Add LM Studio support (#2425)

Co-authored-by: Gaurav Agerwala <ice@Gauravs-MacBook-Pro.local>

.github/workflows/cd.yml

@@ -25,22 +25,23 @@ jobs:
 
       - name: Install dependencies
         run: |
-          cd embedchain
+          cd mem0
           poetry install
 
       - name: Build a binary wheel and a source tarball
         run: |
-          cd embedchain
+          cd mem0
           poetry build
 
-      - name: Publish distribution 📦 to Test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          repository_url: https://test.pypi.org/legacy/
-          packages_dir: embedchain/dist/
+      # TODO: Needs to setup mem0 repo on Test PyPI
+      # - name: Publish distribution 📦 to Test PyPI
+      #   uses: pypa/gh-action-pypi-publish@release/v1
+      #   with:
+      #     repository_url: https://test.pypi.org/legacy/
+      #     packages_dir: dist/
 
       - name: Publish distribution 📦 to PyPI
         if: startsWith(github.ref, 'refs/tags')
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          packages_dir: embedchain/dist/
+          packages_dir: dist/

.github/workflows/ci.yml

@@ -52,7 +52,7 @@ jobs:
           virtualenvs-in-project: true
       - name: Load cached venv
         id: cached-poetry-dependencies
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           path: .venv
           key: venv-mem0-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
@@ -83,7 +83,7 @@ jobs:
           virtualenvs-in-project: true
       - name: Load cached venv
         id: cached-poetry-dependencies
-        uses: actions/cache@v2
+        uses: actions/cache@v3
         with:
           path: .venv
           key: venv-embedchain-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+**/node_modules/
 
 # C extensions
 *.so

Makefile

@@ -12,8 +12,8 @@ install:
 
 install_all:
 	poetry install
-	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
-	                        google-generativeai elasticsearch opensearch-py
+	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
 
 # Format code with ruff
 format:

README.md

@@ -16,6 +16,8 @@
     <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>
 
@@ -71,9 +73,15 @@ Install the Mem0 package via pip:
 pip install mem0ai
 ```
 
+Install the Mem0 package via npm:
+
+```bash
+npm install mem0ai
+```
+
 ### Basic Usage
 
-Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/llms).
+Mem0 requires an LLM to function, with `gpt-4o-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/llms).
 
 First step is to instantiate the memory:
 
@@ -87,7 +95,7 @@ memory = Memory()
 def chat_with_memories(message: str, user_id: str = "default_user") -> str:
     # Retrieve relevant memories
     relevant_memories = memory.search(query=message, user_id=user_id, limit=3)
-    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories)
+    memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"])
     
     # Generate Assistant response
     system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
@@ -114,6 +122,8 @@ if __name__ == "__main__":
     main()
 ```
 
+See the example for [Node.js](https://docs.mem0.ai/examples/ai_companion_js).
+
 For more advanced usage and API documentation, visit our [documentation](https://docs.mem0.ai).
 
 > [!TIP]
@@ -121,6 +131,14 @@ For more advanced usage and API documentation, visit our [documentation](https:/
 
 ## Demos
 
+- Mem0 - ChatGPT with Memory: A personalized AI chat app powered by Mem0 that remembers your preferences, facts, and memories.
+
+[Mem0 - ChatGPT with Memory](https://github.com/user-attachments/assets/cebc4f8e-bdb9-4837-868d-13c5ab7bb433)
+
+Try live [demo](https://mem0.dev/demo/)
+
+<br/><br/>
+
 - AI Companion: Experience personalized conversations with an AI that remembers your preferences and past interactions
 
 [AI Companion Demo](https://github.com/user-attachments/assets/3fc72023-a72c-4593-8be0-3cee3ba744da)

docs/components/embedders/config.mdx

@@ -8,16 +8,18 @@ Config in mem0 is a dictionary that specifies the settings for your embedding mo
 
 ## How to define configurations?
 
-The config is defined as a Python dictionary with two main keys:
+The config is defined as an object (or dictionary) with two main keys:
 - `embedder`: Specifies the embedder provider and its configuration
   - `provider`: The name of the embedder (e.g., "openai", "ollama")
-  - `config`: A nested dictionary containing provider-specific settings
+  - `config`: A nested object or dictionary containing provider-specific settings
+
 
 ## How to use configurations?
 
 Here's a general example of how to use the config with mem0:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -36,6 +38,25 @@ m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'text-embedding-3-small',
+      // Provider-specific settings go here
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
+```
+</CodeGroup>
+
 ## Why is Config Needed?
 
 Config is essential for:
@@ -47,18 +68,32 @@ Config is essential for:
 
 Here's a comprehensive list of all parameters that can be used across different embedders:
 
-| Parameter | Description |
-|-----------|-------------|
-| `model` | Embedding model to use |
-| `api_key` | API key of the provider |
-| `embedding_dims` | Dimensions of the embedding model |
-| `http_client_proxies` | Allow proxy server settings |
-| `ollama_base_url` | Base URL for the Ollama embedding model |
-| `model_kwargs` | Key-Value arguments for the Huggingface embedding model |
-| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model |
+<Tabs>
+<Tab title="Python">
+| Parameter | Description | Provider |
+|-----------|-------------|----------|
+| `model` | Embedding model to use | All |
+| `api_key` | API key of the provider | All |
+| `embedding_dims` | Dimensions of the embedding model | All |
+| `http_client_proxies` | Allow proxy server settings | All |
+| `ollama_base_url` | Base URL for the Ollama embedding model | Ollama |
+| `model_kwargs` | Key-Value arguments for the Huggingface embedding model | Huggingface |
+| `azure_kwargs` | Key-Value arguments for the AzureOpenAI embedding model | Azure OpenAI |
 | `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
-| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file for VertexAI |
-
+| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file for VertexAI                       | VertexAI            |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action                       | VertexAI            |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action                       | VertexAI            |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action                       | VertexAI            |
+| `lmstudio_base_url` | Base URL for LM Studio API                    | LM Studio         |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Provider |
+|-----------|-------------|----------|
+| `model` | Embedding model to use | All |
+| `apiKey` | API key of the provider | All |
+| `embeddingDims` | Dimensions of the embedding model | All |
+</Tab>
+</Tabs>
 
 ## Supported Embedding Models
 

docs/components/embedders/models/azure_openai.mdx

@@ -37,7 +37,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
 ### Config

docs/components/embedders/models/gemini.mdx

@@ -23,7 +23,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
 ### Config

docs/components/embedders/models/huggingface.mdx

@@ -22,7 +22,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
 ### Config

docs/components/embedders/models/lmstudio.mdx

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

docs/components/embedders/models/ollama.mdx

@@ -18,7 +18,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
 ### Config

docs/components/embedders/models/openai.mdx

@@ -6,7 +6,8 @@ To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. Y
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -22,15 +23,50 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  embedder: {
+    provider: 'openai',
+    config: {
+      apiKey: 'your-openai-api-key',
+      model: 'text-embedding-3-large',
+    },
+  },
+};
+
+const memory = new Memory(config);
+await memory.add("I'm visiting Paris", { userId: "john" });
+```
+</CodeGroup>
+
 ### Config
 
 Here are the parameters available for configuring OpenAI embedder:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the embedding model to use | `text-embedding-3-small` |
 | `embedding_dims` | Dimensions of the embedding model | `1536` |
 | `api_key` | The OpenAI API key | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The name of the embedding model to use | `text-embedding-3-small` |
+| `embeddingDims` | Dimensions of the embedding model | `1536` |
+| `apiKey` | The OpenAI API key | `None` |
+</Tab>
+</Tabs>

docs/components/embedders/models/together.mdx

@@ -25,7 +25,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
 
 ### Config

docs/components/embedders/models/vertexai.mdx

@@ -16,15 +16,31 @@ config = {
     "embedder": {
         "provider": "vertexai",
         "config": {
-            "model": "text-embedding-004"
+            "model": "text-embedding-004",
+            "memory_add_embedding_type": "RETRIEVAL_DOCUMENT",
+            "memory_update_embedding_type": "RETRIEVAL_DOCUMENT",
+            "memory_search_embedding_type": "RETRIEVAL_QUERY"
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("I'm visiting Paris", user_id="john")
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="john")
 ```
-
+The embedding types can be one of the following:
+- SEMANTIC_SIMILARITY
+- CLASSIFICATION
+- CLUSTERING
+- RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
+- CODE_RETRIEVAL_QUERY  
+Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.  
+  
 ### Config
 
 Here are the parameters available for configuring the Vertex AI embedder:
@@ -34,3 +50,6 @@ Here are the parameters available for configuring the Vertex AI embedder:
 | `model`                   | The name of the Vertex AI embedding model to use | `text-embedding-004` |
 | `vertex_credentials_json` | Path to the Google Cloud credentials JSON file   | `None`               |
 | `embedding_dims`          | Dimensions of the embedding model                | `256`                |
+| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
+| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |

docs/components/embedders/overview.mdx

@@ -10,6 +10,10 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 
 See the list of supported embedders below.
 
+<Note>
+  The following embedders are supported in the Python implementation. The TypeScript implementation currently only supports OpenAI.
+</Note>
+
 <CardGroup cols={4}>
   <Card title="OpenAI" href="/components/embedders/models/openai"></Card>
   <Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
@@ -18,6 +22,7 @@ See the list of supported embedders below.
   <Card title="Gemini" href="/components/embedders/models/gemini"></Card>
   <Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
   <Card title="Together" href="/components/embedders/models/together"></Card>
+  <Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
 </CardGroup>
 
 ## Usage

docs/components/llms/config.mdx

@@ -6,26 +6,40 @@ iconType: "solid"
 
 ## How to define configurations?
 
-The `config` is defined as a Python dictionary with two main keys:
-- `llm`: Specifies the llm provider and its configuration
-  - `provider`: The name of the llm (e.g., "openai", "groq")
-  - `config`: A nested dictionary containing provider-specific settings
+<Tabs>
+  <Tab title="Python">
+    The `config` is defined as a Python dictionary with two main keys:
+    - `llm`: Specifies the llm provider and its configuration
+      - `provider`: The name of the llm (e.g., "openai", "groq")
+      - `config`: A nested dictionary containing provider-specific settings
+  </Tab>
+  <Tab title="TypeScript">
+    The `config` is defined as a TypeScript object with these keys:
+    - `llm`: Specifies the LLM provider and its configuration (required)
+      - `provider`: The name of the LLM (e.g., "openai", "groq")
+      - `config`: A nested object containing provider-specific settings
+    - `embedder`: Specifies the embedder provider and its configuration (optional)
+    - `vectorStore`: Specifies the vector store provider and its configuration (optional)
+    - `historyDbPath`: Path to the history database file (optional)
+  </Tab>
+</Tabs>
 
 ### Config Values Precedence
 
 Config values are applied in the following order of precedence (from highest to lowest):
 
-1. Values explicitly set in the `config` dictionary
+1. Values explicitly set in the `config` object/dictionary
 2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
 3. Default values defined in the LLM implementation
 
-This means that values specified in the `config` dictionary will override corresponding environment variables, which in turn override default values.
+This means that values specified in the `config` will override corresponding environment variables, which in turn override default values.
 
 ## How to Use Config
 
-Here's a general example of how to use the config with mem0:
+Here's a general example of how to use the config with Mem0:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -44,39 +58,71 @@ m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+// Minimal configuration with just the LLM settings
+const config = {
+  llm: {
+    provider: 'your_chosen_provider',
+    config: {
+      // Provider-specific settings go here
+    }
+  }
+};
+
+const memory = new Memory(config);
+await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });
+```
+</CodeGroup>
+
 ## Why is Config Needed?
 
 Config is essential for:
-1. Specifying which llm to use.
+1. Specifying which LLM to use.
 2. Providing necessary connection details (e.g., model, api_key, temperature).
-3. Ensuring proper initialization and connection to your chosen llm.
+3. Ensuring proper initialization and connection to your chosen LLM.
 
 ## Master List of All Params in Config
 
-Here's a comprehensive list of all parameters that can be used across different llms:
-
-Here's the table based on the provided parameters:
-
-| Parameter            | Description                                   | Provider          |
-|----------------------|-----------------------------------------------|-------------------|
-| `model`              | Embedding model to use                        | All               |
-| `temperature`        | Temperature of the model                      | All               |
-| `api_key`            | API key to use                                | All               |
-| `max_tokens`         | Tokens to generate                            | All               |
-| `top_p`              | Probability threshold for nucleus sampling    | All               |
-| `top_k`              | Number of highest probability tokens to keep  | All               |
-| `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
-| `models`             | List of models                                | Openrouter        |
-| `route`              | Routing strategy                              | Openrouter        |
-| `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
-| `site_url`           | Site URL                                      | Openrouter        |
-| `app_name`           | Application name                              | Openrouter        |
-| `ollama_base_url`    | Base URL for Ollama API                       | Ollama            |
-| `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
-| `azure_kwargs`       | Azure LLM args for initialization             | AzureOpenAI       |
-| `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
+Here's a comprehensive list of all parameters that can be used across different LLMs:
 
+<Tabs>
+  <Tab title="Python">
+    | Parameter            | Description                                   | Provider          |
+    |----------------------|-----------------------------------------------|-------------------|
+    | `model`              | Embedding model to use                        | All               |
+    | `temperature`        | Temperature of the model                      | All               |
+    | `api_key`            | API key to use                                | All               |
+    | `max_tokens`         | Tokens to generate                            | All               |
+    | `top_p`              | Probability threshold for nucleus sampling    | All               |
+    | `top_k`              | Number of highest probability tokens to keep  | All               |
+    | `http_client_proxies`| Allow proxy server settings                   | AzureOpenAI       |
+    | `models`             | List of models                                | Openrouter        |
+    | `route`              | Routing strategy                              | Openrouter        |
+    | `openrouter_base_url`| Base URL for Openrouter API                   | Openrouter        |
+    | `site_url`           | Site URL                                      | Openrouter        |
+    | `app_name`           | Application name                              | Openrouter        |
+    | `ollama_base_url`    | Base URL for Ollama API                       | Ollama            |
+    | `openai_base_url`    | Base URL for OpenAI API                       | OpenAI            |
+    | `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               |
+    | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
+  </Tab>
+  <Tab title="TypeScript">
+    | Parameter            | Description                                   | Provider          |
+    |----------------------|-----------------------------------------------|-------------------|
+    | `model`              | Embedding model to use                        | All               |
+    | `temperature`        | Temperature of the model                      | All               |
+    | `apiKey`             | API key to use                                | All               |
+    | `maxTokens`          | Tokens to generate                            | All               |
+    | `topP`               | Probability threshold for nucleus sampling    | All               |
+    | `topK`               | Number of highest probability tokens to keep  | All               |
+    | `openaiBaseUrl`      | Base URL for OpenAI API                       | OpenAI            |
+  </Tab>
+</Tabs>
 
 ## Supported LLMs
 
-For detailed information on configuring specific llms, please visit the [LLMs](./models) section. There you'll find information for each supported llm with provider-specific usage examples and configuration details.
+For detailed information on configuring specific LLMs, please visit the [LLMs](./models) section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.

docs/components/llms/models/anthropic.mdx

@@ -1,8 +1,13 @@
+---
+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).
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -13,7 +18,7 @@ config = {
     "llm": {
         "provider": "anthropic",
         "config": {
-            "model": "claude-3-5-sonnet-latest",
+            "model": "claude-3-7-sonnet-latest",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -21,9 +26,41 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'anthropic',
+    config: {
+      apiKey: process.env.ANTHROPIC_API_KEY || '',
+      model: 'claude-3-7-sonnet-latest',
+      temperature: 0.1,
+      maxTokens: 2000,
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `anthropic` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/aws_bedrock.mdx

@@ -24,13 +24,19 @@ config = {
         "config": {
             "model": "arn:aws:bedrock:us-east-1:123456789012:model/your-model-name",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/azure_openai.mdx

@@ -36,7 +36,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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"})
 ```
 
 We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.

docs/components/llms/models/deepseek.mdx

@@ -19,14 +19,20 @@ config = {
         "config": {
             "model": "deepseek-chat",  # default model
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
             "top_p": 1.0
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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"})
 ```
 
 You can also configure the API base URL in the config:

docs/components/llms/models/gemini.mdx

@@ -19,13 +19,19 @@ config = {
         "config": {
             "model": "gemini-1.5-flash-latest",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/google_AI.mdx

@@ -19,13 +19,19 @@ config = {
         "config": {
             "model": "gemini/gemini-pro",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/groq.mdx

@@ -1,10 +1,15 @@
+---
+title: Groq
+---
+
 [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.
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -17,15 +22,47 @@ config = {
         "config": {
             "model": "mixtral-8x7b-32768",
             "temperature": 0.1,
-            "max_tokens": 1000,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'groq',
+    config: {
+      apiKey: process.env.GROQ_API_KEY || '',
+      model: 'mixtral-8x7b-32768',
+      temperature: 0.1,
+      maxTokens: 1000,
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ## Config
 
 All available parameters for the `groq` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/litellm.mdx

@@ -14,13 +14,19 @@ config = {
         "config": {
             "model": "gpt-4o-mini",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/lmstudio.mdx

@@ -0,0 +1,82 @@
+---
+title: LM Studio
+---
+
+To use LM Studio with Mem0, you'll need to have LM Studio running locally with its server enabled. LM Studio provides a way to run local LLMs with an OpenAI-compatible API.
+
+## Usage
+
+<CodeGroup>
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+
+config = {
+    "llm": {
+        "provider": "lmstudio",
+        "config": {
+            "model": "lmstudio-community/Meta-Llama-3.1-70B-Instruct-GGUF/Meta-Llama-3.1-70B-Instruct-IQ2_M.gguf",
+            "temperature": 0.2,
+            "max_tokens": 2000,
+            "lmstudio_base_url": "http://localhost:1234/v1", # default LM Studio API URL
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+</CodeGroup>
+
+### Running Completely Locally
+
+You can also use LM Studio for both LLM and embedding to run Mem0 entirely locally:
+
+```python
+from mem0 import Memory
+
+# No external API keys needed!
+config = {
+    "llm": {
+        "provider": "lmstudio"
+    },
+    "embedder": {
+        "provider": "lmstudio"
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice123", metadata={"category": "movies"})
+```
+
+<Note>
+  When using LM Studio for both LLM and embedding, make sure you have:
+  1. An LLM model loaded for generating responses
+  2. An embedding model loaded for vector embeddings
+  3. The server enabled with the correct endpoints accessible
+</Note>
+
+<Note>
+  To use LM Studio, you need to:
+  1. Download and install [LM Studio](https://lmstudio.ai/)
+  2. Start a local server from the "Server" tab
+  3. Set the appropriate `lmstudio_base_url` in your configuration (default is usually http://localhost:1234/v1)
+</Note>
+
+## Config
+
+All available parameters for the `lmstudio` config are present in [Master List of All Params in Config](../config).

docs/components/llms/models/mistral_AI.mdx

@@ -25,7 +25,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/ollama.mdx

@@ -20,7 +20,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/openai.mdx

@@ -6,7 +6,8 @@ To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment varia
 
 ## Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -18,7 +19,7 @@ config = {
         "config": {
             "model": "gpt-4o",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
@@ -35,9 +36,41 @@ config = {
 # }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  llm: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'gpt-4-turbo-preview',
+      temperature: 0.2,
+      maxTokens: 1500,
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 We also support the new [OpenAI structured-outputs](https://platform.openai.com/docs/guides/structured-outputs/introduction) model.
 
 ```python
@@ -59,7 +92,9 @@ config = {
 m = Memory.from_config(config)
 ```
 
-
+<Note>
+  OpenAI structured-outputs is currently only available in the Python implementation.
+</Note>
 
 ## Config
 

docs/components/llms/models/together.mdx

@@ -15,13 +15,19 @@ config = {
         "config": {
             "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/llms/models/xAI.mdx

@@ -0,0 +1,41 @@
+---
+title: xAI
+---
+
+[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.
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
+os.environ["XAI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "xai",
+        "config": {
+            "model": "grok-2-latest",
+            "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 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 `xai` config are present in [Master List of All Params in Config](../config).

docs/components/llms/overview.mdx

@@ -14,19 +14,25 @@ For a comprehensive list of available parameters for llm configuration, please r
 
 To view all supported llms, visit the [Supported LLMs](./models).
 
+<Note>
+  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, and **Groq**.
+</Note>
+
 <CardGroup cols={4}>
-  <Card title="OpenAI" href="/components/llms/models/openai"></Card>
-    <Card title="Ollama" href="/components/llms/models/ollama"></Card>
-    <Card title="Azure OpenAI" href="/components/llms/models/azure_openai"></Card>
-    <Card title="Anthropic" href="/components/llms/models/anthropic"></Card>
-    <Card title="Together" href="/components/llms/models/together"></Card>
-    <Card title="Groq" href="/components/llms/models/groq"></Card>
-    <Card title="Litellm" href="/components/llms/models/litellm"></Card>
-    <Card title="Mistral AI" href="/components/llms/models/mistral_ai"></Card>
-    <Card title="Google AI" href="/components/llms/models/google_ai"></Card>
-    <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock"></Card>
-    <Card title="Gemini" href="/components/llms/models/gemini"></Card>
-    <Card title="DeepSeek" href="/components/llms/models/deepseek"></Card>
+  <Card title="OpenAI" href="/components/llms/models/openai" />
+  <Card title="Ollama" href="/components/llms/models/ollama" />
+  <Card title="Azure OpenAI" href="/components/llms/models/azure_openai" />
+  <Card title="Anthropic" href="/components/llms/models/anthropic" />
+  <Card title="Together" href="/components/llms/models/together" />
+  <Card title="Groq" href="/components/llms/models/groq" />
+  <Card title="Litellm" href="/components/llms/models/litellm" />
+  <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="LM Studio" href="/components/llms/models/lmstudio" />
 </CardGroup>
 
 ## Structured vs Unstructured Outputs

docs/components/vectordbs/config.mdx

@@ -6,16 +6,18 @@ iconType: "solid"
 
 ## How to define configurations?
 
-The `config` is defined as a Python dictionary with two main keys:
+The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus","azure_ai_search", "vertex_ai_vector_search")
   - `config`: A nested dictionary containing provider-specific settings
 
+
 ## How to Use Config
 
 Here's a general example of how to use the config with mem0:
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -34,6 +36,29 @@ m = Memory.from_config(config)
 m.add("Your text here", user_id="user", metadata={"category": "example"})
 ```
 
+```typescript TypeScript
+// Example for in-memory vector database (Only supported in TypeScript)
+import { Memory } from 'mem0ai/oss';
+
+const configMemory = {
+  vector_store: {
+    provider: 'memory',
+    config: {
+      collectionName: 'memories',
+      dimension: 1536,
+    },
+  },
+};
+
+const memory = new Memory(configMemory);
+await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
+```
+</CodeGroup>
+
+<Note>
+  The in-memory vector database is only supported in the TypeScript implementation.
+</Note>
+
 ## Why is Config Needed?
 
 Config is essential for:
@@ -46,6 +71,8 @@ Config is essential for:
 
 Here's a comprehensive list of all parameters that can be used across different vector databases:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description |
 |-----------|-------------|
 | `collection_name` | Name of the collection |
@@ -60,6 +87,33 @@ Here's a comprehensive list of all parameters that can be used across different
 | `url` | Full URL for the server |
 | `api_key` | API key for the server |
 | `on_disk` | Enable persistent storage |
+| `endpoint_id` | Endpoint ID (vertex_ai_vector_search) |
+| `index_id` | Index ID (vertex_ai_vector_search) |
+| `deployment_index_id` | Deployment index ID (vertex_ai_vector_search) |
+| `project_id` | Project ID (vertex_ai_vector_search) |
+| `project_number` | Project number (vertex_ai_vector_search) |
+| `vector_search_api_endpoint` | Vector search API endpoint (vertex_ai_vector_search) |
+| `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
+| `index_method` | Vector index method (for Supabase) |
+| `index_measure` | Distance measure for similarity search (for Supabase) |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description |
+|-----------|-------------|
+| `collectionName` | Name of the collection |
+| `embeddingModelDims` | Dimensions of the embedding model |
+| `dimension` | Dimensions of the embedding model (for memory provider) |
+| `host` | Host where the server is running |
+| `port` | Port where the server is running |
+| `url` | URL for the server |
+| `apiKey` | API key for the server |
+| `path` | Path for the database |
+| `onDisk` | Enable persistent storage |
+| `redisUrl` | URL for the Redis server |
+| `username` | Username for database connection |
+| `password` | Password for database connection |
+</Tab>
+</Tabs>
 
 ## Customizing Config
 

docs/components/vectordbs/dbs/azure_ai_search.mdx

@@ -1,12 +1,12 @@
-[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
+[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search/) (formerly known as "Azure Cognitive Search") provides secure information retrieval at scale over user-owned content in traditional and generative AI search applications.
 
-### Usage
+## Usage
 
 ```python
 import os
 from mem0 import Memory
 
-os.environ["OPENAI_API_KEY"] = "sk-xx"   #this key is used for embedding purpose
+os.environ["OPENAI_API_KEY"] = "sk-xx"   # This key is used for embedding purpose
 
 config = {
     "vector_store": {
@@ -15,24 +15,81 @@ config = {
             "service_name": "ai-search-test",
             "api_key": "*****",
             "collection_name": "mem0", 
-            "embedding_model_dims": 1536 ,
-            "use_compression": False
+            "embedding_model_dims": 1536
         }
     }
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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
+## Using binary compression for large vector collections
 
-Let's see the available parameters for the `qdrant` config:
-service_name (str): Azure Cognitive Search service name.  
-| Parameter | Description | Default Value |
-| --- | --- | --- |
-| `service_name` | Azure AI Search service name | `None` |
-| `api_key` | API key of the Azure AI Search service | `None` |
-| `collection_name` | The name of the collection/index to store the vectors, it will be created automatically if not exist | `mem0` |
-| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `use_compression` | Use scalar quantization vector compression | False |
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "compression_type": "binary",
+            "use_float16": True  # Use half precision for storage efficiency
+        }
+    }
+}
+```
+
+## Using hybrid search
+
+```python
+config = {
+    "vector_store": {
+        "provider": "azure_ai_search",
+        "config": {
+            "service_name": "ai-search-test",
+            "api_key": "*****",
+            "collection_name": "mem0", 
+            "embedding_model_dims": 1536,
+            "hybrid_search": True,
+            "vector_filter_mode": "postFilter"
+        }
+    }
+}
+```
+
+## Configuration Parameters
+
+| Parameter | Description | Default Value | Options |
+| --- | --- | --- | --- |
+| `service_name` | Azure AI Search service name | Required | - |
+| `api_key` | API key of the Azure AI Search service | Required | - |
+| `collection_name` | The name of the collection/index to store vectors | `mem0` | Any valid index name |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` | Any integer value |
+| `compression_type` | Type of vector compression to use | `none` | `none`, `scalar`, `binary` |
+| `use_float16` | Store vectors in half precision (Edm.Half) | `False` | `True`, `False` |
+| `vector_filter_mode` | Vector filter mode to use | `preFilter` | `postFilter`, `preFilter` |
+| `hybrid_search` | Use hybrid search | `False` | `True`, `False` |
+
+## Notes on Configuration Options
+
+- **compression_type**: 
+  - `none`: No compression, uses full vector precision
+  - `scalar`: Scalar quantization with reasonable balance of speed and accuracy
+  - `binary`: Binary quantization for maximum compression with some accuracy trade-off
+
+- **vector_filter_mode**:
+  - `preFilter`: Applies filters before vector search (faster)
+  - `postFilter`: Applies filters after vector search (may provide better relevance)
+
+- **use_float16**: Using half precision (float16) reduces storage requirements but may slightly impact accuracy. Useful for very large vector collections.
+
+- **Filterable Fields**: The implementation automatically extracts `user_id`, `run_id`, and `agent_id` fields from payloads for filtering.

docs/components/vectordbs/dbs/chroma.mdx

@@ -19,7 +19,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/vectordbs/dbs/elasticsearch.mdx

@@ -29,7 +29,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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
@@ -48,6 +54,7 @@ Let's see the available parameters for the `elasticsearch` config:
 | `password`             | Password for basic authentication                  | `None`        |
 | `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
 | `auto_create_index`    | Whether to automatically create the index          | `True`        |
+| `custom_search_query`  | Function returning a custom search query            | `None`        |
 
 ### Features
 
@@ -56,3 +63,46 @@ Let's see the available parameters for the `elasticsearch` config:
 - Multiple authentication methods (Basic Auth, API Key)
 - Automatic index creation with optimized mappings for vector search
 - Memory isolation through payload filtering
+- Custom search query function to customize the search query
+
+### Custom Search Query
+
+The `custom_search_query` parameter allows you to customize the search query when `Memory.search` is called.  
+  
+__Example__  
+```python
+import os
+from typing import List, Optional, Dict
+from mem0 import Memory
+
+def custom_search_query(query: List[float], limit: int, filters: Optional[Dict]) -> Dict:
+    return {
+        "knn": {
+            "field": "vector", 
+            "query_vector": query, 
+            "k": limit, 
+            "num_candidates": limit * 2
+        }
+    }
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536,
+            "custom_search_query": custom_search_query
+        }
+    }
+}
+```
+It should be a function that takes the following parameters:
+- `query`: a query vector used in `Memory.search`
+- `limit`: a number of results used in `Memory.search`
+- `filters`: a dictionary of key-value pairs used in `Memory.search`. You can add custom pairs for the custom search query.  
+  
+The function should return a query body for the Elasticsearch search API.

docs/components/vectordbs/dbs/milvus.mdx

@@ -19,7 +19,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/vectordbs/dbs/opensearch.mdx

@@ -29,7 +29,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/vectordbs/dbs/pgvector.mdx

@@ -21,7 +21,13 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+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

docs/components/vectordbs/dbs/pinecone.mdx

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

docs/components/vectordbs/dbs/qdrant.mdx

@@ -2,7 +2,8 @@
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -20,13 +21,47 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'qdrant',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      host: 'localhost',
+      port: 6333,
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ### Config
 
 Let's see the available parameters for the `qdrant` config:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
@@ -37,4 +72,18 @@ Let's see the available parameters for the `qdrant` config:
 | `path` | Path for the qdrant database | `/tmp/qdrant` |
 | `url` | Full URL for the qdrant server | `None` |
 | `api_key` | API key for the qdrant server | `None` |
-| `on_disk` | For enabling persistent storage | `False` |
+| `on_disk` | For enabling persistent storage | `False` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | The name of the collection to store the vectors | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `host` | The host where the Qdrant server is running | `None` |
+| `port` | The port where the Qdrant server is running | `None` |
+| `path` | Path for the Qdrant database | `/tmp/qdrant` |
+| `url` | Full URL for the Qdrant server | `None` |
+| `apiKey` | API key for the Qdrant server | `None` |
+| `onDisk` | For enabling persistent storage | `False` |
+</Tab>
+</Tabs>

docs/components/vectordbs/dbs/redis.mdx

@@ -12,7 +12,8 @@ docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:lat
 
 ### Usage
 
-```python
+<CodeGroup>
+```python Python
 import os
 from mem0 import Memory
 
@@ -31,15 +32,61 @@ config = {
 }
 
 m = Memory.from_config(config)
-m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
 ```
 
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  vectorStore: {
+    provider: 'redis',
+    config: {
+      collectionName: 'memories',
+      embeddingModelDims: 1536,
+      redisUrl: 'redis://localhost:6379',
+      username: 'your-redis-username',
+      password: 'your-redis-password',
+    },
+  },
+};
+
+const memory = new Memory(config);
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
+```
+</CodeGroup>
+
 ### Config
 
 Let's see the available parameters for the `redis` config:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `collection_name` | The name of the collection to store the vectors | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
-| `redis_url` | The URL of the Redis server | `None` |
+| `redis_url` | The URL of the Redis server | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collectionName` | The name of the collection to store the vectors | `mem0` |
+| `embeddingModelDims` | Dimensions of the embedding model | `1536` |
+| `redisUrl` | The URL of the Redis server | `None` |
+| `username` | Username for Redis connection | `None` |
+| `password` | Password for Redis connection | `None` |
+</Tab>
+</Tabs>

docs/components/vectordbs/dbs/supabase.mdx

@@ -0,0 +1,78 @@
+[Supabase](https://supabase.com/) is an open-source Firebase alternative that provides a PostgreSQL database with pgvector extension for vector similarity search. It offers a powerful and scalable solution for storing and querying vector embeddings.
+
+Create a [Supabase](https://supabase.com/dashboard/projects) account and project, then get your connection string from Project Settings > Database. See the [docs](https://supabase.github.io/vecs/hosting/) for details.
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "supabase",
+        "config": {
+            "connection_string": "postgresql://user:password@host:port/database",
+            "collection_name": "memories",
+            "index_method": "hnsw",  # Optional: defaults to "auto"
+            "index_measure": "cosine_distance"  # Optional: defaults to "cosine_distance"
+        }
+    }
+}
+
+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 Supabase:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `connection_string` | PostgreSQL connection string (required) | None |
+| `collection_name` | Name for the vector collection | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `index_method` | Vector index method to use | `auto` |
+| `index_measure` | Distance measure for similarity search | `cosine_distance` |
+
+### Index Methods
+
+The following index methods are supported:
+
+- `auto`: Automatically selects the best available index method
+- `hnsw`: Hierarchical Navigable Small World graph index (faster search, more memory usage)
+- `ivfflat`: Inverted File Flat index (good balance of speed and memory)
+
+### Distance Measures
+
+Available distance measures for similarity search:
+
+- `cosine_distance`: Cosine similarity (recommended for most embedding models)
+- `l2_distance`: Euclidean distance
+- `l1_distance`: Manhattan distance
+- `max_inner_product`: Maximum inner product similarity
+
+### Best Practices
+
+1. **Index Method Selection**:
+   - Use `hnsw` for fastest search performance when memory is not a constraint
+   - Use `ivfflat` for a good balance of search speed and memory usage
+   - Use `auto` if unsure, it will select the best method based on your data
+
+2. **Distance Measure Selection**:
+   - Use `cosine_distance` for most embedding models (OpenAI, Hugging Face, etc.)
+   - Use `max_inner_product` if your vectors are normalized
+   - Use `l2_distance` or `l1_distance` if working with raw feature vectors
+
+3. **Connection String**:
+   - Always use environment variables for sensitive information in the connection string
+   - Format: `postgresql://user:password@host:port/database`

docs/components/vectordbs/dbs/vertex_ai_vector_search.mdx

@@ -0,0 +1,46 @@
+## Google Cloud Vertex AI Vector Search
+
+
+### Usage
+
+To use Google Cloud Vertex AI Vector Search with `mem0`, you need to configure the `vector_store` in your `mem0` config:
+
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["GEMINI_API_KEY"] = = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "vertex_ai_vector_search",
+        "config": {
+            "endpoint_id": "YOUR_ENDPOINT_ID",            # Required: Vector Search endpoint ID
+            "index_id": "YOUR_INDEX_ID",                  # Required: Vector Search index ID 
+            "deployment_index_id": "YOUR_DEPLOYMENT_INDEX_ID",  # Required: Deployment-specific ID
+            "project_id": "YOUR_PROJECT_ID",              # Required: Google Cloud project ID
+            "project_number": "YOUR_PROJECT_NUMBER",      # Required: Google Cloud project number
+            "region": "YOUR_REGION",                      # Optional: Defaults to GOOGLE_CLOUD_REGION
+            "credentials_path": "path/to/credentials.json", # Optional: Defaults to GOOGLE_APPLICATION_CREDENTIALS
+            "vector_search_api_endpoint": "YOUR_API_ENDPOINT" # Required for get operations
+        }
+    }
+}
+m = Memory.from_config(config)
+m.add("Your text here", user_id="user", metadata={"category": "example"})
+```
+
+
+### Required Parameters
+
+| Parameter | Description | Required |
+|-----------|-------------|----------|
+| `endpoint_id` | Vector Search endpoint ID | Yes |
+| `index_id` | Vector Search index ID | Yes |
+| `deployment_index_id` | Deployment-specific index ID | Yes |
+| `project_id` | Google Cloud project ID | Yes |
+| `project_number` | Google Cloud project number | Yes |
+| `vector_search_api_endpoint` | Vector search API endpoint | Yes (for get operations) |
+| `region` | Google Cloud region | No (defaults to GOOGLE_CLOUD_REGION) |
+| `credentials_path` | Path to service account credentials | No (defaults to GOOGLE_APPLICATION_CREDENTIALS) |

docs/components/vectordbs/dbs/weaviate.mdx

@@ -0,0 +1,47 @@
+[Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.
+
+
+### Installation
+```bash
+pip install weaviate weaviate-client
+```
+
+### Usage
+
+```python Python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "weaviate",
+        "config": {
+            "collection_name": "test",
+            "cluster_url": "http://localhost:8080",
+            "auth_client_secret": None,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+### Config
+
+Let's see the available parameters for the `weaviate` config:
+
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `collection_name` | The name of the collection to store the vectors | `mem0` |
+| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
+| `cluster_url` | URL for the Weaviate server | `None` |
+| `auth_client_secret` | API key for Weaviate authentication | `None` |

docs/components/vectordbs/overview.mdx

@@ -10,15 +10,23 @@ 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.
+</Note>
+
 <CardGroup cols={3}>
   <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card>
   <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card>
   <Card title="Pgvector" href="/components/vectordbs/dbs/pgvector"></Card>
   <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card>
+  <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card>
   <Card title="Azure AI Search" href="/components/vectordbs/dbs/azure_ai_search"></Card>
   <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card>
   <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card>
   <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card>
+  <Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card>
+  <Card title="Vertex AI Vector Search" href="/components/vectordbs/dbs/vertex_ai_vector_search"></Card>
+  <Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card>
 </CardGroup>
 
 ## Usage

docs/contributing/development.mdx

@@ -0,0 +1,87 @@
+---
+title: Development
+icon: "code"
+---
+
+# Development Contributions
+
+We strive to make contributions **easy, collaborative, and enjoyable**. Follow the steps below to ensure a smooth contribution process.
+
+## Submitting Your Contribution through PR
+
+To contribute, follow these steps:
+
+1. **Fork & Clone** the repository: [Mem0 on GitHub](https://github.com/mem0ai/mem0)
+2. **Create a Feature Branch**: Use a dedicated branch for your changes, e.g., `feature/my-new-feature`
+3. **Implement Changes**: If adding a feature or fixing a bug, ensure to:
+   - Write necessary **tests**
+   - Add **documentation, docstrings, and runnable examples**
+4. **Code Quality Checks**:
+   - Run **linting** to catch style issues
+   - Ensure **all tests pass**
+5. **Submit a Pull Request** 🚀
+
+For detailed guidance on pull requests, refer to [GitHub's documentation](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request).
+
+---
+
+## 📦 Dependency Management
+
+We use `poetry` as our package manager. Install it by following the [official instructions](https://python-poetry.org/docs/#installation).
+
+⚠️ **Do NOT use `pip` or `conda` for dependency management.** Instead, run:
+
+```bash
+make install_all
+
+# Activate virtual environment
+poetry shell
+```
+
+---
+
+## 🛠️ Development Standards
+
+### ✅ Pre-commit Hooks
+
+Ensure `pre-commit` is installed before contributing:
+
+```bash
+pre-commit install
+```
+
+### 🔍 Linting with `ruff`
+
+Run the linter and fix any reported issues before submitting your PR:
+
+```bash
+make lint
+```
+
+### 🎨 Code Formatting with `black`
+
+To maintain a consistent code style, format your code using `black`:
+
+```bash
+make format
+```
+
+### 🧪 Testing with `pytest`
+
+Run tests to verify functionality before submitting your PR:
+
+```bash
+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.
+
+---
+
+## 🚀 Release Process
+
+Currently, releases are handled manually. We aim for frequent releases, typically when new features or bug fixes are introduced.
+
+---
+
+Thank you for contributing to Mem0! 🎉

docs/contributing/documentation.mdx

@@ -0,0 +1,55 @@
+---
+title: Documentation
+icon: "book"
+---
+
+# Documentation Contributions
+
+## 📌 Prerequisites
+
+Before getting started, ensure you have **Node.js (version 23.6.0 or higher)** installed on your system.
+
+---
+
+## 🚀 Setting Up Mintlify
+
+### Step 1: Install Mintlify
+
+Install Mintlify globally using your preferred package manager:
+
+<CodeGroup>
+
+```bash npm
+npm i -g mintlify
+```
+
+```bash yarn
+yarn global add mintlify
+```
+
+</CodeGroup>
+
+### Step 2: Run the Documentation Server
+
+Navigate to the `docs/` directory (where `docs.json` is located) and start the development server:
+
+```bash
+mintlify dev
+```
+
+The documentation website will be available at: [http://localhost:3000](http://localhost:3000).
+
+---
+
+## 🔧 Custom Ports
+
+By default, Mintlify runs on **port 3000**. To use a different port, add the `--port` flag:
+
+```bash
+mintlify dev --port 3333
+```
+
+---
+
+By following these steps, you can efficiently contribute to **Mem0's documentation**. Happy documenting! ✍️
+

docs/docs.json

@@ -47,6 +47,7 @@
                     "pages": [
                       "features/platform-overview",
                       "features/advanced-retrieval",
+                      "features/contextual-add",
                       "features/multimodal-support",
                       "features/selective-memory",
                       "features/custom-categories",
@@ -54,7 +55,9 @@
                       "features/direct-import",
                       "features/async-client",
                       "features/memory-export",
-                      "features/webhook"
+                      "features/webhooks",
+                      "features/graph-memory",
+                      "features/feedback-mechanism"
                     ]
                   }
                 ]
@@ -64,12 +67,15 @@
                 "icon": "code-branch",
                 "pages": [
                   "open-source/quickstart",
+                  "open-source/python-quickstart",
+                  "open-source/node-quickstart",
                   {
                     "group": "Features",
                     "icon": "wrench",
                     "pages": [
                       "features/openai_compatibility",
-                      "features/custom-prompts",
+                      "features/custom-fact-extraction-prompt",
+                      "features/custom-update-memory-prompt",
                       "open-source/multimodal-support",
                       "open-source/features/rest-api"
                     ]
@@ -102,7 +108,9 @@
                           "components/llms/models/mistral_AI",
                           "components/llms/models/google_AI",
                           "components/llms/models/aws_bedrock",
-                          "components/llms/models/gemini"
+                          "components/llms/models/gemini",
+                          "components/llms/models/deepseek",
+                          "components/llms/models/xAI"
                         ]
                       }
                     ]
@@ -121,10 +129,14 @@
                           "components/vectordbs/dbs/chroma",
                           "components/vectordbs/dbs/pgvector",
                           "components/vectordbs/dbs/milvus",
+                          "components/vectordbs/dbs/pinecone",
                           "components/vectordbs/dbs/azure_ai_search",
                           "components/vectordbs/dbs/redis",
                           "components/vectordbs/dbs/elasticsearch",
-                          "components/vectordbs/dbs/opensearch"
+                          "components/vectordbs/dbs/opensearch",
+                          "components/vectordbs/dbs/supabase",
+                          "components/vectordbs/dbs/vertex_ai_vector_search",
+                          "components/vectordbs/dbs/weaviate"
                         ]
                       }
                     ]
@@ -150,6 +162,14 @@
                     ]
                   }
                 ]
+              },
+              {
+                "group": "Contribution",
+                "icon": "handshake",
+                "pages": [
+                  "contributing/development",
+                  "contributing/documentation"
+                ]
               }
             ]
           },
@@ -161,11 +181,20 @@
                 "icon": "lightbulb",
                 "pages": [
                   "examples/overview",
+                  "examples/mem0-demo",
+                  "examples/ai_companion_js",
                   "examples/mem0-with-ollama",
                   "examples/personal-ai-tutor",
                   "examples/customer-support-agent",
                   "examples/personal-travel-assistant",
-                  "examples/llama-index-mem0"
+                  "examples/llama-index-mem0",
+                  "examples/chrome-extension",
+                  "examples/document-writing",
+                  "examples/multimodal-demo",
+                  "examples/personalized-deep-research",
+                  "examples/mem0-agentic-tool",
+                  "examples/openai-inbuilt-tools",
+                  "examples/mem0-openai-voice-demo"
                 ]
               }
             ]
@@ -184,7 +213,9 @@
                   "integrations/langchain",
                   "integrations/langgraph",
                   "integrations/llama-index",
-                  "integrations/langchain-tools"
+                  "integrations/langchain-tools",
+                  "integrations/dify",
+                  "integrations/mcp-server"
                 ]
               }
             ]
@@ -259,6 +290,11 @@
         "href": "https://app.mem0.ai",
         "icon": "chart-simple"
       },
+      {
+        "anchor": "Demo",
+        "href": "https://mem0.dev/demo",
+        "icon": "play"
+      },
       {
         "anchor": "Discord",
         "href": "https://mem0.dev/DiD",
@@ -306,6 +342,9 @@
     "posthog": {
       "apiKey": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX",
       "apiHost": "https://mango.mem0.ai"
+    },
+    "intercom": {
+      "appId": "jjv2r0tt"
     }
   }
 }

docs/examples/ai_companion_js.mdx

@@ -0,0 +1,126 @@
+---
+title: AI Companion in Node.js
+---
+
+You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
+
+## Overview
+
+The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates memories for each user interaction and integrates with OpenAI's GPT models to provide detailed and context-aware responses to user queries.
+
+## Setup
+
+Before you begin, ensure you have Node.js installed and create a new project. Install the required dependencies using npm:
+
+```bash
+npm install openai mem0ai
+```
+
+## Full Code Example
+
+Below is the complete code to create and interact with an AI Companion using Mem0:
+
+```javascript
+import { OpenAI } from 'openai';
+import { Memory } from 'mem0ai/oss';
+import * as readline from 'readline';
+
+const openaiClient = new OpenAI();
+const memory = new Memory();
+
+async function chatWithMemories(message, userId = "default_user") {
+  const relevantMemories = await memory.search(message, { userId: userId });
+  
+  const memoriesStr = relevantMemories.results
+    .map(entry => `- ${entry.memory}`)
+    .join('\n');
+  
+  const systemPrompt = `You are a helpful AI. Answer the question based on query and memories.
+User Memories:
+${memoriesStr}`;
+  
+  const messages = [
+    { role: "system", content: systemPrompt },
+    { role: "user", content: message }
+  ];
+  
+  const response = await openaiClient.chat.completions.create({
+    model: "gpt-4o-mini",
+    messages: messages
+  });
+  
+  const assistantResponse = response.choices[0].message.content || "";
+  
+  messages.push({ role: "assistant", content: assistantResponse });
+  await memory.add(messages, { userId: userId });
+  
+  return assistantResponse;
+}
+
+async function main() {
+  const rl = readline.createInterface({
+    input: process.stdin,
+    output: process.stdout
+  });
+  
+  console.log("Chat with AI (type 'exit' to quit)");
+  
+  const askQuestion = () => {
+    return new Promise((resolve) => {
+      rl.question("You: ", (input) => {
+        resolve(input.trim());
+      });
+    });
+  };
+  
+  try {
+    while (true) {
+      const userInput = await askQuestion();
+      
+      if (userInput.toLowerCase() === 'exit') {
+        console.log("Goodbye!");
+        rl.close();
+        break;
+      }
+      
+      const response = await chatWithMemories(userInput, "sample_user");
+      console.log(`AI: ${response}`);
+    }
+  } catch (error) {
+    console.error("An error occurred:", error);
+    rl.close();
+  }
+}
+
+main().catch(console.error);
+```
+
+### Key Components
+
+1. **Initialization**
+   - The code initializes both OpenAI and Mem0 Memory clients
+   - Uses Node.js's built-in readline module for command-line interaction
+
+2. **Memory Management (chatWithMemories function)**
+   - Retrieves relevant memories using Mem0's search functionality
+   - Constructs a system prompt that includes past memories
+   - Makes API calls to OpenAI for generating responses
+   - Stores new interactions in memory
+
+3. **Interactive Chat Interface (main function)**
+   - Creates a command-line interface for user interaction
+   - Handles user input and displays AI responses
+   - Includes graceful exit functionality
+
+### Environment Setup
+
+Make sure to set up your environment variables:
+```bash
+export OPENAI_API_KEY=your_api_key
+```
+
+### Conclusion
+
+This implementation demonstrates how to create an AI Companion that maintains context across conversations using Mem0's memory capabilities. The system automatically stores and retrieves relevant information, creating a more personalized and context-aware interaction experience.
+
+As users interact with the system, Mem0's memory system continuously learns and adapts, making future responses more relevant and personalized. This setup is ideal for creating long-term learning AI assistants that can maintain context and provide increasingly personalized responses over time.

docs/examples/chrome-extension.mdx

@@ -0,0 +1,55 @@
+# Mem0 Chrome Extension
+
+Enhance your AI interactions with **Mem0**, a Chrome extension that introduces a universal memory layer across platforms like `ChatGPT`, `Claude`, and `Perplexity`. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.
+
+<Note>
+  🎉 We now support Grok! The Mem0 Chrome Extension has been updated to work with Grok, bringing the same powerful memory capabilities to your Grok conversations.
+</Note>
+
+
+## Features
+
+- **Universal Memory Layer**: Share context seamlessly across ChatGPT, Claude, Perplexity, and Grok.
+- **Smart Context Detection**: Automatically captures relevant information from your conversations.
+- **Intelligent Memory Retrieval**: Surfaces pertinent memories at the right time.
+- **One-Click Sync**: Easily synchronize with existing ChatGPT memories.
+- **Memory Dashboard**: Manage all your memories in one centralized location.
+
+## Installation
+
+You can install the Mem0 Chrome Extension using one of the following methods:
+
+### Method 1: Chrome Web Store Installation
+
+1. **Download the Extension**: Open Google Chrome and navigate to the [Mem0 Chrome Extension page](https://chromewebstore.google.com/detail/mem0/onihkkbipkfeijkadecaafbgagkhglop?hl=en).
+2. **Add to Chrome**: Click on the "Add to Chrome" button.
+3. **Confirm Installation**: In the pop-up dialog, click "Add extension" to confirm. The Mem0 icon should now appear in your Chrome toolbar.
+
+### Method 2: Manual Installation
+
+1. **Download the Extension**: Clone or download the extension files from the [Mem0 Chrome Extension GitHub repository](https://github.com/mem0ai/mem0-chrome-extension).
+2. **Access Chrome Extensions**: Open Google Chrome and navigate to `chrome://extensions`.
+3. **Enable Developer Mode**: Toggle the "Developer mode" switch in the top right corner.
+4. **Load Unpacked Extension**: Click "Load unpacked" and select the directory containing the extension files.
+5. **Confirm Installation**: The Mem0 Chrome Extension should now appear in your Chrome toolbar.
+
+## Usage
+
+1. **Locate the Mem0 Icon**: After installation, find the Mem0 icon in your Chrome toolbar.
+2. **Sign In**: Click the icon and sign in with your Google account.
+3. **Interact with AI Assistants**:
+   - **ChatGPT and Perplexity**: Continue your conversations as usual; Mem0 operates seamlessly in the background.
+   - **Claude**: Click the Mem0 button or use the shortcut `Ctrl + M` to activate memory functions.
+
+## Configuration
+
+- **API Key**: Obtain your API key from the Mem0 Dashboard to connect the extension to the Mem0 API.
+- **User ID**: This is your unique identifier in the Mem0 system. If not provided, it defaults to 'chrome-extension-user'.
+
+## Demo Video
+
+<iframe width="700" height="400" src="https://www.youtube.com/embed/dqenCMMlfwQ?si=zhGVrkq6IS_0Jwyj" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## Privacy and Data Security
+
+Your messages are sent to the Mem0 API for extracting and retrieving memories. Mem0 is committed to ensuring your data's privacy and security.

docs/examples/customer-support-agent.mdx

@@ -94,8 +94,8 @@ You can fetch all the memories at any point in time using the following code:
 
 ```python
 memories = support_agent.get_memories(user_id=customer_id)
-for m in memories:
-    print(m['text'])
+for m in memories['results']:
+    print(m['memory'])
 ```
 
 ### Key Points

docs/examples/document-writing.mdx

@@ -0,0 +1,184 @@
+---
+title: Document Editing with Mem0
+---
+
+This guide demonstrates how to leverage **Mem0** to edit documents efficiently, ensuring they align with your unique writing style and preferences.
+
+## **Why Use Mem0?**
+
+By integrating Mem0 into your workflow, you can streamline your document editing process with:
+
+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.
+
+---
+## **Setup**
+
+```python
+import os
+from mem0 import MemoryClient
+
+# Set up Mem0 client
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+client = MemoryClient()
+
+# 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).
+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"},
+        {"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.")
+    return response
+```
+
+---
+## **Editing Documents with Mem0**
+
+```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.")
+        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
+```
+
+---
+## **Complete Workflow: Document Editing**
+
+```python
+def document_editing_workflow(content):
+    """Automated workflow for editing a document based on writing preferences."""
+    
+    # Step 1: Store writing preferences (if not already stored)
+    store_writing_preferences()
+    
+    # Step 2: Edit the document with Mem0 preferences
+    edited_content = edit_document_based_on_preferences(content)
+    
+    if not edited_content:
+        return "Failed to edit document."
+    
+    # Step 3: Display results
+    print("\n=== ORIGINAL DOCUMENT ===\n")
+    print(content)
+    
+    print("\n=== EDITED DOCUMENT ===\n")
+    print(edited_content)
+    
+    return edited_content
+```
+
+---
+## **Example Usage**
+
+```python
+# Define your document
+original_content = """Project Proposal
+    
+The following proposal outlines our strategy for the Q3 marketing campaign. 
+We believe this approach will significantly increase our market share.
+
+Increase brand awareness
+Boost sales by 15%
+Expand our social media following
+
+We plan to launch the campaign in July and continue through September.
+"""
+
+# Run the workflow
+result = document_editing_workflow(original_content)
+```
+
+---
+## **Expected Output**
+
+Your document will be transformed into a structured, well-formatted version based on your preferences.
+
+### **Original Document**
+```
+Project Proposal
+    
+The following proposal outlines our strategy for the Q3 marketing campaign. 
+We believe this approach will significantly increase our market share.
+
+Increase brand awareness
+Boost sales by 15%
+Expand our social media following
+
+We plan to launch the campaign in July and continue through September.
+```
+
+### **Edited Document**
+```
+# **Project Proposal**
+
+## **Q3 Marketing Campaign Strategy**
+
+This proposal outlines our strategy for the Q3 marketing campaign. We aim to significantly increase our market share with this approach.
+
+### **Objectives**
+
+- **Increase Brand Awareness**: Implement targeted advertising and community engagement to enhance visibility.
+- **Boost Sales by 15%**: Increase sales by 15% compared to Q2 figures.
+- **Expand Social Media Following**: Grow our social media audience by 20%.
+
+### **Timeline**
+
+- **Launch Date**: July
+- **Duration**: July – September
+
+### **Key Actions**
+
+- **Targeted Advertising**: Utilize platforms like Google Ads and Facebook to reach specific demographics.
+- **Community Engagement**: Host webinars and live Q&A sessions.
+- **Content Creation**: Produce engaging videos and infographics.
+
+### **Supporting Data**
+
+- **Previous Campaign Success**: Our Q2 campaign increased sales by 12%. We will refine similar strategies for Q3.
+- **Social Media Growth**: Last year, our Instagram followers grew by 25% during a similar campaign.
+
+### **Conclusion**
+
+We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let’s work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
+```
+
+Mem0 creates a seamless, intelligent document editing experience—perfect for content creators, technical writers, and businesses alike!

docs/examples/mem0-agentic-tool.mdx

@@ -0,0 +1,226 @@
+---
+title: Mem0 as an Agentic Tool
+---
+
+Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
+You can create agents that remember past conversations and use that context to provide better responses.
+
+## Installation
+
+First, install the required packages:
+```bash
+pip install mem0ai pydantic openai-agents
+```
+
+You'll also need a custom agents framework for this implementation.
+
+## Setting Up Environment Variables
+
+Store your Mem0 API key as an environment variable:
+
+```bash
+export MEM0_API_KEY="your_mem0_api_key"
+```
+
+Or in your Python script:
+
+```python
+import os
+os.environ["MEM0_API_KEY"] = "your_mem0_api_key"
+```
+
+## Code Structure
+
+The integration consists of three main components:
+
+1. **Context Manager**: Defines user context for memory operations
+2. **Memory Tools**: Functions to add, search, and retrieve memories
+3. **Memory Agent**: An agent configured to use these memory tools
+
+## Step-by-Step Implementation
+
+### 1. Import Dependencies
+
+```python
+from __future__ import annotations
+import os
+import asyncio
+from pydantic import BaseModel
+try:
+    from mem0 import AsyncMemoryClient
+except ImportError:
+    raise ImportError("mem0 is not installed. Please install it using 'pip install mem0ai'.")
+from agents import (
+    Agent,
+    ItemHelpers,
+    MessageOutputItem,
+    RunContextWrapper,
+    Runner,
+    ToolCallItem,
+    ToolCallOutputItem,
+    TResponseInputItem,
+    function_tool,
+)
+```
+
+### 2. Define Memory Context
+
+```python
+class Mem0Context(BaseModel):
+    user_id: str | None = None
+```
+
+### 3. Initialize the Mem0 Client
+
+```python
+client = AsyncMemoryClient(api_key=os.getenv("MEM0_API_KEY"))
+```
+
+### 4. Create Memory Tools
+
+#### Add to Memory
+
+```python
+@function_tool
+async def add_to_memory(
+    context: RunContextWrapper[Mem0Context],
+    content: str,
+) -> str:
+    """
+    Add a message to Mem0
+    Args:
+        content: The content to store in memory.
+    """
+    messages = [{"role": "user", "content": content}]
+    user_id = context.context.user_id or "default_user"
+    await client.add(messages, user_id=user_id)
+    return f"Stored message: {content}"
+```
+
+#### Search Memory
+
+```python
+@function_tool
+async def search_memory(
+    context: RunContextWrapper[Mem0Context],
+    query: str,
+) -> str:
+    """
+    Search for memories in Mem0
+    Args:
+        query: The search query.
+    """
+    user_id = context.context.user_id or "default_user"
+    memories = await client.search(query, user_id=user_id, output_format="v1.1")
+    results = '\n'.join([result["memory"] for result in memories["results"]])
+    return str(results)
+```
+
+#### Get All Memories
+
+```python
+@function_tool
+async def get_all_memory(
+    context: RunContextWrapper[Mem0Context],
+) -> str:
+    """Retrieve all memories from Mem0"""
+    user_id = context.context.user_id or "default_user"
+    memories = await client.get_all(user_id=user_id, output_format="v1.1")
+    results = '\n'.join([result["memory"] for result in memories["results"]])
+    return str(results)
+```
+
+### 5. Configure the Memory Agent
+
+```python
+memory_agent = Agent[Mem0Context](
+    name="Memory Assistant",
+    instructions="""You are a helpful assistant with memory capabilities. You can:
+    1. Store new information using add_to_memory
+    2. Search existing information using search_memory
+    3. Retrieve all stored information using get_all_memory
+    When users ask questions:
+    - If they want to store information, use add_to_memory
+    - If they're searching for specific information, use search_memory
+    - If they want to see everything stored, use get_all_memory""",
+    tools=[add_to_memory, search_memory, get_all_memory],
+)
+```
+
+### 6. Implement the Main Runtime Loop
+
+```python
+async def main():
+    current_agent: Agent[Mem0Context] = memory_agent
+    input_items: list[TResponseInputItem] = []
+    context = Mem0Context()
+    while True:
+        user_input = input("Enter your message (or 'quit' to exit): ")
+        if user_input.lower() == 'quit':
+            break
+        input_items.append({"content": user_input, "role": "user"})
+        result = await Runner.run(current_agent, input_items, context=context)
+        for new_item in result.new_items:
+            agent_name = new_item.agent.name
+            if isinstance(new_item, MessageOutputItem):
+                print(f"{agent_name}: {ItemHelpers.text_message_output(new_item)}")
+            elif isinstance(new_item, ToolCallItem):
+                print(f"{agent_name}: Calling a tool")
+            elif isinstance(new_item, ToolCallOutputItem):
+                print(f"{agent_name}: Tool call output: {new_item.output}")
+            else:
+                print(f"{agent_name}: Skipping item: {new_item.__class__.__name__}")
+        input_items = result.to_input_list()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Usage Examples
+
+### Storing Information
+
+```
+User: Remember that my favorite color is blue
+Agent: Calling a tool
+Agent: Tool call output: Stored message: my favorite color is blue
+Agent: I've stored that your favorite color is blue in my memory. I'll remember that for future conversations.
+```
+
+### Searching Memory
+
+```
+User: What's my favorite color?
+Agent: Calling a tool
+Agent: Tool call output: my favorite color is blue
+Agent: Your favorite color is blue, based on what you've told me earlier.
+```
+
+### Retrieving All Memories
+
+```
+User: What do you know about me?
+Agent: Calling a tool
+Agent: Tool call output: favorite color is blue
+my birthday is on March 15
+Agent: Based on our previous conversations, I know that:
+1. Your favorite color is blue
+2. Your birthday is on March 15
+```
+
+## Advanced Configuration
+
+### Custom User IDs
+
+You can specify different user IDs to maintain separate memory stores for multiple users:
+
+```python
+context = Mem0Context(user_id="user123")
+```
+
+
+## Resources
+
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
+- [API Reference](https://docs.mem0.ai/api-reference)

docs/examples/mem0-demo.mdx

@@ -0,0 +1,68 @@
+---
+title: Mem0 Demo
+---
+
+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
+  autoPlay
+  muted
+  loop
+  playsInline
+  className="w-full aspect-video rounded-lg"
+  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.
+
+## Overview
+
+The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates memories for each user interaction and integrates with OpenAI's GPT models to provide detailed and context-aware responses to user queries.
+
+## Setup
+
+Before you begin, follow these steps to set up the demo application:
+
+1. Clone the Mem0 repository:
+   ```bash
+   git clone https://github.com/mem0ai/mem0.git
+   ```
+
+2. Navigate to the demo application folder:
+   ```bash
+   cd mem0/examples/mem0-demo
+   ```
+
+3. Install dependencies:
+   ```bash
+   pnpm install
+   ```
+
+4. Set up environment variables by creating a `.env` file in the project root with the following content:
+   ```bash
+   OPENAI_API_KEY=your_openai_api_key
+   MEM0_API_KEY=your_mem0_api_key
+   ```
+   You can obtain your `MEM0_API_KEY` by signing up at [Mem0 API Dashboard](https://app.mem0.ai/dashboard/api-keys).
+
+5. Start the development server:
+   ```bash
+   pnpm run dev
+   ```
+
+## Enhancing the Next.js Application
+
+Once the demo is running, you can customize and enhance the Next.js application by modifying the components in the `mem0-demo` folder. Consider:
+- Adding new memory features to improve contextual retention.
+- Customizing the UI to better suit your application needs.
+- Integrating additional APIs or third-party services to extend functionality.
+
+## Full Code
+
+You can find the complete source code for this demo on GitHub:
+[Mem0 Demo GitHub](https://github.com/mem0ai/mem0/tree/main/examples/mem0-demo)
+
+## Conclusion
+
+This setup demonstrates how to build an AI Companion that maintains memory across interactions using Mem0. The system continuously adapts to user interactions, making future responses more relevant and personalized. Experiment with the application and enhance it further to suit your use case!
+

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

@@ -0,0 +1,538 @@
+---
+title: 'Mem0 with OpenAI Agents SDK for Voice'
+description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
+---
+
+# Building Voice Agents with Memory using Mem0 and OpenAI Agents SDK
+
+This guide demonstrates how to combine OpenAI's Agents SDK for voice applications with Mem0's memory capabilities to create a voice assistant that remembers user preferences and past interactions.
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+1. Installed OpenAI Agents SDK with voice dependencies:
+```bash
+pip install 'openai-agents[voice]'
+```
+
+2. Installed Mem0 SDK:
+```bash
+pip install mem0ai
+```
+
+3. Installed other required dependencies:
+```bash
+pip install numpy sounddevice pydantic
+```
+
+4. Set up your API keys:
+   - OpenAI API key for the Agents SDK
+   - Mem0 API key from the Mem0 Platform
+
+## Code Breakdown
+
+Let's break down the key components of this implementation:
+
+### 1. Setting Up Dependencies and Environment
+
+```python
+# OpenAI Agents SDK imports
+from agents import (
+    Agent,
+    function_tool
+)
+from agents.voice import (
+    AudioInput,
+    SingleAgentVoiceWorkflow,
+    VoicePipeline
+)
+from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
+
+# Mem0 imports
+from mem0 import AsyncMemoryClient
+
+# Set up API keys (replace with your actual keys)
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Define a global user ID for simplicity
+USER_ID = "voice_user"
+
+# Initialize Mem0 client
+mem0_client = AsyncMemoryClient()
+```
+
+This section handles:
+- Importing required modules from OpenAI Agents SDK and Mem0
+- Setting up environment variables for API keys
+- Defining a simple user identification system (using a global variable)
+- Initializing the Mem0 client that will handle memory operations
+
+### 2. Memory Tools with Function Decorators
+
+The `@function_tool` decorator transforms Python functions into callable tools for the OpenAI agent. Here are the key memory tools:
+
+#### Storing User Memories
+
+```python
+import logging
+
+# Set up logging at the top of your file
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    force=True
+)
+logger = logging.getLogger("memory_voice_agent")
+
+# Then use logger in your function tools
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """Store a user memory in memory."""
+    # This will be visible in your console
+    logger.debug(f"Saving memory: {memory} for user {USER_ID}")
+    
+    # Store the preference in Mem0
+    memory_content = f"User memory - {memory}"
+    await mem0_client.add(
+        memory_content,
+        user_id=USER_ID,
+    )
+
+    return f"I've saved your memory: {memory}"
+```
+
+This function:
+- Takes a memory string
+- Creates a formatted memory string
+- Stores it in Mem0 using the `add()` method
+- Includes metadata to categorize the memory for easier retrieval
+- Returns a confirmation message that the agent will speak
+
+#### Finding Relevant Memories
+
+```python
+@function_tool
+async def search_memories(
+    query: str
+) -> str:
+    """
+    Find memories relevant to the current conversation.
+    Args:
+        query: The search query to find relevant memories
+    """
+    print(f"Finding memories related to: {query}")
+    results = await mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+    
+    # Format and return the results
+    if not results.get('results', []):
+        return "I don't have any relevant memories about this topic."
+    
+    memories = [f"• {result['memory']}" for result in results.get('results', [])]
+    return "Here's what I remember that might be relevant:\n" + "\n".join(memories)
+```
+
+This tool:
+- Takes a search query string
+- Passes it to Mem0's semantic search to find related memories
+- Sets a threshold for relevance to ensure quality results
+- Returns a formatted list of relevant memories or a default message
+
+### 3. Creating the Voice Agent
+
+```python
+def create_memory_voice_agent():
+    # Create the agent with memory-enabled tools
+    agent = Agent(
+        name="Memory Assistant",
+        instructions=prompt_with_handoff_instructions(
+            """You're speaking to a human, so be polite and concise.
+            Always respond in clear, natural English.
+            You have the ability to remember information about the user.
+            Use the save_memories tool when the user shares an important information worth remembering.
+            Use the search_memories tool when you need context from past conversations or user asks you to recall something.
+            """,
+        ),
+        model="gpt-4o",
+        tools=[save_memories, search_memories],
+    )
+    
+    return agent
+```
+
+This function:
+- Creates an OpenAI Agent with specific instructions
+- Configures it to use gpt-4o (you can use other models)
+- Registers the memory-related tools with the agent
+- Uses `prompt_with_handoff_instructions` to include standard voice agent behaviors
+
+### 4. Microphone Recording Functionality
+
+```python
+async def record_from_microphone(duration=5, samplerate=24000):
+    """Record audio from the microphone for a specified duration."""
+    print(f"Recording for {duration} seconds...")
+    
+    # Create a buffer to store the recorded audio
+    frames = []
+    
+    # Callback function to store audio data
+    def callback(indata, frames_count, time_info, status):
+        frames.append(indata.copy())
+    
+    # Start recording
+    with sd.InputStream(samplerate=samplerate, channels=1, callback=callback, dtype=np.int16):
+        await asyncio.sleep(duration)
+    
+    # Combine all frames into a single numpy array
+    audio_data = np.concatenate(frames)
+    return audio_data
+```
+
+This function:
+- Creates a simple asynchronous microphone recording function
+- Uses the sounddevice library to capture audio input
+- Stores frames in a buffer during recording
+- Combines frames into a single numpy array when complete
+- Returns the audio data for processing
+
+### 5. Main Loop and Voice Processing
+
+```python
+async def main():
+    # Create the agent
+    agent = create_memory_voice_agent()
+    
+    # Set up the voice pipeline
+    pipeline = VoicePipeline(
+        workflow=SingleAgentVoiceWorkflow(agent)
+    )
+    
+    # Configure TTS settings
+    pipeline.config.tts_settings.voice = "alloy"
+    pipeline.config.tts_settings.speed = 1.0
+    
+    try:
+        while True:
+            # Get user input
+            print("\nPress Enter to start recording (or 'q' to quit)...")
+            user_input = input()
+            if user_input.lower() == 'q':
+                break
+            
+            # Record and process audio
+            audio_data = await record_from_microphone(duration=5)
+            audio_input = AudioInput(buffer=audio_data)
+            result = await pipeline.run(audio_input)
+            
+            # Play response and handle events
+            player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
+            player.start()
+            
+            agent_response = ""
+            print("\nAgent response:")
+            
+            async for event in result.stream():
+                if event.type == "voice_stream_event_audio":
+                    player.write(event.data)
+                elif event.type == "voice_stream_event_content":
+                    content = event.data
+                    agent_response += content
+                    print(content, end="", flush=True)
+            
+            # Save the agent's response to memory
+            if agent_response:
+                try:
+                    await mem0_client.add(
+                        f"Agent response: {agent_response}", 
+                        user_id=USER_ID,
+                        metadata={"type": "agent_response"}
+                    )
+                except Exception as e:
+                    print(f"Failed to store memory: {e}")
+    
+    except KeyboardInterrupt:
+        print("\nExiting...")
+```
+
+This main function orchestrates the entire process:
+1. Creates the memory-enabled voice agent
+2. Sets up the voice pipeline with TTS settings
+3. Implements an interactive loop for recording and processing voice input
+4. Handles streaming of response events (both audio and text)
+5. Automatically saves the agent's responses to memory
+6. Includes proper error handling and exit mechanisms
+
+## Create a Memory-Enabled Voice Agent
+
+Now that we've explained each component, here's the complete implementation that combines OpenAI Agents SDK for voice with Mem0's memory capabilities:
+
+```python
+import asyncio
+import os
+import logging
+from typing import Optional, List, Dict, Any
+import numpy as np
+import sounddevice as sd
+from pydantic import BaseModel
+
+# OpenAI Agents SDK imports
+from agents import (
+    Agent,
+    function_tool
+)
+from agents.voice import (
+    AudioInput,
+    SingleAgentVoiceWorkflow,
+    VoicePipeline
+)
+from agents.extensions.handoff_prompt import prompt_with_handoff_instructions
+
+# Mem0 imports
+from mem0 import AsyncMemoryClient
+
+# Set up API keys (replace with your actual keys)
+os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+
+# Define a global user ID for simplicity
+USER_ID = "voice_user"
+
+# Initialize Mem0 client
+mem0_client = AsyncMemoryClient()
+
+# Create tools that utilize Mem0's memory
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """
+    Store a user memory in memory.
+    Args:
+        memory: The memory to save
+    """
+    print(f"Saving memory: {memory} for user {USER_ID}")
+
+    # Store the preference in Mem0
+    memory_content = f"User memory - {memory}"
+    await mem0_client.add(
+        memory_content,
+        user_id=USER_ID,
+    )
+
+    return f"I've saved your memory: {memory}"
+
+@function_tool
+async def search_memories(
+    query: str
+) -> str:
+    """
+    Find memories relevant to the current conversation.
+    Args:
+        query: The search query to find relevant memories
+    """
+    print(f"Finding memories related to: {query}")
+    results = await mem0_client.search(
+        query,
+        user_id=USER_ID,
+        limit=5,
+        threshold=0.7,  # Higher threshold for more relevant results
+        output_format="v1.1"
+    )
+    
+    # Format and return the results
+    if not results.get('results', []):
+        return "I don't have any relevant memories about this topic."
+    
+    memories = [f"• {result['memory']}" for result in results.get('results', [])]
+    return "Here's what I remember that might be relevant:\n" + "\n".join(memories)
+
+# Create the agent with memory-enabled tools
+def create_memory_voice_agent():
+    # Create the agent with memory-enabled tools
+    agent = Agent(
+        name="Memory Assistant",
+        instructions=prompt_with_handoff_instructions(
+            """You're speaking to a human, so be polite and concise.
+            Always respond in clear, natural English.
+            You have the ability to remember information about the user.
+            Use the save_memories tool when the user shares an important information worth remembering.
+            Use the search_memories tool when you need context from past conversations or user asks you to recall something.
+            """,
+        ),
+        model="gpt-4o",
+        tools=[save_memories, search_memories],
+    )
+    
+    return agent
+
+async def record_from_microphone(duration=5, samplerate=24000):
+    """Record audio from the microphone for a specified duration."""
+    print(f"Recording for {duration} seconds...")
+    
+    # Create a buffer to store the recorded audio
+    frames = []
+    
+    # Callback function to store audio data
+    def callback(indata, frames_count, time_info, status):
+        frames.append(indata.copy())
+    
+    # Start recording
+    with sd.InputStream(samplerate=samplerate, channels=1, callback=callback, dtype=np.int16):
+        await asyncio.sleep(duration)
+    
+    # Combine all frames into a single numpy array
+    audio_data = np.concatenate(frames)
+    return audio_data
+
+async def main():
+    print("Starting Memory Voice Agent")
+    
+    # Create the agent and context
+    agent = create_memory_voice_agent()
+    
+    # Set up the voice pipeline
+    pipeline = VoicePipeline(
+        workflow=SingleAgentVoiceWorkflow(agent)
+    )
+    
+    # Configure TTS settings
+    pipeline.config.tts_settings.voice = "alloy"
+    pipeline.config.tts_settings.speed = 1.0
+    
+    try:
+        while True:
+            # Get user input
+            print("\nPress Enter to start recording (or 'q' to quit)...")
+            user_input = input()
+            if user_input.lower() == 'q':
+                break
+            
+            # Record and process audio
+            audio_data = await record_from_microphone(duration=5)
+            audio_input = AudioInput(buffer=audio_data)
+            
+            print("Processing your request...")
+            
+            # Process the audio input
+            result = await pipeline.run(audio_input)
+            
+            # Create an audio player
+            player = sd.OutputStream(samplerate=24000, channels=1, dtype=np.int16)
+            player.start()
+            
+            # Store the agent's response for adding to memory
+            agent_response = ""
+            
+            print("\nAgent response:")
+            # Play the audio stream as it comes in
+            async for event in result.stream():
+                if event.type == "voice_stream_event_audio":
+                    player.write(event.data)
+                elif event.type == "voice_stream_event_content":
+                    # Accumulate and print the text response
+                    content = event.data
+                    agent_response += content
+                    print(content, end="", flush=True)
+            
+            print("\n")
+            
+            # Example of saving the conversation to Mem0 after completion
+            if agent_response:
+                try:
+                    await mem0_client.add(
+                        f"Agent response: {agent_response}", 
+                        user_id=USER_ID,
+                        metadata={"type": "agent_response"}
+                    )
+                except Exception as e:
+                    print(f"Failed to store memory: {e}")
+    
+    except KeyboardInterrupt:
+        print("\nExiting...")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+## Key Features of This Implementation
+
+This implementation offers several key features:
+
+1. **Simplified User Management**: Uses a global `USER_ID` variable for simplicity, but can be extended to manage multiple users.
+
+2. **Real Microphone Input**: Includes a `record_from_microphone()` function that captures actual voice input from your microphone.
+
+3. **Interactive Voice Loop**: Implements a continuous interaction loop, allowing for multiple back-and-forth exchanges.
+
+4. **Memory Management Tools**:
+   - `save_memories`: Stores user memories in Mem0
+   - `search_memories`: Searches for relevant past information
+
+5. **Voice Configuration**: Demonstrates how to configure TTS settings for the voice response.
+
+## Running the Example
+
+To run this example:
+
+1. Replace the placeholder API keys with your actual keys
+2. Make sure your microphone is properly connected
+3. Run the script with Python 3.8 or newer
+4. Press Enter to start recording, then speak your request
+5. Press 'q' to quit the application
+
+The agent will listen to your request, process it through the OpenAI model, utilize Mem0 for memory operations as needed, and respond both through text output and voice speech.
+
+## Best Practices for Voice Agents with Memory
+
+1. **Optimizing Memory for Voice**: Keep memories concise and relevant for voice responses.
+
+2. **Forgetting Mechanism**: Implement a way to delete or expire memories that are no longer relevant.
+
+3. **Context Preservation**: Store enough context with each memory to make retrieval effective.
+
+4. **Error Handling**: Implement robust error handling for memory operations, as voice interactions should continue smoothly even if memory operations fail.
+
+## Conclusion
+
+By combining OpenAI's Agents SDK with Mem0's memory capabilities, you can create voice agents that maintain persistent memory of user preferences and past interactions. This significantly enhances the user experience by making conversations more natural and personalized.
+
+As you build your voice application, experiment with different memory strategies and filtering approaches to find the optimal balance between comprehensive memory and efficient retrieval for your specific use case.
+
+## Debugging Function Tools
+
+When working with the OpenAI Agents SDK, you might notice that regular `print()` statements inside `@function_tool` decorated functions don't appear in your console output. This is because the Agents SDK captures and redirects standard output when executing these functions.
+
+To effectively debug your function tools, use Python's `logging` module instead:
+
+```python
+import logging
+
+# Set up logging at the top of your file
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    force=True
+)
+logger = logging.getLogger("memory_voice_agent")
+
+# Then use logger in your function tools
+@function_tool
+async def save_memories(
+    memory: str
+) -> str:
+    """Store a user memory in memory."""
+    # This will be visible in your console
+    logger.debug(f"Saving memory: {memory} for user {USER_ID}")
+    
+    # Rest of your function...
+```

docs/examples/mem0-with-ollama.mdx

@@ -37,7 +37,7 @@ config = {
         "config": {
             "model": "llama3.1:latest",
             "temperature": 0,
-            "max_tokens": 8000,
+            "max_tokens": 2000,
             "ollama_base_url": "http://localhost:11434",  # Ensure this URL is correct
         },
     },

docs/examples/multimodal-demo.mdx

@@ -0,0 +1,31 @@
+---
+title: Multimodal Demo with Mem0
+---
+
+Enhance your AI interactions with **Mem0**'s multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.
+
+> 🎉 Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
+
+## 🚀 Features
+
+- **🖼️ Image Understanding**: Share and discuss images with AI assistants while maintaining context.
+- **🔍 Smart Visual Context**: Automatically capture and reference visual elements in conversations.
+- **🔗 Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
+- **📌 Cross-Session Recall**: Reference previously discussed visual content across different conversations.
+- **⚡ Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
+
+## 📖 How It Works
+
+1. **📂 Upload Visual Content**: Simply drag and drop or paste images into your conversations.
+2. **💬 Natural Interaction**: Discuss the visual content naturally with AI assistants.
+3. **📚 Memory Integration**: Visual context is automatically stored and linked with your conversation history.
+4. **🔄 Persistent Recall**: Retrieve and reference past visual content effortlessly.
+
+## Demo Video
+
+<iframe width="700" height="400" src="https://www.youtube.com/embed/2Md5AEFVpmg?si=rXXupn6CiDUPJsi3" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## 🔥 Try It Out
+
+Visit [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai) to experience Mem0's multimodal capabilities firsthand. Upload images and see how Mem0 understands and remembers visual context across your conversations.
+

docs/examples/openai-inbuilt-tools.mdx

@@ -0,0 +1,312 @@
+---
+title: OpenAI Inbuilt Tools
+---
+
+Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
+
+## Getting Started
+
+### Installation
+
+```bash
+npm install mem0ai openai zod
+```
+
+## Environment Setup
+
+Save your Mem0 and OpenAI API keys in a `.env` file:
+
+```
+MEM0_API_KEY=your_mem0_api_key
+OPENAI_API_KEY=your_openai_api_key
+```
+
+Get your Mem0 API key from the [Mem0 Dashboard](https://app.mem0.ai/dashboard/api-keys).
+
+### Configuration
+
+```javascript
+const mem0Config = {
+    apiKey: process.env.MEM0_API_KEY,
+    user_id: "sample-user",
+};
+
+const openAIClient = new OpenAI();
+const mem0Client = new MemoryClient(mem0Config);
+```
+
+### Adding Memories
+
+Store user preferences, past interactions, or any relevant information:
+<CodeGroup>
+```javascript JavaScript
+async function addUserPreferences() {
+    const mem0Client = new MemoryClient(mem0Config);
+    
+    const userPreferences = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
+    
+    await mem0Client.add([{
+        role: "user",
+        content: userPreferences,
+    }], mem0Config);
+}
+
+await addUserPreferences();
+```
+
+```json Output (Memories)
+ [
+  {
+    "id": "ff9f3367-9e83-415d-b9c5-dc8befd9a4b4",
+    "data": { "memory": "Loves BMW, Audi, and Porsche" },
+    "event": "ADD"
+  },
+  {
+    "id": "04172ce6-3d7b-45a3-b4a1-ee9798593cb4",
+    "data": { "memory": "Hates Mercedes" },
+    "event": "ADD"
+  },
+  {
+    "id": "db363a5d-d258-4953-9e4c-777c120de34d",
+    "data": { "memory": "Loves red cars and maroon cars" },
+    "event": "ADD"
+  },
+  {
+    "id": "5519aaad-a2ac-4c0d-81d7-0d55c6ecdba8",
+    "data": { "memory": "Has a budget of 120K to 150K USD" },
+    "event": "ADD"
+  },
+  {
+    "id": "523b7693-7344-4563-922f-5db08edc8634",
+    "data": { "memory": "Likes Audi the most" },
+    "event": "ADD"
+  }
+]
+```
+</CodeGroup>
+### Retrieving Memories
+
+Search for relevant memories based on the current user input:
+
+```javascript
+const relevantMemories = await mem0Client.search(userInput, mem0Config);
+```
+
+### Structured Responses with Zod
+
+Define structured response schemas to get consistent output formats:
+
+```javascript
+// Define the schema for a car recommendation
+const CarSchema = z.object({
+  car_name: z.string(),
+  car_price: z.string(),
+  car_url: z.string(),
+  car_image: z.string(),
+  car_description: z.string(),
+});
+
+// Schema for a list of car recommendations
+const Cars = z.object({
+  cars: z.array(CarSchema),
+});
+
+// Create a function tool based on the schema
+const carRecommendationTool = zodResponsesFunction({ 
+    name: "carRecommendations", 
+    parameters: Cars 
+});
+
+// Use the tool in your OpenAI request
+const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, carRecommendationTool],
+    input: `${getMemoryString(relevantMemories)}\n${userInput}`,
+});
+```
+
+### Using Web Search
+
+Combine memory with web search for up-to-date recommendations:
+
+```javascript
+const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, carRecommendationTool],
+    input: `${getMemoryString(relevantMemories)}\n${userInput}`,
+});
+```
+
+## Examples
+
+### Complete Car Recommendation System
+
+```javascript
+import MemoryClient from "mem0ai";
+import { OpenAI } from "openai";
+import { zodResponsesFunction } from "openai/helpers/zod";
+import { z } from "zod";
+import dotenv from 'dotenv';
+
+dotenv.config();
+
+const mem0Config = {
+    apiKey: process.env.MEM0_API_KEY,
+    user_id: "sample-user",
+};
+
+async function run() {
+    // Responses without memories
+    console.log("\n\nRESPONSES WITHOUT MEMORIES\n\n");
+    await main();
+
+    // Adding sample memories
+    await addSampleMemories();
+
+    // Responses with memories
+    console.log("\n\nRESPONSES WITH MEMORIES\n\n");
+    await main(true);
+}
+
+// OpenAI Response Schema
+const CarSchema = z.object({
+  car_name: z.string(),
+  car_price: z.string(),
+  car_url: z.string(),
+  car_image: z.string(),
+  car_description: z.string(),
+});
+
+const Cars = z.object({
+  cars: z.array(CarSchema),
+});
+
+async function main(memory = false) {
+  const openAIClient = new OpenAI();
+  const mem0Client = new MemoryClient(mem0Config);
+
+  const input = "Suggest me some cars that I can buy today.";
+
+  const tool = zodResponsesFunction({ name: "carRecommendations", parameters: Cars });
+
+  // Store the user input as a memory
+  await mem0Client.add([{
+    role: "user",
+    content: input,
+  }], mem0Config);
+
+  // Search for relevant memories
+  let relevantMemories = []
+  if (memory) {
+    relevantMemories = await mem0Client.search(input, mem0Config);
+  }
+
+  const response = await openAIClient.responses.create({
+    model: "gpt-4o",
+    tools: [{ type: "web_search_preview" }, tool],
+    input: `${getMemoryString(relevantMemories)}\n${input}`,
+  });
+
+  console.log(response.output);
+}
+
+async function addSampleMemories() {
+  const mem0Client = new MemoryClient(mem0Config);
+
+  const myInterests = "I Love BMW, Audi and Porsche. I Hate Mercedes. I love Red cars and Maroon cars. I have a budget of 120K to 150K USD. I like Audi the most.";
+  
+  await mem0Client.add([{
+    role: "user",
+    content: myInterests,
+  }], mem0Config);
+}
+
+const getMemoryString = (memories) => {
+    const MEMORY_STRING_PREFIX = "These are the memories I have stored. Give more weightage to the question by users and try to answer that first. You have to modify your answer based on the memories I have provided. If the memories are irrelevant you can ignore them. Also don't reply to this section of the prompt, or the memories, they are only for your reference. The MEMORIES of the USER are: \n\n";
+    const memoryString = memories.map((mem) => `${mem.memory}`).join("\n") ?? "";
+    return memoryString.length > 0 ? `${MEMORY_STRING_PREFIX}${memoryString}` : "";
+};
+
+run().catch(console.error);
+```
+
+### Responses
+
+<CodeGroup>
+    ```json Without Memories
+    {
+      "cars": [
+        {
+          "car_name": "Toyota Camry",
+          "car_price": "$25,000",
+          "car_url": "https://www.toyota.com/camry/",
+          "car_image": "https://link-to-toyota-camry-image.com",
+          "car_description": "Reliable mid-size sedan with great fuel efficiency."
+        },
+        {
+          "car_name": "Honda Accord",
+          "car_price": "$26,000",
+          "car_url": "https://www.honda.com/accord/",
+          "car_image": "https://link-to-honda-accord-image.com",
+          "car_description": "Comfortable and spacious with advanced safety features."
+        },
+        {
+          "car_name": "Ford Mustang",
+          "car_price": "$28,000",
+          "car_url": "https://www.ford.com/mustang/",
+          "car_image": "https://link-to-ford-mustang-image.com",
+          "car_description": "Iconic sports car with powerful engine options."
+        },
+        {
+          "car_name": "Tesla Model 3",
+          "car_price": "$38,000",
+          "car_url": "https://www.tesla.com/model3",
+          "car_image": "https://link-to-tesla-model3-image.com",
+          "car_description": "Electric vehicle with advanced technology and long range."
+        },
+        {
+          "car_name": "Chevrolet Equinox",
+          "car_price": "$24,000",
+          "car_url": "https://www.chevrolet.com/equinox/",
+          "car_image": "https://link-to-chevron-equinox-image.com",
+          "car_description": "Compact SUV with a spacious interior and user-friendly technology."
+        }
+      ]
+    }
+    ```
+  
+    ```json With Memories
+    {
+      "cars": [
+        {
+          "car_name": "Audi RS7",
+          "car_price": "$118,500",
+          "car_url": "https://www.audiusa.com/us/web/en/models/rs7/2023/overview.html",
+          "car_image": "https://www.audiusa.com/content/dam/nemo/us/models/rs7/my23/gallery/1920x1080_AOZ_A717_191004.jpg",
+          "car_description": "The Audi RS7 is a high-performance hatchback with a sleek design, powerful 591-hp twin-turbo V8, and luxurious interior. It's available in various colors including red."
+        },
+        {
+          "car_name": "Porsche Panamera GTS",
+          "car_price": "$129,300",
+          "car_url": "https://www.porsche.com/usa/models/panamera/panamera-models/panamera-gts/",
+          "car_image": "https://files.porsche.com/filestore/image/multimedia/noneporsche-panamera-gts-sample-m02-high/normal/8a6327c3-6c7f-4c6f-a9a8-fb9f58b21795;sP;twebp/porsche-normal.webp",
+          "car_description": "The Porsche Panamera GTS is a luxury sports sedan with a 473-hp V8 engine, exquisite handling, and available in stunning red. Balances sportiness and comfort."
+        },
+        {
+          "car_name": "BMW M5",
+          "car_price": "$105,500",
+          "car_url": "https://www.bmwusa.com/vehicles/m-models/m5/sedan/overview.html",
+          "car_image": "https://www.bmwusa.com/content/dam/bmwusa/M/m5/2023/bmw-my23-m5-sapphire-black-twilight-purple-exterior-02.jpg",
+          "car_description": "The BMW M5 is a powerhouse sedan with a 600-hp V8 engine, known for its great handling and luxury. It comes in several distinctive colors including maroon."
+        }
+      ]
+    }
+    ```
+</CodeGroup>
+
+## Resources
+
+- [Mem0 Documentation](https://docs.mem0.ai)
+- [Mem0 Dashboard](https://app.mem0.ai/dashboard)
+- [API Reference](https://docs.mem0.ai/api-reference)
+- [OpenAI Documentation](https://platform.openai.com/docs)

docs/examples/overview.mdx

@@ -16,20 +16,58 @@ Here are some examples of how Mem0 can be integrated into various applications:
 
 ## Examples
 
-<CardGroup cols={2}>
-  <Card title="Mem0 with Ollama" icon="square-1" href="/examples/mem0-with-ollama">
-    Run Mem0 locally with Ollama.
-  </Card>
-  <Card title="Personal AI Tutor" icon="square-2" href="/examples/personal-ai-tutor">
-    Create a Personalized AI Tutor that adapts to student progress and learning preferences.
-  </Card>
-  <Card title="Personal Travel Assistant" icon="square-3" href="/examples/personal-travel-assistant">
-    Build a Personalized AI Travel Assistant that understands your travel preferences and past itineraries.
-  </Card>
-  <Card title="Customer Support Agent" icon="square-4" href="/examples/customer-support-agent">
-    Develop a Personal AI Assistant that remembers user preferences, past interactions, and context to provide personalized and efficient assistance.
-  </Card>
-  <Card title="LlamaIndex Mem0" icon="square-5" href="/examples/llama-index-mem0">
-    Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
-  </Card>
+Explore how **Mem0** can power real-world applications and bring personalized, intelligent experiences to life:
+
+<CardGroup cols={2}>  
+  <Card title="AI Companion in Node.js" icon="node" href="/examples/ai_companion_js">  
+    Build a personalized AI Companion in **Node.js** that remembers conversations and adapts over time using Mem0.  
+  </Card>  
+
+  <Card title="Mem0 with Ollama" icon="server" href="/examples/mem0-with-ollama">  
+    Run **Mem0 locally** with **Ollama** to create private, stateful AI experiences without relying on cloud APIs.  
+  </Card>  
+
+  <Card title="Personal AI Tutor" icon="graduation-cap" href="/examples/personal-ai-tutor">  
+    Create an **AI Tutor** that adapts to student progress, learning style, and history — for a truly customized learning experience.  
+  </Card>  
+
+  <Card title="Personal Travel Assistant" icon="plane" href="/examples/personal-travel-assistant">  
+    Develop a **Personal Travel Assistant** that remembers your preferences, past trips, and helps plan future adventures.  
+  </Card>  
+
+  <Card title="Customer Support Agent" icon="headset" href="/examples/customer-support-agent">  
+    Build a **Customer Support AI** that recalls user preferences, past chats, and provides context-aware, efficient help.  
+  </Card>  
+
+  <Card title="LlamaIndex + Mem0" icon="book-open" href="/examples/llama-index-mem0">  
+    Combine **LlamaIndex** and Mem0 to create a powerful **ReAct Agent** with persistent memory for smarter interactions.  
+  </Card>  
+
+  <Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">  
+    Add **long-term memory** to ChatGPT, Claude, or Perplexity via the **Mem0 Chrome Extension** — personalize your AI chats anywhere.  
+  </Card>  
+
+  <Card title="Document Writing Assistant" icon="pen" href="/examples/document-writing">  
+    Create a **Writing Assistant** that understands and adapts to your unique style, improving consistency and productivity.  
+  </Card>  
+
+  <Card title="Multimodal AI Demo" icon="image" href="/examples/multimodal-demo">  
+    Supercharge AI with **Mem0's multimodal memory** — blend text, images, and more for richer, context-aware interactions.  
+  </Card>  
+
+  <Card title="Personalized Research Agent" icon="robot" href="/examples/personalized-deep-research">  
+    Build a **Deep Research AI** that remembers your research goals and compiles insights from vast information sources.  
+  </Card>  
+
+  <Card title="Mem0 as an Agentic Tool" icon="robot" href="/examples/mem0-agentic-tool">  
+    Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.  
+  </Card>  
+
+  <Card title="OpenAI Inbuilt Tools" icon="robot" href="/examples/openai-inbuilt-tools">  
+    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.  
+  </Card>  
+
+  <Card title="Mem0 OpenAI Voice Demo" icon="robot" href="/examples/mem0-openai-voice-demo">  
+    Use Mem0's memory capabilities with OpenAI's Inbuilt Tools to create AI agents with persistent memory.  
+  </Card>  
 </CardGroup>

docs/examples/personal-ai-tutor.mdx

@@ -96,8 +96,8 @@ You can fetch all the memories at any point in time using the following code:
 
 ```python
 memories = ai_tutor.get_memories(user_id=user_id)
-for m in memories:
-    print(m['text'])
+for m in memories['results']:
+    print(m['memory'])
 ```
 
 ### Key Points

docs/examples/personal-travel-assistant.mdx

@@ -82,11 +82,11 @@ class PersonalTravelAssistant:
 
     def get_memories(self, user_id):
         memories = self.memory.get_all(user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories['results']]
 
     def search_memories(self, query, user_id):
         memories = self.memory.search(query, user_id=user_id)
-        return [m['memory'] for m in memories['memories']]
+        return [m['memory'] for m in memories['results']]
 
 # Usage example
 user_id = "traveler_123"

docs/examples/personalized-deep-research.mdx

@@ -0,0 +1,70 @@
+---
+title: Personalized Deep Research
+---
+
+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.
+
+## Overview
+
+Deep Research leverages Mem0's memory capabilities to:
+- Synthesize large amounts of online data
+- Complete complex research tasks
+- Customize results to your preferences
+- Store and utilize personal insights
+- Maintain context across research sessions
+
+## Demo
+
+Watch Deep Research in action:
+
+<iframe 
+  width="700" 
+  height="400" 
+  src="https://www.youtube.com/embed/8vQlCtXzF60?si=b8iTOgummAVzR7ia" 
+  title="YouTube video player" 
+  frameborder="0" 
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" 
+  referrerpolicy="strict-origin-when-cross-origin" 
+  allowfullscreen
+></iframe>
+
+## 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
+- Analyzes your background and expertise
+- Tailors research depth and complexity to your level
+- Incorporates your previous research context
+
+### 2. Comprehensive Data Synthesis
+- Processes multiple online sources
+- Extracts relevant information
+- Provides coherent summaries
+
+### 3. Memory Integration
+- Stores research findings for future reference
+- Maintains context across sessions
+- Links related research topics
+
+### 4. Interactive Exploration
+- Allows real-time query refinement
+- Supports follow-up questions
+- Enables deep-diving into specific areas
+
+## Use Cases
+
+- **Academic Research**: Literature reviews, thesis research, paper writing
+- **Market Research**: Industry analysis, competitor research, trend identification
+- **Technical Research**: Technology evaluation, solution comparison
+- **Business Research**: Strategic planning, opportunity analysis
+
+
+## Try It Out
+
+Experience AI-powered research personalization at [deep-research.mem0.ai](https://deep-research.mem0.ai/)

docs/faqs.mdx

@@ -84,9 +84,48 @@ iconType: "solid"
         - Include specific examples or cases rather than general definitions
     </Accordion>
 
+    <Accordion title="How do I configure Mem0 for AWS Lambda?">
+        When deploying Mem0 on AWS Lambda, you'll need to modify the storage directory configuration due to Lambda's file system restrictions. By default, Lambda only allows writing to the `/tmp` directory.
+
+        To configure Mem0 for AWS Lambda, set the `MEM0_DIR` environment variable to point to a writable directory in `/tmp`:
+
+        ```bash
+        MEM0_DIR=/tmp/.mem0
+        ```
+
+        If you're not using environment variables, you'll need to modify the storage path in your code:
+
+        ```python
+        # Change from
+        home_dir = os.path.expanduser("~")
+        mem0_dir = os.environ.get("MEM0_DIR") or os.path.join(home_dir, ".mem0")
+
+        # To
+        mem0_dir = os.environ.get("MEM0_DIR", "/tmp/.mem0")
+        ```
+
+        Note that the `/tmp` directory in Lambda has a size limit of 512MB and its contents are not persistent between function invocations.
+    </Accordion>
+
+    <Accordion title="How can I use metadata with Mem0?">
+        Metadata is the recommended approach for incorporating additional information with Mem0. You can store any type of structured data as metadata during the `add` method, such as location, timestamp, weather conditions, user state, or application context. This enriches your memories with valuable contextual information that can be used for more precise retrieval and filtering.
+
+        During retrieval, you have two main approaches for using metadata:
+
+        1. **Pre-filtering**: Include metadata parameters in your initial search query to narrow down the memory pool
+        2. **Post-processing**: Retrieve a broader set of memories based on query, then apply metadata filters to refine the results
+
+        Examples of useful metadata you might store:
+
+        - **Contextual information**: Location, time, device type, application state
+        - **User attributes**: Preferences, skill levels, demographic information
+        - **Interaction details**: Conversation topics, sentiment, urgency levels
+        - **Custom tags**: Any domain-specific categorization relevant to your application
+
+        This flexibility allows you to create highly contextually aware AI applications that can adapt to specific user needs and situations. Metadata provides an additional dimension for memory retrieval, enabling more precise and relevant responses.
+    </Accordion>
+
 </AccordionGroup>
 
 
 
-
-

docs/features/advanced-retrieval.mdx

@@ -14,23 +14,77 @@ Mem0's **Advanced Retrieval** feature delivers superior search results by levera
    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**
 
-   Reranking allows you to reorder the memories returned by the default search based on relevance. This parameter is set to `false` by default. When enabled, it reorders the memories based on the relevance score.
+   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 enables you to narrow down the search results by applying specific criteria. This parameter is set to `false` by default. Activating it enhances search precision, potentially reducing recall by a small margin.
+   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')
    ```
 
-**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.
+   **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

docs/features/async-client.mdx

@@ -14,13 +14,17 @@ To use the async client, you first need to initialize it:
 <CodeGroup>
 
 ```python Python
+import os
 from mem0 import AsyncMemoryClient
-client = AsyncMemoryClient(api_key="your-api-key")
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = AsyncMemoryClient()
 ```
 
 ```javascript JavaScript
 const { MemoryClient } = require('mem0ai');
-const client = new MemoryClient('your-api-key');
+const client = new MemoryClient({ apiKey: 'your-api-key'});
 ```
 
 </CodeGroup>
@@ -60,7 +64,7 @@ Search for memories based on a query asynchronously.
 <CodeGroup>
 
 ```python Python
-await client.search(query="What is Alice's favorite sport?", user_id="alice")
+await client.search("What is Alice's favorite sport?", user_id="alice")
 ```
 
 ```javascript JavaScript

docs/features/contextual-add.mdx

@@ -0,0 +1,205 @@
+---
+title: Contextual Add (ADD v2)
+icon: "square-plus"
+iconType: "solid"
+---
+
+Mem0 now supports an contextual add version (v2). To use it, set `version="v2"` during the add call. The default version is v1, which is deprecated now. We recommend migrating to `v2` for new applications.
+
+## Key Differences Between v1 and v2
+
+### Version 1 (Legacy)
+In v1 (default), users needed to pass either the entire conversation history or past k messages with each new message to generate properly contextualized memories. This approach required:
+
+- Manually tracking and sending previous messages using a sliding window approach
+- Increased payload sizes as conversations grew longer, requiring careful window size management
+
+<CodeGroup>
+
+```python Python
+# First interaction
+messages1 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."}
+]
+client.add(messages1, user_id="alex")
+
+# Second interaction - must include previous messages for context
+messages2 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."},
+    {"role": "user", "content": "I like to eat sushi, and yesterday I went to Sunnyvale to eat sushi with my friends."},
+    {"role": "assistant", "content": "Sushi is really a tasty choice. What did you do this weekend?"}
+]
+client.add(messages2, user_id="alex")
+```
+
+```javascript JavaScript
+// First interaction
+const messages1 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."}
+];
+client.add(messages1, { user_id: "alex" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+
+// Second interaction - must include previous messages for context
+const messages2 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."},
+    {"role": "user", "content": "I like to eat sushi, and yesterday I went to Sunnyvale to eat sushi with my friends."},
+    {"role": "assistant", "content": "Sushi is really a tasty choice. What did you do this weekend?"}
+];
+client.add(messages2, { user_id: "alex" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+</CodeGroup>
+
+### Version 2 (Recommended)
+In v2, Mem0 automatically manages conversation context. Users only need to send new messages, and the system will:
+
+- Automatically retrieve relevant conversation history
+- Generate properly contextualized memories
+- Reduce payload sizes and simplify integration
+
+<CodeGroup>
+
+```python Python
+# First interaction
+messages1 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."}
+]
+client.add(messages1, user_id="alex", version="v2")
+
+# Second interaction - only need to send new messages
+messages2 = [
+    {"role": "user", "content": "I like to eat sushi, and yesterday I went to Sunnyvale to eat sushi with my friends."},
+    {"role": "assistant", "content": "Sushi is really a tasty choice. What did you do this weekend?"}
+]
+client.add(messages2, user_id="alex", version="v2")
+```
+
+```javascript JavaScript
+// First interaction
+const messages1 = [
+    {"role": "user", "content": "Hi, I'm Alex and I live in San Francisco."},
+    {"role": "assistant", "content": "Hello Alex! Nice to meet you. San Francisco is a beautiful city."}
+];
+client.add(messages1, { user_id: "alex", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+
+// Second interaction - only need to send new messages
+const messages2 = [
+    {"role": "user", "content": "I like to eat sushi, and yesterday I went to Sunnyvale to eat sushi with my friends."},
+    {"role": "assistant", "content": "Sushi is really a tasty choice. What did you do this weekend?"}
+];
+client.add(messages2, { user_id: "alex", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+</CodeGroup>
+
+## Benefits of Using v2
+
+1. **Simplified Integration**: No need to track and manage conversation history
+2. **Reduced Payload Size**: Only send new messages, not the entire conversation
+3. **Improved Memory Quality**: Automatic context retrieval ensures better memory generation
+
+## Understanding ID Parameters in v2
+
+When using contextual add v2, you have different options for how to organize and retrieve memories:
+
+### Using Only `user_id`
+
+When you provide only a `user_id`:
+
+- Memories are associated with this user's long-term memory store
+- The system will automatically retrieve relevant context from all of the user's previous conversations
+- These memories persist indefinitely across all of the user's sessions
+- Ideal for maintaining persistent user information (preferences, personal details, etc.)
+
+<CodeGroup>
+
+```python Python
+# Adding to long-term user memory
+messages = [
+    {"role": "user", "content": "I'm allergic to peanuts and shellfish."},
+    {"role": "assistant", "content": "I've noted your allergies to peanuts and shellfish."}
+]
+client.add(messages, user_id="alex", version="v2")
+```
+
+```javascript JavaScript
+// Adding to long-term user memory
+const messages = [
+    {"role": "user", "content": "I'm allergic to peanuts and shellfish."},
+    {"role": "assistant", "content": "I've noted your allergies to peanuts and shellfish."}
+];
+client.add(messages, { user_id: "alex", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+</CodeGroup>
+
+### Using `user_id` with `run_id`
+
+When you provide both `user_id` and `run_id`:
+
+- Memories are associated with a specific conversation session or interaction
+- The system will retrieve context primarily from this specific session
+- These memories are still tied to the user but are organized by the specific session
+- Ideal for maintaining context within a specific conversation flow or task
+- Helps prevent context from different conversations from interfering with each other
+
+<CodeGroup>
+
+```python Python
+# Adding to a specific conversation session
+messages = [
+    {"role": "user", "content": "For this trip to Paris, I want to focus on art museums."},
+    {"role": "assistant", "content": "Great! I'll help you plan your Paris trip with a focus on art museums."}
+]
+client.add(messages, user_id="alex", run_id="paris-trip-2024", version="v2")
+
+# Later in the same conversation session
+messages2 = [
+    {"role": "user", "content": "I'd like to visit the Louvre on Monday."},
+    {"role": "assistant", "content": "The Louvre is a great choice for Monday. Would you like information about opening hours?"}
+]
+client.add(messages2, user_id="alex", run_id="paris-trip-2024", version="v2")
+```
+
+```javascript JavaScript
+// Adding to a specific conversation session
+const messages = [
+    {"role": "user", "content": "For this trip to Paris, I want to focus on art museums."},
+    {"role": "assistant", "content": "Great! I'll help you plan your Paris trip with a focus on art museums."}
+];
+client.add(messages, { user_id: "alex", run_id: "paris-trip-2024", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+
+// Later in the same conversation session
+const messages2 = [
+    {"role": "user", "content": "I'd like to visit the Louvre on Monday."},
+    {"role": "assistant", "content": "The Louvre is a great choice for Monday. Would you like information about opening hours?"}
+];
+client.add(messages2, { user_id: "alex", run_id: "paris-trip-2024", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+</CodeGroup>
+
+Using `run_id` helps you organize memories into logical sessions or tasks, making it easier to maintain context for specific interactions while still associating everything with the user's overall profile.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/features/custom-categories.mdx

@@ -19,9 +19,12 @@ Here's how to set custom categories:
 
 <CodeGroup>
 ```python Code
+import os
 from mem0 import MemoryClient
 
-client = MemoryClient(api_key="<your_mem0_api_key>")
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
 
 # Update custom categories
 new_categories = [
@@ -95,8 +98,11 @@ You can also set custom categories during the `add` API call. This will override
 
 <CodeGroup>
 ```python Code
+import os
 from mem0 import MemoryClient
 
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
 client = MemoryClient(api_key="<your_mem0_api_key>")
 
 custom_categories = [
@@ -148,9 +154,12 @@ Here is the list of **default categories**. If you don't specify any custom cate
 
 <CodeGroup>
 ```python Code
+import os
 from mem0 import MemoryClient
 
-client = MemoryClient(api_key="<your_mem0_api_key>")
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
 
 messages = [
     {"role": "user", "content": "Hi, my name is Alice."},

docs/features/custom-fact-extraction-prompt.mdx

@@ -0,0 +1,169 @@
+---
+title: Custom Fact Extraction Prompt
+description: 'Enhance your product experience by adding custom fact extraction prompt tailored to your needs'
+icon: "pencil"
+iconType: "solid"
+---
+
+## Introduction to Custom Fact Extraction Prompt
+
+Custom fact extraction prompt allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
+By defining it, you can control how information is extracted from the user's message.
+
+To create an effective custom fact extraction prompt:
+1. Be specific about the information to extract.
+2. Provide few-shot examples to guide the LLM.
+3. Ensure examples follow the format shown below.
+
+Example of a custom fact extraction prompt:
+
+<CodeGroup>
+```python Python
+custom_fact_extraction_prompt = """
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:
+
+Input: Hi.
+Output: {{"facts" : []}}
+
+Input: The weather is nice today.
+Output: {{"facts" : []}}
+
+Input: My order #12345 hasn't arrived yet.
+Output: {{"facts" : ["Order #12345 not received"]}}
+
+Input: I'm John Doe, and I'd like to return the shoes I bought last week.
+Output: {{"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}}
+
+Input: I ordered a red shirt, size medium, but received a blue one instead.
+Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}}
+
+Return the facts and customer information in a json format as shown above.
+"""
+```
+
+```typescript TypeScript
+const customPrompt = `
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:
+
+Input: Hi.
+Output: {"facts" : []}
+
+Input: The weather is nice today.
+Output: {"facts" : []}
+
+Input: My order #12345 hasn't arrived yet.
+Output: {"facts" : ["Order #12345 not received"]}
+
+Input: I am John Doe, and I would like to return the shoes I bought last week.
+Output: {"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}
+
+Input: I ordered a red shirt, size medium, but received a blue one instead.
+Output: {"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}
+
+Return the facts and customer information in a json format as shown above.
+`;
+```
+</CodeGroup>
+
+Here we initialize the custom fact extraction prompt in the config:
+
+<CodeGroup>
+```python Python
+from mem0 import Memory
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.2,
+            "max_tokens": 2000,
+        }
+    },
+    "custom_fact_extraction_prompt": custom_fact_extraction_prompt,
+    "version": "v1.1"
+}
+
+m = Memory.from_config(config_dict=config, user_id="alice")
+```
+
+```typescript TypeScript
+import { Memory } from 'mem0ai/oss';
+
+const config = {
+  version: 'v1.1',
+  llm: {
+    provider: 'openai',
+    config: {
+      apiKey: process.env.OPENAI_API_KEY || '',
+      model: 'gpt-4-turbo-preview',
+      temperature: 0.2,
+      maxTokens: 1500,
+    },
+  },
+  customPrompt: customPrompt
+};
+
+const memory = new Memory(config);
+```
+</CodeGroup>
+
+### Example 1
+
+In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
+
+<CodeGroup>
+```python Python
+m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
+```
+
+```typescript TypeScript
+await memory.add('Yesterday, I ordered a laptop, the order id is 12345', { userId: "user123" });
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Ordered a laptop",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order ID: 12345",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order placed yesterday",
+      "event": "ADD"
+    }
+  ],
+  "relations": []
+}
+```
+</CodeGroup>
+
+### Example 2
+
+In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
+Hence, the memory is not added.
+
+<CodeGroup>
+```python Python
+m.add("I like going to hikes", user_id="alice")
+```
+
+```typescript TypeScript
+await memory.add('I like going to hikes', { userId: "user123" });
+```
+
+```json Output
+{
+  "results": [],
+  "relations": []
+}
+```
+</CodeGroup>
+
+The custom fact extraction prompt will process both the user and assistant messages to extract relevant information according to the defined format.

docs/features/custom-prompts.mdx

@@ -1,111 +0,0 @@
----
-title: Custom Prompts
-description: 'Enhance your product experience by adding custom prompts tailored to your needs'
-icon: "pencil"
-iconType: "solid"
----
-
-## Introduction to Custom Prompts
-
-Custom prompts allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
-By defining a custom prompt, you can control how information is extracted, processed, and stored in your memory system.
-
-To create an effective custom prompt:
-1. Be specific about the information to extract.
-2. Provide few-shot examples to guide the LLM.
-3. Ensure examples follow the format shown below.
-
-Example of a custom prompt:
-
-```python
-custom_prompt = """
-Please only extract entities containing customer support information, order details, and user information. 
-Here are some few shot examples:
-
-Input: Hi.
-Output: {{"facts" : []}}
-
-Input: The weather is nice today.
-Output: {{"facts" : []}}
-
-Input: My order #12345 hasn't arrived yet.
-Output: {{"facts" : ["Order #12345 not received"]}}
-
-Input: I'm John Doe, and I'd like to return the shoes I bought last week.
-Output: {{"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}}
-
-Input: I ordered a red shirt, size medium, but received a blue one instead.
-Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}}
-
-Return the facts and customer information in a json format as shown above.
-"""
-
-```
-
-Here we initialize the custom prompt in the config.
-
-```python
-from mem0 import Memory
-
-config = {
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "model": "gpt-4o",
-            "temperature": 0.2,
-            "max_tokens": 1500,
-        }
-    },
-    "custom_prompt": custom_prompt,
-    "version": "v1.1"
-}
-
-m = Memory.from_config(config_dict=config, user_id="alice")
-```
-
-### Example 1
-
-In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
-
-<CodeGroup>
-```python Code
-m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
-```
-
-```json Output
-{
-  "results": [
-    {
-      "memory": "Ordered a laptop",
-      "event": "ADD"
-    },
-    {
-      "memory": "Order ID: 12345",
-      "event": "ADD"
-    },
-    {
-      "memory": "Order placed yesterday",
-      "event": "ADD"
-    }
-  ],
-  "relations": []
-}
-```
-</CodeGroup>
-
-### Example 2
-
-In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
-Hence, the memory is not added.
-<CodeGroup>
-```python Code
-m.add("I like going to hikes", user_id="alice")
-```
-
-```json Output
-{
-  "results": [],
-  "relations": []
-}
-```
-</CodeGroup>

docs/features/custom-update-memory-prompt.mdx

@@ -0,0 +1,239 @@
+---
+title: Custom Update Memory Prompt
+icon: "pencil"
+iconType: "solid"
+---
+Update memory prompt is a prompt used to determine the action to be performed on the memory. 
+By customizing this prompt, you can control how the memory is updated.  
+  
+
+## Introduction  
+Mem0 memory system compares the newly retrieved facts with the existing memory and determines the action to be performed on the memory.  
+The kinds of actions are:
+- Add
+    - Add the newly retrieved facts to the memory.
+- Update
+    - Update the existing memory with the newly retrieved facts.
+- Delete
+    - Delete the existing memory.
+- No Change
+    - Do not make any changes to the memory.
+  
+### Example
+Example of a custom update memory prompt:
+
+<CodeGroup>
+```python Python
+UPDATE_MEMORY_PROMPT = """You are a smart memory manager which controls the memory of a system.
+You can perform four operations: (1) add into the memory, (2) update the memory, (3) delete from the memory, and (4) no change.
+
+Based on the above four operations, the memory will change.
+
+Compare newly retrieved facts with the existing memory. For each new fact, decide whether to:
+- ADD: Add it to the memory as a new element
+- UPDATE: Update an existing memory element
+- DELETE: Delete an existing memory element
+- NONE: Make no change (if the fact is already present or irrelevant)
+
+There are specific guidelines to select which operation to perform:
+
+1. **Add**: If the retrieved facts contain new information not present in the memory, then you have to add it by generating a new ID in the id field.
+- **Example**:
+    - Old Memory:
+        [
+            {
+                "id" : "0",
+                "text" : "User is a software engineer"
+            }
+        ]
+    - Retrieved facts: ["Name is John"]
+    - New Memory:
+        {
+            "memory" : [
+                {
+                    "id" : "0",
+                    "text" : "User is a software engineer",
+                    "event" : "NONE"
+                },
+                {
+                    "id" : "1",
+                    "text" : "Name is John",
+                    "event" : "ADD"
+                }
+            ]
+
+        }
+
+2. **Update**: If the retrieved facts contain information that is already present in the memory but the information is totally different, then you have to update it. 
+If the retrieved fact contains information that conveys the same thing as the elements present in the memory, then you have to keep the fact which has the most information. 
+Example (a) -- if the memory contains "User likes to play cricket" and the retrieved fact is "Loves to play cricket with friends", then update the memory with the retrieved facts.
+Example (b) -- if the memory contains "Likes cheese pizza" and the retrieved fact is "Loves cheese pizza", then you do not need to update it because they convey the same information.
+If the direction is to update the memory, then you have to update it.
+Please keep in mind while updating you have to keep the same ID.
+Please note to return the IDs in the output from the input IDs only and do not generate any new ID.
+- **Example**:
+    - Old Memory:
+        [
+            {
+                "id" : "0",
+                "text" : "I really like cheese pizza"
+            },
+            {
+                "id" : "1",
+                "text" : "User is a software engineer"
+            },
+            {
+                "id" : "2",
+                "text" : "User likes to play cricket"
+            }
+        ]
+    - Retrieved facts: ["Loves chicken pizza", "Loves to play cricket with friends"]
+    - New Memory:
+        {
+        "memory" : [
+                {
+                    "id" : "0",
+                    "text" : "Loves cheese and chicken pizza",
+                    "event" : "UPDATE",
+                    "old_memory" : "I really like cheese pizza"
+                },
+                {
+                    "id" : "1",
+                    "text" : "User is a software engineer",
+                    "event" : "NONE"
+                },
+                {
+                    "id" : "2",
+                    "text" : "Loves to play cricket with friends",
+                    "event" : "UPDATE",
+                    "old_memory" : "User likes to play cricket"
+                }
+            ]
+        }
+
+
+3. **Delete**: If the retrieved facts contain information that contradicts the information present in the memory, then you have to delete it. Or if the direction is to delete the memory, then you have to delete it.
+Please note to return the IDs in the output from the input IDs only and do not generate any new ID.
+- **Example**:
+    - Old Memory:
+        [
+            {
+                "id" : "0",
+                "text" : "Name is John"
+            },
+            {
+                "id" : "1",
+                "text" : "Loves cheese pizza"
+            }
+        ]
+    - Retrieved facts: ["Dislikes cheese pizza"]
+    - New Memory:
+        {
+        "memory" : [
+                {
+                    "id" : "0",
+                    "text" : "Name is John",
+                    "event" : "NONE"
+                },
+                {
+                    "id" : "1",
+                    "text" : "Loves cheese pizza",
+                    "event" : "DELETE"
+                }
+        ]
+        }
+
+4. **No Change**: If the retrieved facts contain information that is already present in the memory, then you do not need to make any changes.
+- **Example**:
+    - Old Memory:
+        [
+            {
+                "id" : "0",
+                "text" : "Name is John"
+            },
+            {
+                "id" : "1",
+                "text" : "Loves cheese pizza"
+            }
+        ]
+    - Retrieved facts: ["Name is John"]
+    - New Memory:
+        {
+        "memory" : [
+                {
+                    "id" : "0",
+                    "text" : "Name is John",
+                    "event" : "NONE"
+                },
+                {
+                    "id" : "1",
+                    "text" : "Loves cheese pizza",
+                    "event" : "NONE"
+                }
+            ]
+        }
+"""
+```
+</CodeGroup>  
+
+## Output format
+The prompt needs to guide the output to follow the structure as shown below:
+<CodeGroup>
+```json Add
+{
+    "memory": [
+        {
+            "id" : "0",
+            "text" : "This information is new",
+            "event" : "ADD"
+        }
+    ]
+}
+```
+
+```json Update
+{
+    "memory": [
+        {
+            "id" : "0",
+            "text" : "This information replaces the old information",
+            "event" : "UPDATE",
+            "old_memory" : "Old information"
+        }
+    ]
+}
+```
+
+```json Delete
+{
+    "memory": [
+        {
+            "id" : "0",
+            "text" : "This information will be deleted",
+            "event" : "DELETE"
+        }
+    ]
+}
+```
+
+```json No Change
+{
+    "memory": [
+        {
+            "id" : "0",
+            "text" : "No changes for this information",
+            "event" : "NONE"
+        }
+    ]
+}
+```
+</CodeGroup>
+  
+  
+## custom update memory prompt vs custom prompt
+
+| Feature | `custom_update_memory_prompt` | `custom_prompt` |
+|---------|-------------------------------|-----------------|
+| Use case | Determine the action to be performed on the memory | Extract the facts from messages |
+| Reference | Retrieved facts from messages and old memory | Messages |
+| Output | Action to be performed on the memory | Extracted facts |

docs/features/direct-import.mdx

@@ -41,7 +41,7 @@ You can retrieve memories using the `search` method.
 <CodeGroup>
 
 ```python Python
-client.search(query="What is Alice's favorite sport?", user_id="alice", output_format="v1.1")
+client.search("What is Alice's favorite sport?", user_id="alice", output_format="v1.1")
 ```
 
 ```json Output

docs/features/feedback-mechanism.mdx

@@ -0,0 +1,62 @@
+---
+title: Feedback Mechanism
+icon: "thumbs-up"
+iconType: "solid"
+---
+
+Mem0's **Feedback Mechanism** allows you to provide feedback on the memories generated by your application. This feedback is used to improve the accuracy of the memories and the search results.
+
+## How it works
+
+The feedback mechanism is a simple API that allows you to provide feedback on the memories generated by your application. The feedback is stored in the database and is used to improve the accuracy of the memories and the search results. Over time, Mem0 continuously learns from this feedback, refining its memory generation and search capabilities for better performance.
+
+## Give Feedback
+
+You can give feedback on a memory by calling the `feedback` method on the Mem0 client.
+
+<CodeGroup>
+
+```python Python
+from mem0 import Mem0
+
+client = Mem0(api_key="your_api_key")
+
+client.feedback(memory_id="your-memory-id", feedback="NEGATIVE", feedback_reason="I don't like this memory because it is not relevant.")
+```
+
+```javascript JavaScript
+import MemoryClient from 'mem0ai';
+
+const client = new MemoryClient({ apiKey: 'your-api-key'});
+
+client.feedback({
+    memory_id: "your-memory-id", 
+    feedback: "NEGATIVE", 
+    feedback_reason: "I don't like this memory because it is not relevant."
+})
+```
+
+</CodeGroup>
+
+## Feedback Types
+
+The `feedback` parameter can be one of the following values:
+
+- `POSITIVE`: The memory is useful.
+- `NEGATIVE`: The memory is not useful.
+- `VERY_NEGATIVE`: The memory is not useful at all.
+
+## Parameters
+
+The `feedback` method takes the following parameters:
+
+- `memory_id`: The ID of the memory to give feedback on.
+- `feedback`: The feedback to give on the memory. (Optional)
+- `feedback_reason`: The reason for the feedback. (Optional)
+
+The `feedback_reason` parameter is optional and can be used to provide a reason for the feedback.
+
+<Note>
+You can pass `None` or `null` to the `feedback` and `feedback_reason` parameters to remove the feedback for a memory.
+</Note>
+

docs/features/graph-memory.mdx

@@ -0,0 +1,295 @@
+---
+title: Graph Memory
+icon: "circle-nodes"
+iconType: "solid"
+description: "Enable graph-based memory retrieval for more contextually relevant results"
+---
+
+## Overview
+
+Graph Memory enhances memory pipeline by creating relationships between entities in your data. It builds a network of interconnected information for more contextually relevant search results.
+
+This feature allows your AI applications to understand connections between entities, providing richer context for responses. It's ideal for applications needing relationship tracking and nuanced information retrieval across related memories.
+
+## How Graph Memory Works
+
+The Graph Memory feature analyzes how each entity connects and relates to each other. When enabled:
+
+1. Mem0 automatically builds a graph representation of entities
+2. Retrieval considers graph relationships between entities
+3. Results include entities that may be contextually important even if they're not direct semantic matches
+
+## Using Graph Memory
+
+To use Graph Memory, you need to enable it in your API calls by setting the `enable_graph=True` parameter. You'll also need to specify `output_format="v1.1"` to receive the enriched response format.
+
+### Adding Memories with Graph Memory
+
+When adding new memories, enable Graph Memory to automatically build relationships with existing memories:
+
+<CodeGroup>
+
+```python Python
+from mem0 import MemoryClient
+
+client = MemoryClient(
+    api_key="your-api-key",
+    org_id="your-org-id",
+    project_id="your-project-id"
+)
+
+messages = [
+    {"role": "user", "content": "My name is Joseph"},
+    {"role": "assistant", "content": "Hello Joseph, it's nice to meet you!"},
+    {"role": "user", "content": "I'm from Seattle and I work as a software engineer"}
+]
+
+# Enable graph memory when adding
+client.add(
+    messages, 
+    user_id="joseph", 
+    version="v1", 
+    enable_graph=True, 
+    output_format="v1.1"
+)
+```
+
+```javascript JavaScript
+import { MemoryClient } from "mem0";
+
+const client = new MemoryClient({
+  apiKey: "your-api-key",
+  orgId: "your-org-id",
+  projectId: "your-project-id"
+});
+
+const messages = [
+  { role: "user", content: "My name is Joseph" },
+  { role: "assistant", content: "Hello Joseph, it's nice to meet you!" },
+  { role: "user", content: "I'm from Seattle and I work as a software engineer" }
+];
+
+// Enable graph memory when adding
+await client.add({
+  messages,
+  userId: "joseph",
+  version: "v1",
+  enableGraph: true,
+  outputFormat: "v1.1"
+});
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Name is Joseph",
+      "event": "ADD",
+      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438"
+    },
+    {
+      "memory": "Is from Seattle",
+      "event": "ADD",
+      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d"
+    },
+    {
+      "memory": "Is a software engineer",
+      "event": "ADD",
+      "id": "5f0a184e-ddea-4fe6-9b92-692d6a901df8"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+The graph memory would look like this:
+
+<Frame>
+  <img src="/images/graph-platform.png" alt="Graph Memory Visualization showing relationships between entities" />
+</Frame>
+
+<Caption>Graph Memory creates a network of relationships between entities, enabling more contextual retrieval</Caption>
+
+
+<Note>
+Response for the graph memory's `add` operation will not be available directly in the response. 
+As adding graph memories is an asynchronous operation due to heavy processing, 
+you can use the `get_all()` endpoint to retrieve the memory with the graph metadata.
+</Note>
+
+
+### Searching with Graph Memory
+
+When searching memories, Graph Memory helps retrieve entities that are contextually important even if they're not direct semantic matches.
+
+<CodeGroup>
+
+```python Python
+# Search with graph memory enabled
+results = client.search(
+    "what is my name?", 
+    user_id="joseph", 
+    enable_graph=True, 
+    output_format="v1.1"
+)
+
+print(results)
+```
+
+```javascript JavaScript
+// Search with graph memory enabled
+const results = await client.search({
+  query: "what is my name?",
+  userId: "joseph",
+  enableGraph: true,
+  outputFormat: "v1.1"
+});
+
+console.log(results);
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438",
+      "memory": "Name is Joseph",
+      "user_id": "joseph",
+      "metadata": null,
+      "categories": ["personal_details"],
+      "immutable": false,
+      "created_at": "2025-03-19T09:09:00.146390-07:00",
+      "updated_at": "2025-03-19T09:09:00.146404-07:00",
+      "score": 0.3621795393335552
+    },
+    {
+      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d",
+      "memory": "Is from Seattle",
+      "user_id": "joseph",
+      "metadata": null,
+      "categories": ["personal_details"],
+      "immutable": false,
+      "created_at": "2025-03-19T09:09:00.170680-07:00",
+      "updated_at": "2025-03-19T09:09:00.170692-07:00",
+      "score": 0.31212713194651254
+    }
+  ],
+  "relations": [
+    {
+      "source": "joseph",
+      "source_type": "person",
+      "relationship": "name",
+      "target": "joseph",
+      "target_type": "person",
+      "score": 0.39
+    }
+  ]
+}
+```
+
+</CodeGroup>
+
+### Retrieving All Memories with Graph Memory
+
+When retrieving all memories, Graph Memory provides additional relationship context:
+
+<CodeGroup>
+
+```python Python
+# Get all memories with graph context
+memories = client.get_all(
+    user_id="joseph", 
+    enable_graph=True, 
+    output_format="v1.1"
+)
+
+print(memories)
+```
+
+```javascript JavaScript
+// Get all memories with graph context
+const memories = await client.getAll({
+  userId: "joseph",
+  enableGraph: true,
+  outputFormat: "v1.1"
+});
+
+console.log(memories);
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "5f0a184e-ddea-4fe6-9b92-692d6a901df8",
+      "memory": "Is a software engineer",
+      "user_id": "joseph",
+      "metadata": null,
+      "categories": ["professional_details"],
+      "immutable": false,
+      "created_at": "2025-03-19T09:09:00.194116-07:00",
+      "updated_at": "2025-03-19T09:09:00.194128-07:00",
+    },
+    {
+      "id": "8d268d0f-5452-4714-b27d-ae46f676a49d",
+      "memory": "Is from Seattle",
+      "user_id": "joseph",
+      "metadata": null,
+      "categories": ["personal_details"],
+      "immutable": false,
+      "created_at": "2025-03-19T09:09:00.170680-07:00",
+      "updated_at": "2025-03-19T09:09:00.170692-07:00",
+    },
+    {
+      "id": "4a5a417a-fa10-43b5-8c53-a77c45e80438",
+      "memory": "Name is Joseph",
+      "user_id": "joseph",
+      "metadata": null,
+      "categories": ["personal_details"],
+      "immutable": false,
+      "created_at": "2025-03-19T09:09:00.146390-07:00",
+      "updated_at": "2025-03-19T09:09:00.146404-07:00",
+    }
+  ],
+  "relations": [
+    {
+      "source": "joseph",
+      "source_type": "person",
+      "relationship": "name",
+      "target": "joseph",
+      "target_type": "person"
+    },
+    {
+      "source": "joseph",
+      "source_type": "person",
+      "relationship": "city",
+      "target": "seattle",
+      "target_type": "city"
+    },
+    {
+      "source": "joseph",
+      "source_type": "person",
+      "relationship": "job",
+      "target": "software engineer",
+      "target_type": "job"
+    }
+  ]
+}
+```
+
+</CodeGroup>
+
+## Best Practices
+
+- Enable Graph Memory for applications where understanding context and relationships between memories is important
+- Graph Memory works best with a rich history of related conversations
+- Consider Graph Memory for long-running assistants that need to track evolving information
+
+## Performance Considerations
+
+Graph Memory requires additional processing and may increase response times slightly for very large memory stores. However, for most use cases, the improved retrieval quality outweighs the minimal performance impact.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
+

docs/features/memory-export.mdx

@@ -71,13 +71,32 @@ Here's an example schema for extracting professional profile information:
 
 ### Submit Export Job
 
+You can optionally provide additional instructions to guide how memories are processed and structured during export using the `export_instructions` parameter.
+
 <CodeGroup>
 
 ```python Python
+# Basic export request
 response = client.create_memory_export(
     schema=json_schema,
-    user_id="user123"
+    user_id="alice"
 )
+
+# Export with custom instructions
+export_instructions = """
+1. Create a comprehensive profile with detailed information in each category
+2. Only mark fields as "None" when absolutely no relevant information exists
+3. Base all information directly on the user's memories
+4. When contradictions exist, prioritize the most recent information
+5. Clearly distinguish between factual statements and inferences
+"""
+
+response = client.create_memory_export(
+    schema=json_schema,
+    user_id="alice",
+    export_instructions=export_instructions
+)
+
 print(response)
 ```
 
@@ -87,7 +106,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/export/" \
      -H "Content-Type: application/json" \
      -d '{
          "schema": {json_schema},
-         "user_id": "user123"
+         "user_id": "alice",
+         "export_instructions": "1. Create a comprehensive profile with detailed information\n2. Only mark fields as \"None\" when absolutely no relevant information exists"
      }'
 ```
 
@@ -107,12 +127,12 @@ Once the export job is complete, you can retrieve the structured data:
 <CodeGroup>
 
 ```python Python
-response = client.get_memory_export(user_id="user123")
+response = client.get_memory_export(user_id="alice")
 print(response)
 ```
 
 ```bash cURL
-curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=user123" \
+curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=alice" \
      -H "Authorization: Token your-api-key"
 ```
 

docs/features/multimodal-support.mdx

@@ -1,21 +1,24 @@
 ---
 title: Multimodal Support
+description: Integrate images into your interactions with Mem0
 icon: "image"
 iconType: "solid"
 ---
 
-Mem0 extends its capabilities beyond text by supporting multimodal data, including images. Users can seamlessly integrate images into their interactions, allowing Mem0 to extract pertinent information from visual content and enrich the memory system.
+Mem0 extends its capabilities beyond text by supporting multimodal data, including images. With this feature, users can seamlessly integrate images into their interactions—allowing Mem0 to extract relevant information from visual content and enrich the memory system.
 
 ## How It Works
 
-When a user provides an image, Mem0 processes the image to extract textual information and relevant details, which are then added to the user's memory. This feature enhances the system's ability to understand and remember details based on visual inputs.
+When a user submits an image, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall visual inputs.
 
 <CodeGroup>
-```python Code
+```python Python
+import os
 from mem0 import MemoryClient
 
-# Initialize the MemoryClient with your API key
-client = MemoryClient(api_key="your_api_key_here")
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
 
 messages = [
     {
@@ -41,6 +44,34 @@ messages = [
 client.add(messages, user_id="alice")
 ```
 
+```typescript TypeScript
+import MemoryClient from "mem0ai";
+
+const client = new MemoryClient();
+
+const messages = [
+    {
+        role: "user",
+        content: "Hi, my name is Alice."
+    },
+    {
+        role: "assistant",
+        content: "Nice to meet you, Alice! What do you like to eat?"
+    },
+    {
+        role: "user",
+        content: {
+            type: "image_url",
+            image_url: {
+                url: "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+            }
+        }
+    },
+]
+
+await client.add(messages, { user_id: "alice" })
+```
+
 ```json Output
 {
   "results": [
@@ -61,13 +92,13 @@ client.add(messages, user_id="alice")
 
 ## Image Integration Methods
 
-Mem0 allows you to add images to user interactions through two primary methods: by providing an image URL or by using a Base64-encoded image. Below are examples demonstrating each approach.
+Mem0 supports incorporating images into user interactions using two primary methods: by providing an image URL or by using a Base64-encoded image. The examples below demonstrate both approaches.
 
 ## 1. Using an Image URL (Recommended)
 
-You can include an image by passing its direct URL. This method is simple and efficient for online images.
+You can include an image by providing its direct URL. This method is simple and efficient for online images.
 
-```python
+```python {2, 5-13}
 # Define the image URL
 image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
 
@@ -81,13 +112,15 @@ image_message = {
         }
     }
 }
+client.add([image_message], user_id="alice")
 ```
 
 ## 2. Using Base64 Image Encoding for Local Files
 
-For local images or scenarios where embedding the image directly is preferable, you can use a Base64-encoded string.
+For local images—or when embedding the image directly is preferable—you can use a Base64-encoded string.
 
-```python
+<CodeGroup>
+```python Python
 import base64
 
 # Path to the image file
@@ -107,9 +140,31 @@ image_message = {
         }
     }
 }
+client.add([image_message], user_id="alice")
 ```
 
-By utilizing these methods, you can effectively incorporate images into user interactions, enhancing the multimodal capabilities of your Mem0 instance.
+```typescript TypeScript
+import MemoryClient from "mem0ai";
+import fs from 'fs';
+
+const imagePath = 'path/to/your/image.jpg';
+
+const base64Image = fs.readFileSync(imagePath, { encoding: 'base64' });
+
+const imageMessage = {
+    role: "user",
+    content: {
+        type: "image_url",
+        image_url: {
+            url: `data:image/jpeg;base64,${base64Image}`
+        }
+    }
+};
+
+await client.add([imageMessage], { user_id: "alice" })
+```
+</CodeGroup>
+Using these methods, you can seamlessly incorporate images into your interactions, further enhancing Mem0's multimodal capabilities.
 
 If you have any questions, please feel free to reach out to us using one of the following methods:
 

docs/features/platform-overview.mdx

@@ -12,6 +12,9 @@ Learn about the key features and capabilities that make Mem0 a powerful platform
   <Card title="Advanced Retrieval" icon="magnifying-glass" href="/features/advanced-retrieval">
     Superior search results using state-of-the-art algorithms, including keyword search, reranking, and filtering capabilities.
   </Card>
+  <Card title="Contextual Add" icon="square-plus" href="/features/contextual-add">
+    Only send your latest conversation history - we automatically retrieve the rest and generate properly contextualized memories.
+  </Card>
   <Card title="Multimodal Support" icon="photo-film" href="/features/multimodal-support">
     Process and analyze various types of content including images.
   </Card>
@@ -33,6 +36,9 @@ Learn about the key features and capabilities that make Mem0 a powerful platform
   <Card title="Memory Export" icon="file-export" href="/features/memory-export">
     Export memories in structured formats using customizable Pydantic schemas.
   </Card>
+  <Card title="Graph Memory" icon="graph" href="/features/graph-memory">
+    Add memories in the form of nodes and edges in a graph database and search for related memories.
+  </Card>
 </CardGroup>
 
 ## Getting Help

docs/features/selective-memory.mdx

@@ -29,9 +29,12 @@ Users can define specific kinds of memories to store. This feature enhances memo
 Here’s how you can do it:
 
 ```python
+import os
 from mem0 import MemoryClient
 
-m = MemoryClient(api_key="xxx")
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+m = MemoryClient()
 
 # Define what to include
 includes = "sports related things"

docs/features/webhooks.mdx

@@ -7,41 +7,43 @@ iconType: "solid"
 
 ## Overview
 
-Webhooks allow you to receive real-time notifications when memory events occur in your Mem0 project. Webhooks are configured at the project level, meaning each webhook is associated with a specific project and will only receive events from that project. You can configure webhooks to send HTTP POST requests to your specified URLs whenever memories are created, updated, or deleted.
+Webhooks enable real-time notifications for memory events in your Mem0 project. Webhooks are configured at the project level, meaning each webhook is tied to a specific project and receives events solely from that project. You can configure webhooks to send HTTP POST requests to your specified URLs whenever memories are created, updated, or deleted.
 
 ## Managing Webhooks
 
 ### Create Webhook
 
-Create a new webhook for your project. The webhook will only receive events from the specified project:
-
+Create a webhook for your project; it will receive events only from that project:
 <CodeGroup>
 
 ```python Python
+import os
 from mem0 import MemoryClient
 
-client = MemoryClient(api_key="your-api-key")
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
 
 # Create webhook in a specific project
 webhook = client.create_webhook(
     url="https://your-app.com/webhook",
     name="Memory Logger",
     project_id="proj_123",
-    event_types=["memory:add"]
+    event_types=["memory_add"]
 )
 print(webhook)
 ```
 
 ```javascript JavaScript
 const { MemoryClient } = require('mem0ai');
-const client = new MemoryClient('your-api-key');
+const client = new MemoryClient({ apiKey: 'your-api-key'});
 
 // Create webhook in a specific project
 const webhook = await client.createWebhook({
     url: "https://your-app.com/webhook",
     name: "Memory Logger",
     projectId: "proj_123",
-    eventTypes: ["memory:add"]
+    eventTypes: ["memory_add"]
 });
 console.log(webhook);
 ```
@@ -63,7 +65,7 @@ console.log(webhook);
 
 ### Get Webhooks
 
-Retrieve all webhooks configured for your project:
+Retrieve all webhooks for your project:
 
 <CodeGroup>
 
@@ -100,7 +102,7 @@ console.log(webhooks);
 
 ### Update Webhook
 
-Modify an existing webhook's configuration. Use `webhook_id` to update the parameters of the webhook:
+Update an existing webhook’s configuration by specifying its `webhook_id`:
 
 <CodeGroup>
 
@@ -109,7 +111,7 @@ Modify an existing webhook's configuration. Use `webhook_id` to update the param
 updated_webhook = client.update_webhook(
     name="Updated Logger",
     url="https://your-app.com/new-webhook",
-    event_types=["memory:update", "memory:add"],
+    event_types=["memory_update", "memory_add"],
     webhook_id="wh_123"
 )
 print(updated_webhook)
@@ -120,8 +122,7 @@ print(updated_webhook)
 const updatedWebhook = await client.updateWebhook({
     name: "Updated Logger",
     url: "https://your-app.com/new-webhook",
-    eventTypes: ["memory:update", "memory:add"],
-    projectId: "proj_123",
+    eventTypes: ["memory_update", "memory_add"],
     webhookId: "wh_123"
 });
 console.log(updatedWebhook);
@@ -137,7 +138,7 @@ console.log(updatedWebhook);
 
 ### Delete Webhook
 
-Webhook can be deleted by using the `webhook_id` of the webhook:
+Delete a webhook by providing its `webhook_id`:
 
 <CodeGroup>
 
@@ -165,13 +166,13 @@ console.log(response);
 
 Mem0 supports the following event types for webhooks:
 
-- `memory:add`: Triggered when a new memory is added.
-- `memory:update`: Triggered when an existing memory is updated.
-- `memory:delete`: Triggered when a memory is deleted.
+- `memory_add`: Triggered when a memory is added.
+- `memory_update`: Triggered when an existing memory is updated.
+- `memory_delete`: Triggered when a memory is deleted.
 
 ## Webhook Payload
 
-When a memory event occurs in your project, Mem0 sends a POST request to your webhook URL with the following example payload structure:
+When a memory event occurs, Mem0 sends an HTTP POST request to your webhook URL with the following payload:
 
 ```json
 {
@@ -187,13 +188,13 @@ When a memory event occurs in your project, Mem0 sends a POST request to your we
 
 ## Best Practices
 
-1. **Implement Retry Logic**: Your webhook endpoint should be able to handle temporary failures and implement appropriate retry mechanisms.
+1. **Implement Retry Logic**: Ensure your webhook endpoint can handle temporary failures by implementing retry logic.
 
-2. **Verify Webhook Source**: Implement security measures to verify that webhook requests are coming from Mem0.
+2. **Verify Webhook Source**: Implement security measures to verify that webhook requests originate from Mem0.
 
-3. **Process Events Asynchronously**: Handle webhook events asynchronously to prevent timeouts and ensure reliable processing.
+3. **Process Events Asynchronously**: Process webhook events asynchronously to avoid timeouts and ensure reliable handling.
 
-4. **Monitor Webhook Health**: Regularly check your webhook logs to ensure proper functionality and handle any delivery failures.
+4. **Monitor Webhook Health**: Regularly review your webhook logs to ensure functionality and promptly address any delivery failures.
 
 If you have any questions, please feel free to reach out to us using one of the following methods:
 

docs/integrations/autogen.mdx

@@ -30,9 +30,10 @@ USER_ID = "customer_service_bot"
 
 # Set up OpenAI API key
 os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
+os.environ['MEM0_API_KEY'] = MEM0_API_KEY
 
 # Initialize Mem0 and AutoGen agents
-memory_client = MemoryClient(api_key=MEM0_API_KEY)
+memory_client = MemoryClient()
 agent = ConversableAgent(
     "chatbot",
     llm_config={"config_list": [{"model": "gpt-4", "api_key": OPENAI_API_KEY}]},

docs/integrations/crewai.mdx

@@ -164,3 +164,5 @@ By combining CrewAI with Mem0, you can create sophisticated AI systems that main
 
 - For CrewAI documentation, visit [CrewAI Documentation](https://docs.crewai.com/)
 - For Mem0 documentation, refer to the [Mem0 Platform](https://app.mem0.ai/)
+
+<Snippet file="get-help.mdx" />

docs/integrations/dify.mdx

@@ -0,0 +1,34 @@
+---
+title: Dify
+---
+
+# 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.
+
+---
+
+## How to Integrate Mem0 in Your Dify Workflow
+
+1. **Install the Mem0 Plugin:**  
+   Head to the [Dify Marketplace](https://marketplace.dify.ai/plugins/yevanchen/mem0) and install the Mem0 plugin. This is your first step toward adding intelligent memory to your AI applications.
+
+2. **Create or Open Your Dify Project:**  
+   Whether you're starting fresh or updating an existing project, simply create or open your Dify workspace.
+
+3. **Add the Mem0 Plugin to Your Project:**  
+   Within your project, add the Mem0 plugin. This integration connects Mem0’s memory management capabilities directly to your Dify application.
+
+4. **Configure Your Mem0 Settings:**  
+   Customize Mem0 to suit your needs—set preferences for how conversation history is stored, the search parameters, and any other context-aware features.
+
+5. **Leverage Mem0 in Your Workflow:**  
+   Use Mem0 to store every conversation turn and retrieve past interactions seamlessly. This integration ensures that your AI agents can refer back to important context, making multi-turn dialogues more effective and user-centric.
+
+---
+
+![Mem0 Dify Integration](/images/dify-mem0-integration.png)
+
+Enhance your Dify-powered AI with Mem0 and transform your conversational experiences. Start integrating intelligent memory management today and give your agents the context they need to excel!
+
+[Explore Mem0 on Dify Marketplace](https://marketplace.dify.ai/plugins/yevanchen/mem0)

docs/integrations/langchain-tools.mdx

@@ -25,9 +25,11 @@ from langchain_core.tools import StructuredTool
 from mem0 import MemoryClient
 from pydantic import BaseModel, Field
 from typing import List, Dict, Any, Optional
+import os
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
 
 client = MemoryClient(
-    api_key=your_api_key,
     org_id=your_org_id,
     project_id=your_project_id
 )
@@ -93,7 +95,7 @@ add_input = {
         {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
         {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy."}
     ],
-    "user_id": "alex123",
+    "user_id": "alex",
     "output_format": "v1.1",
     "metadata": {"food": "vegan"}
 }
@@ -171,7 +173,7 @@ search_input = {
     "filters": {
         "AND": [
             {"created_at": {"gte": "2024-07-20", "lte": "2024-12-10"}},
-            {"user_id": "alex123"}
+            {"user_id": "alex"}
         ]
     },
     "version": "v2"
@@ -184,7 +186,7 @@ result = search_tool.invoke(search_input)
   {
     "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
     "memory": "Name is Alex",
-    "user_id": "alex123", 
+    "user_id": "alex", 
     "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
     "metadata": {
       "food": "vegan"
@@ -253,7 +255,7 @@ get_all_input = {
     "version": "v2",
     "filters": {
         "AND": [
-            {"user_id": "alex123"},
+            {"user_id": "alex"},
             {"created_at": {"gte": "2024-07-01", "lte": "2024-12-31"}}
         ]
     },
@@ -272,7 +274,7 @@ get_all_result = get_all_tool.invoke(get_all_input)
     {
       "id": "1a75e827-7eca-45ea-8c5c-cfd43299f061",
       "memory": "Name is Alex",
-      "user_id": "alex123", 
+      "user_id": "alex", 
       "hash": "d0fccc8fa47f7a149ee95750c37bb0ca",
       "metadata": {
         "food": "vegan"
@@ -286,7 +288,7 @@ get_all_result = get_all_tool.invoke(get_all_input)
     {
       "id": "91509588-0b39-408a-8df3-84b3bce8c521",
       "memory": "Is a vegetarian",
-      "user_id": "alex123",
+      "user_id": "alex",
       "hash": "ce6b1c84586772ab9995a9477032df99", 
       "metadata": {
         "food": "vegan"
@@ -301,7 +303,7 @@ get_all_result = get_all_tool.invoke(get_all_input)
     {
       "id": "8d74f7a0-6107-4589-bd6f-210f6bf4fbbb",
       "memory": "Is allergic to nuts",
-      "user_id": "alex123",
+      "user_id": "alex",
       "hash": "7873cd0e5a29c513253d9fad038e758b",
       "metadata": {
         "food": "vegan"
@@ -325,4 +327,10 @@ All tools are implemented as Langchain `StructuredTool` instances, making them c
 2. Add the tools to your agent's toolset
 3. The agent can now use these tools to manage memories through natural language interactions
 
-Each tool provides structured input validation through Pydantic models and returns consistent responses that can be processed by your agent.
+Each tool provides structured input validation through Pydantic models and returns consistent responses that can be processed by your agent.
+
+## Help
+
+In case of any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/integrations/langchain.mdx

@@ -37,7 +37,7 @@ os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize LangChain and Mem0
 llm = ChatOpenAI(model="gpt-4o-mini")
-mem0 = MemoryClient(api_key=os.environ["MEM0_API_KEY"])
+mem0 = MemoryClient()
 ```
 
 ## Create Prompt Template

docs/integrations/llama-index.mdx

@@ -80,7 +80,7 @@ config = {
         "config": {
             "model": "gpt-4o",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         },
     },
     "embedder": {

docs/integrations/mcp-server.mdx

@@ -0,0 +1,60 @@
+---
+title: MCP Server
+---
+
+## Integrating mem0 as an MCP Server in Cursor
+[mem0](https://github.com/mem0ai/mem0-mcp) is a powerful tool designed to enhance AI-driven workflows, particularly in code generation and contextual memory. In this guide, we'll walk through integrating mem0 as an **MCP (Model Context Protocol) server** within [Cursor](https://cursor.sh/), an AI-powered coding editor.
+
+## Prerequisites
+Before proceeding, ensure you have the following installed:
+- Cursor IDE
+- Python (>=3.8)
+- Git
+- [mem0-mcp](https://github.com/mem0ai/mem0-mcp) (Clone the repository and set up as per the instructions in the README)
+
+
+## Configuring Cursor to use mem0 as an MCP Server
+
+1. **Open Cursor.**
+2. **Navigate to `Settings` > `Cursor Settings` > `Features` > `MCP Servers`.**
+3. **Add a new provider using the MCP server:**
+   - Click on **`Add new MCP server`**
+   - Provide a name for the server, e.g. `mem0` and select type as `sse`
+   - Enter the **SSE Endpoint**: `http://0.0.0.0:8080/sse`
+4. **Save and Restart Cursor** to apply changes.
+
+## Demo
+<iframe width="560" height="315" src="https://www.youtube.com/embed/fWa6KX7cpG8?si=cmJDz2sQevGnItSI" 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>
+
+## Using mem0 in Cursor
+Once integrated, mem0 can assist with contextual memory and AI-driven coding enhancements. Some key functionalities include:
+
+### 1. Storing Coding Preferences
+Mem0 can store and manage coding preferences, including:
+- Complete code snippets with dependencies
+- Language/framework versions
+- Documentation and comments
+- Best practices and example usage
+
+### 2. Retrieving Stored Preferences
+Access all stored coding references to:
+- Review implementations
+- Maintain consistency in coding practices
+
+### 3. Semantic Search for Preferences
+Use natural language queries to find:
+- Code snippets
+- Technical documentation
+- Best practices
+- Setup guides
+
+## Benefits of Using mem0 in Cursor
+- **Persistent Context Storage**: Retain and reuse coding insights across sessions.
+- **Seamless Integration**: Works directly within Cursor as an MCP server.
+- **Efficient Search**: Retrieve relevant coding insights using semantic search.
+
+## Conclusion
+By integrating mem0 as an MCP server within Cursor, you enhance your development workflow with AI-powered memory and context-aware assistance. Follow the steps above to set up and start leveraging mem0 in your coding environment.
+
+For more details on MCP integration, refer to Cursor's [Model Context Protocol documentation](https://docs.cursor.com/context/model-context-protocol).
+

docs/integrations/multion.mdx

@@ -34,10 +34,11 @@ USER_ID = "your-user-id"
 
 # Set up OpenAI API key
 os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
+os.environ['MEM0_API_KEY'] = MEM0_API_KEY
 
 # Initialize Mem0 and MultiOn
 memory = Memory()  # For local usage
-memory_client = MemoryClient(api_key=MEM0_API_KEY)  # For API usage
+memory_client = MemoryClient()  # For API usage
 multion = MultiOn(api_key=MULTION_API_KEY)
 ```
 
@@ -209,3 +210,5 @@ These examples illustrate how combining memory management with web browsing capa
 - For more details and advanced usage, refer to the full [cookbooks here](https://github.com/mem0ai/mem0/blob/main/cookbooks).
 - Feel free to visit our [Github](https://github.com/mem0ai/mem0) or [Mem0 Platform](https://app.mem0.ai/).
 - For any questions or assistance, please reach out to `taranjeetio` on [Discord](https://mem0.dev/DiD).
+
+<Snippet file="get-help.mdx" />

docs/integrations/overview.mdx

@@ -72,7 +72,7 @@ Here are the available integrations for Mem0:
         </defs>
       </svg>
     }
-    href="/integrations/llamaindex"
+    href="/integrations/llama-index"
   >
     Build RAG applications with LlamaIndex and Mem0.
   </Card>
@@ -178,4 +178,46 @@ Here are the available integrations for Mem0:
   >
     Use Mem0 with LangChain Tools for enhanced agent capabilities.
   </Card>
+  <Card
+    title="Dify"
+    icon={
+      <svg
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+        viewBox="0 0 200 200"
+        fill="none"
+      >
+        <path
+          d="M40 20 H120 C160 20, 160 180, 120 180 H40 V20"
+          fill="currentColor"
+        />
+      </svg>
+    }
+    href="/integrations/dify"
+  >
+    Build AI applications with persistent memory using Dify and Mem0.
+  </Card>
+  <Card
+    title="MCP Server"
+    icon={
+      <svg
+        viewBox="0 0 180 180"
+        xmlns="http://www.w3.org/2000/svg"
+        width="24"
+        height="24"
+      >
+        <path
+          d="M45 45 L135 45 M45 90 L135 90 M45 135 L135 135"
+          stroke="currentColor"
+          strokeWidth="12"
+          strokeLinecap="round"
+          fill="none"
+        />
+      </svg>
+    }
+    href="/integrations/mcp-server"
+  >
+    Integrate Mem0 as an MCP Server in Cursor.
+  </Card>
 </CardGroup>

docs/open-source/graph_memory/features.mdx

@@ -16,24 +16,43 @@ Users can add a customized prompt that will be used to extract specific entities
 This allows for more targeted and relevant information extraction based on the user's needs. 
 Here's an example of how to add a customized prompt:
 
-```python
-from mem0 import Memory
+<CodeGroup>
+    ```python Python
+    from mem0 import Memory
 
-config = {
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {
-            "url": "neo4j+s://xxx",
-            "username": "neo4j",
-            "password": "xxx"
-        },
-        "custom_prompt": "Please only extract entities containing sports related relationships and nothing else.",
-    },
-    "version": "v1.1"
-}
+    config = {
+        "graph_store": {
+            "provider": "neo4j",
+            "config": {
+                "url": "neo4j+s://xxx",
+                "username": "neo4j",
+                "password": "xxx"
+            },
+            "custom_prompt": "Please only extract entities containing sports related relationships and nothing else.",
+        }
+    }
 
-m = Memory.from_config(config_dict=config)
-```
+    m = Memory.from_config(config_dict=config)
+    ```
+
+    ```typescript TypeScript
+    import { Memory } from "mem0ai/oss";
+
+    const config = {
+        graphStore: {
+            provider: "neo4j",
+            config: {
+                url: "neo4j+s://xxx",
+                username: "neo4j",
+                password: "xxx",
+            },
+            customPrompt: "Please only extract entities containing sports related relationships and nothing else.",
+        }
+    }
+
+    const memory = new Memory(config);
+    ```
+</CodeGroup>
 
 If you want to use a managed version of Mem0, please check out [Mem0](https://mem0.dev/pd). If you have any questions, please feel free to reach out to us using one of the following methods:
 

docs/open-source/graph_memory/overview.mdx

@@ -9,14 +9,24 @@ Mem0 now supports **Graph Memory**.
 With Graph Memory, users can now create and utilize complex relationships between pieces of information, allowing for more nuanced and context-aware responses. 
 This integration enables users to leverage the strengths of both vector-based and graph-based approaches, resulting in more accurate and comprehensive information retrieval and generation.
 
+<Note>
+NodeSDK now supports Graph Memory. 🎉
+</Note>
+
 ## Installation
 
 To use Mem0 with Graph Memory support, install it using pip:
 
-```bash
+<CodeGroup>
+```bash Python
 pip install "mem0ai[graph]"
 ```
 
+```bash TypeScript
+npm install mem0ai
+```
+</CodeGroup>
+
 This command installs Mem0 along with the necessary dependencies for graph functionality.
 
 Try Graph Memory on Google Colab.
@@ -38,12 +48,10 @@ allowfullscreen
 ## Initialize Graph Memory
 
 To initialize Graph Memory you'll need to set up your configuration with graph store providers.
-Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/). 
-Moreover, you also need to set the version to `v1.1` (*prior versions are not supported*).
+Currently, we support Neo4j as a graph store provider. You can setup [Neo4j](https://neo4j.com/) locally or use the hosted [Neo4j AuraDB](https://neo4j.com/product/auradb/).
 
 <Note>If you are using Neo4j locally, then you need to install [APOC plugins](https://neo4j.com/labs/apoc/4.1/installation/).</Note>
 
-
 User can also customize the LLM for Graph Memory from the [Supported LLM list](https://docs.mem0.ai/components/llms/overview) with three levels of configuration:
 
 1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
@@ -54,7 +62,7 @@ Here's how you can do it:
 
 
 <CodeGroup>
-```python Basic
+```python Python
 from mem0 import Memory
 
 config = {
@@ -65,23 +73,38 @@ config = {
             "username": "neo4j",
             "password": "xxx"
         }
-    },
-    "version": "v1.1"
+    }
 }
 
 m = Memory.from_config(config_dict=config)
 ```
 
-```python Advanced (Custom LLM)
-from mem0 import Memory
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
 
+const config = {
+    enableGraph: true,
+    graphStore: {
+        provider: "neo4j",
+        config: {
+            url: "neo4j+s://xxx",
+            username: "neo4j",
+            password: "xxx",
+        }
+    }
+}
+
+const memory = new Memory(config);
+```
+
+```python Python (Advanced)
 config = {
     "llm": {
         "provider": "openai",
         "config": {
             "model": "gpt-4o",
             "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
         }
     },
     "graph_store": {
@@ -98,28 +121,66 @@ config = {
                 "temperature": 0.0,
             }
         }
-    },
-    "version": "v1.1"
+    }
 }
 
 m = Memory.from_config(config_dict=config)
 ```
+
+```typescript TypeScript (Advanced)
+const config = {
+    llm: {
+        provider: "openai",
+        config: {
+            model: "gpt-4o",
+            temperature: 0.2,
+            max_tokens: 2000,
+        }
+    },
+    enableGraph: true,
+    graphStore: {
+        provider: "neo4j",
+        config: {
+            url: "neo4j+s://xxx",
+            username: "neo4j",
+            password: "xxx",
+        },
+        llm: {
+            provider: "openai",
+            config: {
+                model: "gpt-4o-mini",
+                temperature: 0.0,
+            }
+        }
+    }
+}
+
+const memory = new Memory(config);
+```
 </CodeGroup>
 
+<Note>
+If you are using NodeSDK, you need to pass `enableGraph` as `true` in the `config` object.
+</Note>
+
 ## Graph Operations
 The Mem0's graph supports the following operations:
 
 ### Add Memories
 
 <Note>
-If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. The default value of `user_id` (in case of graph memory) is `user`.
+If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. Use `userId` in NodeSDK.
 </Note>
 
 <CodeGroup>
-```python Code
+```python Python
 m.add("I like pizza", user_id="alice")
 ```
 
+```typescript TypeScript
+memory.add("I like pizza", { userId: "alice" });
+```
+
 ```json Output
 {'message': 'ok'}
 ```
@@ -129,10 +190,14 @@ m.add("I like pizza", user_id="alice")
 ### Get all memories
 
 <CodeGroup>
-```python Code
+```python Python
 m.get_all(user_id="alice")
 ```
 
+```typescript TypeScript
+memory.getAll({ userId: "alice" });
+```
+
 ```json Output
 {
     'memories': [
@@ -160,10 +225,14 @@ m.get_all(user_id="alice")
 ### Search Memories
 
 <CodeGroup>
-```python Code
+```python Python
 m.search("tell me my name.", user_id="alice")
 ```
 
+```typescript TypeScript
+memory.search("tell me my name.", { userId: "alice" });
+```
+
 ```json Output
 {
     'memories': [
@@ -190,10 +259,16 @@ m.search("tell me my name.", user_id="alice")
 
 
 ### Delete all Memories
-```python
+
+<CodeGroup>
+```python Python
 m.delete_all(user_id="alice")
 ```
 
+```typescript TypeScript
+memory.deleteAll({ userId: "alice" });
+```
+</CodeGroup>
 
 # Example Usage
 Here's an example of how to use Mem0's graph operations:
@@ -209,64 +284,110 @@ Below are the steps to add memories and visualize the graph:
 <Steps>
   <Step title="Add memory 'I like going to hikes'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("I like going to hikes", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("I like going to hikes", { userId: "alice123" });
+```
+</CodeGroup>
 ![Graph Memory Visualization](/images/graph_memory/graph_example1.png)
 
 </Step>
 <Step title="Add memory 'I love to play badminton'">
 
-
-```python
+<CodeGroup>
+```python Python
 m.add("I love to play badminton", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("I love to play badminton", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example2.png)
 
 </Step>
 
 <Step title="Add memory 'I hate playing badminton'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("I hate playing badminton", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("I hate playing badminton", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example3.png)
 
 </Step>
 
 <Step title="Add memory 'My friend name is john and john has a dog named tommy'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("My friend name is john and john has a dog named tommy", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("My friend name is john and john has a dog named tommy", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example4.png)
 
 </Step>
 
 <Step title="Add memory 'My name is Alice'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("My name is Alice", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("My name is Alice", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example5.png)
 
 </Step>
 
 <Step title="Add memory 'John loves to hike and Harry loves to hike as well'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("John loves to hike and Harry loves to hike as well", user_id="alice123")
 ```
+
+```typescript TypeScript
+memory.add("John loves to hike and Harry loves to hike as well", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example6.png)
 
 </Step>
 
 <Step title="Add memory 'My friend peter is the spiderman'">
 
-```python
+<CodeGroup>
+```python Python
 m.add("My friend peter is the spiderman", user_id="alice123")
 ```
 
+```typescript TypeScript
+memory.add("My friend peter is the spiderman", { userId: "alice123" });
+```
+</CodeGroup>
+
 ![Graph Memory Visualization](/images/graph_memory/graph_example7.png)
 
 </Step>
@@ -277,10 +398,14 @@ m.add("My friend peter is the spiderman", user_id="alice123")
 ### Search Memories
 
 <CodeGroup>
-```python Code
+```python Python
 m.search("What is my name?", user_id="alice123")
 ```
 
+```typescript TypeScript
+memory.search("What is my name?", { userId: "alice123" });
+```
+
 ```json Output
 {
     'memories': [...],
@@ -300,10 +425,14 @@ Below graph visualization shows what nodes and relationships are fetched from th
 ![Graph Memory Visualization](/images/graph_memory/graph_example8.png)
 
 <CodeGroup>
-```python Code
+```python Python
 m.search("Who is spiderman?", user_id="alice123")
 ```
 
+```typescript TypeScript
+memory.search("Who is spiderman?", { userId: "alice123" });
+```
+
 ```json Output
 {
     'memories': [...],

docs/open-source/multimodal-support.mdx

@@ -10,11 +10,25 @@ Mem0 extends its capabilities beyond text by supporting multimodal data, includi
 
 When a user provides an image, Mem0 processes the image to extract textual information and relevant details, which are then added to the user's memory. This feature enhances the system's ability to understand and remember details based on visual inputs.
 
+<Note>
+To enable multimodal support, you must set `enable_vision = True` in your configuration. The `vision_details` parameter can be set to "auto" (default), "low", or "high" to control the level of detail in image processing.
+</Note>
+
 <CodeGroup>
 ```python Code
 from mem0 import Memory
 
-client = Memory()
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "enable_vision": True,
+            "vision_details": "high"
+        }
+    }
+}
+
+client = Memory.from_config(config=config)
 
 messages = [
     {
@@ -40,6 +54,34 @@ messages = [
 client.add(messages, user_id="alice")
 ```
 
+```typescript TypeScript
+import { Memory, Message } from "mem0ai/oss";
+
+const client = new Memory();
+
+const messages: Message[] = [
+    {
+        role: "user",
+        content: "Hi, my name is Alice."
+    },
+    {
+        role: "assistant",
+        content: "Nice to meet you, Alice! What do you like to eat?"
+    },
+    {
+        role: "user",
+        content: {
+            type: "image_url",
+            image_url: {
+                url: "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+            }
+        }
+    },
+]
+
+await client.add(messages, { userId: "alice" })
+```
+
 ```json Output
 {
   "results": [
@@ -66,6 +108,7 @@ Mem0 allows you to add images to user interactions through two primary methods:
 
 You can include an image by passing its direct URL. This method is simple and efficient for online images.
 
+<CodeGroup>
 ```python
 # Define the image URL
 image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
@@ -82,11 +125,33 @@ image_message = {
 }
 ```
 
+```typescript TypeScript
+import { Memory, Message } from "mem0ai/oss";
+
+const client = new Memory();
+
+const imageUrl = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg";
+
+const imageMessage: Message = {
+    role: "user",
+    content: {
+        type: "image_url",
+        image_url: {
+            url: imageUrl
+        }
+    }
+}
+
+await client.add([imageMessage], { userId: "alice" })
+```
+</CodeGroup>
+
 ## 2. Using Base64 Image Encoding for Local Files
 
 For local images or scenarios where embedding the image directly is preferable, you can use a Base64-encoded string.
 
-```python
+<CodeGroup>
+```python Python
 import base64
 
 # Path to the image file
@@ -108,11 +173,95 @@ image_message = {
 }
 ```
 
-By utilizing these methods, you can effectively incorporate images into user interactions, enhancing the multimodal capabilities of your Mem0 instance.
+```typescript TypeScript
+import { Memory, Message } from "mem0ai/oss";
 
-<Note>
-Currently, we support only OpenAI models for image description.
-</Note>
+const client = new Memory();
+
+const imagePath = "path/to/your/image.jpg";
+
+const base64Image = fs.readFileSync(imagePath, { encoding: 'base64' });
+
+const imageMessage: Message = {
+    role: "user",
+    content: {
+        type: "image_url",
+        image_url: {
+            url: `data:image/jpeg;base64,${base64Image}`
+        }
+    }
+}
+
+await client.add([imageMessage], { userId: "alice" })
+```
+</CodeGroup>
+
+## 3. OpenAI-Compatible Message Format
+
+You can also use the OpenAI-compatible format to combine text and images in a single message:
+
+<CodeGroup>
+```python Python
+import base64
+
+# Path to the image file
+image_path = "path/to/your/image.jpg"
+
+# Encode the image in Base64
+with open(image_path, "rb") as image_file:
+    base64_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+# Create the message using OpenAI-compatible format
+message = {
+    "role": "user",
+    "content": [
+        {
+            "type": "text",
+            "text": "What is in this image?",
+        },
+        {
+            "type": "image_url",
+            "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
+        },
+    ],
+}
+
+# Add the message to memory
+client.add([message], user_id="alice")
+```
+
+```typescript TypeScript
+import { Memory, Message } from "mem0ai/oss";
+
+const client = new Memory();
+
+const imagePath = "path/to/your/image.jpg";
+
+const base64Image = fs.readFileSync(imagePath, { encoding: 'base64' });
+
+const message: Message = {
+    role: "user",
+    content: [
+        {
+            type: "text",
+            text: "What is in this image?",
+        },
+        {
+            type: "image_url",
+            image_url: {
+                url: `data:image/jpeg;base64,${base64Image}`
+            }
+        },
+    ],
+}
+
+await client.add([message], { userId: "alice" })
+```
+</CodeGroup>
+
+This format allows you to combine text and images in a single message, making it easier to provide context along with visual content.
+
+By utilizing these methods, you can effectively incorporate images into user interactions, enhancing the multimodal capabilities of your Mem0 instance.
 
 If you have any questions, please feel free to reach out to us using one of the following methods:
 

docs/open-source/node-quickstart.mdx

@@ -0,0 +1,389 @@
+---
+title: Node SDK
+description: 'Get started with Mem0 quickly!'
+icon: "node"
+iconType: "solid"
+---
+
+> Welcome to the Mem0 quickstart guide. This guide will help you get up and running with Mem0 in no time.
+
+## Installation
+
+To install Mem0, you can use npm. Run the following command in your terminal:
+
+```bash
+npm install mem0ai
+```
+
+## Basic Usage
+
+### Initialize Mem0
+
+<Tabs>
+  <Tab title="Basic">
+```typescript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory();
+```
+  </Tab>
+  <Tab title="Advanced">
+If you want to run Mem0 in production, initialize using the following method:
+
+```typescript
+import { Memory } from 'mem0ai/oss';
+
+const memory = new Memory({
+    version: 'v1.1',
+    embedder: {
+      provider: 'openai',
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || '',
+        model: 'text-embedding-3-small',
+      },
+    },
+    vectorStore: {
+      provider: 'memory',
+      config: {
+        collectionName: 'memories',
+        dimension: 1536,
+      },
+    },
+    llm: {
+      provider: 'openai',
+      config: {
+        apiKey: process.env.OPENAI_API_KEY || '',
+        model: 'gpt-4-turbo-preview',
+      },
+    },
+    historyDbPath: 'memory.db',
+  });
+```
+  </Tab>
+</Tabs>
+
+
+### Store a Memory
+
+<CodeGroup>
+```typescript Code
+const messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+
+await memory.add(messages, { userId: "alice", metadata: { category: "movie_recommendations" } });
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+      "memory": "User is planning to watch a movie tonight.",
+      "metadata": {
+        "category": "movie_recommendations"
+      }
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "metadata": {
+        "category": "movie_recommendations"
+      }
+    },
+    {
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+      "memory": "User loves sci-fi movies.",
+      "metadata": {
+        "category": "movie_recommendations"
+      }
+    }
+  ]
+}
+```
+</CodeGroup>
+
+### Retrieve Memories
+
+<CodeGroup>
+```typescript Code
+// Get all memories
+const allMemories = await memory.getAll({ userId: "alice" });
+console.log(allMemories)
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+      "memory": "User is planning to watch a movie tonight.",
+      "hash": "1a271c007316c94377175ee80e746a19",
+      "createdAt": "2025-02-27T16:33:20.557Z",
+      "updatedAt": "2025-02-27T16:33:27.051Z",
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    },
+    {
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+      "memory": "User loves sci-fi movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+
+<br />
+
+<CodeGroup>
+```typescript Code
+// Get a single memory by ID
+const singleMemory = await memory.get('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
+console.log(singleMemory);
+```
+
+```json Output
+{
+  "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+  "memory": "User is planning to watch a movie tonight.",
+  "hash": "1a271c007316c94377175ee80e746a19",
+  "createdAt": "2025-02-27T16:33:20.557Z",
+  "updatedAt": undefined,
+  "metadata": {
+    "category": "movie_recommendations"
+  },
+  "userId": "alice"
+}
+```
+</CodeGroup>
+
+### Search Memories
+
+<CodeGroup>
+```typescript Code
+const result = await memory.search('What do you know about me?', { userId: "alice" });
+console.log(result);
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+      "memory": "User is planning to watch a movie tonight.",
+      "hash": "1a271c007316c94377175ee80e746a19",
+      "createdAt": "2025-02-27T16:33:20.557Z",
+      "updatedAt": undefined,
+      "score": 0.38920719231944799,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    },
+    {
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+      "memory": "User loves sci-fi movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "score": 0.36869761478135689,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "createdAt": "2025-02-27T16:33:20.560Z",
+      "updatedAt": undefined,
+      "score": 0.33855272141248272,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "userId": "alice"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+### Update a Memory
+
+<CodeGroup>
+```typescript Code
+const result = await memory.update(
+  '892db2ae-06d9-49e5-8b3e-585ef9b85b8e',
+  'I love India, it is my favorite country.'
+);
+console.log(result);
+```
+
+```json Output
+{
+  "message": "Memory updated successfully!"
+}
+```
+</CodeGroup>
+
+### Memory History
+
+<CodeGroup>
+```typescript Code
+const history = await memory.history('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
+console.log(history);
+```
+
+```json Output
+[
+  {
+    "id": 39,
+    "memoryId": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+    "previousValue": "User is planning to watch a movie tonight.",
+    "newValue": "I love India, it is my favorite country.",
+    "action": "UPDATE",
+    "createdAt": "2025-02-27T16:33:20.557Z",
+    "updatedAt": "2025-02-27T16:33:27.051Z",
+    "isDeleted": 0
+  },
+  {
+    "id": 37,
+    "memoryId": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+    "previousValue": null,
+    "newValue": "User is planning to watch a movie tonight.",
+    "action": "ADD",
+    "createdAt": "2025-02-27T16:33:20.557Z",
+    "updatedAt": null,
+    "isDeleted": 0
+  }
+]
+```
+</CodeGroup>
+
+### Delete Memory
+
+```typescript
+// Delete a memory by id
+await memory.delete('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
+
+// Delete all memories for a user
+await memory.deleteAll({ userId: "alice" });
+```
+
+### Reset Memory
+
+```typescript
+await memory.reset(); // Reset all memories
+```
+
+## Configuration Parameters
+
+Mem0 offers extensive configuration options to customize its behavior according to your needs. These configurations span across different components like vector stores, language models, embedders, and graph stores.
+
+<AccordionGroup>
+<Accordion title="Vector Store Configuration">
+| Parameter    | Description                     | Default     |
+|-------------|---------------------------------|-------------|
+| `provider`   | Vector store provider (e.g., "memory") | "memory"   |
+| `host`       | Host address                    | "localhost" |
+| `port`       | Port number                     | undefined       |
+</Accordion>
+
+<Accordion title="LLM Configuration">
+| Parameter              | Description                                   | Provider          |
+|-----------------------|-----------------------------------------------|-------------------|
+| `provider`            | LLM provider (e.g., "openai", "anthropic")    | All              |
+| `model`               | Model to use                                  | All              |
+| `temperature`         | Temperature of the model                      | All              |
+| `apiKey`             | API key to use                                | All              |
+| `maxTokens`          | Tokens to generate                            | All              |
+| `topP`               | Probability threshold for nucleus sampling    | All              |
+| `topK`               | Number of highest probability tokens to keep  | All              |
+| `openaiBaseUrl`     | Base URL for OpenAI API                      | OpenAI           |
+</Accordion>
+
+<Accordion title="Graph Store Configuration">
+| Parameter    | Description                     | Default     |
+|-------------|---------------------------------|-------------|
+| `provider`   | Graph store provider (e.g., "neo4j") | "neo4j"    |
+| `url`        | Connection URL                  | env.NEO4J_URL        |
+| `username`   | Authentication username         | env.NEO4J_USERNAME        |
+| `password`   | Authentication password         | env.NEO4J_PASSWORD        |
+</Accordion>
+
+<Accordion title="Embedder Configuration">
+| Parameter    | Description                     | Default                      |
+|-------------|---------------------------------|------------------------------|
+| `provider`   | Embedding provider              | "openai"                     |
+| `model`      | Embedding model to use          | "text-embedding-3-small"     |
+| `apiKey`    | API key for embedding service   | None                        |
+</Accordion>
+
+<Accordion title="General Configuration">
+| Parameter         | Description                          | Default                    |
+|------------------|--------------------------------------|----------------------------|
+| `historyDbPath` | Path to the history database         | "{mem0_dir}/history.db"    |
+| `version`         | API version                          | "v1.0"                     |
+| `customPrompt`   | Custom prompt for memory processing  | None                       |
+</Accordion>
+
+<Accordion title="Complete Configuration Example">
+```typescript
+const config = {
+      version: 'v1.1',
+      embedder: {
+        provider: 'openai',
+        config: {
+          apiKey: process.env.OPENAI_API_KEY || '',
+          model: 'text-embedding-3-small',
+        },
+      },
+      vectorStore: {
+        provider: 'memory',
+        config: {
+          collectionName: 'memories',
+          dimension: 1536,
+        },
+      },
+      llm: {
+        provider: 'openai',
+        config: {
+          apiKey: process.env.OPENAI_API_KEY || '',
+          model: 'gpt-4-turbo-preview',
+        },
+      },
+      historyDbPath: 'memory.db',
+      customPrompt: "I'm a virtual assistant. I'm here to help you with your queries.",
+    }
+```
+</Accordion>
+</AccordionGroup>
+
+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/open-source/python-quickstart.mdx

@@ -0,0 +1,573 @@
+---
+title: Python SDK
+description: 'Get started with Mem0 quickly!'
+icon: "python"
+iconType: "solid"
+---
+
+> Welcome to the Mem0 quickstart guide. This guide will help you get up and running with Mem0 in no time.
+
+## Installation
+
+To install Mem0, you can use pip. Run the following command in your terminal:
+
+```bash
+pip install mem0ai
+```
+
+## Basic Usage
+
+### Initialize Mem0
+
+<Tabs>
+  <Tab title="Basic">
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+m = Memory()
+```
+  </Tab>
+  <Tab title="Advanced">
+If you want to run Mem0 in production, initialize using the following method:
+
+Run Qdrant first:
+
+```bash
+docker pull qdrant/qdrant
+
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+```
+
+Then, instantiate memory with qdrant server:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
+}
+
+m = Memory.from_config(config)
+```
+  </Tab>
+
+<Tab title="Advanced (Graph Memory)">
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+config = {
+    "graph_store": {
+        "provider": "neo4j",
+        "config": {
+            "url": "neo4j+s://---",
+            "username": "neo4j",
+            "password": "---"
+        }
+    }
+}
+
+m = Memory.from_config(config_dict=config)
+```
+
+</Tab>
+</Tabs>
+
+
+### Store a Memory
+
+<CodeGroup>
+```python Code
+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"})
+
+# Store raw messages without inference
+# result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"}, infer=False)
+```
+
+```json Output
+{
+    "results": [
+        {
+            "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+            "memory": "User is planning to watch a movie tonight.",
+            "metadata": {
+                "category": "movie_recommendations"
+            },
+            "event": "ADD"
+        },
+        {
+            "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+            "memory": "User is not a big fan of thriller movies.",
+            "metadata": {
+                "category": "movie_recommendations"
+            },
+            "event": "ADD"
+        },
+        {
+            "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+            "memory": "User loves sci-fi movies.",
+            "metadata": {
+                "category": "movie_recommendations"
+            },
+            "event": "ADD"
+        }
+    ]
+}
+```
+</CodeGroup>
+
+### Retrieve Memories
+
+<CodeGroup>
+```python Code
+# Get all memories
+all_memories = m.get_all(user_id="alice")
+```
+
+```json Output
+{
+    "results": [
+            {
+        "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+        "memory": "User is planning to watch a movie tonight.",
+        "hash": "1a271c007316c94377175ee80e746a19",
+        "created_at": "2025-02-27T16:33:20.557Z",
+        "updated_at": "2025-02-27T16:33:27.051Z",
+        "metadata": {
+            "category": "movie_recommendations"
+        },
+        "user_id": "alice"
+        },
+        {
+        "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+        "memory": "User loves sci-fi movies.",
+        "hash": "285d07801ae42054732314853e9eadd7",
+        "created_at": "2025-02-27T16:33:20.560Z",
+        "updated_at": None,
+        "metadata": {
+            "category": "movie_recommendations"
+        },
+        "user_id": "alice"
+        },
+        {
+        "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+        "memory": "User is not a big fan of thriller movies.",
+        "hash": "285d07801ae42054732314853e9eadd7",
+        "created_at": "2025-02-27T16:33:20.560Z",
+        "updated_at": None,
+        "metadata": {
+            "category": "movie_recommendations"
+        },
+        "user_id": "alice"
+        }
+    ]
+}
+```
+</CodeGroup>
+
+
+<br />
+
+<CodeGroup>
+```python Code
+# Get a single memory by ID
+specific_memory = m.get("892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
+```
+
+```json Output
+{
+  "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+  "memory": "User is planning to watch a movie tonight.",
+  "hash": "1a271c007316c94377175ee80e746a19",
+  "created_at": "2025-02-27T16:33:20.557Z",
+  "updated_at": None,
+  "metadata": {
+    "category": "movie_recommendations"
+  },
+  "user_id": "alice"
+}
+```
+</CodeGroup>
+
+### Search Memories
+
+<CodeGroup>
+```python Code
+related_memories = m.search(query="What do you know about me?", user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+      "memory": "User is planning to watch a movie tonight.",
+      "hash": "1a271c007316c94377175ee80e746a19",
+      "created_at": "2025-02-27T16:33:20.557Z",
+      "updated_at": None,
+      "score": 0.38920719231944799,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "user_id": "alice"
+    },
+    {
+      "id": "475bde34-21e6-42ab-8bef-0ab84474f156",
+      "memory": "User loves sci-fi movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "created_at": "2025-02-27T16:33:20.560Z",
+      "updated_at": None,
+      "score": 0.36869761478135689,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "user_id": "alice"
+    },
+    {
+      "id": "cbb1fe73-0bf1-4067-8c1f-63aa53e7b1a4",
+      "memory": "User is not a big fan of thriller movies.",
+      "hash": "285d07801ae42054732314853e9eadd7",
+      "created_at": "2025-02-27T16:33:20.560Z",
+      "updated_at": None,
+      "score": 0.33855272141248272,
+      "metadata": {
+        "category": "movie_recommendations"
+      },
+      "user_id": "alice"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+### Update a Memory
+
+<CodeGroup>
+```python Code
+result = m.update(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e", data="I love India, it is my favorite country.")
+```
+
+```json Output
+{'message': 'Memory updated successfully!'}
+```
+</CodeGroup>
+
+### Memory History
+
+<CodeGroup>
+```python Code
+history = m.history(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
+```
+
+```json Output
+[
+  {
+    "id": 39,
+    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+    "previous_value": "User is planning to watch a movie tonight.",
+    "new_value": "I love India, it is my favorite country.",
+    "action": "UPDATE",
+    "created_at": "2025-02-27T16:33:20.557Z",
+    "updated_at": "2025-02-27T16:33:27.051Z",
+    "is_deleted": 0
+  },
+  {
+    "id": 37,
+    "memory_id": "892db2ae-06d9-49e5-8b3e-585ef9b85b8e",
+    "previous_value": null,
+    "new_value": "User is planning to watch a movie tonight.",
+    "action": "ADD",
+    "created_at": "2025-02-27T16:33:20.557Z",
+    "updated_at": null,
+    "is_deleted": 0
+  }
+]
+```
+</CodeGroup>
+
+### Delete Memory
+
+```python
+# Delete a memory by id
+m.delete(memory_id="892db2ae-06d9-49e5-8b3e-585ef9b85b8e")
+# Delete all memories for a user
+m.delete_all(user_id="alice")
+```
+
+### Reset Memory
+
+```python
+m.reset() # Reset all memories
+```
+
+## Configuration Parameters
+
+Mem0 offers extensive configuration options to customize its behavior according to your needs. These configurations span across different components like vector stores, language models, embedders, and graph stores.
+
+<AccordionGroup>
+<Accordion title="Vector Store Configuration">
+| Parameter    | Description                     | Default     |
+|-------------|---------------------------------|-------------|
+| `provider`   | Vector store provider (e.g., "qdrant") | "qdrant"   |
+| `host`       | Host address                    | "localhost" |
+| `port`       | Port number                     | 6333       |
+</Accordion>
+
+<Accordion title="LLM Configuration">
+| Parameter              | Description                                   | Provider          |
+|-----------------------|-----------------------------------------------|-------------------|
+| `provider`            | LLM provider (e.g., "openai", "anthropic")    | All              |
+| `model`               | Model to use                                  | All              |
+| `temperature`         | Temperature of the model                      | All              |
+| `api_key`             | API key to use                                | All              |
+| `max_tokens`          | Tokens to generate                            | All              |
+| `top_p`               | Probability threshold for nucleus sampling    | All              |
+| `top_k`               | Number of highest probability tokens to keep  | All              |
+| `http_client_proxies` | Allow proxy server settings                   | AzureOpenAI      |
+| `models`              | List of models                                | Openrouter       |
+| `route`               | Routing strategy                              | Openrouter       |
+| `openrouter_base_url` | Base URL for Openrouter API                  | Openrouter       |
+| `site_url`            | Site URL                                      | Openrouter       |
+| `app_name`            | Application name                              | Openrouter       |
+| `ollama_base_url`     | Base URL for Ollama API                      | Ollama           |
+| `openai_base_url`     | Base URL for OpenAI API                      | OpenAI           |
+| `azure_kwargs`        | Azure LLM args for initialization            | AzureOpenAI      |
+| `deepseek_base_url`   | Base URL for DeepSeek API                    | DeepSeek         |
+</Accordion>
+
+<Accordion title="Embedder Configuration">
+| Parameter    | Description                     | Default                      |
+|-------------|---------------------------------|------------------------------|
+| `provider`   | Embedding provider              | "openai"                     |
+| `model`      | Embedding model to use          | "text-embedding-3-small"     |
+| `api_key`    | API key for embedding service   | None                        |
+</Accordion>
+
+<Accordion title="Graph Store Configuration">
+| Parameter    | Description                     | Default     |
+|-------------|---------------------------------|-------------|
+| `provider`   | Graph store provider (e.g., "neo4j") | "neo4j"    |
+| `url`        | Connection URL                  | None        |
+| `username`   | Authentication username         | None        |
+| `password`   | Authentication password         | None        |
+</Accordion>
+
+<Accordion title="General Configuration">
+| Parameter         | Description                          | Default                    |
+|------------------|--------------------------------------|----------------------------|
+| `history_db_path` | Path to the history database         | "{mem0_dir}/history.db"    |
+| `version`         | API version                          | "v1.1"                     |
+| `custom_fact_extraction_prompt`   | Custom prompt for memory processing  | None                       |
+| `custom_update_memory_prompt` | Custom prompt for update memory | None                |
+</Accordion>
+
+<Accordion title="Complete Configuration Example">
+```python
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "gpt-4"
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "api_key": "your-api-key",
+            "model": "text-embedding-3-small"
+        }
+    },
+    "graph_store": {
+        "provider": "neo4j",
+        "config": {
+            "url": "neo4j+s://your-instance",
+            "username": "neo4j",
+            "password": "password"
+        }
+    },
+    "history_db_path": "/path/to/history.db",
+    "version": "v1.1",
+    "custom_fact_extraction_prompt": "Optional custom prompt for fact extraction for memory",
+    "custom_update_memory_prompt": "Optional custom prompt for update memory"
+}
+```
+</Accordion>
+</AccordionGroup>
+
+## Run Mem0 Locally
+
+Please refer to the example [Mem0 with Ollama](../examples/mem0-with-ollama) to run Mem0 locally.
+
+
+## Chat Completion
+
+Mem0 can be easily integrated into chat applications to enhance conversational agents with structured memory. Mem0's APIs are designed to be compatible with OpenAI's, with the goal of making it easy to leverage Mem0 in applications you may have already built.
+
+If you have a `Mem0 API key`, you can use it to initialize the client. Alternatively, you can initialize Mem0 without an API key if you're using it locally.
+
+Mem0 supports several language models (LLMs) through integration with various [providers](https://litellm.vercel.app/docs/providers).
+
+## Use Mem0 Platform
+
+```python
+from mem0.proxy.main import Mem0
+
+client = Mem0(api_key="m0-xxx")
+
+# First interaction: Storing user preferences
+messages = [
+  {
+    "role": "user",
+    "content": "I love indian food but I cannot eat pizza since allergic to cheese."
+  },
+]
+user_id = "alice"
+chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
+# Memory saved after this will look like: "Loves Indian food. Allergic to cheese and cannot eat pizza."
+
+# Second interaction: Leveraging stored memory
+messages = [
+  {
+    "role": "user",
+    "content": "Suggest restaurants in San Francisco to eat.",
+  }
+]
+
+chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
+print(chat_completion.choices[0].message.content)
+# Answer: You might enjoy Indian restaurants in San Francisco, such as Amber India, Dosa, or Curry Up Now, which offer delicious options without cheese.
+```
+
+In this example, you can see how the second response is tailored based on the information provided in the first interaction. Mem0 remembers the user's preference for Indian food and their cheese allergy, using this information to provide more relevant and personalized restaurant suggestions in San Francisco.
+
+### Use Mem0 OSS
+
+```python
+config = {
+    "vector_store": {
+        "provider": "qdrant",
+        "config": {
+            "host": "localhost",
+            "port": 6333,
+        }
+    },
+}
+
+client = Mem0(config=config)
+
+chat_completion = client.chat.completions.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "What's the capital of France?",
+        }
+    ],
+    model="gpt-4o",
+)
+```
+
+## APIs
+
+Get started with using Mem0 APIs in your applications. For more details, refer to the [Platform](/platform/quickstart.mdx).
+
+Here is an example of how to use Mem0 APIs:
+
+```python
+import os
+from mem0 import MemoryClient
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient() # get api_key from https://app.mem0.ai/
+
+# Store messages
+messages = [
+    {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
+    {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions."}
+]
+result = client.add(messages, user_id="alex")
+print(result)
+
+# Retrieve memories
+all_memories = client.get_all(user_id="alex")
+print(all_memories)
+
+# Search memories
+query = "What do you know about me?"
+related_memories = client.search(query, user_id="alex")
+
+# Get memory history
+history = client.history(memory_id="m1")
+print(history)
+```
+
+
+## Contributing
+
+We welcome contributions to Mem0! Here's how you can contribute:
+
+1. Fork the repository and create your branch from `main`.
+2. Clone the forked repository to your local machine.
+3. Install the project dependencies:
+
+   ```bash
+   poetry install
+   ```
+
+4. Install pre-commit hooks:
+
+   ```bash
+   pip install pre-commit  # If pre-commit is not already installed
+   pre-commit install
+   ```
+
+5. Make your changes and ensure they adhere to the project's coding standards.
+
+6. Run the tests locally:
+
+   ```bash
+   poetry run pytest
+   ```
+
+7. If all tests pass, commit your changes and push to your fork.
+8. Open a pull request with a clear title and description.
+
+Please make sure your code follows our coding conventions and is well-documented. We appreciate your contributions to make Mem0 better!
+
+
+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/open-source/quickstart.mdx

@@ -1,489 +1,28 @@
 ---
-title: Guide
-description: 'Get started with Mem0 quickly!'
-icon: "book"
+title: Overview
+icon: "info"
 iconType: "solid"
 ---
 
-> Welcome to the Mem0 quickstart guide. This guide will help you get up and running with Mem0 in no time.
+Welcome to Mem0 Open Source - a powerful, self-hosted memory management solution for AI agents and assistants. With Mem0 OSS, you get full control over your infrastructure while maintaining complete customization flexibility.
 
-## Installation
+We offer two SDKs for Python and Node.js.
 
-To install Mem0, you can use pip. Run the following command in your terminal:
+Check out our [GitHub repository](https://mem0.dev/gd) to explore the source code.
 
-```bash
-pip install mem0ai
-```
+<CardGroup cols={2}>
+<Card title="Python SDK Guide" icon="python" href="/open-source/python-quickstart">
+  Learn more about Mem0 OSS Python SDK
+</Card>
+<Card title="Node.js SDK Guide" icon="node" href="/open-source/node-quickstart">
+  Learn more about Mem0 OSS Node.js SDK
+</Card>
+</CardGroup>
 
-## Basic Usage
+## Key Features
 
-### Initialize Mem0
-
-<Tabs>
-  <Tab title="Basic">
-```python
-from mem0 import Memory
-m = Memory()
-```
-  </Tab>
-  <Tab title="Advanced">
-If you want to run Mem0 in production, initialize using the following method:
-
-Run Qdrant first:
-
-```bash
-docker pull qdrant/qdrant
-
-docker run -p 6333:6333 -p 6334:6334 \
-    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
-    qdrant/qdrant
-```
-
-Then, instantiate memory with qdrant server:
-
-```python
-from mem0 import Memory
-
-config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "host": "localhost",
-            "port": 6333,
-        }
-    },
-}
-
-m = Memory.from_config(config)
-```
-  </Tab>
-
-<Tab title="Advanced (Graph Memory)">
-
-```python
-from mem0 import Memory
-
-config = {
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {
-            "url": "neo4j+s://---",
-            "username": "neo4j",
-            "password": "---"
-        }
-    },
-    "version": "v1.1"
-}
-
-m = Memory.from_config(config_dict=config)
-```
-
-</Tab>
-</Tabs>
-
-
-### Store a Memory
-
-<CodeGroup>
-```python Code
-# For a user
-result = m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
-
-# messages = [
-#    {"role": "user", "content": "Hi, I'm Alex. I like to play cricket on weekends."},
-#    {"role": "assistant", "content": "Hello Alex! It's great to know that you enjoy playing cricket on weekends. I'll remember that for future reference."}
-# ]
-# client.add(messages, user_id="alice")
-```
-
-```json Output
-{
-    "results": [
-        {"id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", "memory": "Likes to play cricket on weekends", "event": "ADD"}
-    ],
-    "relations": [
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}, 
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}
-    ]
-}
-```
-</CodeGroup>
-
-### Retrieve Memories
-
-<CodeGroup>
-```python Code
-# Get all memories
-all_memories = m.get_all(user_id="alice")
-```
-
-```json Output
-{
-    "results": [
-        {
-            "id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", 
-            "memory": "Likes to play cricket on weekends", 
-            "hash": "285d07801ae42054732314853e9eadd7", 
-            "metadata": {"category": "hobbies"}, 
-            "created_at": "2024-10-28T12:32:07.744891-07:00", 
-            "updated_at": None, 
-            "user_id": "alice"
-        }
-    ], 
-    "relations": [
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}, 
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}
-    ]
-}
-```
-</CodeGroup>
-
-
-<br />
-
-<CodeGroup>
-```python Code
-# Get a single memory by ID
-specific_memory = m.get("bf4d4092-cf91-4181-bfeb-b6fa2ed3061b")
-```
-
-```json Output
-{
-    "id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", 
-    "memory": "Likes to play cricket on weekends", 
-    "hash": "285d07801ae42054732314853e9eadd7", 
-    "metadata": {"category": "hobbies"}, 
-    "created_at": "2024-10-28T12:32:07.744891-07:00", 
-    "updated_at": None, 
-    "user_id": "alice"
-}
-```
-</CodeGroup>
-
-### Search Memories
-
-<CodeGroup>
-```python Code
-related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
-```
-
-```json Output
-{
-    "results": [
-        {
-            "id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", 
-            "memory": "Likes to play cricket on weekends", 
-            "hash": "285d07801ae42054732314853e9eadd7", 
-            "metadata": {"category": "hobbies"}, 
-            "score": 0.30808347, 
-            "created_at": "2024-10-28T12:32:07.744891-07:00", 
-            "updated_at": None, 
-            "user_id": "alice"
-        }
-    ], 
-    "relations": [
-        {"source": "alice", "relationship": "plays_on", "target": "weekends"}, 
-        {"source": "alice", "relationship": "likes_to_play", "target": "cricket"}
-    ]
-}
-```
-</CodeGroup>
-
-### Update a Memory
-
-<CodeGroup>
-```python Code
-result = m.update(memory_id="bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", data="Likes to play tennis on weekends")
-```
-
-```json Output
-{'message': 'Memory updated successfully!'}
-```
-</CodeGroup>
-
-### Memory History
-
-<CodeGroup>
-```python Code
-history = m.history(memory_id="bf4d4092-cf91-4181-bfeb-b6fa2ed3061b")
-```
-
-```json Output
-[
-    {
-        "id": "96d2821d-e551-4089-aa57-9398c421d450", 
-        "memory_id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", 
-        "old_memory": None, 
-        "new_memory": "Likes to play cricket on weekends", 
-        "event": "ADD", 
-        "created_at": "2024-10-28T12:32:07.744891-07:00", 
-        "updated_at": None
-    }, 
-    {
-        "id": "3db4cb58-c0f1-4dd0-b62a-8123068ebfe7", 
-        "memory_id": "bf4d4092-cf91-4181-bfeb-b6fa2ed3061b", 
-        "old_memory": "Likes to play cricket on weekends", 
-        "new_memory": "Likes to play tennis on weekends", 
-        "event": "UPDATE", 
-        "created_at": "2024-10-28T12:32:07.744891-07:00", 
-        "updated_at": "2024-10-28T13:05:46.987978-07:00"
-    }
-]
-```
-</CodeGroup>
-
-### Delete Memory
-
-```python
-# Delete a memory by id
-m.delete(memory_id="bf4d4092-cf91-4181-bfeb-b6fa2ed3061b")
-# Delete all memories for a user
-m.delete_all(user_id="alice")
-```
-
-### Reset Memory
-
-```python
-m.reset() # Reset all memories
-```
-
-## Configuration Parameters
-
-Mem0 offers extensive configuration options to customize its behavior according to your needs. These configurations span across different components like vector stores, language models, embedders, and graph stores.
-
-<AccordionGroup>
-<Accordion title="Vector Store Configuration">
-| Parameter    | Description                     | Default     |
-|-------------|---------------------------------|-------------|
-| `provider`   | Vector store provider (e.g., "qdrant") | "qdrant"   |
-| `host`       | Host address                    | "localhost" |
-| `port`       | Port number                     | 6333       |
-</Accordion>
-
-<Accordion title="LLM Configuration">
-| Parameter              | Description                                   | Provider          |
-|-----------------------|-----------------------------------------------|-------------------|
-| `provider`            | LLM provider (e.g., "openai", "anthropic")    | All              |
-| `model`               | Model to use                                  | All              |
-| `temperature`         | Temperature of the model                      | All              |
-| `api_key`             | API key to use                                | All              |
-| `max_tokens`          | Tokens to generate                            | All              |
-| `top_p`               | Probability threshold for nucleus sampling    | All              |
-| `top_k`               | Number of highest probability tokens to keep  | All              |
-| `http_client_proxies` | Allow proxy server settings                   | AzureOpenAI      |
-| `models`              | List of models                                | Openrouter       |
-| `route`               | Routing strategy                              | Openrouter       |
-| `openrouter_base_url` | Base URL for Openrouter API                  | Openrouter       |
-| `site_url`            | Site URL                                      | Openrouter       |
-| `app_name`            | Application name                              | Openrouter       |
-| `ollama_base_url`     | Base URL for Ollama API                      | Ollama           |
-| `openai_base_url`     | Base URL for OpenAI API                      | OpenAI           |
-| `azure_kwargs`        | Azure LLM args for initialization            | AzureOpenAI      |
-| `deepseek_base_url`   | Base URL for DeepSeek API                    | DeepSeek         |
-</Accordion>
-
-<Accordion title="Embedder Configuration">
-| Parameter    | Description                     | Default                      |
-|-------------|---------------------------------|------------------------------|
-| `provider`   | Embedding provider              | "openai"                     |
-| `model`      | Embedding model to use          | "text-embedding-3-small"     |
-| `api_key`    | API key for embedding service   | None                        |
-</Accordion>
-
-<Accordion title="Graph Store Configuration">
-| Parameter    | Description                     | Default     |
-|-------------|---------------------------------|-------------|
-| `provider`   | Graph store provider (e.g., "neo4j") | "neo4j"    |
-| `url`        | Connection URL                  | None        |
-| `username`   | Authentication username         | None        |
-| `password`   | Authentication password         | None        |
-</Accordion>
-
-<Accordion title="General Configuration">
-| Parameter         | Description                          | Default                    |
-|------------------|--------------------------------------|----------------------------|
-| `history_db_path` | Path to the history database         | "{mem0_dir}/history.db"    |
-| `version`         | API version                          | "v1.0"                     |
-| `custom_prompt`   | Custom prompt for memory processing  | None                       |
-</Accordion>
-
-<Accordion title="Complete Configuration Example">
-```python
-config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "host": "localhost",
-            "port": 6333
-        }
-    },
-    "llm": {
-        "provider": "openai",
-        "config": {
-            "api_key": "your-api-key",
-            "model": "gpt-4"
-        }
-    },
-    "embedder": {
-        "provider": "openai",
-        "config": {
-            "api_key": "your-api-key",
-            "model": "text-embedding-3-small"
-        }
-    },
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {
-            "url": "neo4j+s://your-instance",
-            "username": "neo4j",
-            "password": "password"
-        }
-    },
-    "history_db_path": "/path/to/history.db",
-    "version": "v1.1",
-    "custom_prompt": "Optional custom prompt for memory processing"
-}
-```
-</Accordion>
-</AccordionGroup>
-
-## Run Mem0 Locally
-
-Please refer to the example [Mem0 with Ollama](../examples/mem0-with-ollama) to run Mem0 locally.
-
-
-## Chat Completion
-
-Mem0 can be easily integrated into chat applications to enhance conversational agents with structured memory. Mem0's APIs are designed to be compatible with OpenAI's, with the goal of making it easy to leverage Mem0 in applications you may have already built.
-
-If you have a `Mem0 API key`, you can use it to initialize the client. Alternatively, you can initialize Mem0 without an API key if you're using it locally.
-
-Mem0 supports several language models (LLMs) through integration with various [providers](https://litellm.vercel.app/docs/providers).
-
-## Use Mem0 Platform
-
-```python
-from mem0.proxy.main import Mem0
-
-client = Mem0(api_key="m0-xxx")
-
-# First interaction: Storing user preferences
-messages = [
-  {
-    "role": "user",
-    "content": "I love indian food but I cannot eat pizza since allergic to cheese."
-  },
-]
-user_id = "alice"
-chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
-# Memory saved after this will look like: "Loves Indian food. Allergic to cheese and cannot eat pizza."
-
-# Second interaction: Leveraging stored memory
-messages = [
-  {
-    "role": "user",
-    "content": "Suggest restaurants in San Francisco to eat.",
-  }
-]
-
-chat_completion = client.chat.completions.create(messages=messages, model="gpt-4o-mini", user_id=user_id)
-print(chat_completion.choices[0].message.content)
-# Answer: You might enjoy Indian restaurants in San Francisco, such as Amber India, Dosa, or Curry Up Now, which offer delicious options without cheese.
-```
-
-In this example, you can see how the second response is tailored based on the information provided in the first interaction. Mem0 remembers the user's preference for Indian food and their cheese allergy, using this information to provide more relevant and personalized restaurant suggestions in San Francisco.
-
-### Use Mem0 OSS
-
-```python
-config = {
-    "vector_store": {
-        "provider": "qdrant",
-        "config": {
-            "host": "localhost",
-            "port": 6333,
-        }
-    },
-}
-
-client = Mem0(config=config)
-
-chat_completion = client.chat.completions.create(
-    messages=[
-        {
-            "role": "user",
-            "content": "What's the capital of France?",
-        }
-    ],
-    model="gpt-4o",
-)
-```
-
-## APIs
-
-Get started with using Mem0 APIs in your applications. For more details, refer to the [Platform](/platform/quickstart.mdx).
-
-Here is an example of how to use Mem0 APIs:
-
-```python
-from mem0 import MemoryClient
-client = MemoryClient(api_key="your-api-key") # get api_key from https://app.mem0.ai/
-
-# Store messages
-messages = [
-    {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
-    {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions."}
-]
-result = client.add(messages, user_id="alex")
-print(result)
-
-# Retrieve memories
-all_memories = client.get_all(user_id="alex")
-print(all_memories)
-
-# Search memories
-query = "What do you know about me?"
-related_memories = client.search(query, user_id="alex")
-
-# Get memory history
-history = client.history(memory_id="m1")
-print(history)
-```
-
-
-## Contributing
-
-We welcome contributions to Mem0! Here's how you can contribute:
-
-1. Fork the repository and create your branch from `main`.
-2. Clone the forked repository to your local machine.
-3. Install the project dependencies:
-
-   ```bash
-   poetry install
-   ```
-
-4. Install pre-commit hooks:
-
-   ```bash
-   pip install pre-commit  # If pre-commit is not already installed
-   pre-commit install
-   ```
-
-5. Make your changes and ensure they adhere to the project's coding standards.
-
-6. Run the tests locally:
-
-   ```bash
-   poetry run pytest
-   ```
-
-7. If all tests pass, commit your changes and push to your fork.
-8. Open a pull request with a clear title and description.
-
-Please make sure your code follows our coding conventions and is well-documented. We appreciate your contributions to make Mem0 better!
-
-
-If you have any questions, please feel free to reach out to us using one of the following methods:
-
-<Snippet file="get-help.mdx" />
+- **Full Infrastructure Control**: Host Mem0 on your own servers
+- **Customizable Implementation**: Modify and extend functionality as needed
+- **Local Development**: Perfect for development and testing
+- **No Vendor Lock-in**: Own your data and infrastructure
+- **Community Driven**: Benefit from and contribute to community improvements

docs/openapi.json

@@ -774,26 +774,27 @@
 												"type": "string",
 												"format": "date-time"
 											},
-											"total_memories": {
-												"type": "integer"
-											},
 											"owner": {
 												"type": "string"
 											},
+											"immutable": {
+												"type": "boolean",
+												"description": "Whether the memory is immutable.",
+												"title": "Immutable",
+												"default": false
+											},
+											"expiration_date": {
+												"type": "string",
+												"format": "date-time",
+												"description": "The date and time when the memory will expire. The default expiration date is 30 days from the date of creation. Format: YYYY-MM-DD",
+												"title": "Expiration date",
+												"nullable": true
+											},
 											"organization": {
 												"type": "string"
 											},
 											"metadata": {
 												"type": "object"
-											},
-											"type": {
-												"type": "string",
-												"enum": [
-													"user",
-													"agent",
-													"app",
-													"run"
-												]
 											}
 										},
 										"required": [
@@ -938,27 +939,27 @@
 				"x-code-samples": [
 				{
 				"lang": "Python",
-				"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your_api_key\", org_id=\"your_org_id\", project_id=\"your_project_id\")\n\nmessages = [\n    {\"role\": \"user\", \"content\": \"<user-message>\"},\n    {\"role\": \"assistant\", \"content\": \"<assistant-response>\"}\n]\n\nclient.add(messages, user_id=\"<user-id>\")"
+				"source": "# To use the Python SDK, install the package:\n# pip install mem0ai\n\nfrom mem0 import MemoryClient\n\nclient = MemoryClient(api_key=\"your_api_key\", org_id=\"your_org_id\", project_id=\"your_project_id\")\n\nmessages = [\n    {\"role\": \"user\", \"content\": \"<user-message>\"},\n    {\"role\": \"assistant\", \"content\": \"<assistant-response>\"}\n]\n\nclient.add(messages, user_id=\"<user-id>\", version=\"v2\")"
 				},
 				{
 				"lang": "JavaScript",
-				"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst messages = [\n  { role: \"user\", content: \"Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts.\" },\n  { role: \"assistant\", content: \"Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions.\" }\n];\n\nclient.add(messages, { user_id: \"<user_id>\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
+				"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\nconst messages = [\n  { role: \"user\", content: \"Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts.\" },\n  { role: \"assistant\", content: \"Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions.\" }\n];\n\nclient.add(messages, { user_id: \"<user_id>\", version: \"v2\" })\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
 				},				  
 				{
 				"lang": "cURL",
-				"source": "curl --request POST \\\n  --url https://api.mem0.ai/v1/memories/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"messages\": [\n    {}\n  ],\n  \"agent_id\": \"<string>\",\n  \"user_id\": \"<string>\",\n  \"app_id\": \"<string>\",\n  \"run_id\": \"<string>\",\n  \"metadata\": {},\n  \"includes\": \"<string>\",\n  \"excludes\": \"<string>\",\n  \"infer\": true,\n  \"custom_categories\": {}, \n  \"org_id\": \"<string>\",\n  \"project_id\": \"<string>\"\n}'"
+				"source": "curl --request POST \\\n  --url https://api.mem0.ai/v1/memories/ \\\n  --header 'Authorization: Token <api-key>' \\\n  --header 'Content-Type: application/json' \\\n  --data '{\n  \"messages\": [\n    {}\n  ],\n  \"agent_id\": \"<string>\",\n  \"user_id\": \"<string>\",\n  \"app_id\": \"<string>\",\n  \"run_id\": \"<string>\",\n  \"metadata\": {},\n  \"includes\": \"<string>\",\n  \"excludes\": \"<string>\",\n  \"infer\": true,\n  \"custom_categories\": {}, \n  \"org_id\": \"<string>\",\n  \"project_id\": \"<string>\",\n  \"version\": \"v2\"\n}'"
 				},
 				{
 				"lang": "Go",
-				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/v1/memories/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {},\n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
+				"source": "package main\n\nimport (\n\t\"fmt\"\n\t\"strings\"\n\t\"net/http\"\n\t\"io/ioutil\"\n)\n\nfunc main() {\n\n\turl := \"https://api.mem0.ai/v1/memories/\"\n\n\tpayload := strings.NewReader(\"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {},\n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\",\n  \\\"version\\\": \"v2\"\n}\")\n\n\treq, _ := http.NewRequest(\"POST\", url, payload)\n\n\treq.Header.Add(\"Authorization\", \"Token <api-key>\")\n\treq.Header.Add(\"Content-Type\", \"application/json\")\n\n\tres, _ := http.DefaultClient.Do(req)\n\n\tdefer res.Body.Close()\n\tbody, _ := ioutil.ReadAll(res.Body)\n\n\tfmt.Println(res)\n\tfmt.Println(string(body))\n\n}"
 				},
 				{
 				"lang": "PHP",
-				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/v1/memories/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {}, \n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
+				"source": "<?php\n\n$curl = curl_init();\n\ncurl_setopt_array($curl, [\n  CURLOPT_URL => \"https://api.mem0.ai/v1/memories/\",\n  CURLOPT_RETURNTRANSFER => true,\n  CURLOPT_ENCODING => \"\",\n  CURLOPT_MAXREDIRS => 10,\n  CURLOPT_TIMEOUT => 30,\n  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,\n  CURLOPT_CUSTOMREQUEST => \"POST\",\n  CURLOPT_POSTFIELDS => \"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {}, \n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\",\n  \\\"version\\\": \"v2\"\n}\",\n  CURLOPT_HTTPHEADER => [\n    \"Authorization: Token <api-key>\",\n    \"Content-Type: application/json\"\n  ],\n]);\n\n$response = curl_exec($curl);\n$err = curl_error($curl);\n\ncurl_close($curl);\n\nif ($err) {\n  echo \"cURL Error #:\" . $err;\n} else {\n  echo $response;\n}"
 				},
 				{
 				"lang": "Java",
-				"source": "HttpResponse<String> response = Unirest.post(\"https://api.mem0.ai/v1/memories/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {}, \n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\"\n}\")\n  .asString();"
+				"source": "HttpResponse<String> response = Unirest.post(\"https://api.mem0.ai/v1/memories/\")\n  .header(\"Authorization\", \"Token <api-key>\")\n  .header(\"Content-Type\", \"application/json\")\n  .body(\"{\n  \\\"messages\\\": [\n    {}\n  ],\n  \\\"agent_id\\\": \\\"<string>\\\",\n  \\\"user_id\\\": \\\"<string>\\\",\n  \\\"app_id\\\": \\\"<string>\\\",\n  \\\"run_id\\\": \\\"<string>\\\",\n  \\\"metadata\\\": {},\n  \\\"includes\\\": \\\"<string>\\\",\n  \\\"excludes\\\": \\\"<string>\\\",\n  \\\"infer\\\": true,\n  \\\"custom_categories\\\": {}, \n  \\\"org_id\\\": \\\"<string>\\\",\n  \\\"project_id\\\": \\\"<string>\",\n  \\\"version\\\": \"v2\"\n}\")\n  .asString();"
 					}
 				],
 				"x-codegen-request-body-name": "data"
@@ -1097,6 +1098,7 @@
 								"created_at": {"type": "string", "format": "date-time"},
 								"updated_at": {"type": "string", "format": "date-time"},
 								"categories": {"type": "array", "items": {"type": "string"}},
+								"metadata": {"type": "object"},
 								"keywords": {"type": "string"}
 							},
 							"additionalProperties": {
@@ -1117,6 +1119,15 @@
 						"style": "deepObject",
 						"explode": true
 					},
+					{
+						"name": "fields",
+						"in": "query",
+						"schema": {
+							"type": "array",
+							"items": {"type": "string"}
+						},
+						"description": "A list of field names to include in the response. If not provided, all fields will be returned."
+					},
 					{
 						"name": "org_id",
 						"in": "query",
@@ -1170,26 +1181,27 @@
 												"type": "string",
 												"format": "date-time"
 											},
-											"total_memories": {
-												"type": "integer"
-											},
 											"owner": {
 												"type": "string"
 											},
+											"immutable": {
+												"type": "boolean",
+												"description": "Whether the memory is immutable.",
+												"title": "Immutable",
+												"default": false
+											},
+											"expiration_date": {
+												"type": "string",
+												"format": "date-time",
+												"description": "The date and time when the memory will expire. The default expiration date is 30 days from the date of creation. Format: YYYY-MM-DD",
+												"title": "Expiration date",
+												"nullable": true
+											},
 											"organization": {
 												"type": "string"
 											},
 											"metadata": {
 												"type": "object"
-											},
-											"type": {
-												"type": "string",
-												"enum": [
-													"user",
-													"agent",
-													"app",
-													"run"
-												]
 											}
 										},
 										"required": [
@@ -1320,6 +1332,19 @@
 												},
 												"description": "Categories associated with the memory"
 											},
+											"immutable": {
+												"type": "boolean",
+												"description": "Whether the memory is immutable.",
+												"title": "Immutable",
+												"default": false
+											},
+											"expiration_date": {
+												"type": "string",
+												"format": "date-time",
+												"description": "The date and time when the memory will expire. The default expiration date is 30 days from the date of creation. Format: YYYY-MM-DD",
+												"title": "Expiration date",
+												"nullable": true
+											},
 											"created_at": {
 												"type": "string",
 												"format": "date-time",
@@ -1441,6 +1466,19 @@
 												},
 												"description": "Categories associated with the memory"
 											},
+											"immutable": {
+												"type": "boolean",
+												"description": "Whether the memory is immutable.",
+												"title": "Immutable",
+												"default": false
+											},
+											"expiration_date": {
+												"type": "string",
+												"format": "date-time",
+												"description": "The date and time when the memory will expire. The default expiration date is 30 days from the date of creation. Format: YYYY-MM-DD",
+												"title": "Expiration date",
+												"nullable": true
+											},
 											"created_at": {
 												"type": "string",
 												"format": "date-time",
@@ -1751,7 +1789,7 @@
 				},
 				{
 				"lang": "JavaScript",
-				"source": "const options = {\n  method: 'PUT',\n  headers: {\n    'Authorization': 'Token <api-key>',\n    'Content-Type': 'application/json'\n  },\n  body: JSON.stringify({\n    text: 'Your updated memory text here'\n  })\n};\n\nfetch('https://api.mem0.ai/v1/memories/{memory_id}/', options)\n  .then(response => response.json())\n  .then(response => console.log(response))\n  .catch(err => console.error(err));"
+				"source": "// To use the JavaScript SDK, install the package:\n// npm i mem0ai\n\nimport MemoryClient from 'mem0ai';\nconst client = new MemoryClient({ apiKey: \"your-api-key\" });\n\n// Update a specific memory\nconst memory_id=<memory_id>\nconst message=\"Your updated memory message here\"\nclient.update(memory_id, message)\n  .then(result => console.log(result))\n  .catch(error => console.error(error));"
 				},
 				{
 				"lang": "cURL",
@@ -4733,7 +4771,7 @@
 						"nullable": true
 					},
 					"metadata": {
-						"description": "Additional metadata associated with the memory, which can be used to store any additional information or context about the memory.",
+						"description": "Additional metadata associated with the memory, which can be used to store any additional information or context about the memory. Best practice for incorporating additional information is through metadata (e.g. location, time, ids, etc.). During retrieval, you can either use these metadata alongside the query to fetch relevant memories or retrieve memories based on the query first and then refine the results using metadata during post-processing.",
 						"title": "Metadata",
 						"type": "object",
 						"properties": {
@@ -4782,6 +4820,18 @@
 						"type": "string",
 						"nullable": true
 					},
+					"immutable": {
+						"description": "Whether the memory is immutable.",
+						"title": "Immutable",
+						"type": "boolean",
+						"default": false
+					},
+					"expiration_date": {
+						"description": "The date and time when the memory will expire. The default expiration date is 30 days from the date of creation. Format: YYYY-MM-DD",
+						"title": "Expiration date",
+						"type": "string",
+						"nullable": true
+					},
 					"org_id": {
 						"description": "The unique identifier of the organization associated with this memory.",
 						"title": "Organization id",
@@ -4793,6 +4843,12 @@
 						"title": "Project id",
 						"type": "string",
 						"nullable": true
+					},
+					"version": {
+						"description": "The version of the memory to use. The default version is v1, which is deprecated. We recommend using v2 for new applications.",
+						"title": "Version", 
+						"type": "string",
+						"nullable": true
 					}
 				}
 			},

docs/overview.mdx

@@ -5,7 +5,7 @@ iconType: "solid"
 ---
 
 <Note type="info">
-  🔔 New Feature: [Webhooks](/features/webhook) are now available! Configure real-time notifications for memory events in your Mem0 project.
+  🎉 We now support [Claude 3.7 Sonnet](components/llms/models/anthropic)! Enhance your AI assistants with the latest and most capable language model from Anthropic.
 </Note>
 
 # Introduction

docs/platform/quickstart.mdx

@@ -87,10 +87,10 @@ messages = [
 ]
 
 # The default output_format is v1.0
-client.add(messages, user_id="alex", output_format="v1.0")
+client.add(messages, user_id="alex", output_format="v1.0", version="v2")
 
 # To use the latest output_format, set the output_format parameter to "v1.1"
-client.add(messages, user_id="alex", output_format="v1.1", metadata={"food": "vegan"})
+client.add(messages, user_id="alex", output_format="v1.1", metadata={"food": "vegan"}, version="v2")
 ```
 
 ```javascript JavaScript
@@ -98,7 +98,7 @@ const messages = [
     {"role": "user", "content": "Hi, I'm Alex. I'm a vegetarian and I'm allergic to nuts."},
     {"role": "assistant", "content": "Hello Alex! I've noted that you're a vegetarian and have a nut allergy. I'll keep this in mind for any food-related recommendations or discussions."}
 ];
-client.add(messages, { user_id: "alex", output_format: "v1.1", metadata: { food: "vegan" } })
+client.add(messages, { user_id: "alex", output_format: "v1.1", metadata: { food: "vegan" }, version: "v2" })
     .then(response => console.log(response))
     .catch(error => console.error(error));
 ```
@@ -116,7 +116,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
          "output_format": "v1.1",
          "metadata": {
              "food": "vegan"
-         }
+         },
+         "version": "v2"
      }'
 ```
 
@@ -173,6 +174,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
     Messages passed along with `user_id`, `run_id`, or `app_id` are stored as user memories, while messages from the assistant are excluded from memory. To store messages for the assistant, use `agent_id` exclusively and avoid including other IDs, such as user_id, alongside it. This ensures the memory is properly attributed to the assistant.
 </Note>
 
+<Note>Metadata allows you to store structured information (location, timestamp, user state) with memories. Add it during creation to enable precise filtering and retrieval during searches.</Note>
+
 
 #### Short-term memory for a user session
 
@@ -189,10 +192,10 @@ messages = [
 ]
 
 # The default output_format is v1.0
-client.add(messages, user_id="alex123", run_id="trip-planning-2024", output_format="v1.0")
+client.add(messages, user_id="alex", run_id="trip-planning-2024", output_format="v1.0", version="v2")
 
 # To use the latest output_format, set the output_format parameter to "v1.1"
-client.add(messages, user_id="alex123", run_id="trip-planning-2024", output_format="v1.1")
+client.add(messages, user_id="alex", run_id="trip-planning-2024", output_format="v1.1", version="v2")
 ```
 
 ```javascript JavaScript
@@ -202,7 +205,7 @@ const messages = [
     {"role": "user", "content": "Yes, please! Especially in Tokyo."},
     {"role": "assistant", "content": "Great! I'll remember that you're interested in vegetarian restaurants in Tokyo for your upcoming trip. I'll prepare a list for you in our next interaction."}
 ];
-client.add(messages, { user_id: "alex123", run_id: "trip-planning-2024", output_format: "v1.1" })
+client.add(messages, { user_id: "alex", run_id: "trip-planning-2024", output_format: "v1.1", version: "v2" })
     .then(response => console.log(response))
     .catch(error => console.error(error));
 ```
@@ -218,9 +221,10 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
              {"role": "user", "content": "Yes, please! Especially in Tokyo."},
              {"role": "assistant", "content": "Great! I'll remember that you're interested in vegetarian restaurants in Tokyo for your upcoming trip. I'll prepare a list for you in our next interaction."}
          ],
-         "user_id": "alex123",
+         "user_id": "alex",
          "run_id": "trip-planning-2024",
-         "output_format": "v1.1"
+         "output_format": "v1.1",
+         "version": "v2"
      }'
 ```
 
@@ -272,10 +276,10 @@ messages = [
 ]
 
 # The default output_format is v1.0
-client.add(messages, agent_id="ai-tutor", output_format="v1.0")
+client.add(messages, agent_id="ai-tutor", output_format="v1.0", version="v2")
 
 # To use the latest output_format, set the output_format parameter to "v1.1"
-client.add(messages, agent_id="ai-tutor", output_format="v1.1")
+client.add(messages, agent_id="ai-tutor", output_format="v1.1", version="v2")
 ```
 
 ```javascript JavaScript
@@ -283,7 +287,7 @@ const messages = [
     {"role": "system", "content": "You are an AI tutor with a personality. Give yourself a name for the user."},
     {"role": "assistant", "content": "Understood. I'm an AI tutor with a personality. My name is Alice."}
 ];
-client.add(messages, { agent_id: "ai-tutor", output_format: "v1.1" })
+client.add(messages, { agent_id: "ai-tutor", output_format: "v1.1", version: "v2" })
     .then(response => console.log(response))
     .catch(error => console.error(error));
 ```
@@ -298,7 +302,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
              {"role": "assistant", "content": "Understood. I'm an AI tutor with a personality. My name is Alice."}
          ],
          "agent_id": "ai-tutor",
-         "output_format": "v1.1"
+         "output_format": "v1.1",
+         "version": "v2"
      }'
 ```
 
@@ -352,6 +357,148 @@ curl -X POST "https://api.mem0.ai/v1/memories/" \
     The `agent_id` retains memories exclusively based on messages generated by the assistant or those explicitly provided as input to the assistant. Messages outside these criteria are not stored as memory.
 </Note>
 
+#### Long-term memory for both users and agents
+When you provide both `user_id` and `agent_id`, Mem0 will store memories with both identifiers attached:
+- Each memory will be tagged with both the specified `user_id` and `agent_id`
+- During retrieval, you'll need to provide both IDs to access the memories
+- This enables tracking the full context of conversations between specific users and agents
+
+<CodeGroup>
+
+```python Python
+messages = [
+    {"role": "user", "content": "I'm travelling to San Francisco"},
+    {"role": "assistant", "content": "That's great! I'm going to Dubai next month."},
+]
+
+client.add(messages=messages, user_id="user1", agent_id="agent1", version="v2")
+```
+
+```javascript JavaScript
+const messages = [
+    {"role": "user", "content": "I'm travelling to San Francisco"},
+    {"role": "assistant", "content": "That's great! I'm going to Dubai next month."},
+]
+
+client.add(messages, { user_id: "user1", agent_id: "agent1", version: "v2" })
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "messages": [
+             {"role": "user", "content": "I'm travelling to San Francisco"},
+             {"role": "assistant", "content": "That's great! I'm going to Dubai next month."},
+         ],
+         "user_id": "user1",
+         "agent_id": "agent1",
+         "version": "v2"
+     }'
+```
+
+```json Output
+[
+    {
+        'id': 'c57abfa2-f0ac-48af-896a-21728dbcecee0',
+        'data': {'memory': 'Travelling to San Francisco'},
+        'event': 'ADD'
+    },
+    {   'id': '0e8c003f-7db7-426a-9fdc-a46f9331a0c2',
+        'data': {'memory': 'Going to Dubai next month'},
+        'event': 'ADD'
+    }
+]
+```
+
+</CodeGroup>
+
+#### Setting Memory Expiration Date
+
+You can set an expiration date for memories, after which they will no longer be retrieved in searches. This is useful for creating temporary memories or memories that are only relevant for a specific time period.
+
+<CodeGroup>
+
+```python Python
+import datetime
+
+messages = [
+    {
+        "role": "user", 
+        "content": "I'll be in San Francisco until August 31st."
+    }
+]
+
+# Set an expiration date for this memory
+client.add(messages=messages, user_id="alex", expiration_date=str(datetime.datetime.now().date() + datetime.timedelta(days=30)))
+
+# You can also use an explicit date string
+client.add(messages=messages, user_id="alex", expiration_date="2023-08-31")
+```
+
+```javascript JavaScript
+const messages = [
+    {
+        "role": "user", 
+        "content": "I'll be in San Francisco until August 31st."
+    }
+];
+
+// Set an expiration date 30 days from now
+const expirationDate = new Date();
+expirationDate.setDate(expirationDate.getDate() + 30);
+client.add(messages, { 
+    user_id: "alex", 
+    expiration_date: expirationDate.toISOString().split('T')[0] 
+})
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+
+// You can also use an explicit date string
+client.add(messages, { 
+    user_id: "alex", 
+    expiration_date: "2023-08-31" 
+})
+    .then(response => console.log(response))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "messages": [
+             {
+                "role": "user", 
+                "content": "I'll be in San Francisco until August 31st."
+            }
+         ],
+         "user_id": "alex",
+         "expiration_date": "2023-08-31"
+     }'
+```
+
+```json Output
+[
+  {
+    "id": "a1b2c3d4-e5f6-4g7h-8i9j-k0l1m2n3o4p5",
+    "data": {
+      "memory": "In San Francisco until August 31st"
+    },
+    "event": "ADD"
+  }
+]
+```
+
+</CodeGroup>
+
+<Note>
+Once a memory reaches its expiration date, it won't be included in search or get results.
+</Note>
 
 #### Monitor Memories
 
@@ -405,6 +552,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/" \
         "user_id": "alex",
         "metadata": {"food": "vegan"},
         "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
         "created_at": "2024-07-20T01:30:36.275141-07:00",
         "updated_at": "2024-07-20T01:30:36.275172-07:00"
     }
@@ -420,6 +569,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/" \
             "user_id": "alex",
             "metadata": {"food": "vegan"},
             "categories": ["food_preferences"],
+            "immutable": false,
+            "expiration_date": null,
             "created_at": "2024-07-20T01:30:36.275141-07:00",
             "updated_at": "2024-07-20T01:30:36.275172-07:00"
         }
@@ -429,6 +580,7 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/" \
 </CodeGroup>
 
 Use category and metadata filters:
+
 <CodeGroup>
 
 ```python Python
@@ -463,6 +615,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
         "user_id": "alex",
         "metadata": {"food": "vegan"},
         "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
         "created_at": "2024-07-20T01:30:36.275141-07:00",
         "updated_at": "2024-07-20T01:30:36.275172-07:00"
     }
@@ -552,6 +706,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
         "user_id": "alex",
         "metadata": null,
         "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
         "created_at": "2024-07-20T01:30:36.275141-07:00",
         "updated_at": "2024-07-20T01:30:36.275172-07:00"
     }
@@ -616,6 +772,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
         "user_id": "alex",
         "metadata": null,
         "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
         "created_at": "2024-07-20T01:30:36.275141-07:00",
         "updated_at": "2024-07-20T01:30:36.275172-07:00"
     }
@@ -689,6 +847,8 @@ curl -X POST "https://api.mem0.ai/v1/memories/search/?version=v2" \
         "user_id": "alex",
         "metadata": {"food": "vegan"},
         "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
         "created_at": "2024-07-20T01:30:36.275141-07:00",
         "updated_at": "2024-07-20T01:30:36.275172-07:00"
     }
@@ -788,6 +948,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&page=1&page_size=50"
             "agent_id":"travel-assistant",
             "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-25T23:57:00.108347-07:00",
             "updated_at":"2024-07-25T23:57:00.108367-07:00",
             "categories":None
@@ -798,6 +960,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&page=1&page_size=50"
             "agent_id":"travel-assistant",
             "hash":"35a305373d639b0bffc6c2a3e2eb4244",
             "metadata":"None",
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-26T00:31:03.543759-07:00",
             "updated_at":"2024-07-26T00:31:03.543778-07:00",
             "categories":None
@@ -820,6 +984,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&page=1&page_size=50"
                 "agent_id":"travel-assistant",
                 "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at":"2024-07-25T23:57:00.108347-07:00",
                 "updated_at":"2024-07-25T23:57:00.108367-07:00",
                 "categories":None
@@ -830,6 +996,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&page=1&page_size=50"
                 "agent_id":"travel-assistant",
                 "hash":"35a305373d639b0bffc6c2a3e2eb4244",
                 "metadata":"None",
+                "immutable": false,
+                "expiration_date": null,
                 "created_at":"2024-07-26T00:31:03.543759-07:00",
                 "updated_at":"2024-07-26T00:31:03.543778-07:00",
                 "categories":None
@@ -873,6 +1041,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?agent_id=ai-tutor&page=1&page_size
             "agent_id":"ai-tutor",
             "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-25T23:57:00.108347-07:00",
             "updated_at":"2024-07-25T23:57:00.108367-07:00",
             "categories":None
@@ -883,6 +1053,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?agent_id=ai-tutor&page=1&page_size
             "agent_id":"ai-tutor",
             "hash":"35a305373d639b0bffc6c2a3e2eb4244",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-26T00:31:03.543759-07:00",
             "updated_at":"2024-07-26T00:31:03.543778-07:00",
             "categories":None
@@ -905,6 +1077,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?agent_id=ai-tutor&page=1&page_size
                 "agent_id": "ai-tutor",
                 "hash": "62bc074f56d1f909f1b4c2b639f56f6a",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at": "2024-07-25T23:57:00.108347-07:00",
                 "updated_at": "2024-07-25T23:57:00.108367-07:00"
             },
@@ -914,6 +1088,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?agent_id=ai-tutor&page=1&page_size
                 "agent_id": "ai-tutor",
                 "hash": "35a305373d639b0bffc6c2a3e2eb4244",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at": "2024-07-26T00:31:03.543759-07:00",
                 "updated_at": "2024-07-26T00:31:03.543778-07:00"
             }
@@ -929,17 +1105,17 @@ curl -X GET "https://api.mem0.ai/v1/memories/?agent_id=ai-tutor&page=1&page_size
 <CodeGroup>
 
 ```python Python
-short_term_memories = client.get_all(user_id="alex123", run_id="trip-planning-2024", page=1, page_size=50)
+short_term_memories = client.get_all(user_id="alex", run_id="trip-planning-2024", page=1, page_size=50)
 ```
 
 ```javascript JavaScript
-client.getAll({ user_id: "alex123", run_id: "trip-planning-2024", page: 1, page_size: 50 })
+client.getAll({ user_id: "alex", run_id: "trip-planning-2024", page: 1, page_size: 50 })
     .then(memories => console.log(memories))
     .catch(error => console.error(error));
 ```
 
 ```bash cURL
-curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planning-2024&page=1&page_size=50" \
+curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&run_id=trip-planning-2024&page=1&page_size=50" \
      -H "Authorization: Token your-api-key"
 ```
 
@@ -952,9 +1128,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
         {
             "id":"06d8df63-7bd2-4fad-9acb-60871bcecee0",
             "memory":"Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-            "user_id":"alex123",
+            "user_id":"alex",
             "hash":"d2088c936e259f2f5d2d75543d31401c",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-26T00:25:16.566471-07:00",
             "updated_at":"2024-07-26T00:25:16.566492-07:00",
             "categories":None
@@ -962,9 +1140,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
         {
             "id":"b4229775-d860-4ccb-983f-0f628ca112f5",
             "memory":"Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-            "user_id":"alex123",
+            "user_id":"alex",
             "hash":"d2088c936e259f2f5d2d75543d31401c",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-26T00:33:20.350542-07:00",
             "updated_at":"2024-07-26T00:33:20.350560-07:00",
             "categories":None
@@ -972,9 +1152,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
         {
             "id":"df1aca24-76cf-4b92-9f58-d03857efcb64",
             "memory":"Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-            "user_id":"alex123",
+            "user_id":"alex",
             "hash":"d2088c936e259f2f5d2d75543d31401c",
             "metadata":None,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-26T00:51:09.642275-07:00",
             "updated_at":"2024-07-26T00:51:09.642295-07:00",
             "categories":None
@@ -994,9 +1176,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
             {
                 "id": "06d8df63-7bd2-4fad-9acb-60871bcecee0",
                 "memory": "Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-                "user_id": "alex123",
+                "user_id": "alex",
                 "hash": "d2088c936e259f2f5d2d75543d31401c",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at": "2024-07-26T00:25:16.566471-07:00",
                 "updated_at": "2024-07-26T00:25:16.566492-07:00",
                 "categories": ["food_preferences"]
@@ -1004,9 +1188,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
             {
                 "id": "b4229775-d860-4ccb-983f-0f628ca112f5",
                 "memory": "Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-                "user_id": "alex123",
+                "user_id": "alex",
                 "hash": "d2088c936e259f2f5d2d75543d31401c",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at": "2024-07-26T00:33:20.350542-07:00",
                 "updated_at": "2024-07-26T00:33:20.350560-07:00",
                 "categories": ["food_preferences"]
@@ -1014,9 +1200,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&run_id=trip-planni
             {
                 "id": "df1aca24-76cf-4b92-9f58-d03857efcb64",
                 "memory": "Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-                "user_id": "alex123",
+                "user_id": "alex",
                 "hash": "d2088c936e259f2f5d2d75543d31401c",
                 "metadata":None,
+                "immutable": false,
+                "expiration_date": null,
                 "created_at": "2024-07-26T00:51:09.642275-07:00",
                 "updated_at": "2024-07-26T00:51:09.642295-07:00",
                 "categories": None
@@ -1052,9 +1240,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/582bbe6d-506b-48c6-a4c6-5df3b1e6342
 {
    "id":"06d8df63-7bd2-4fad-9acb-60871bcecee0",
    "memory":"Planning a trip to Japan next month. Interested in vegetarian restaurants in Tokyo.",
-   "user_id":"alex123",
+   "user_id":"alex",
    "hash":"d2088c936e259f2f5d2d75543d31401c",
    "metadata":"None",
+   "immutable": false,
+   "expiration_date": null,
    "created_at":"2024-07-26T00:25:16.566471-07:00",
    "updated_at":"2024-07-26T00:25:16.566492-07:00",
    "categories": ["travel"]
@@ -1070,55 +1260,55 @@ You can filter memories by their categories when using get_all:
 
 ```python Python
 # Get memories with specific categories
-memories = client.get_all(user_id="alex123", categories=["likes"])
+memories = client.get_all(user_id="alex", categories=["likes"])
 
 # Get memories with multiple categories
-memories = client.get_all(user_id="alex123", categories=["likes", "food_preferences"])
+memories = client.get_all(user_id="alex", categories=["likes", "food_preferences"])
 
 # Custom pagination with categories
-memories = client.get_all(user_id="alex123", categories=["likes"], page=1, page_size=50)
+memories = client.get_all(user_id="alex", categories=["likes"], page=1, page_size=50)
 
 # Get memories with specific keywords
-memories = client.get_all(user_id="alex123", keywords="to play", page=1, page_size=50)
+memories = client.get_all(user_id="alex", keywords="to play", page=1, page_size=50)
 ```
 
 ```javascript JavaScript
 // Get memories with specific categories
-client.getAll({ user_id: "alex123", categories: ["likes"] })
+client.getAll({ user_id: "alex", categories: ["likes"] })
     .then(memories => console.log(memories))
     .catch(error => console.error(error));
 
 // Get memories with multiple categories
-client.getAll({ user_id: "alex123", categories: ["likes", "food_preferences"] })
+client.getAll({ user_id: "alex", categories: ["likes", "food_preferences"] })
     .then(memories => console.log(memories))
     .catch(error => console.error(error));
 
 // Custom pagination with categories
-client.getAll({ user_id: "alex123", categories: ["likes"], page: 1, page_size: 50 })
+client.getAll({ user_id: "alex", categories: ["likes"], page: 1, page_size: 50 })
     .then(memories => console.log(memories))
     .catch(error => console.error(error));
 
 // Get memories with specific keywords
-client.getAll({ user_id: "alex123", keywords: "to play", page: 1, page_size: 50 })
+client.getAll({ user_id: "alex", keywords: "to play", page: 1, page_size: 50 })
     .then(memories => console.log(memories))
     .catch(error => console.error(error));
 ```
 
 ```bash cURL
 # Get memories with specific categories
-curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&categories=likes" \
+curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&categories=likes" \
      -H "Authorization: Token your-api-key"
 
 # Get memories with multiple categories
-curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&categories=likes,food_preferences" \
+curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&categories=likes,food_preferences" \
      -H "Authorization: Token your-api-key"
 
 # Custom pagination with categories
-curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&categories=likes&page=1&page_size=50" \
+curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&categories=likes&page=1&page_size=50" \
      -H "Authorization: Token your-api-key"
 
 # Get memories with specific keywords
-curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&keywords=to play&page=1&page_size=50" \
+curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex&keywords=to play&page=1&page_size=50" \
      -H "Authorization: Token your-api-key"
 ```
 
@@ -1131,9 +1321,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&keywords=to play&p
         {
             "id": "06d8df63-7bd2-4fad-9acb-60871bcecee0",
             "memory": "Likes pizza and pasta",
-            "user_id": "alex123",
+            "user_id": "alex",
             "hash": "d2088c936e259f2f5d2d75543d31401c",
             "metadata": null,
+            "immutable": false,
+            "expiration_date": null,
             "created_at": "2024-07-26T00:25:16.566471-07:00",
             "updated_at": "2024-07-26T00:25:16.566492-07:00",
             "categories": ["likes", "food_preferences"]
@@ -1141,9 +1333,11 @@ curl -X GET "https://api.mem0.ai/v1/memories/?user_id=alex123&keywords=to play&p
         {
             "id": "b4229775-d860-4ccb-983f-0f628ca112f5",
             "memory": "Likes to travel to beach destinations",
-            "user_id": "alex123",
+            "user_id": "alex",
             "hash": "d2088c936e259f2f5d2d75543d31401c",
             "metadata": null,
+            "immutable": false,
+            "expiration_date": null,
             "created_at": "2024-07-26T00:33:20.350542-07:00",
             "updated_at": "2024-07-26T00:33:20.350560-07:00",
             "categories": ["likes"]
@@ -1160,7 +1354,7 @@ Our advanced retrieval allows you to set custom filters when fetching memories.
 
 Here you need to define `version` as `v2` in the get_all method.
 
-Example: Get all memories using user_id and date filters
+Example 1. Get all memories using user_id and date filters
 
 <CodeGroup>
 
@@ -1207,11 +1401,6 @@ const filters = {
          "categories":{
             "contains": "food_preferences"
          }
-      },
-      {
-         "keywords":{
-            "contains": "to play"
-         }
       }
    ]
 };
@@ -1235,20 +1424,14 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2" \
      -d '{
          "filters": {
              "AND": [
-                 {
-                     "user_id": "alex"
-                 },
-                 {
-                     "created_at": {
-                         "gte": "2024-07-01",
-                         "lte": "2024-07-31"
-                     }
-                 },
-                 {
-                    "categories":{
-                        "contains": "food_preferences"
-                    }
-                }
+                {"user_id":"alex"},
+                {"created_at":{
+                    "gte":"2024-07-01",
+                    "lte":"2024-07-31"
+                }},
+                {"categories":{
+                    "contains": "food_preferences"
+                }}
              ]
          }
      }'
@@ -1260,15 +1443,14 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
      -d '{
          "filters": {
              "AND": [
-                 {
-                     "user_id": "alex"
-                 },
-                 {
-                     "created_at": {
-                         "gte": "2024-07-01",
-                         "lte": "2024-07-31"
-                     }
-                 }
+                {"user_id":"alex"},
+                {"created_at":{
+                    "gte":"2024-07-01",
+                    "lte":"2024-07-31"
+                }},
+                {"categories":{
+                    "contains": "food_preferences"
+                }}
              ]
          }
      }'
@@ -1282,6 +1464,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
       "user_id":"alex",
       "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
       "metadata":null,
+      "immutable": false,
+      "expiration_date": null,
       "created_at":"2024-07-25T23:57:00.108347-07:00",
       "updated_at":"2024-07-25T23:57:00.108367-07:00",
       "categories": ["food_preferences"]
@@ -1301,6 +1485,8 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
             "user_id":"alex",
             "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
             "metadata":null,
+            "immutable": false,
+            "expiration_date": null,
             "created_at":"2024-07-25T23:57:00.108347-07:00",
             "updated_at":"2024-07-25T23:57:00.108367-07:00",
             "categories": ["food_preferences"]
@@ -1311,6 +1497,104 @@ curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
 
 </CodeGroup>
 
+Example 2: Search using metadata and categories Filters
+<CodeGroup>
+```python Python
+filters = {
+    "AND": [
+        {"metadata": {"food": "vegan"}},
+        {
+         "categories":{
+            "contains": "food_preferences"
+         }
+    }
+    ]
+}
+# Default (No Pagination)
+client.get_all(version="v2", filters=filters)
+
+# Pagination (You can also use the page and page_size parameters)
+client.get_all(version="v2", filters=filters, page=1, page_size=50)
+```
+
+```javascript JavaScript
+const filters = {
+    "AND": [
+        {"metadata": {"food": "vegan"}},
+        {
+            "categories": {
+                "contains": "food_preferences"
+            }
+        }
+    ]
+};
+
+// Default (No Pagination)
+client.getAll({ version: "v2", filters })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+
+// Pagination (You can also use the page and page_size parameters)
+client.getAll({ version: "v2", filters, page: 1, page_size: 50 })
+    .then(memories => console.log(memories))
+    .catch(error => console.error(error));
+```
+
+```bash cURL
+# Default (No Pagination)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+             "AND": [
+                {"metadata": {"food": "vegan"}},
+                {
+                    "categories": {
+                        "contains": "food_preferences"
+                    }
+                }}
+             ]
+         }
+     }'
+
+# Pagination (You can also use the page and page_size parameters)
+curl -X GET "https://api.mem0.ai/v1/memories/?version=v2&page=1&page_size=50" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "filters": {
+                    "AND": [
+                {"metadata": {"food": "vegan"}},
+                {
+                    "categories": {
+                        "contains": "food_preferences"
+                    }
+                }}
+            ]
+         }
+     }'
+```
+
+```json Output
+[
+    {
+        "id": "654fee-b411-4afe-b7e5-35789b72c4a5",
+        "memory": "Name: Alex. Vegetarian. Allergic to nuts.",
+        "user_id": "alex",
+        "metadata": {"food": "vegan"},
+        "categories": ["food_preferences"],
+        "immutable": false,
+        "expiration_date": null,
+        "created_at": "2024-07-20T01:30:36.275141-07:00",
+        "updated_at": "2024-07-20T01:30:36.275172-07:00"
+    }
+]
+```
+
+</CodeGroup>
+
+
 ### 4.5 Memory History
 
 Get history of how a memory has changed over time.
@@ -1393,7 +1677,7 @@ curl -X GET "https://api.mem0.ai/v1/memories/<memory-id-here>/history/" \
       ],
       "old_memory":"None",
       "new_memory":"Turned vegetarian.",
-      "user_id":"alex123456",
+      "user_id":"alex",
       "event":"ADD",
       "metadata":"None",
       "created_at":"2024-07-26T01:02:41.737310-07:00",
@@ -1623,13 +1907,10 @@ response = client.batch_update(update_memories)
 print(response)
 ```
 ```javascript JavaScript
-const updateMemories = [
-    {
-        memoryId: "285ed74b-6e05-4043-b16b-3abd5b533496",
+const updateMemories = [{"memory_id": "285ed74b-6e05-4043-b16b-3abd5b533496",
         text: "Watches football"
     },
-    {
-        memoryId: "2c9bd859-d1b7-4d33-a6b8-94e0147c4f07",
+    {"memory_id": "2c9bd859-d1b7-4d33-a6b8-94e0147c4f07",
         text: "Loves to travel"
     }
 ];
@@ -1674,8 +1955,7 @@ response = client.batch_delete(delete_memories)
 print(response)
 ```
 ```javascript JavaScript
-const deleteMemories = [
-    {"memory_id": "285ed74b-6e05-4043-b16b-3abd5b533496"},
+const deleteMemories = [{"memory_id": "285ed74b-6e05-4043-b16b-3abd5b533496"},
     {"memory_id": "2c9bd859-d1b7-4d33-a6b8-94e0147c4f07"}
 ];