:**
+- **Rate Limiting:** New rate limit for V2 Search
+
+**Bug Fixes:**
+- **Slack:** Fixed Slack rate limit error with backend improvements
+
+</Update>
+
+<Update label="2025-07-10" description="">
 
 **Improvements:**
 - **Performance:** 
-  - Optimized graph for better performance
-  - Optimized database calls in ADD method
-- **Analytics:** Added flagging of paid users in Posthog
-- **CI/CD:** Improved CI pipeline and fixed lint issues
+  - Changed connection pooling time to 5 minutes
+  - Separated graph lambdas for better performance
 
 </Update>
 
-<Update label="2025-04-10" description="">
-
-**New Features:**
-- **Notifications:** Implemented email notifications for organization owners when new members join
+<Update label="2025-07-09" description="">
 
 **Improvements:**
-- **CI/CD:** Fixed Dockerfile for CI tests
+- **Graph:** Graph Optimizations V2 and memory improvements
 
 </Update>
 
-<Update label="2025-04-09" description="">
-
-**Improvements:**
-- **Integrations:** Updated chat model for Together Qwen
-- **Platform:** Removed older platforms
-- **Bug Fixes:** Fixed FILTER_MAPPING
-
-</Update>
-
-<Update label="2025-04-03" description="">
+<Update label="2025-07-08" description="">
 
 **New Features:**
-- **Memory:** Added implicit memory capabilities
-- **API:** Improved implicit lambda and get_all v2 functionality
+- **Database:** Added read replica support for improved database performance
+- **UI:** Implemented UI changes for Users Page
+- **Feedback:** Enabled feedback functionality
+
+**Bug Fixes:**
+- **Serializer:** Fixed GET ALL Serializer
 
 </Update>
 
-<Update label="2025-04-02" description="">
+<Update label="2025-07-05" description="">
 
 **New Features:**
-- **Integrations:** Added Clay integration
+- **UI:** User Page Revamp and New Users Page
+
+</Update>
+
+<Update label="2025-07-04" description="">
+
+**New Features:**
+- **Users:** New Users Page implementation
+- **Tools:** Added script to backfill memory categories
+
+**Bug Fixes:**
+- **Filters:** Fixed Filters Get All functionality
+
+</Update>
+
+<Update label="2025-07-03" description="">
 
 **Improvements:**
-- **Integrations:** Removed deepseek coder from Together
-- **API:** Added custom instructions for add v2
+- **Graph:** Graph Memory optimization
+- **Memory:** Fixed exact memories and semantically similar memories retrieval
 
 </Update>
 
-<Update label="2025-03-31" description="">
+<Update label="2025-07-02" description="">
 
-**Security:**
-- **Validation:** Added key validation in messages
+**Improvements:**
+- **Categorization:** Refactored categorization logic to utilize Gemini 2.5 Flash and improve message handling
 
 </Update>
 
-<Update label="2025-03-28" description="">
-- **Updated Playground Prompt**
-- **Send Email on User Addition to Org/Proj**
-- **Fix Search Entity**
+<Update label="2025-07-01" description="">
+
+**Bug Fixes:**
+- **Memory:** Fixed old_memory issue in Async memory addition lambda
+- **Events:** Fixed missing events
+
 </Update>
 
-<Update label="2025-03-19" description="">
-- **General Stability & Performance Improvements**
+<Update label="2025-06-30" description="">
+
+**Improvements:**
+- **Graph:** Improvements to graph memory and added user to LTM-STM
+
+</Update>
+
+<Update label="2025-06-28" description="">
+
+**New Features:**
+- **Graph:** Added support for SQS in graph memory addition
+- **Testing:** Added Locust load testing script and Grafana Dashboard
+
+</Update>
+
+<Update label="2025-06-27" description="">
+
+**Improvements:**
+- **Rate Limiting:** Updated rate limiting for ADD API to 1000/min
+- **Performance:** Improved Neo4j performance
+
+</Update>
+
+<Update label="2025-06-26" description="">
+
+**New Features:**
+- **Memory:** Edit Memory From Drawer functionality
+- **API:** Added Topic Suggestions API Endpoint
+
+</Update>
+
+<Update label="2025-06-25" description="">
+
+**New Features:**
+- **Group Chat:** Group-Chat v2 with Actor-Aware Memories
+- **Memory:** Editable Metadata in Memories
+- **UI:** Memory Actions Badges
+
+</Update>
+
+<Update label="2025-06-19" description="">
+
+**New Features:**
+- **Rate Limiting:** Implemented comprehensive rate limiting system
+
+**Improvements:**
+- **Performance:** Added performance indexes for memory stats query
+
+**Bug Fixes:**
+- **Search:** Fixed search events not respecting top-k parameter
+
+</Update>
+
+<Update label="2025-06-18" description="">
+
+**New Features:**
+- **Memory Management:** Implemented OpenAI Batch API for Memory Cleaning with fallback
+- **Playground:** Added Claude 4 support on Playground
+
+**Improvements:**
+- **Memory:** Added ability to update memory metadata
+
+</Update>
+
+<Update label="2025-06-17" description="">
+
+**New Features:**
+- **UI:** New Memories Page UI design
+
+</Update>
+
+<Update label="2025-06-16" description="">
+
+**Improvements:**
+- **Infrastructure:** Migrated to Application Load Balancer (ALB)
+
+</Update>
+
+<Update label="2025-06-13" description="">
+
+**Improvements:**
+- **Memory Management:** Enhanced Memory Management with Cosine Similarity Fallback
+
+</Update>
+
+<Update label="2025-06-11" description="">
+
+**New Features:**
+- **OMM:** Added OMM Script and UI functionality
+
+**Improvements:**
+- **API:** Added filters validation to semantic_search_v2 endpoint
+
+</Update>
+
+<Update label="2025-06-09" description="">
+
+**New Features:**
+- **Intercom:** Set Intercom events for ADD and SEARCH operations
+- **OpenMemory:** Added Posthog integration and feedback functionality
+- **MCP:** New JavaScript MCP Server with feedback support
+
+**Improvements:**
+- **Structured Data:** Enhanced structured data handling in memory management
+
+</Update>
+
+<Update label="2025-06-06" description="">
+
+**New Features:**
+- **OAuth:** Added Mem0 OAuth integration
+- **OMM:** Added OMM-Mem0 sync for deleted memories
+
+</Update>
+
+<Update label="2025-06-05" description="">
+
+**New Features:**
+- **Filters:** Implemented Wildcard Filters and refactored filter logic in V2 Views
+
+</Update>
+
+<Update label="2025-06-02" description="">
+
+**New Features:**
+- **OpenMemory Cloud:** Added OpenMemory Cloud support
+- **Structured Data:** Added 'structured_attributes' field to Memory model
+
+</Update>
+
+<Update label="2025-05-30" description="">
+
+**New Features:**
+- **Projects:** Added version and enable_graph to project views
+- **OpenMemory:** Added Postgres support for OpenMemory
+
+</Update>
+
+<Update label="2025-05-19" description="">
+
+**Bug Fixes:**
+- **Core:** Fixed unicode error in user_id, agent_id, run_id and app_id
+
 </Update>
 
 </Tab>
+
+<Tab title="Vercel AI SDK">
+
+<Update label="2025-08-05" description="v2.0.0">
+**New Features:**
+- **Vercel AI SDK:** Migration to AI SDK V5.
+</Update>
+
+<Update label="2025-06-15" description="v1.0.6">
+**New Features:**
+- **Vercel AI SDK:** Added param `filter_memories`.
+</Update>
+
+<Update label="2025-05-23" description="v1.0.5">
+**New Features:**
+- **Vercel AI SDK:** Added support for Google provider.
+</Update>
+
+<Update label="2025-05-10" description="v1.0.4">
+**New Features:**
+- **Vercel AI SDK:** Added support for new param `output_format`.
+</Update>
+
+<Update label="2025-05-08" description="v1.0.3">
+**Improvements:**
+- **Vercel AI SDK:** Added support for graceful failure in cases services are down. 
+</Update>
+
+<Update label="2025-05-01" description="v1.0.1">
+**New Features:**
+- **Vercel AI SDK:** Added support for graph memories
+</Update>
+
+</Tab>
+
 </Tabs>
 

docs/components/embedders/config.mdx

@@ -4,6 +4,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.
 
 ## How to define configurations?

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/google_AI.mdx

@@ -1,8 +1,8 @@
 ---
-title: Gemini
+title: Google AI
 ---
 
-To use Gemini embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
+To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
 
 ### Usage
 
@@ -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` |
-| `api_key` | The Gemini API key | `None` |
+| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
+| `api_key` | The Google API key | `None` |

docs/components/embedders/models/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/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
 
 ## Supported Embedders
@@ -19,11 +21,12 @@ See the list of supported embedders below.
   <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
   <Card title="Ollama" href="/components/embedders/models/ollama"></Card>
   <Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
-  <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
+  <Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
   <Card title="Together" href="/components/embedders/models/together"></Card>
   <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
   <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="blank-notif.mdx" />
+
 ## How to define configurations?
 
 <Tabs>
@@ -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?
@@ -108,7 +112,14 @@ 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         |
+    | `response_callback`  | LLM response callback function                | OpenAI            |
   </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="blank-notif.mdx" />
+
+To use Anthropic's models, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
 
 ## 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="blank-notif.mdx" />
+
 ### Setup
 - Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
 - You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
@@ -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

@@ -2,6 +2,8 @@
 title: Azure OpenAI
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 <Note> Mem0 Now Supports Azure OpenAI Models in TypeScript SDK </Note>
 
 To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM_AZURE_ENDPOINT`, `LLM_AZURE_DEPLOYMENT` and `LLM_AZURE_API_VERSION` environment variables. You can obtain the Azure API key from the [Azure](https://azure.microsoft.com/).
@@ -16,6 +18,8 @@ To use Azure OpenAI models, you have to set the `LLM_AZURE_OPENAI_API_KEY`, `LLM
 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"

docs/components/llms/models/deepseek.mdx

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

docs/components/llms/models/gemini.mdx

@@ -1,39 +0,0 @@
----
-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)
-
-## Usage
-
-```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"
-
-config = {
-    "llm": {
-        "provider": "gemini",
-        "config": {
-            "model": "gemini-1.5-flash-latest",
-            "temperature": 0.2,
-            "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 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
-
-All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/google_AI.mdx

@@ -2,38 +2,75 @@
 title: Google AI
 ---
 
-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)
+<Snippet file="blank-notif.mdx" />
+
+To use the Gemini model, set the `GOOGLE_API_KEY` environment variable. You can obtain the Google/Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
+
+> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
+
+> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
-os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
-os.environ["GEMINI_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"  # Used for embedding model
+os.environ["GOOGLE_API_KEY"] = "your-gemini-api-key"
 
 config = {
     "llm": {
-        "provider": "litellm",
+        "provider": "gemini",
         "config": {
-            "model": "gemini/gemini-pro",
+            "model": "gemini-2.0-flash-001",
             "temperature": 0.2,
             "max_tokens": 2000,
+            "top_p": 1.0
         }
     }
 }
 
 m = Memory.from_config(config)
+
 messages = [
     {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
-    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
-    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
-    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
 ]
+
 m.add(messages, user_id="alice", metadata={"category": "movies"})
+
 ```
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+const config = {
+    llm: {
+        // You can also use "google" as provider ( for backward compatibility )
+        provider: "gemini",
+        config: {
+            model: "gemini-2.0-flash-001",
+            temperature: 0.1
+        }
+    }
+}
+
+const memory = new Memory(config);
+
+const messages = [
+    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+    { role: "user", content: "I’m not a big fan of thrillers, but I love sci-fi movies." },
+    { role: "assistant", content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead." }
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
 
 ## Config
 
-All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).
+All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/groq.mdx

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

docs/components/llms/models/langchain.mdx

@@ -2,6 +2,8 @@
 title: LangChain
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 supports LangChain as a provider to access a wide range of LLM models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various LLM providers through a consistent interface.
 
 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).

docs/components/llms/models/litellm.mdx

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

docs/components/llms/models/lmstudio.mdx

@@ -2,6 +2,8 @@
 title: LM Studio
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.
 
 ## Usage
@@ -21,6 +23,7 @@ config = {
             "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": {}}},
         }
     }
 }

docs/components/llms/models/mistral_AI.mdx

@@ -2,6 +2,8 @@
 title: Mistral AI
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 To use mistral's models, please obtain the Mistral AI api key from their [console](https://console.mistral.ai/). Set the `MISTRAL_API_KEY` environment variable to use the model as given below in the example.
 
 ## Usage

docs/components/llms/models/ollama.mdx

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

docs/components/llms/models/openai.mdx

@@ -2,8 +2,12 @@
 title: OpenAI
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
 
+> **Note**: The following are currently unsupported with reasoning models `Parallel tool calling`,`temperature`, `top_p`, `presence_penalty`, `frequency_penalty`, `logprobs`, `top_logprobs`, `logit_bias`, `max_tokens`
+
 ## Usage
 
 <CodeGroup>

docs/components/llms/models/sarvam.mdx

@@ -0,0 +1,75 @@
+---
+title: Sarvam AI
+---
+
+<Snippet file="blank-notif.mdx" />
+
+**Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.
+
+To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get from their [platform](https://dashboard.sarvam.ai/).
+
+## 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="blank-notif.mdx" />
+
 To use TogetherAI LLM models, you have to set the `TOGETHER_API_KEY` environment variable. You can obtain the TogetherAI API key from their [Account settings page](https://api.together.xyz/settings/api-keys).
 
 ## Usage

docs/components/llms/models/vllm.mdx

@@ -0,0 +1,109 @@
+---
+title: vLLM
+---
+
+<Snippet file="blank-notif.mdx" />
+
+[vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.
+
+## Prerequisites
+
+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="blank-notif.mdx" />
+
 [xAI](https://x.ai/) is a new AI company founded by Elon Musk that develops large language models, including Grok. Grok is trained on real-time data from X (formerly Twitter) and aims to provide accurate, up-to-date responses with a touch of wit and humor.
 
 In order to use LLMs from xAI, go to their [platform](https://console.x.ai) and get the API key. Set the API key as `XAI_API_KEY` environment variable to use the model as given below in the example.

docs/components/llms/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
 
 ## Usage
@@ -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**.
@@ -29,9 +33,9 @@ To view all supported llms, visit the [Supported LLMs](./models).
   <Card title="Mistral AI" href="/components/llms/models/mistral_ai" />
   <Card title="Google AI" href="/components/llms/models/google_ai" />
   <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
-  <Card title="Gemini" href="/components/llms/models/gemini" />
   <Card title="DeepSeek" href="/components/llms/models/deepseek" />
   <Card title="xAI" href="/components/llms/models/xAI" />
+  <Card title="Sarvam AI" href="/components/llms/models/sarvam" />
   <Card title="LM Studio" href="/components/llms/models/lmstudio" />
   <Card title="Langchain" href="/components/llms/models/langchain" />
 </CardGroup>

docs/components/vectordbs/config.mdx

@@ -4,6 +4,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 ## How to define configurations?
 
 The `config` is defined as an object with two main keys:

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/milvus.mdx

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

docs/components/vectordbs/dbs/mongodb.mdx

@@ -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/pgvector.mdx

@@ -36,7 +36,7 @@ Here's the parameters available for configuring pgvector:
 
 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `dbname` | The name of the  | `postgres` |
+| `dbname` | The name of the database | `postgres` |
 | `collection_name` | The name of the collection | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `user` | User name to connect to the database | `None` |
@@ -44,4 +44,12 @@ Here's the parameters available for configuring pgvector:
 | `host` | The host where the Postgres server is running | `None` |
 | `port` | The port where the Postgres server is running | `None` |
 | `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
-| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
+| `sslmode` | SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable') | `None` |
+| `connection_string` | PostgreSQL connection string (overrides individual connection parameters) | `None` |
+| `connection_pool` | psycopg2 connection pool object (overrides connection string and individual parameters) | `None` |
+
+**Note**: The connection parameters have the following priority:
+1. `connection_pool` (highest priority)
+2. `connection_string`
+3. Individual connection parameters (`user`, `password`, `host`, `port`, `sslmode`)

docs/components/vectordbs/dbs/pinecone.mdx

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

docs/components/vectordbs/dbs/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

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

docs/components/vectordbs/overview.mdx

@@ -4,6 +4,8 @@ icon: "info"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
 
 ## Supported Vector Databases
@@ -11,7 +13,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
 See the list of supported vector databases below.
 
 <Note>
-  The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis 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}>
@@ -21,6 +23,7 @@ See the list of supported vector databases below.
   <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card>
   <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></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>

docs/contributing/development.mdx

@@ -3,6 +3,8 @@ title: Development
 icon: "code"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 # Development Contributions
 
 We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
@@ -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="blank-notif.mdx" />
+
 # Documentation Contributions
 
 ## 📌 Prerequisites

docs/core-concepts/memory-operations.mdx

@@ -5,6 +5,8 @@ icon: "gear"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 provides two core operations for managing memories in AI applications: adding new memories and searching existing ones. This guide covers how these operations work and how to use them effectively in your application.
 
 

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="blank-notif.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="blank-notif.mdx" />
+
+## Overview
+
+Memories can become outdated, irrelevant, or need to be removed for privacy or compliance reasons. Mem0 offers flexible ways to delete memory:
+
+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="blank-notif.mdx" />
+
+## Overview
+
+The `search` operation allows you to retrieve relevant memories based on a natural language query and optional filters like user ID, agent ID, categories, and more. This is the foundation of giving your agents memory-aware behavior.
+
+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="blank-notif.mdx" />
+
+## Overview
+
+User preferences, interests, and behaviors often evolve over time. The `update` operation lets you revise a stored memory, whether it's updating facts and memories, rephrasing a message, or enriching metadata.
+
+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="blank-notif.mdx" />
+
 To build useful AI applications, we need to understand how different memory systems work together. This guide explores the fundamental types of memory in AI systems and shows how Mem0 implements these concepts.
 
 ## Why Memory Matters

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"
+                    ]
+                  }
                 ]
               },
               {
@@ -45,19 +54,22 @@
                     "group": "Features",
                     "icon": "star",
                     "pages": [
-                      "features/platform-overview",
-                      "features/advanced-retrieval",
-                      "features/contextual-add",
-                      "features/multimodal-support",
-                      "features/selective-memory",
-                      "features/custom-categories",
-                      "features/custom-instructions",
-                      "features/direct-import",
-                      "features/async-client",
-                      "features/memory-export",
-                      "features/webhooks",
-                      "features/graph-memory",
-                      "features/feedback-mechanism"
+                      "platform/features/platform-overview",
+                      "platform/features/contextual-add",
+                      "platform/features/async-client",
+                      "platform/features/advanced-retrieval",
+                      "platform/features/criteria-retrieval",
+                      "platform/features/multimodal-support",
+                      "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"
                     ]
                   }
                 ]
@@ -66,18 +78,19 @@
                 "group": "Open Source",
                 "icon": "code-branch",
                 "pages": [
-                  "open-source/quickstart",
+                  "open-source/overview",
                   "open-source/python-quickstart",
                   "open-source/node-quickstart",
                   {
                     "group": "Features",
-                    "icon": "wrench",
+                    "icon": "star",
                     "pages": [
+                      "open-source/features/overview",
                       "open-source/features/async-memory",
-                      "features/openai_compatibility",
-                      "features/custom-fact-extraction-prompt",
-                      "features/custom-update-memory-prompt",
-                      "open-source/multimodal-support",
+                      "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"
                     ]
                   },
@@ -109,11 +122,12 @@
                           "components/llms/models/mistral_AI",
                           "components/llms/models/google_AI",
                           "components/llms/models/aws_bedrock",
-                          "components/llms/models/gemini",
                           "components/llms/models/deepseek",
                           "components/llms/models/xAI",
+                          "components/llms/models/sarvam",
                           "components/llms/models/lmstudio",
-                          "components/llms/models/langchain"
+                          "components/llms/models/langchain",
+                          "components/llms/models/vllm"
                         ]
                       }
                     ]
@@ -133,6 +147,7 @@
                           "components/vectordbs/dbs/pgvector",
                           "components/vectordbs/dbs/milvus",
                           "components/vectordbs/dbs/pinecone",
+                          "components/vectordbs/dbs/mongodb",
                           "components/vectordbs/dbs/azure",
                           "components/vectordbs/dbs/redis",
                           "components/vectordbs/dbs/elasticsearch",
@@ -141,7 +156,8 @@
                           "components/vectordbs/dbs/vertex_ai",
                           "components/vectordbs/dbs/weaviate",
                           "components/vectordbs/dbs/faiss",
-                          "components/vectordbs/dbs/langchain"
+                          "components/vectordbs/dbs/langchain",
+                          "components/vectordbs/dbs/baidu"
                         ]
                       }
                     ]
@@ -161,10 +177,11 @@
                           "components/embedders/models/ollama",
                           "components/embedders/models/huggingface",
                           "components/embedders/models/vertexai",
-                          "components/embedders/models/gemini",
+                          "components/embedders/models/google_AI",
                           "components/embedders/models/lmstudio",
                           "components/embedders/models/together",
-                          "components/embedders/models/langchain"
+                          "components/embedders/models/langchain",
+                          "components/embedders/models/aws_bedrock"
                         ]
                       }
                     ]
@@ -181,6 +198,15 @@
               }
             ]
           },
+          {
+            "tab": "OpenMemory",
+            "icon": "square-terminal",
+            "pages": [
+              "openmemory/overview",
+              "openmemory/quickstart",
+              "openmemory/integrations"
+            ]
+          },
           {
             "tab": "Examples",
             "groups": [
@@ -189,8 +215,13 @@
                 "icon": "lightbulb",
                 "pages": [
                   "examples",
+                  "examples/aws_example",
                   "examples/mem0-demo",
                   "examples/ai_companion_js",
+                  "examples/collaborative-task-agent",
+                  "examples/llamaindex-multiagent-learning-system",
+                  "examples/personalized-search-tavily-mem0",
+                  "examples/eliza_os",
                   "examples/mem0-mastra",
                   "examples/mem0-with-ollama",
                   "examples/personal-ai-tutor",
@@ -198,12 +229,13 @@
                   "examples/personal-travel-assistant",
                   "examples/llama-index-mem0",
                   "examples/chrome-extension",
-                  "examples/document-writing",
+                  "examples/memory-guided-content-writing",
                   "examples/multimodal-demo",
                   "examples/personalized-deep-research",
                   "examples/mem0-agentic-tool",
                   "examples/openai-inbuilt-tools",
                   "examples/mem0-openai-voice-demo",
+                  "examples/mem0-google-adk-healthcare-assistant",
                   "examples/email_processing",
                   "examples/youtube-assistant"
                 ]
@@ -218,21 +250,26 @@
                 "icon": "plug",
                 "pages": [
                   "integrations",
-                  "integrations/vercel-ai-sdk",
-                  "integrations/flowise",
-                  "integrations/crewai",
-                  "integrations/autogen",
                   "integrations/langchain",
                   "integrations/langgraph",
                   "integrations/llama-index",
-                  "integrations/langchain-tools",
-                  "integrations/dify",
-                  "integrations/mcp-server",
-                  "integrations/livekit",
-                  "integrations/elevenlabs",
-                  "integrations/pipecat",
                   "integrations/agno",
-                  "integrations/keywords"
+                  "integrations/autogen",
+                  "integrations/crewai",
+                  "integrations/openai-agents-sdk",
+                  "integrations/google-ai-adk",
+                  "integrations/mastra",
+                  "integrations/vercel-ai-sdk",
+                  "integrations/livekit",
+                  "integrations/pipecat",
+                  "integrations/elevenlabs",
+                  "integrations/aws-bedrock",
+                  "integrations/flowise",
+                  "integrations/langchain-tools",
+                  "integrations/agentops",
+                  "integrations/keywords",
+                  "integrations/dify",
+                  "integrations/raycast"
                 ]
               }
             ]
@@ -327,31 +364,6 @@
             ]
           }
         ]
-      },
-      {
-        "anchor": "Your Dashboard",
-        "href": "https://app.mem0.ai",
-        "icon": "chart-simple"
-      },
-      {
-        "anchor": "Demo",
-        "href": "https://mem0.dev/demo",
-        "icon": "play"
-      },
-      {
-        "anchor": "Discord",
-        "href": "https://mem0.dev/DiD",
-        "icon": "discord"
-      },
-      {
-        "anchor": "GitHub",
-        "href": "https://github.com/mem0ai/mem0",
-        "icon": "github"
-      },
-      {
-        "anchor": "Support",
-        "href": "mailto:founders@mem0.ai",
-        "icon": "envelope"
       }
     ]
   },

docs/examples.mdx

@@ -3,6 +3,8 @@ title: Overview
 description: How to use mem0 in your existing applications?
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 
 With Mem0, you can create stateful LLM-based applications such as chatbots, virtual assistants, or AI agents. Mem0 enhances your applications by providing a memory layer that makes responses:
 
@@ -37,10 +39,14 @@ Explore how **Mem0** can power real-world applications and bring personalized, i
 
   <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>
 
-  <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 title="Personalized Search Assistant" icon="magnifying-glass" href="/examples/personalized-search-tavily-mem0">
+    Build a **Personalized Search Assistant** that tailors search according to user preferences.
+  </Card>
+
+  <Card title="LlamaIndex + Mem0 Learning System" icon="book-open" href="/examples/llama-index-mem0">
+    Multi-agent learning system powered by memory.
   </Card>  
 
   <Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">  
@@ -73,7 +79,11 @@ Explore how **Mem0** can power real-world applications and bring personalized, i
 
   <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>
+
+  <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.  

docs/examples/ai_companion.mdx

@@ -2,6 +2,8 @@
 title: AI Companion
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
 ## Overview
@@ -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
@@ -85,8 +87,8 @@ class Companion:
 
         previous_memories = self.memory.search(question, user_id=user_id_to_use)
         relevant_memories_text = ""
-        if previous_memories:
-            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
+        if previous_memories and previous_memories.get('results'):
+            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
 
         prompt = f"User input: {question}\nPrevious {check_answer} memories: {relevant_memories_text}"
 
@@ -144,7 +146,7 @@ def print_memories(user_id, label):
     memories = ai_companion.get_memories(user_id=user_id)
     if memories:
         for m in memories:
-            print(f"- {m['text']}")
+            print(f"- {m['memory']}")
     else:
         print("No memories found.")
 

docs/examples/ai_companion_js.mdx

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

docs/examples/aws_example.mdx

@@ -0,0 +1,132 @@
+---
+title: Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics
+---
+
+<Snippet file="blank-notif.mdx" />
+
+This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock**, **OpenSearch Service (AOSS)**, and **AWS Neptune Analytics** for persistent memory capabilities in Python.
+
+## Installation
+
+Install the required dependencies to include the Amazon data stack, including **boto3**, **opensearch-py**, and **langchain-aws**:
+
+```bash
+pip install "mem0ai[graph,extras]"
+```
+
+## Environment Setup
+
+Set your AWS environment variables:
+
+```python
+import os
+
+# Set these in your environment or notebook
+os.environ['AWS_REGION'] = 'us-west-2'
+os.environ['AWS_ACCESS_KEY_ID'] = 'AK00000000000000000'
+os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS00000000000000000'
+
+# Confirm they are set
+print(os.environ['AWS_REGION'])
+print(os.environ['AWS_ACCESS_KEY_ID'])
+print(os.environ['AWS_SECRET_ACCESS_KEY'])
+```
+
+## Configuration and Usage
+
+This sets up Mem0 with:
+- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
+- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
+- [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)
+- [Neptune Analytics as your graph store](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-neptune-analytics).
+
+```python
+import boto3
+from opensearchpy import 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": "us.anthropic.claude-3-7-sonnet-20250219-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,
+            "connection_class": RequestsHttpConnection,
+            "pool_maxsize": 20,
+            "use_ssl": True,
+            "verify_certs": True,
+            "embedding_model_dims": 1024,
+        }
+    },
+    "graph_store": {
+        "provider": "neptune",
+        "config": {
+            "endpoint": f"neptune-graph://my-graph-identifier",
+        },
+    },
+}
+
+# Initialize the memory system
+m = Memory.from_config(config)
+```
+
+## Usage
+
+Reference [Notebook example](https://github.com/mem0ai/mem0/blob/main/examples/graph-db-demo/neptune-example.ipynb)
+
+#### Add a memory:
+
+```python
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+# Store inferred memories (default behavior)
+result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+```
+
+#### Search a memory:
+```python
+relevant_memories = m.search(query, user_id="alice")
+```
+
+#### Get all memories:
+```python
+all_memories = m.get_all(user_id="alice")
+```
+
+#### Get a specific memory:
+```python
+memory = m.get(memory_id)
+```
+
+
+---
+
+## Conclusion
+
+With Mem0 and AWS services like Bedrock, OpenSearch, and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

docs/examples/chrome-extension.mdx

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

docs/examples/collaborative-task-agent.mdx

@@ -0,0 +1,125 @@
+---
+title: Multi-User Collaboration with Mem0
+---
+
+<Snippet file="blank-notif.mdx" />
+
+## Overview
+
+Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.
+
+## 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="blank-notif.mdx" />
+
 You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
 
 ## Overview
@@ -21,6 +23,7 @@ pip install openai mem0ai
 Below is the simplified code to create and interact with a Customer Support AI Agent using Mem0:
 
 ```python
+import os
 from openai import OpenAI
 from mem0 import Memory
 

docs/examples/eliza_os.mdx

@@ -0,0 +1,75 @@
+---
+title: Eliza OS Character
+---
+
+<Snippet file="blank-notif.mdx" />
+
+You can create a personalised Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+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

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

docs/examples/llama-index-mem0.mdx

@@ -1,6 +1,8 @@
 ---
 title: LlamaIndex ReAct Agent
 ---
+<Snippet file="blank-notif.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="blank-notif.mdx" />
+
+Build an intelligent multi-agent learning system that uses Mem0 to maintain persistent memory across multiple specialized agents. This example demonstrates how to create a tutoring system where different agents collaborate while sharing a unified memory layer.
+
+## Overview
+
+This example showcases a **Multi-Agent Personal Learning System** that combines:
+- **LlamaIndex AgentWorkflow** for multi-agent orchestration
+- **Mem0** for persistent, shared memory across agents
+- **Multi-agents** that collaborate on teaching tasks
+
+The system consists of two agents:
+- **TutorAgent**: Primary instructor for explanations and concept teaching
+- **PracticeAgent**: Generates exercises and tracks learning progress
+
+Both agents share the same memory context, enabling seamless collaboration and continuous learning from student interactions.
+
+## Key Features
+
+- **Persistent Memory**: Agents remember previous interactions across sessions
+- **Multi-Agent Collaboration**: Agents can hand off tasks to each other
+- **Personalized Learning**: Adapts to individual student needs and learning styles
+- **Progress Tracking**: Monitors learning patterns and skill development
+- **Memory-Driven Teaching**: References past struggles and successes
+
+## Prerequisites
+
+Install the required packages:
+
+```bash
+pip install llama-index-core llama-index-memory-mem0 openai python-dotenv
+```
+
+Set up your environment variables:
+- `MEM0_API_KEY`: Your Mem0 Platform API key
+- `OPENAI_API_KEY`: Your OpenAI API key
+
+You can obtain your Mem0 Platform API key from the [Mem0 Platform](https://app.mem0.ai).
+
+## Complete Implementation
+
+```python
+"""
+Multi-Agent Personal Learning System: Mem0 + LlamaIndex AgentWorkflow Example
+
+INSTALLATIONS:
+!pip install llama-index-core llama-index-memory-mem0 openai
+
+You need MEM0_API_KEY and OPENAI_API_KEY to run the example.
+"""
+
+import asyncio
+from datetime import datetime
+from dotenv import load_dotenv
+
+# LlamaIndex imports
+from llama_index.core.agent.workflow import AgentWorkflow, FunctionAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+
+# Memory integration
+from llama_index.memory.mem0 import Mem0Memory
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+load_dotenv()
+
+
+class MultiAgentLearningSystem:
+    """
+    Multi-Agent Architecture:
+    - TutorAgent: Main teaching and explanations
+    - PracticeAgent: Exercises and skill reinforcement
+    - Shared Memory: Both agents learn from student interactions
+    """
+
+    def __init__(self, student_id: str):
+        self.student_id = student_id
+        self.llm = OpenAI(model="gpt-4o", temperature=0.2)
+
+        # Memory context for this student
+        self.memory_context = {"user_id": student_id, "app": "learning_assistant"}
+        self.memory = Mem0Memory.from_client(
+            context=self.memory_context
+        )
+
+        self._setup_agents()
+
+    def _setup_agents(self):
+        """Setup two agents that work together and share memory"""
+
+        # TOOLS
+        async def assess_understanding(topic: str, student_response: str) -> str:
+            """Assess student's understanding of a topic and save insights"""
+            # Simulate assessment logic
+            if "confused" in student_response.lower() or "don't understand" in student_response.lower():
+                assessment = f"STRUGGLING with {topic}: {student_response}"
+                insight = f"Student needs more help with {topic}. Prefers step-by-step explanations."
+            elif "makes sense" in student_response.lower() or "got it" in student_response.lower():
+                assessment = f"UNDERSTANDS {topic}: {student_response}"
+                insight = f"Student grasped {topic} quickly. Can move to advanced concepts."
+            else:
+                assessment = f"PARTIAL understanding of {topic}: {student_response}"
+                insight = f"Student has basic understanding of {topic}. Needs reinforcement."
+
+            return f"Assessment: {assessment}\nInsight saved: {insight}"
+
+        async def track_progress(topic: str, success_rate: str) -> str:
+            """Track learning progress and identify patterns"""
+            progress_note = f"Progress on {topic}: {success_rate} - {datetime.now().strftime('%Y-%m-%d')}"
+            return f"Progress tracked: {progress_note}"
+
+        # Convert to FunctionTools
+        tools = [
+            FunctionTool.from_defaults(async_fn=assess_understanding),
+            FunctionTool.from_defaults(async_fn=track_progress)
+        ]
+
+        # AGENTS
+        # Tutor Agent - Main teaching and explanation
+        self.tutor_agent = FunctionAgent(
+            name="TutorAgent",
+            description="Primary instructor that explains concepts and adapts to student needs",
+            system_prompt="""
+            You are a patient, adaptive programming tutor. Your key strength is REMEMBERING and BUILDING on previous interactions.
+
+            Key Behaviors:
+            1. Always check what the student has learned before (use memory context)
+            2. Adapt explanations based on their preferred learning style
+            3. Reference previous struggles or successes
+            4. Build progressively on past lessons
+            5. Use assess_understanding to evaluate responses and save insights
+
+            MEMORY-DRIVEN TEACHING:
+            - "Last time you struggled with X, so let's approach Y differently..."
+            - "Since you prefer visual examples, here's a diagram..."
+            - "Building on the functions we covered yesterday..."
+
+            When student shows understanding, hand off to PracticeAgent for exercises.
+            """,
+            tools=tools,
+            llm=self.llm,
+            can_handoff_to=["PracticeAgent"]
+        )
+
+        # Practice Agent - Exercises and reinforcement
+        self.practice_agent = FunctionAgent(
+            name="PracticeAgent",
+            description="Creates practice exercises and tracks progress based on student's learning history",
+            system_prompt="""
+            You create personalized practice exercises based on the student's learning history and current level.
+
+            Key Behaviors:
+            1. Generate problems that match their skill level (from memory)
+            2. Focus on areas they've struggled with previously
+            3. Gradually increase difficulty based on their progress
+            4. Use track_progress to record their performance
+            5. Provide encouraging feedback that references their growth
+
+            MEMORY-DRIVEN PRACTICE:
+            - "Let's practice loops again since you wanted more examples..."
+            - "Here's a harder version of the problem you solved yesterday..."
+            - "You've improved a lot in functions, ready for the next level?"
+
+            After practice, can hand back to TutorAgent for concept review if needed.
+            """,
+            tools=tools,
+            llm=self.llm,
+            can_handoff_to=["TutorAgent"]
+        )
+
+        # Create the multi-agent workflow
+        self.workflow = AgentWorkflow(
+            agents=[self.tutor_agent, self.practice_agent],
+            root_agent=self.tutor_agent.name,
+            initial_state={
+                "current_topic": "",
+                "student_level": "beginner",
+                "learning_style": "unknown",
+                "session_goals": []
+            }
+        )
+
+    async def start_learning_session(self, topic: str, student_message: str = "") -> str:
+        """
+        Start a learning session with multi-agent memory-aware teaching
+        """
+
+        if student_message:
+            request = f"I want to learn about {topic}. {student_message}"
+        else:
+            request = f"I want to learn about {topic}."
+
+        # The magic happens here - multi-agent memory is automatically shared!
+        response = await self.workflow.run(
+            user_msg=request,
+            memory=self.memory
+        )
+
+        return str(response)
+
+    async def get_learning_history(self) -> str:
+        """Show what the system remembers about this student"""
+        try:
+            # Search memory for learning patterns
+            memories = self.memory.search(
+                user_id=self.student_id,
+                query="learning machine learning"
+            )
+
+            if memories and memories.get('results'):
+                history = "\n".join(f"- {m['memory']}" for m in memories['results'])
+                return history
+            else:
+                return "No learning history found yet. Let's start building your profile!"
+
+        except Exception as e:
+            return f"Memory retrieval error: {str(e)}"
+
+
+async def run_learning_agent():
+
+    learning_system = MultiAgentLearningSystem(student_id="Alexander")
+
+    # First session
+    print("Session 1:")
+    response = await learning_system.start_learning_session(
+        "Vision Language Models",
+        "I'm new to machine learning but I have good hold on Python and have 4 years of work experience.")
+    print(response)
+
+    # Second session - multi-agent memory will remember the first
+    print("\nSession 2:")
+    response2 = await learning_system.start_learning_session(
+        "Machine Learning", "what all did I cover so far?")
+    print(response2)
+
+    # Show what the multi-agent system remembers
+    print("\nLearning History:")
+    history = await learning_system.get_learning_history()
+    print(history)
+
+
+if __name__ == "__main__":
+    """Run the example"""
+    print("Multi-agent Learning System powered by LlamaIndex and Mem0")
+
+    async def main():
+        await run_learning_agent()
+
+    asyncio.run(main())
+```
+
+## How It Works
+
+### 1. Memory Context Setup
+
+```python
+# Memory context for this student
+self.memory_context = {"user_id": student_id, "app": "learning_assistant"}
+self.memory = Mem0Memory.from_client(context=self.memory_context)
+```
+
+The memory context identifies the specific student and application, ensuring memory isolation and proper retrieval.
+
+### 2. Agent Collaboration
+
+```python
+# Agents can hand off to each other
+can_handoff_to=["PracticeAgent"]  # TutorAgent can hand off to PracticeAgent
+can_handoff_to=["TutorAgent"]     # PracticeAgent can hand off back
+```
+
+Agents collaborate seamlessly, with the TutorAgent handling explanations and the PracticeAgent managing exercises.
+
+### 3. Shared Memory
+
+```python
+# Both agents share the same memory instance
+response = await self.workflow.run(
+    user_msg=request,
+    memory=self.memory  # Shared across all agents
+)
+```
+
+All agents in the workflow share the same memory context, enabling true collaborative learning.
+
+### 4. Memory-Driven Interactions
+
+The system prompts guide agents to:
+- Reference previous learning sessions
+- Adapt to discovered learning styles
+- Build progressively on past lessons
+- Track and respond to learning patterns
+
+## Running the Example
+
+```python
+# Initialize the learning system
+learning_system = MultiAgentLearningSystem(student_id="Alexander")
+
+# Start a learning session
+response = await learning_system.start_learning_session(
+    "Vision Language Models",
+    "I'm new to machine learning but I have good hold on Python and have 4 years of work experience."
+)
+
+# Continue learning in a new session (memory persists)
+response2 = await learning_system.start_learning_session(
+    "Machine Learning",
+    "what all did I cover so far?"
+)
+
+# Check learning history
+history = await learning_system.get_learning_history()
+```
+
+## Expected Output
+
+The system will demonstrate memory-aware interactions:
+
+```
+Session 1:
+I understand you want to learn about Vision Language Models and you mentioned you're new to machine learning but have a strong Python background with 4 years of experience. That's a great foundation to build on!
+
+Let me start with an explanation tailored to your programming background...
+[Agent provides explanation and may hand off to PracticeAgent for exercises]
+
+Session 2:
+Based on our previous session, I remember we covered Vision Language Models and I noted that you have a strong Python background with 4 years of experience. You mentioned being new to machine learning, so we started with foundational concepts...
+[Agent references previous session and builds upon it]
+```
+
+## Key Benefits
+
+1. **Persistent Learning**: Agents remember across sessions, creating continuity
+2. **Collaborative Teaching**: Multiple specialized agents work together seamlessly
+3. **Personalized Adaptation**: System learns and adapts to individual learning styles
+4. **Scalable Architecture**: Easy to add more specialized agents
+5. **Memory Efficiency**: Shared memory prevents duplication and ensures consistency
+
+
+## Best Practices
+
+1. **Clear Agent Roles**: Define specific responsibilities for each agent
+2. **Memory Context**: Use descriptive context for memory isolation
+3. **Handoff Strategy**: Design clear handoff criteria between agents
+5. **Memory Hygiene**: Regularly review and clean memory for optimal performance
+
+## Help & Resources
+
+- [LlamaIndex Agent Workflows](https://docs.llamaindex.ai/en/stable/use_cases/agents/)
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/examples/mem0-agentic-tool.mdx

@@ -2,6 +2,8 @@
 title: Mem0 as an Agentic Tool
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
 You can create agents that remember past conversations and use that context to provide better responses.
 

docs/examples/mem0-demo.mdx

@@ -2,6 +2,9 @@
 title: Mem0 Demo
 ---
 
+
+<Snippet file="blank-notif.mdx" />
+
 You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.
 
 <video
@@ -13,7 +16,7 @@ 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.dev/demo) live here.
+You can try the [Mem0 Demo](https://mem0-4vmi.vercel.app) live here.
 
 ## Overview
 

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="blank-notif.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

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

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

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

docs/examples/mem0-with-ollama.mdx

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

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

@@ -1,102 +1,133 @@
 ---
-title: Document Editing with Mem0
+title: Memory-Guided Content Writing
 ---
+<Snippet file="blank-notif.mdx" />
 
-This guide demonstrates how to leverage **Mem0** to edit documents efficiently, ensuring they align with your unique writing style and preferences.
+This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
 
-## **Why Use Mem0?**
+## Why Use Mem0?
 
-By integrating Mem0 into your workflow, you can streamline your document editing process with:
+Integrating Mem0 into your writing workflow helps you:
 
-1. **Persistent Writing Preferences**: Mem0 stores and recalls your style preferences, ensuring consistency across all documents.
-2. **Automated Enhancements**: Your stored preferences guide document refinements, making edits seamless and efficient.
-3. **Scalability & Reusability**: Your writing style can be applied to multiple documents, saving time and effort.
+1. **Store persistent writing preferences** ensuring consistent tone, formatting, and structure.
+2. **Automate content refinement** by retrieving preferences when rewriting or reviewing content.
+3. **Scale your writing style** so it applies consistently across multiple documents or sessions.
 
----
-## **Setup**
+## Setup
 
 ```python
 import os
+from openai import OpenAI
 from mem0 import MemoryClient
 
-# Set up Mem0 client
 os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+
+
+# Set up Mem0 and OpenAI client
 client = MemoryClient()
+openai = OpenAI()
 
-# Define constants
 USER_ID = "content_writer"
 RUN_ID = "smart_editing_session"
 ```
 
----
 ## **Storing Your Writing Preferences in Mem0**
 
 ```python
 def store_writing_preferences():
     """Store your writing preferences in Mem0."""
     
-    # Define writing preferences
     preferences = """My writing preferences:
 1. Use headings and sub-headings for structure.
-2. Keep paragraphs concise (8-10 sentences max).
+2. Keep paragraphs concise (8–10 sentences max).
 3. Incorporate specific numbers and statistics.
 4. Provide concrete examples.
 5. Use bullet points for clarity.
 6. Avoid jargon and buzzwords."""
 
-    # Store preferences in Mem0
-    preference_message = [
-        {"role": "user", "content": "Here are my writing style preferences"},
+    messages = [
+        {"role": "user", "content": "Here are my writing style preferences."},
         {"role": "assistant", "content": preferences}
     ]
-    
-    response = client.add(preference_message, user_id=USER_ID, run_id=RUN_ID, metadata={"type": "preferences", "category": "writing_style"})
-    
-    print("Writing preferences stored successfully.")
+
+    response = client.add(
+        messages,
+        user_id=USER_ID,
+        run_id=RUN_ID,
+        metadata={"type": "preferences", "category": "writing_style"}
+    )
+
     return response
 ```
 
----
-## **Editing Documents with Mem0**
+## **Editing Content Using Stored Preferences**
 
 ```python
-def edit_document_based_on_preferences(original_content):
-    """Edit a document using Mem0-based stored preferences."""
-    
-    # Retrieve stored preferences
-    query = "What are my writing style preferences?"
-    preferences_results = client.search(query, user_id=USER_ID, run_id=RUN_ID)
-    
-    if not preferences_results:
-        print("No writing preferences found.")
+def apply_writing_style(original_content):
+    """Use preferences stored in Mem0 to guide content rewriting."""
+
+    results = client.search(
+        query="What are my writing style preferences?",
+        version="v2",
+        filters={
+            "AND": [
+                {
+                    "user_id": USER_ID
+                },
+                {
+                    "run_id": RUN_ID
+                }
+            ]
+        },
+    )
+
+    if not results:
+        print("No preferences found.")
         return None
-    
-    # Extract preferences
-    preferences = ' '.join(memory["memory"] for memory in preferences_results)
-    
-    # Apply stored preferences to refine the document
-    edited_content = f"Applying stored preferences:\n{preferences}\n\nEdited Document:\n{original_content}"
-    
-    return edited_content
+
+    preferences = "\n".join(r["memory"] for r in results.get('results', []))
+
+    system_prompt = f"""
+You are a writing assistant.
+
+Apply the following writing style preferences to improve the user's content:
+
+Preferences:
+{preferences}
+"""
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": f"""Original Content:
+    {original_content}"""}
+    ]
+
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=messages
+    )
+    clean_response = response.choices[0].message.content.strip()
+
+    return clean_response
 ```
 
----
-## **Complete Workflow: Document Editing**
+## **Complete Workflow: Content Editing**
 
 ```python
-def document_editing_workflow(content):
+def content_writing_workflow(content):
     """Automated workflow for editing a document based on writing preferences."""
     
-    # Step 1: Store writing preferences (if not already stored)
-    store_writing_preferences()
+    # Store writing preferences (if not already stored)
+    store_writing_preferences()  # Ideally done once, or with a conditional check
     
-    # Step 2: Edit the document with Mem0 preferences
-    edited_content = edit_document_based_on_preferences(content)
+    # Edit the document with Mem0 preferences
+    edited_content = apply_writing_style(content)
     
     if not edited_content:
         return "Failed to edit document."
     
-    # Step 3: Display results
+    # Display results
     print("\n=== ORIGINAL DOCUMENT ===\n")
     print(content)
     
@@ -106,7 +137,6 @@ def document_editing_workflow(content):
     return edited_content
 ```
 
----
 ## **Example Usage**
 
 ```python
@@ -124,10 +154,9 @@ We plan to launch the campaign in July and continue through September.
 """
 
 # Run the workflow
-result = document_editing_workflow(original_content)
+result = content_writing_workflow(original_content)
 ```
 
----
 ## **Expected Output**
 
 Your document will be transformed into a structured, well-formatted version based on your preferences.
@@ -181,4 +210,10 @@ This proposal outlines our strategy for the Q3 marketing campaign. We aim to sig
 We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let’s work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
 ```
 
-Mem0 creates a seamless, intelligent document editing experience—perfect for content creators, technical writers, and businesses alike!
+Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.
+
+## Help & Resources
+
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/examples/multimodal-demo.mdx

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

docs/examples/openai-inbuilt-tools.mdx

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

docs/examples/personal-ai-tutor.mdx

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

docs/examples/personal-travel-assistant.mdx

@@ -1,6 +1,9 @@
 ---
 title: Personal AI Travel Assistant
 ---
+
+<Snippet file="blank-notif.mdx" />
+
 Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
 
 ## Overview
@@ -152,11 +155,11 @@ class PersonalTravelAssistant:
 
     def get_memories(self, user_id):
         memories = self.memory.get_all(user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
     def search_memories(self, query, user_id):
         memories = self.memory.search(query, user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories.get('results', [])]
 
 # Usage example
 user_id = "traveler_123"

docs/examples/personalized-deep-research.mdx

@@ -2,8 +2,12 @@
 title: Personalized Deep Research
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.
 
+You can checkout GitHub repositry here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
+
 ## Overview
 
 Deep Research leverages Mem0's memory capabilities to:
@@ -28,13 +32,6 @@ Watch Deep Research in action:
   allowfullscreen
 ></iframe>
 
-## Getting Started
-
-1. Visit [deep-research.mem0.ai](https://deep-research.mem0.ai/)
-2. Upload your resume (PDF or text) or manually enter information about yourself
-3. Enter your research topic
-4. Click "Start Research" to begin
-
 ## Features
 
 ### 1. Personalized Research
@@ -67,4 +64,6 @@ Watch Deep Research in action:
 
 ## Try It Out
 
-Experience AI-powered research personalization at [deep-research.mem0.ai](https://deep-research.mem0.ai/)
+> To try it yourself, clone the repository and follow the instructions in the README to run it locally or deploy it.
+
+- [Personalized Deep Research GitHub](https://github.com/mem0ai/personalized-deep-research/tree/mem0)

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

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

docs/examples/youtube-assistant.mdx

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

docs/faqs.mdx

@@ -4,6 +4,7 @@ icon: "question"
 iconType: "solid"
 ---
 
+<Snippet file="async-memory-add.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?">

docs/features.mdx

@@ -4,6 +4,8 @@ icon: "wrench"
 iconType: "solid"
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 ## Core features
 
 - **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
@@ -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

@@ -1,103 +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')
-   ```
-
-   **Example:**
-   ```python
-   # Search for memories about food preferences with keyword search enabled
-   query = "What are my food preferences?"
-   results = client.search(query, keyword_search=True, user_id='alex')
-
-   # Output might include:
-   # - "Vegetarian. Allergic to nuts." (highly relevant)
-   # - "Prefers spicy food and enjoys Thai cuisine" (relevant)
-   # - "Mentioned disliking sea food during restaurant discussion" (keyword match)
-
-   # Without keyword_search=True, only the most relevant memories would be returned:
-   # - "Vegetarian. Allergic to nuts." (highly relevant)
-   # - "Prefers spicy food and enjoys Thai cuisine" (relevant)
-   # The keyword-based match about "sea food" would be excluded
-   ```
-
-2. **Reranking**
-
-   Normal retrieval gives you memories sorted in order of their relevancy, but the order may not be perfect. Reranking uses a deep neural network to correct this order, ensuring the most relevant memories appear first. If you are concerned about the order of memories, or want that the best results always comes at top then use reranking. This parameter is set to `false` by default. When enabled, it reorders the memories based on a more accurate relevance score.
-
-   ```python
-   client.search(query, rerank=True, user_id='alex')
-   ```
-
-   **Example:**
-   ```python
-   # Search for travel plans with reranking enabled
-   query = "What are my travel plans?"
-   results = client.search(query, rerank=True, user_id='alex')
-
-   # Without reranking, results might be ordered like:
-   # 1. "Traveled to France last year" (less relevant to current plans)
-   # 2. "Planning a trip to Japan next month" (more relevant to current plans)
-   # 3. "Interested in visiting Tokyo restaurants" (relevant to current plans)
-
-   # With reranking enabled, results would be reordered:
-   # 1. "Planning a trip to Japan next month" (most relevant to current plans)
-   # 2. "Interested in visiting Tokyo restaurants" (highly relevant to current plans)
-   # 3. "Traveled to France last year" (less relevant to current plans)
-   ```
-
-3. **Filtering**
-
-   Filtering allows you to narrow down search results by applying specific criterias. This parameter is set to `false` by default. When activated, it significantly enhances search precision by removing irrelevant memories, though it may slightly reduce recall. Filtering is particularly useful when you need highly specific information.
-
-   ```python
-   client.search(query, filter_memories=True, user_id='alex')
-   ```
-
-   **Example:**
-   ```python
-   # Search for dietary restrictions with filtering enabled
-   query = "What are my dietary restrictions?"
-   results = client.search(query, filter_memories=True, user_id='alex')
-
-   # Without filtering, results might include:
-   # - "Vegetarian. Allergic to nuts." (directly relevant)
-   # - "I enjoy cooking Italian food on weekends" (somewhat related to food)
-   # - "Mentioned disliking seafood during restaurant discussion" (food-related)
-   # - "Prefers to eat dinner at 7pm" (tangentially food-related)
-
-   # With filtering enabled, results would be focused:
-   # - "Vegetarian. Allergic to nuts." (directly relevant)
-   # - "Mentioned disliking seafood during restaurant discussion" (relevant restriction)
-   #
-   # The filtering process removes memories that are about food preferences
-   # but not specifically about dietary restrictions
-   ```
-
-<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. </Note>
-
-
-### Latency Numbers
-
-Here are the typical latency ranges for each search mode:
-
-| **Mode**            | **Latency**      |
-|---------------------|------------------|
-| **Keyword Search**  | **&lt;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/integrations.mdx

@@ -3,6 +3,8 @@ title: Overview
 description: How to integrate Mem0 into other frameworks
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Mem0 seamlessly integrates with popular AI frameworks and tools to enhance your LLM-based applications with persistent memory capabilities. By integrating Mem0, your applications benefit from:
 
 - Enhanced context management across multiple frameworks
@@ -16,6 +18,23 @@ Here are the available integrations for Mem0:
 ## Integrations
 
 <CardGroup cols={2}>
+  <Card
+    title="AgentOps"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="25"
+        height="26"
+        viewBox="0 0 30 36"
+        fill="none"
+      >
+      <path d="M10.4659 6.47277C10.45 6.37428 10.4381 6.27986 10.4303 6.18101L10.4285 6.16388C10.4212 6.09482 10.414 6.02566 10.4106 5.95626L1.18538 21.8752C0.505422 23.0493 0.323356 24.4208 0.675227 25.7289C0.849119 26.3869 1.14971 26.9859 1.55323 27.5098C1.95675 28.0338 2.46282 28.4751 3.05175 28.8143C3.83464 29.2675 4.70856 29.5 5.59028 29.5C6.03318 29.5 6.4798 29.4408 6.91899 29.3226C8.23581 28.972 9.3349 28.1326 10.0152 26.9545L15.9268 16.749V16.7449L16.5001 15.7637L17.6431 13.7936L16.5001 11.8234L15.9309 10.8381L15.9268 10.8341L13.7836 7.13406C13.6651 6.933 13.5741 6.72418 13.5109 6.51165C13.2817 5.80223 13.3292 5.04172 13.6097 4.37599L13.8115 4.02535C14.3532 3.09155 15.31 2.53987 16.3184 2.47692C16.3738 2.46915 16.4251 2.46915 16.4804 2.46915C16.5421 2.46915 16.6038 2.47257 16.6654 2.47599L16.6822 2.47692C17.6906 2.53987 18.6474 3.09155 19.1892 4.02535L21.2216 7.52838L21.8146 8.55289L21.8421 8.60399L30.1024 22.8601C30.5174 23.5814 30.6281 24.4167 30.4148 25.2205C30.1975 26.0244 29.6832 26.6942 28.9598 27.1081C28.2364 27.5258 27.3977 27.6361 26.5911 27.4195C25.7844 27.2066 25.1123 26.6905 24.6968 25.9696L18.2119 14.7788L17.069 16.7449L22.9847 26.9545C23.6646 28.1326 24.7641 28.972 26.0809 29.3226C26.5197 29.4408 26.9626 29.5 27.4096 29.5C28.2914 29.5 29.1612 29.2675 29.9482 28.8143C31.1264 28.1367 31.9728 27.0411 32.3247 25.7289C32.6766 24.4208 32.4949 23.0493 31.8145 21.8752L21.1261 3.43034C20.7029 2.51617 20.0033 1.72011 19.0621 1.18027C18.5281 0.877027 17.9708 0.675975 17.3975 0.581189C17.3027 0.565268 17.2076 0.549717 17.1129 0.537868C17.0099 0.52602 16.9074 0.518244 16.8045 0.510469C16.6027 0.498621 16.3972 0.494548 16.1914 0.510469C16.0885 0.518244 15.9859 0.52639 15.883 0.537868C15.795 0.54887 15.7067 0.563384 15.6187 0.577852L15.5984 0.581189C15.0291 0.675605 14.4673 0.876657 13.9375 1.18027C12.9885 1.72789 12.2766 2.53579 11.8537 3.46181C11.7742 3.63473 11.707 3.81282 11.6471 3.99314C11.6361 4.02668 11.6269 4.06051 11.6177 4.09435C11.612 4.11503 11.6064 4.13579 11.6003 4.15642C11.5624 4.28601 11.5275 4.41634 11.4996 4.54853C11.4885 4.60231 11.4794 4.65668 11.4703 4.71111L11.4666 4.73329C11.4443 4.86399 11.4264 4.99543 11.4145 5.12762C11.4093 5.18686 11.4045 5.24573 11.4012 5.30534C11.3934 5.44567 11.3923 5.58637 11.3963 5.72744C11.3969 5.74403 11.3962 5.76062 11.3956 5.7772C11.3949 5.79616 11.3942 5.81512 11.3952 5.83407C11.3952 5.86184 11.3952 5.88924 11.3993 5.92071C11.3998 5.9291 11.4006 5.93736 11.4014 5.94564C11.402 5.95125 11.4026 5.95687 11.403 5.96255C11.4045 5.98181 11.4064 6.00106 11.4082 6.02031C11.4097 6.03577 11.4109 6.05122 11.4122 6.06674C11.4142 6.09134 11.4163 6.11621 11.419 6.14139L11.4428 6.32282C11.4506 6.38983 11.4625 6.46092 11.4744 6.52757C11.5063 6.68863 11.5468 6.84896 11.5936 7.0078C11.5944 7.0102 11.5949 7.0127 11.5955 7.0152C11.5958 7.01662 11.5961 7.01804 11.5965 7.01944C11.5967 7.02051 11.597 7.02157 11.5974 7.02261C11.6483 7.19293 11.7081 7.36177 11.7787 7.52838C11.8619 7.72943 11.9607 7.92641 12.0715 8.11932L12.3245 8.5566V8.56067L12.4984 8.85614L12.7199 9.24232H12.7239L12.728 9.25417L14.7802 12.7927V12.7968L14.7883 12.805V12.809L15.3576 13.7943L14.7883 14.7796L8.30344 25.9703C7.88431 26.6912 7.21216 27.2077 6.40921 27.4202C6.14019 27.4913 5.86338 27.5306 5.59474 27.5306C5.053 27.5306 4.51906 27.3888 4.04085 27.1089C3.31705 26.6953 2.79909 26.0251 2.58581 25.2213C2.36845 24.4174 2.47917 23.5821 2.89829 22.8609L11.1585 8.60473L11.186 8.56141V8.55734C11.1266 8.45478 11.0753 8.35629 11.024 8.25409C11.0105 8.22496 10.9969 8.19611 10.9834 8.16739C10.9458 8.08735 10.9086 8.00836 10.8739 7.92715C10.8718 7.92504 10.8708 7.92194 10.8698 7.91887C10.8688 7.91602 10.8679 7.91319 10.8661 7.91123V7.90346C10.8423 7.8483 10.8186 7.79311 10.7989 7.73795C10.7476 7.60799 10.7041 7.47803 10.6644 7.3477C10.6012 7.15479 10.5536 6.96152 10.518 6.76861C10.4942 6.67012 10.4786 6.5757 10.4667 6.47684C10.4667 6.47684 10.47 6.47684 10.4659 6.47277Z" fill="currentColor"></path>
+      </svg>
+    }
+    href="/integrations/agentops"
+  >
+    Monitor and analyze Mem0 operations with comprehensive AI agent analytics and LLM observability.
+  </Card>
   <Card
     title="LangChain"
     icon={
@@ -303,6 +322,7 @@ Here are the available integrations for Mem0:
   >
     Build autonomous agents with memory using Agno framework.
   </Card>
+
   <Card
     title="Keywords AI"
     icon={
@@ -320,4 +340,60 @@ Here are the available integrations for Mem0:
   >
     Build AI applications with persistent memory and comprehensive LLM observability.
   </Card>
+  <Card
+    title="Raycast"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+        viewBox="0 0 24 24"
+        fill="none"
+      >
+        <path
+          d="M3 12L21 12M12 3L12 21M7.5 7.5L16.5 16.5M16.5 7.5L7.5 16.5"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinecap="round"
+        />
+      </svg>
+    }
+    href="/integrations/raycast"
+  >
+    Mem0 Raycast extension for intelligent memory management and retrieval.
+  </Card>
+  <Card
+    title="Mastra"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+        viewBox="0 0 24 24"
+        fill="none"
+      >
+        <path
+          d="M12 2L22 7L12 12L2 7L12 2Z"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+        <path
+          d="M2 17L12 22L22 17"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+        <path
+          d="M2 12L12 17L22 12"
+          stroke="currentColor"
+          strokeWidth="2"
+          strokeLinejoin="round"
+        />
+      </svg>
+    }
+    href="/integrations/mastra"
+  >
+    Build AI agents with persistent memory using Mastra's framework and tools.
+  </Card>
 </CardGroup>

docs/integrations/agentops.mdx

@@ -0,0 +1,172 @@
+---
+title: AgentOps
+---
+<Snippet file="blank-notif.mdx" />
+
+Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.
+
+## Overview
+
+1. Automatic monitoring of Mem0 operations and performance metrics
+2. Real-time tracking of memory add, search, and retrieval operations
+3. Analytics dashboard with memory usage patterns and insights
+4. Error tracking and debugging capabilities for memory operations
+
+## Prerequisites
+
+Before setting up Mem0 with AgentOps, ensure you have:
+
+1. Installed the required packages:
+```bash
+pip install mem0ai agentops
+```
+
+2. Valid API keys:
+   - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
+   - OpenAI API Key (for LLM operations)
+   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys) (optional, for cloud operations)
+
+## Basic Integration Example
+
+The following example demonstrates how to integrate Mem0 with AgentOps monitoring for comprehensive memory operation tracking:
+
+```python
+#Import the required libraries for local memory management with Mem0
+from mem0 import Memory, AsyncMemory
+import os
+import asyncio
+import logging
+from dotenv import load_dotenv
+import agentops
+
+#Set up environment variables for API keys
+os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+#Set up the configuration for local memory storage and define sample user data. 
+local_config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        },
+    }
+}
+user_id = "alice_demo"
+agent_id = "assistant_demo"
+run_id = "session_001"
+
+sample_messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller? 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.",
+    },
+]
+
+sample_preferences = [
+    "I prefer dark roast coffee over light roast",
+    "I exercise every morning at 6 AM",
+    "I'm vegetarian and avoid all meat products",
+    "I love reading science fiction novels",
+    "I work in software engineering",
+]
+
+#This function demonstrates sequential memory operations using the synchronous Memory class
+def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id):
+    """
+    Demonstrate synchronous Memory class operations.
+    """
+
+    agentops.start_trace("mem0_memory_example", tags=["mem0_memory_example"])
+    try:
+        
+        memory = Memory.from_config(local_config)
+
+        result = memory.add(
+            sample_messages, user_id=user_id, metadata={"category": "movie_preferences", "session": "demo"}
+        )
+
+        for i, preference in enumerate(sample_preferences):
+            result = memory.add(preference, user_id=user_id, metadata={"type": "preference", "index": i})
+       
+        search_queries = [
+            "What movies does the user like?",
+            "What are the user's food preferences?",
+            "When does the user exercise?",
+        ]
+
+        for query in search_queries:
+            results = memory.search(query, user_id=user_id)
+        
+            if results and "results" in results:
+                for j, result in enumerate(results['results']): 
+                    print(f"Result {j+1}: {result.get('memory', 'N/A')}")
+            else:
+                print("No results found")
+
+        all_memories = memory.get_all(user_id=user_id)
+        if all_memories and "results" in all_memories:
+            print(f"Total memories: {len(all_memories['results'])}")
+
+        delete_all_result = memory.delete_all(user_id=user_id)
+        print(f"Delete all result: {delete_all_result}")
+
+        agentops.end_trace(end_state="success")
+    except Exception as e:
+        agentops.end_trace(end_state="error")
+
+# Execute sync demonstrations
+demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id)
+
+```
+
+For detailed information on this integration, refer to the official [Agentops Mem0 integration documentation](https://docs.agentops.ai/v2/integrations/mem0).
+
+
+## Key Features
+
+### 1. Automatic Operation Tracking
+
+AgentOps automatically monitors all Mem0 operations:
+
+- **Memory Operations**: Track add, search, get_all, delete operations and much more
+- **Performance Metrics**: Monitor response times and success rates
+- **Error Tracking**: Capture and analyze operation failures
+
+### 2. Real-time Analytics Dashboard
+
+Access comprehensive analytics through the AgentOps dashboard:
+
+- **Usage Patterns**: Visualize memory usage trends over time
+- **User Behavior**: Analyze how different users interact with memory
+- **Performance Insights**: Identify bottlenecks and optimization opportunities
+
+### 3. Session Management
+
+Organize your monitoring with structured sessions:
+
+- **Session Tracking**: Group related operations into logical sessions
+- **Success/Failure Rates**: Track session outcomes for reliability monitoring
+- **Custom Metadata**: Add context to sessions for better analysis
+
+## Best Practices
+
+1. **Initialize Early**: Always initialize AgentOps before importing Mem0 classes
+2. **Session Management**: Use meaningful session names and end sessions appropriately
+3. **Error Handling**: Wrap operations in try-catch blocks and report failures
+4. **Tagging**: Use tags to organize different types of memory operations
+5. **Environment Separation**: Use different projects or tags for dev/staging/prod
+
+## Help & Resources
+
+- [AgentOps Documentation](https://docs.agentops.ai/)
+- [AgentOps Dashboard](https://app.agentops.ai/)
+- [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" /> 
+ 

docs/integrations/agno.mdx

@@ -1,15 +1,18 @@
 ---
 title: Agno
 ---
+<Snippet file="blank-notif.mdx" />
 
-Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-ai/agno), a Python framework for building autonomous agents. This integration enables Agno agents to access persistent memory across conversations, enhancing context retention and personalization.
+
+This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno, enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
 
 ## Overview
 
-1. 🧠 Store and retrieve memories from Mem0 within Agno agents
-2. 🖼️ Support for multimodal interactions (text and images)
-3. 🔍 Semantic search for relevant past conversations
-4. 🌐 Personalized responses based on user history
+1. Store and retrieve memories from Mem0 within Agno agents
+2. Support for multimodal interactions (text and images)
+3. Semantic search for relevant past conversations
+4. Personalized responses based on user history
+5. One-line memory integration via `Mem0Tools`
 
 ## Prerequisites
 
@@ -17,14 +20,41 @@ Before setting up Mem0 with Agno, ensure you have:
 
 1. Installed the required packages:
 ```bash
-pip install agno-ai mem0ai
+pip install agno mem0ai
 ```
 
 2. Valid API keys:
    - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys)
    - OpenAI API Key (for the agent model)
 
-## Integration Example
+## Quick Integration (Using `Mem0Tools`)
+
+The simplest way to integrate Mem0 with Agno Agents is to use Mem0 as a tool using built-in `Mem0Tools`:
+
+```python
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+from agno.tools.mem0 import Mem0Tools
+
+agent = Agent(
+    name="Memory Agent",
+    model=OpenAIChat(id="gpt-4o-mini"),
+    tools=[Mem0Tools()],
+    description="An assistant that remembers and personalizes using Mem0 memory."
+)
+```
+
+This enables memory functionality out of the box:
+
+- **Persistent memory writing**: `Mem0Tools` uses `MemoryClient.add(...)` to store messages from user-agent interactions, including optional metadata such as user ID or session.
+- **Contextual memory search**: Compatible queries use `MemoryClient.search(...)` to retrieve relevant past messages, improving contextual understanding.
+- **Multimodal support**: Both text and image inputs are supported, allowing richer memory records.
+
+> `Mem0Tools` uses the `MemoryClient` under the hood and requires no additional setup. You can customize its behavior by modifying your tools list or extending it in code.
+
+## Full Manual Example
+
+> Note: Mem0 can also be used with Agno Agents as a separate memory layer.
 
 The following example demonstrates how to create an Agno agent with Mem0 memory integration, including support for image processing:
 
@@ -38,7 +68,6 @@ from agno.media import Image
 from agno.models.openai import OpenAIChat
 from mem0 import MemoryClient
 
-
 # Initialize the Mem0 client
 client = MemoryClient()
 
@@ -99,7 +128,7 @@ def chat_user(
     if user_input:
         # Search for relevant memories
         memories = client.search(user_input, user_id=user_id)
-        memory_context = "\n".join(f"- {m['memory']}" for m in memories)
+        memory_context = "\n".join(f"- {m['memory']}" for m in memories.get('results', []))
 
         # Construct the prompt
         prompt = f"""
@@ -161,6 +190,8 @@ Improve your agent's context awareness:
 
 Customize the integration to your needs:
 
+- **Use `Mem0Tools()`** for drop-in memory support
+- **Use `MemoryClient` directly** for advanced control
 - **User Identification**: Organize memories by user ID
 - **Memory Search**: Configure search relevance and result count
 - **Memory Formatting**: Support for various OpenAI message formats

docs/integrations/autogen.mdx

@@ -1,5 +1,7 @@
 Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
 
+<Snippet file="blank-notif.mdx" />
+
 ## Overview
 
 In this guide, we'll explore an example of creating a conversational AI system with memory:
@@ -66,7 +68,7 @@ Create a function to get context-aware responses based on user's question and pr
 ```python
 def get_context_aware_response(question):
     relevant_memories = memory_client.search(question, user_id=USER_ID)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
     Previous interactions:
@@ -98,7 +100,7 @@ manager = ConversableAgent(
 
 def escalate_to_manager(question):
     relevant_memories = memory_client.search(question, user_id=USER_ID)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""
     Context from previous interactions:

docs/integrations/aws-bedrock.mdx

@@ -0,0 +1,132 @@
+---
+title: AWS Bedrock
+---
+
+<Snippet file="blank-notif.mdx" />
+
+This integration demonstrates how to use **Mem0** with **AWS Bedrock** and **Amazon OpenSearch Service (AOSS)** to enable persistent, semantic memory in intelligent agents.
+
+## Overview
+
+In this guide, you'll:
+
+1. Configure AWS credentials to enable Bedrock and OpenSearch access
+2. Set up the Mem0 SDK to use Bedrock for embeddings and LLM
+3. Store and retrieve memories using OpenSearch as a vector store
+4. Build memory-aware applications with scalable cloud infrastructure
+
+## Prerequisites
+
+- AWS account with access to:
+  - Bedrock foundation models (e.g., Titan, Claude)
+  - OpenSearch Service with a configured domain
+- Python 3.8+
+- Valid AWS credentials (via environment or IAM role)
+
+## Setup and Installation
+
+Install required packages:
+
+```bash
+pip install mem0ai boto3 opensearch-py
+```
+
+Set environment variables:
+
+Be sure to configure your AWS credentials using environment variables, IAM roles, or the AWS CLI.
+
+```python
+import os
+
+os.environ['AWS_REGION'] = 'us-west-2'
+os.environ['AWS_ACCESS_KEY_ID'] = 'AKIA...'
+os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS...'
+```
+
+## Initialize Mem0 Integration
+
+Import necessary modules and configure Mem0:
+
+```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)
+```
+
+## Memory Operations
+
+Use Mem0 with your Bedrock-powered LLM and OpenSearch storage backend:
+
+```python
+# Store conversational context
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller?"},
+    {"role": "user", "content": "I prefer sci-fi."},
+    {"role": "assistant", "content": "Noted! I'll suggest sci-fi movies next time."}
+]
+
+m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
+
+# Search for memory
+relevant = m.search("What kind of movies does Alice like?", user_id="alice")
+
+# Retrieve all user memories
+all_memories = m.get_all(user_id="alice")
+```
+
+## Key Features
+
+1. **Serverless Memory Embeddings**: Use Titan or other Bedrock models for fast, cloud-native embeddings
+2. **Scalable Vector Search**: Store and retrieve vectorized memories via OpenSearch
+3. **Seamless AWS Auth**: Uses AWS IAM or environment variables to securely authenticate
+4. **User-specific Memory Spaces**: Memories are isolated per user ID
+5. **Persistent Memory Context**: Maintain and recall history across sessions
+
+## Help
+
+- [AWS Bedrock Documentation](https://docs.aws.amazon.com/bedrock/)
+- [Amazon OpenSearch Service Docs](https://docs.aws.amazon.com/opensearch-service/)
+- [Mem0 Platform](https://app.mem0.ai)
+
+<Snippet file="get-help.mdx" />
+

docs/integrations/crewai.mdx

@@ -2,6 +2,8 @@
 title: CrewAI
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Build an AI system that combines CrewAI's agent-based architecture with Mem0's memory capabilities. This integration enables persistent memory across agent interactions and personalized task execution based on user history.
 
 ## Overview
@@ -162,7 +164,7 @@ By combining CrewAI with Mem0, you can create sophisticated AI systems that main
 
 ## Help
 
-- For CrewAI documentation, visit [CrewAI Documentation](https://docs.crewai.com/)
-- For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/)
+- [CrewAI Documentation](https://docs.crewai.com/)
+- [Mem0 Platform](https://app.mem0.ai/)
 
 <Snippet file="get-help.mdx" />

docs/integrations/dify.mdx

@@ -2,6 +2,8 @@
 title: Dify
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 # Integrating Mem0 with Dify AI
 
 Mem0 brings a robust memory layer to Dify AI, empowering your AI agents with persistent conversation storage and retrieval capabilities. With Mem0, your Dify applications gain the ability to recall past interactions and maintain context, ensuring more natural and insightful conversations.

docs/integrations/elevenlabs.mdx

@@ -2,6 +2,8 @@
 title: ElevenLabs
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables persistent, context-aware voice interactions that remember past conversations.
 
 ## Overview
@@ -150,7 +152,7 @@ Define the two key memory functions that will be registered as tools:
         )
 
         # Extract and join the memory texts
-        memories = ' '.join([result["memory"] for result in results])
+        memories = ' '.join([result["memory"] for result in results.get('results', [])])
         print("[ Memories ]", memories)
 
         if memories:

docs/integrations/flowise.mdx

@@ -2,6 +2,8 @@
 title: Flowise
 ---
 
+<Snippet file="blank-notif.mdx" />
+
 The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.
 
 ## Overview