Update pyproject.toml (#2301 )

[Misc] Clean up unnecessary checks in chromadb vector store integration (#2284 )
Add Mem0 Demo (#2291 )
2025-03-04 14:22:12 -08:00 · 2025-03-04 14:21:27 -08:00 · 2025-03-04 10:04:59 -08:00 · 2025-03-04 17:51:27 +05:30 · 2025-03-04 15:47:38 +05:30 · 2025-03-04 14:29:25 +05:30
274 changed files with 18970 additions and 3563 deletions
@@ -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') }}
@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+**/node_modules/

 # C extensions
 *.so
@@ -13,7 +13,7 @@ install:
 install_all:
 	poetry install
 	poetry run pip install groq together boto3 litellm ollama chromadb sentence_transformers vertexai \
-	                        google-generativeai
+	                        google-generativeai elasticsearch opensearch-py vecs

 # Format code with ruff
 format:
@@ -2,7 +2,14 @@
  <a href="https://github.com/mem0ai/mem0">
  <img src="docs/images/banner-sm.png" width="800px" alt="Mem0 - The Memory Layer for Personalized AI">
  </a>
-<p align="center"><a href=https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps target='_blank'><img alt=Launch YC: Mem0 - Open Source Memory Layer for AI Apps src=https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps/upvote_embed.svg/></a></p>
+<p align="center" style="display: flex; justify-content: center; gap: 20px; align-items: center;">
+  <a href="https://trendshift.io/repositories/11194" target="_blank">
+    <img src="https://trendshift.io/api/badge/repositories/11194" alt="mem0ai%2Fmem0 | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
+  </a>
+  <a href="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps" target="_blank">
+    <img alt="Launch YC: Mem0 - Open Source Memory Layer for AI Apps" src="https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps/upvote_embed.svg"/>
+  </a>
+</p>


  <p align="center">
@@ -19,6 +26,9 @@
  <a href="https://pepy.tech/project/mem0ai">
    <img src="https://img.shields.io/pypi/dm/mem0ai" alt="Mem0 PyPI - Downloads" >
  </a>
+  <a href="https://github.com/mem0ai/mem0">
+    <img src="https://img.shields.io/github/commit-activity/m/mem0ai/mem0?style=flat-square" alt="GitHub commit activity">
+  </a>
  <a href="https://pypi.org/project/mem0ai" target="_blank">
        <img src="https://img.shields.io/pypi/v/mem0ai?color=%2334D058&label=pypi%20package" alt="Package version">
    </a>
@@ -35,50 +45,25 @@

 [Mem0](https://mem0.ai) (pronounced as "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.

-<!-- Start of Selection -->
-<p style="display: flex;">
-  <span style="font-size: 1.2em;">New Feature: Introducing Graph Memory. Check out our <a href="https://docs.mem0.ai/open-source/graph-memory" target="_blank">documentation</a>.</span>
-</p>
-<!-- End of Selection -->
+### Features & Use Cases

+Core Capabilities:
+- **Multi-Level Memory**: User, Session, and AI Agent memory retention with adaptive personalization
+- **Developer-Friendly**: Simple API integration, cross-platform consistency, and hassle-free managed service

-### Core Features
-
- **Multi-Level Memory**: User, Session, and AI Agent memory retention
- **Adaptive Personalization**: Continuous improvement based on interactions
- **Developer-Friendly API**: Simple integration into various applications
- **Cross-Platform Consistency**: Uniform behavior across devices
- **Managed Service**: Hassle-free hosted solution
-
-### How Mem0 works?
-
-Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
-
-When a message is added to the Mem0 using add()  method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
-
-When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
-
-The retrieved memories can then be appended to the LLM's prompt as needed, enhancing the personalization and relevance of its responses.
-
-### Use Cases
-
-Mem0 empowers organizations and individuals to enhance:
-
- **AI Assistants and agents**: Seamless conversations with a touch of déjà vu
- **Personalized Learning**: Tailored content recommendations and progress tracking
- **Customer Support**: Context-aware assistance with user preference memory
- **Healthcare**: Patient history and treatment plan management
- **Virtual Companions**: Deeper user relationships through conversation memory
- **Productivity**: Streamlined workflows based on user habits and task history
- **Gaming**: Adaptive environments reflecting player choices and progress
+Applications:
+- **AI Assistants**: Seamless conversations with context and personalization
+- **Learning & Support**: Tailored content recommendations and context-aware customer assistance
+- **Healthcare & Companions**: Patient history tracking and deeper relationship building
+- **Productivity & Gaming**: Streamlined workflows and adaptive environments based on user behavior

 ## Get Started

-The easiest way to set up Mem0 is through the managed [Mem0 Platform](https://app.mem0.ai). This hosted solution offers automatic updates, advanced analytics, and dedicated support. [Sign up](https://app.mem0.ai) to get started.
+Get started quickly with [Mem0 Platform](https://app.mem0.ai) - our fully managed solution that provides automatic updates, advanced analytics, enterprise security, and dedicated support. [Create a free account](https://app.mem0.ai) to begin.

-If you prefer to self-host, use the open-source Mem0 package. Follow the [installation instructions](#install) to get started.
+For complete control, you can self-host Mem0 using our open-source package. See the [Quickstart guide](#quickstart) below to set up your own instance.

-## Installation Instructions <a name="install"></a>
+## Quickstart Guide <a name="quickstart"></a>

 Install the Mem0 package via pip:

@@ -86,7 +71,11 @@ Install the Mem0 package via pip:
 pip install mem0ai
 ```

-Alternatively, you can use Mem0 with one click on the hosted platform [here](https://app.mem0.ai/).
+Install the Mem0 package via npm:
+
+```bash
+npm install mem0ai
+```

 ### Basic Usage

@@ -95,101 +84,93 @@ Mem0 requires an LLM to function, with `gpt-4o` from OpenAI as the default. Howe
 First step is to instantiate the memory:

 ```python
+from openai import OpenAI
 from mem0 import Memory

-m = Memory()
+openai_client = OpenAI()
+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)
+    
+    # Generate Assistant response
+    system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
+    messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
+    response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
+    assistant_response = response.choices[0].message.content
+
+    # Create new memories from the conversation
+    messages.append({"role": "assistant", "content": assistant_response})
+    memory.add(messages, user_id=user_id)
+
+    return assistant_response
+
+def main():
+    print("Chat with AI (type 'exit' to quit)")
+    while True:
+        user_input = input("You: ").strip()
+        if user_input.lower() == 'exit':
+            print("Goodbye!")
+            break
+        print(f"AI: {chat_with_memories(user_input)}")
+
+if __name__ == "__main__":
+    main()
 ```

-<details>
-<summary>How to set OPENAI_API_KEY</summary>
+See the example for [Node.js](https://docs.mem0.ai/examples/ai_companion_js).

-```python
-import os
-os.environ["OPENAI_API_KEY"] = "sk-xxx"
-```
-</details>
-
-
-You can perform the following task on the memory:
-
-1. Add: Store a memory from any unstructured text
-2. Update: Update memory of a given memory_id
-3. Search: Fetch memories based on a query
-4. Get: Return memories for a certain user/agent/session
-5. History: Describe how a memory has changed over time for a specific memory ID
-
-```python
-# 1. Add: Store a memory from any unstructured text
-result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})
-
-# Created memory --> 'Improving her tennis skills.' and 'Looking for online suggestions.'
-```
-
-```python
-# 2. Update: update the memory
-result = m.update(memory_id=<memory_id_1>, data="Likes to play tennis on weekends")
-
-# Updated memory --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
-```
-
-```python
-# 3. Search: search related memories
-related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
-
-# Retrieved memory --> 'Likes to play tennis on weekends'
-```
-
-```python
-# 4. Get all memories
-all_memories = m.get_all()
-memory_id = all_memories["memories"][0] ["id"] # get a memory_id
-
-# All memory items --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'
-```
-
-```python
-# 5. Get memory history for a particular memory_id
-history = m.history(memory_id=<memory_id_1>)
-
-# Logs corresponding to memory_id_1 --> {'prev_value': 'Working on improving tennis skills and interested in online courses for tennis.', 'new_value': 'Likes to play tennis on weekends' }
-```
+For more advanced usage and API documentation, visit our [documentation](https://docs.mem0.ai).

 > [!TIP]
-> If you prefer a hosted version without the need to set up infrastructure yourself, check out the [Mem0 Platform](https://app.mem0.ai/) to get started in minutes.
+> For a hassle-free experience, try our [hosted platform](https://app.mem0.ai) with automatic updates and enterprise features.
+
+## Demos
+
+- 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)
+
+<br/><br/>
+
+- Mem0 Demo: A personalized AI chat app powered by Mem0 that remembers your preferences, facts, and memories.
+
+[Mem0 Demo](https://github.com/user-attachments/assets/cebc4f8e-bdb9-4837-868d-13c5ab7bb433)
+
+<br/><br/>
+
+- Enhance your AI interactions by storing memories across ChatGPT, Perplexity, and Claude using our browser extension. Get [chrome extension](https://chromewebstore.google.com/detail/mem0/onihkkbipkfeijkadecaafbgagkhglop?hl=en).


-### 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*). 
-Here's how you can do it:
+[Chrome Extension Demo](https://github.com/user-attachments/assets/ca92e40b-c453-4ff6-b25e-739fb18a8650)

-```python
-from mem0 import Memory
+<br/><br/>

-config = {
-    "graph_store": {
-        "provider": "neo4j",
-        "config": {
-            "url": "neo4j+s://xxx",
-            "username": "neo4j",
-            "password": "xxx"
-        }
-    },
-    "version": "v1.1"
-}
+- Customer support bot using <strong>Langgraph and Mem0</strong>. Get the complete code from [here](https://docs.mem0.ai/integrations/langgraph)
+
+
+[Langgraph: Customer Bot](https://github.com/user-attachments/assets/ca6b482e-7f46-42c8-aa08-f88d1d93a5f4)
+
+<br/><br/>
+
+- Use Mem0 with CrewAI to get personalized results. Full example [here](https://docs.mem0.ai/integrations/crewai)
+
+[CrewAI Demo](https://github.com/user-attachments/assets/69172a79-ccb9-4340-91f1-caa7d2dd4213)

-m = Memory.from_config(config_dict=config)

-```

 ## Documentation

-For detailed usage instructions and API reference, visit our documentation at [docs.mem0.ai](https://docs.mem0.ai). Here, you can find more information on both the open-source version and the hosted [Mem0 Platform](https://app.mem0.ai).
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=mem0ai/mem0&type=Date)](https://star-history.com/#mem0ai/mem0&Date)
+For detailed usage instructions and API reference, visit our [documentation](https://docs.mem0.ai). You'll find:
+- Complete API reference
+- Integration guides
+- Advanced configuration options
+- Best practices and examples
+- More details about:
+  - Open-source version
+  - [Hosted Mem0 Platform](https://app.mem0.ai)

 ## Support

@@ -199,20 +180,6 @@ Join our community for support and discussions. If you have any questions, feel
 - [Follow us on Twitter](https://x.com/mem0ai)
 - [Email founders](mailto:founders@mem0.ai)

-## Contributors
-
-Join our [Discord community](https://mem0.dev/DiG) to learn about memory management for AI agents and LLMs, and connect with Mem0 users and contributors. Share your ideas, questions, or feedback in our [GitHub Issues](https://github.com/mem0ai/mem0/issues).
-
-We value and appreciate the contributions of our community. Special thanks to our contributors for helping us improve Mem0.
-
-<a href="https://github.com/mem0ai/mem0/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=mem0ai/mem0" />
-</a>
-
-## Anonymous Telemetry
-
-We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable MEM0_TELEMETRY=false. We prioritize data security and don't share this data externally.
-
 ## License

 This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.
@@ -0,0 +1,6 @@
+---
+title: 'Create Memory Export'
+openapi: post /v1/exports/
+---
+
+Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., user_id, agent_id, run_id, or session_id) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
@@ -0,0 +1,6 @@
+---
+title: 'Get Memory Export'
+openapi: get /v1/exports/
+---
+
+Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.
@@ -1,4 +1,4 @@
 ---
-title: 'V1 Get Memories'
+title: 'Get Memories (v1 - Deprecated)'
 openapi: get /v1/memories/
 ---
@@ -1,4 +1,4 @@
 ---
-title: 'V1 Search Memories'
+title: 'Search Memories (v1 - Deprecated)'
 openapi: post /v1/memories/search/
---
+---
@@ -1,74 +1,43 @@
 ---
-title: 'V2 Get Memories'
+title: 'Get Memories (v2)'
 openapi: post /v2/memories/
 ---

+The v2 get memories API is powerful and flexible, allowing for more precise memory listing without the need for a search query. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+- `in`: Matches any of the values specified
+- `gte`: Greater than or equal to
+- `lte`: Less than or equal to
+- `gt`: Greater than
+- `lt`: Less than

-Mem0 offers two versions of the get memories API: v1 and v2. Here's how they differ:
+<CodeGroup>
+```python Code
+memories = m.get_all(
+    filters={
+        "AND": [
+            {
+                "user_id": "alex"
+            },
+            {
+                "created_at": {"gte": "2024-07-01", "lte": "2024-07-31"}
+            }
+        ]
+    },
+    version="v2"
+)
+```

-<Tabs>
-  <Tab title="v1 Get Memories">
-    <CodeGroup>
-    ```python Code
-    memories = m.get_all(user_id="alex")
-    ```
-
-    ```json Output
-    [
-   {
-      "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
-      "memory":"travelling to Paris",
-      "user_id":"alex",
-      "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
-      "metadata":null,
-      "created_at":"2023-02-25T23:57:00.108347-07:00",
-      "updated_at":"2024-07-25T23:57:00.108367-07:00"
-   }
+```json Output
+[
+{
+    "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
+    "memory":"Name: Alex. Vegetarian. Allergic to nuts.",
+    "user_id":"alex",
+    "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
+    "metadata":null,
+    "created_at":"2024-07-25T23:57:00.108347-07:00",
+    "updated_at":"2024-07-25T23:57:00.108367-07:00"
+}
 ]
-    ```
-    </CodeGroup>
-  </Tab>
-
-  <Tab title="v2 Get Memories">
-    <CodeGroup>
-    ```python Code
-    memories = m.get_all(
-        filters={
-            "AND": [
-                {
-                    "user_id": "alex"
-                },
-                {
-                    "created_at": {
-                        "gte": "2024-07-01",
-                        "lte": "2024-07-31"
-                    }
-                }
-            ]
-        },
-        version="v2"
-    )
-    ```
-
-    ```json Output
-    [
-   {
-      "id":"f38b689d-6b24-45b7-bced-17fbb4d8bac7",
-      "memory":"Name: Alex. Vegetarian. Allergic to nuts.",
-      "user_id":"alex",
-      "hash":"62bc074f56d1f909f1b4c2b639f56f6a",
-      "metadata":null,
-      "created_at":"2024-07-25T23:57:00.108347-07:00",
-      "updated_at":"2024-07-25T23:57:00.108367-07:00"
-   }
-]
-    ```
-    </CodeGroup>
-  </Tab>
-</Tabs>
-
-Key difference between v1 and v2 get memories:
-
-• **Filters**: v2 allows you to apply filters to narrow down memory retrieval based on specific criteria. This includes support for complex logical operations (AND, OR) and comparison operators (IN, gte, lte, gt, lt, ne, icontains) for advanced filtering capabilities.
-
-The v2 get memories API is more powerful and flexible, allowing for more precise memory retrieval without the need for a search query.
+```
+</CodeGroup>
@@ -1,85 +1,51 @@
 ---
-title: 'V2 Search Memories'
+title: 'Search Memories (v2)'
 openapi: post /v2/memories/search/
 ---

-Mem0 offers two versions of the search API: v1 and v2. Here's how they differ:
+The v2 search API is powerful and flexible, allowing for more precise memory retrieval. It supports complex logical operations (AND, OR) and comparison operators for advanced filtering capabilities. The comparison operators include:
+- `in`: Matches any of the values specified
+- `gte`: Greater than or equal to
+- `lte`: Less than or equal to
+- `gt`: Greater than
+- `lt`: Less than
+- `ne`: Not equal to
+- `icontains`: Case-insensitive containment check

-<Tabs>
-  <Tab title="v1 Search">
-    <CodeGroup>
-    ```python Code
-    related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")
-    ```
+  <CodeGroup>
+  ```python Code
+  related_memories = m.vsearch(
+      query="What are Alice's hobbies?",
+      version="v2",
+      filters={
+          "AND": [
+              {
+                "user_id": "alice"
+              },
+              {
+                "agent_id": {"in": ["travel-agent", "sports-agent"]}
+              }
+          ]
+      },
+  )
+  ```

-    ```json Output
-    [
-       {
-          "id":"ea925981-272f-40dd-b576-be64e4871429",
-          "memory":"Likes to play cricket and plays cricket on weekends.",
-          "hash":"c8809002-25c1-4c97-a3a2-227ce9c20c53",
-          "metadata":{
-             "category":"hobbies"
-          },
-          "score":0.32116443111457704,
-          "created_at":"2024-07-26T10:29:36.630547-07:00",
-          "updated_at":"None",
-          "user_id":"alice"
-       }
-    ]
-    ```
-    </CodeGroup>
-  </Tab>
-
-  <Tab title="v2 Search">
-    <CodeGroup>
-    ```python Code
-    related_memories = m.vsearch(
-        query="What are Alice's hobbies?",
-        filters={
-        "AND":[
-            {
-                "user_id":"alice"
-            },
-            {
-                "agent_id":{
-                    "in":[
-                    "travelling",
-                    "sports"
-                    ]
-                }
-            }
-        ]
-    },
-    version="v2"
-    )
-    ```
-
-    ```json Output
-    {
-      "memories": [
-        {
-          "id": "ea925981-272f-40dd-b576-be64e4871429",
-          "memory": "Likes to play cricket and plays cricket on weekends.",
-          "hash": "c8809002-25c1-4c97-a3a2-227ce9c20c53",
-          "metadata": {
-            "category": "hobbies"
-          },
-          "score": 0.32116443111457704,
-          "created_at": "2024-07-26T10:29:36.630547-07:00",
-          "updated_at": null,
-          "user_id": "alice",
-          "agent_id": "sports"
-        }
-      ],
-    }
-    ```
-    </CodeGroup>
-  </Tab>
-</Tabs>
-
-Key difference between v1 and v2 search:
-
-• **Filters**: v2 allows you to apply filters to narrow down search results based on specific criteria. This includes support for complex logical operations (AND, OR) and comparison operators (IN, gte, lte, gt, lt, ne, icontains) for advanced filtering capabilities.
-
-The v2 search API is more powerful and flexible, allowing for more precise memory retrieval.
+  ```json Output
+  {
+    "memories": [
+      {
+        "id": "ea925981-272f-40dd-b576-be64e4871429",
+        "memory": "Likes to play cricket and plays cricket on weekends.",
+        "metadata": {
+          "category": "hobbies"
+        },
+        "score": 0.32116443111457704,
+        "created_at": "2024-07-26T10:29:36.630547-07:00",
+        "updated_at": null,
+        "user_id": "alice",
+        "agent_id": "sports-agent"
+      }
+    ],
+  }
+  ```
+  </CodeGroup>
@@ -1,4 +1,8 @@
-# Mem0 API Overview
+---
+title: Overview
+icon: "info"
+iconType: "solid"
+---

 Mem0 provides a powerful set of APIs that allow you to integrate advanced memory management capabilities into your applications. Our APIs are designed to be intuitive, efficient, and scalable, enabling you to create, retrieve, update, and delete memories across various entities such as users, agents, apps, and runs.

@@ -34,30 +38,26 @@ Organizations and projects provide the following capabilities:

 Example with the mem0 Python package:

+<Tabs>
+  <Tab title="Python">
+
 ```python
 from mem0 import MemoryClient
-
-# Recommended: Using organization and project IDs
-client = MemoryClient(
-    org_id='YOUR_ORG_ID', # It can be found on the organization settings page in dashboard
-    project_id='YOUR_PROJECT_ID',
-)
+client = MemoryClient(org_id='YOUR_ORG_ID', project_id='YOUR_PROJECT_ID')
 ```
-> **Note**: The use of `organization` and `project` parameters is deprecated and will be removed in version `0.1.40`. Please use `org_id` and `project_id` instead.

+  </Tab>

-Example with the mem0 Node.js package:
+  <Tab title="Node.js">

 ```javascript
 import { MemoryClient } from "mem0ai";
-
-# Recommended: Using organization and project IDs
-const client = new MemoryClient({
-    organizationId: "YOUR_ORG_ID",
-    projectId: "YOUR_PROJECT_ID"
-});
+const client = new MemoryClient({organizationId: "YOUR_ORG_ID", projectId: "YOUR_PROJECT_ID"});
 ```

+  </Tab>
+</Tabs>
+
 ## Getting Started

 To begin using the Mem0 API, you'll need to:
@@ -0,0 +1,4 @@
+---
+title: 'Update Project'
+openapi: patch /api/v1/orgs/organizations/{org_id}/projects/{project_id}/
+---
@@ -0,0 +1,9 @@
+---
+title: 'Create Webhook'
+openapi: post /api/v1/webhooks/projects/{project_id}/
+---
+
+## Create Webhook
+
+Create a webhook by providing the project ID and the webhook details.
+
@@ -0,0 +1,8 @@
+---
+title: 'Delete Webhook'
+openapi: delete /api/v1/webhooks/{webhook_id}/
+---
+
+## Delete Webhook
+
+Delete a webhook by providing the webhook ID.
@@ -0,0 +1,9 @@
+---
+title: 'Get Webhook'
+openapi: get /api/v1/webhooks/projects/{project_id}/
+---
+
+## Get Webhook
+
+Get a webhook by providing the project ID.
+
@@ -0,0 +1,9 @@
+---
+title: 'Update Webhook'
+openapi: put /api/v1/webhooks/{webhook_id}/
+---
+
+## Update Webhook
+
+Update a webhook by providing the webhook ID and the fields to update.
+
@@ -1,19 +1,25 @@
-## What is Config?
+---
+title: Configurations
+icon: "gear"
+iconType: "solid"
+---

 Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.

-## How to Define Config
+## 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 Config
+
+## 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

@@ -32,6 +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:
@@ -43,18 +68,31 @@ 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            |
+</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

@@ -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
@@ -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
@@ -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
@@ -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
@@ -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>
@@ -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
@@ -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` |
@@ -1,5 +1,7 @@
 ---
 title: Overview
+icon: "info"
+iconType: "solid"
 ---

 Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
@@ -8,6 +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>
@@ -1,29 +1,45 @@
-## What is Config?
+---
+title: Configurations
+icon: "gear"
+iconType: "solid"
+---

-Config in mem0 is a dictionary that specifies the settings for your llms. It allows you to customize the behavior and connection details of your chosen llm.
+## How to define configurations?

-## How to Define Config
-
-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

@@ -42,38 +58,70 @@ 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       |
+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               |
+  </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.
@@ -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).
@@ -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
@@ -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.
@@ -0,0 +1,55 @@
+---
+title: DeepSeek
+---
+
+To use DeepSeek LLM models, you have to set the `DEEPSEEK_API_KEY` environment variable. You can also optionally set `DEEPSEEK_API_BASE` if you need to use a different API endpoint (defaults to "https://api.deepseek.com").
+
+## Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["DEEPSEEK_API_KEY"] = "your-api-key"
+os.environ["OPENAI_API_KEY"] = "your-api-key" # for embedder model
+
+config = {
+    "llm": {
+        "provider": "deepseek",
+        "config": {
+            "model": "deepseek-chat",  # default model
+            "temperature": 0.2,
+            "max_tokens": 2000,
+            "top_p": 1.0
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+You can also configure the API base URL in the config:
+
+```python
+config = {
+    "llm": {
+        "provider": "deepseek",
+        "config": {
+            "model": "deepseek-chat",
+            "deepseek_base_url": "https://your-custom-endpoint.com",
+            "api_key": "your-api-key"  # alternatively to using environment variable
+        }
+    }
+}
+```
+
+## Config
+
+All available parameters for the `deepseek` config are present in [Master List of All Params in Config](../config).
@@ -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
@@ -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
@@ -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).
@@ -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
@@ -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
@@ -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
@@ -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

@@ -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
@@ -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).
@@ -1,5 +1,7 @@
 ---
 title: Overview
+icon: "info"
+iconType: "solid"
 ---

 Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.
@@ -12,18 +14,24 @@ 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="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" />
 </CardGroup>

 ## Structured vs Unstructured Outputs
@@ -1,19 +1,22 @@
-## What is Config?
+---
+title: Configurations
+icon: "gear"
+iconType: "solid"
+---

-Config in mem0 is a dictionary that specifies the settings for your vector database. It allows you to customize the behavior and connection details of your chosen vector store.
+## How to define configurations?

-## How to Define Config
-
-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")
-  - `config`: A nested dictionary containing provider-specific settings
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "azure_ai_search")
+  - `config`: A nested object 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

@@ -32,6 +35,29 @@ 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:
@@ -44,6 +70,8 @@ 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 |
@@ -58,6 +86,27 @@ 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 |
+| `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

@@ -22,7 +22,13 @@ 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
@@ -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
@@ -0,0 +1,64 @@
+[Elasticsearch](https://www.elastic.co/) is a distributed, RESTful search and analytics engine that can efficiently store and search vector data using dense vectors and k-NN search.
+
+### Installation
+
+Elasticsearch support requires additional dependencies. Install them with:
+
+```bash
+pip install elasticsearch>=8.0.0
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "elasticsearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+### Config
+
+Let's see the available parameters for the `elasticsearch` config:
+
+| Parameter              | Description                                        | Default Value |
+| ---------------------- | -------------------------------------------------- | ------------- |
+| `collection_name`      | The name of the index to store the vectors         | `mem0`        |
+| `embedding_model_dims` | Dimensions of the embedding model                  | `1536`        |
+| `host`                 | The host where the Elasticsearch server is running | `localhost`   |
+| `port`                 | The port where the Elasticsearch server is running | `9200`        |
+| `cloud_id`             | Cloud ID for Elastic Cloud deployment              | `None`        |
+| `api_key`              | API key for authentication                         | `None`        |
+| `user`                 | Username for basic authentication                  | `None`        |
+| `password`             | Password for basic authentication                  | `None`        |
+| `verify_certs`         | Whether to verify SSL certificates                 | `True`        |
+| `auto_create_index`    | Whether to automatically create the index          | `True`        |
+
+### Features
+
+- Efficient vector search using Elasticsearch's native k-NN search
+- Support for both local and cloud deployments (Elastic Cloud)
+- Multiple authentication methods (Basic Auth, API Key)
+- Automatic index creation with optimized mappings for vector search
+- Memory isolation through payload filtering
@@ -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
@@ -0,0 +1,65 @@
+[OpenSearch](https://opensearch.org/) is an open-source, enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently.
+
+### Installation
+
+OpenSearch support requires additional dependencies. Install them with:
+
+```bash
+pip install opensearch>=2.8.0
+```
+
+### Usage
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["OPENAI_API_KEY"] = "sk-xx"
+
+config = {
+    "vector_store": {
+        "provider": "opensearch",
+        "config": {
+            "collection_name": "mem0",
+            "host": "localhost",
+            "port": 9200,
+            "embedding_model_dims": 1536
+        }
+    }
+}
+
+m = Memory.from_config(config)
+messages = [
+    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
+    {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
+    {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
+    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
+]
+m.add(messages, user_id="alice", metadata={"category": "movies"})
+```
+
+### Config
+
+Let's see the available parameters for the `opensearch` config:
+
+| Parameter              | Description                                        | Default Value |
+| ---------------------- | -------------------------------------------------- | ------------- |
+| `collection_name`      | The name of the index to store the vectors         | `mem0`        |
+| `embedding_model_dims` | Dimensions of the embedding model                  | `1536`        |
+| `host`                 | The host where the OpenSearch server is running    | `localhost`   |
+| `port`                 | The port where the OpenSearch server is running    | `9200`        |
+| `api_key`              | API key for authentication                         | `None`        |
+| `user`                 | Username for basic authentication                  | `None`        |
+| `password`             | Password for basic authentication                  | `None`        |
+| `verify_certs`         | Whether to verify SSL certificates                 | `False`        |
+| `auto_create_index`    | Whether to automatically create the index          | `True`        |
+| `use_ssl`              | Whether to use SSL for connection                  | `False`        |
+
+### Features
+
+- Fast and Efficient Vector Search
+- Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service.
+- Multiple Authentication and Security Methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect)
+- Automatic index creation with optimized mappings for vector search
+- Memory Optimization through Disk-Based Vector Search and Quantization
+- Real-Time Analytics and Observability
@@ -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
@@ -30,11 +36,12 @@ Here's the parameters available for configuring pgvector:

 | Parameter | Description | Default Value |
 | --- | --- | --- |
-| `dbname` | The name of the database | `postgres` |
+| `dbname` | The name of the  | `postgres` |
 | `collection_name` | The name of the collection | `mem0` |
 | `embedding_model_dims` | Dimensions of the embedding model | `1536` |
 | `user` | User name to connect to the database | `None` |
 | `password` | Password to connect to the database | `None` |
 | `host` | The host where the Postgres server is running | `None` |
 | `port` | The port where the Postgres server is running | `None` |
-| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
+| `diskann` | Whether to use diskann for vector similarity search (requires pgvectorscale) | `True` |
+| `hnsw` | Whether to use hnsw for vector similarity search | `False` |
@@ -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>
@@ -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

@@ -26,19 +27,66 @@ config = {
            "embedding_model_dims": 1536,
            "redis_url": "redis://localhost:6379"
        }
-    }
+    },
+    "version": "v1.1"
 }

 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>
@@ -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`
@@ -1,5 +1,7 @@
 ---
 title: Overview
+icon: "info"
+iconType: "solid"
 ---

 Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -8,6 +10,10 @@ 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>
@@ -15,6 +21,9 @@ See the list of supported vector databases below.
  <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></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>
 </CardGroup>

 ## Usage
@@ -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! 🎉
@@ -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! ✍️
+
@@ -0,0 +1,60 @@
+---
+title: Memory Operations
+description: Understanding the core operations for managing memories in AI applications
+icon: "gear"
+iconType: "solid"
+---
+
+Mem0 provides two core operations for managing memories in AI applications: adding new memories and searching existing ones. This guide covers how these operations work and how to use them effectively in your application.
+
+
+## Core Operations
+
+Mem0 exposes two main endpoints for interacting with memories:
+- The `add` endpoint for ingesting conversations and storing them as memories
+- The `search` endpoint for retrieving relevant memories based on queries
+
+### Adding Memories
+
+<Frame caption="Architecture diagram illustrating the process of adding memories.">
+  <img src="../images/add_architecture.png" />
+</Frame>
+
+The add operation processes conversations through several steps:
+
+1. **Information Extraction**
+   * An LLM extracts relevant memories from the conversation
+   * It identifies important entities and their relationships
+
+2. **Conflict Resolution**
+   * The system compares new information with existing data
+   * It identifies and resolves any contradictions
+
+3. **Memory Storage**
+   * Vector database stores the actual memories
+   * Graph database maintains relationship information
+   * Information is continuously updated with each interaction
+
+### Searching Memories
+
+<Frame caption="Architecture diagram illustrating the memory search process.">
+  <img src="../images/search_architecture.png" />
+</Frame>
+
+The search operation retrieves memories through a multi-step process:
+
+1. **Query Processing**
+   * LLM processes and optimizes the search query
+   * System prepares filters for targeted search
+
+2. **Vector Search**
+   * Performs semantic search using the optimized query
+   * Ranks results by relevance to the query
+   * Applies specified filters (user, agent, metadata, etc.)
+
+3. **Result Processing**
+   * Combines and ranks the search results
+   * Returns memories with relevance scores
+   * Includes associated metadata and timestamps
+
+This semantic search approach ensures accurate memory retrieval, whether you're looking for specific information or exploring related concepts.
@@ -0,0 +1,48 @@
+---
+title: Memory Types
+description: Understanding different types of memory in AI Applications
+icon: "memory"
+iconType: "solid"
+---
+To build useful AI applications, we need to understand how different memory systems work together. This guide explores the fundamental types of memory in AI systems and shows how Mem0 implements these concepts.
+
+## Why Memory Matters
+
+AI systems need memory for three key purposes:
+1. Maintaining context during conversations
+2. Learning from past interactions
+3. Building personalized experiences over time
+
+Without proper memory systems, AI applications would treat each interaction as completely new, losing valuable context and personalization opportunities.
+
+## Short-Term Memory
+
+The most basic form of memory in AI systems holds immediate context - like a person remembering what was just said in a conversation. This includes:
+
+- **Conversation History**: Recent messages and their order
+- **Working Memory**: Temporary variables and state
+- **Attention Context**: Current focus of the conversation
+
+## Long-Term Memory
+
+More sophisticated AI applications implement long-term memory to retain information across conversations. This includes:
+
+- **Factual Memory**: Stored knowledge about users, preferences, and domain-specific information
+- **Episodic Memory**: Past interactions and experiences
+- **Semantic Memory**: Understanding of concepts and their relationships
+
+## Memory Characteristics
+
+Each memory type has distinct characteristics:
+
+| Type | Persistence | Access Speed | Use Case |
+|------|-------------|--------------|-----------|
+| Short-Term | Temporary | Instant | Active conversations |
+| Long-Term | Persistent | Fast | User preferences and history |
+
+## How Mem0 Implements Long-Term Memory
+Mem0's long-term memory system builds on these foundations by:
+
+1. Using vector embeddings to store and retrieve semantic information
+2. Maintaining user-specific context across sessions
+3. Implementing efficient retrieval mechanisms for relevant past interactions
@@ -0,0 +1,332 @@
+{
+  "$schema": "https://mintlify.com/docs.json",
+  "theme": "maple",
+  "name": "Mem0",
+  "description": "Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users.",
+  "colors": {
+    "primary": "#6c60f0",
+    "light": "#E6FFA2",
+    "dark": "#a3df02"
+  },
+  "favicon": "/logo/favicon.png",
+  "navigation": {
+    "anchors": [
+      {
+        "anchor": "Documentation",
+        "icon": "book-open",
+        "tabs": [
+          {
+            "tab": "Documentation",
+            "groups": [
+              {
+                "group": "Get Started",
+                "icon": "rocket",
+                "pages": [
+                  "overview",
+                  "quickstart",
+                  "faqs"
+                ]
+              },
+              {
+                "group": "Core Concepts",
+                "icon": "brain",
+                "pages": [
+                  "core-concepts/memory-types",
+                  "core-concepts/memory-operations"
+                ]
+              },
+              {
+                "group": "Platform",
+                "icon": "cogs",
+                "pages": [
+                  "platform/overview",
+                  "platform/quickstart",
+                  {
+                    "group": "Features",
+                    "icon": "star",
+                    "pages": [
+                      "features/platform-overview",
+                      "features/advanced-retrieval",
+                      "features/multimodal-support",
+                      "features/selective-memory",
+                      "features/custom-categories",
+                      "features/custom-instructions",
+                      "features/direct-import",
+                      "features/async-client",
+                      "features/memory-export",
+                      "features/webhooks"
+                    ]
+                  }
+                ]
+              },
+              {
+                "group": "Open Source",
+                "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",
+                      "open-source/multimodal-support",
+                      "open-source/features/rest-api"
+                    ]
+                  },
+                  {
+                    "group": "Graph Memory",
+                    "icon": "spider-web",
+                    "pages": [
+                      "open-source/graph_memory/overview",
+                      "open-source/graph_memory/features"
+                    ]
+                  },
+                  {
+                    "group": "LLMs",
+                    "icon": "brain",
+                    "pages": [
+                      "components/llms/overview",
+                      "components/llms/config",
+                      {
+                        "group": "Supported LLMs",
+                        "icon": "list",
+                        "pages": [
+                          "components/llms/models/openai",
+                          "components/llms/models/anthropic",
+                          "components/llms/models/azure_openai",
+                          "components/llms/models/ollama",
+                          "components/llms/models/together",
+                          "components/llms/models/groq",
+                          "components/llms/models/litellm",
+                          "components/llms/models/mistral_AI",
+                          "components/llms/models/google_AI",
+                          "components/llms/models/aws_bedrock",
+                          "components/llms/models/gemini",
+                          "components/llms/models/deepseek",
+                          "components/llms/models/xAI"
+                        ]
+                      }
+                    ]
+                  },
+                  {
+                    "group": "Vector Databases",
+                    "icon": "database",
+                    "pages": [
+                      "components/vectordbs/overview",
+                      "components/vectordbs/config",
+                      {
+                        "group": "Supported Vector Databases",
+                        "icon": "server",
+                        "pages": [
+                          "components/vectordbs/dbs/qdrant",
+                          "components/vectordbs/dbs/chroma",
+                          "components/vectordbs/dbs/pgvector",
+                          "components/vectordbs/dbs/milvus",
+                          "components/vectordbs/dbs/azure_ai_search",
+                          "components/vectordbs/dbs/redis",
+                          "components/vectordbs/dbs/elasticsearch",
+                          "components/vectordbs/dbs/opensearch",
+                          "components/vectordbs/dbs/supabase"
+                        ]
+                      }
+                    ]
+                  },
+                  {
+                    "group": "Embedding Models",
+                    "icon": "layer-group",
+                    "pages": [
+                      "components/embedders/overview",
+                      "components/embedders/config",
+                      {
+                        "group": "Supported Embedding Models",
+                        "icon": "list",
+                        "pages": [
+                          "components/embedders/models/openai",
+                          "components/embedders/models/azure_openai",
+                          "components/embedders/models/ollama",
+                          "components/embedders/models/huggingface",
+                          "components/embedders/models/vertexai",
+                          "components/embedders/models/gemini"
+                        ]
+                      }
+                    ]
+                  }
+                ]
+              },
+              {
+                "group": "Contribution",
+                "icon": "handshake",
+                "pages": [
+                  "contributing/development",
+                  "contributing/documentation"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Examples",
+            "groups": [
+              {
+                "group": "💡 Examples",
+                "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"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "Integrations",
+            "groups": [
+              {
+                "group": "Integrations",
+                "icon": "plug",
+                "pages": [
+                  "integrations/overview",
+                  "integrations/vercel-ai-sdk",
+                  "integrations/crewai",
+                  "integrations/autogen",
+                  "integrations/langchain",
+                  "integrations/langgraph",
+                  "integrations/llama-index",
+                  "integrations/langchain-tools",
+                  "integrations/dify"
+                ]
+              }
+            ]
+          },
+          {
+            "tab": "API Reference",
+            "icon": "square-terminal",
+            "groups": [
+              {
+                "group": "API Reference",
+                "icon": "terminal",
+                "pages": [
+                  "api-reference/overview",
+                  {
+                    "group": "Memory APIs",
+                    "icon": "microchip",
+                    "pages": [
+                      "api-reference/memory/add-memories",
+                      "api-reference/memory/v2-search-memories",
+                      "api-reference/memory/v1-search-memories",
+                      "api-reference/memory/v2-get-memories",
+                      "api-reference/memory/v1-get-memories",
+                      "api-reference/memory/history-memory",
+                      "api-reference/memory/get-memory",
+                      "api-reference/memory/update-memory",
+                      "api-reference/memory/batch-update",
+                      "api-reference/memory/delete-memory",
+                      "api-reference/memory/batch-delete",
+                      "api-reference/memory/delete-memories",
+                      "api-reference/memory/create-memory-export",
+                      "api-reference/memory/get-memory-export"
+                    ]
+                  },
+                  {
+                    "group": "Entities APIs",
+                    "icon": "users",
+                    "pages": [
+                      "api-reference/entities/get-users",
+                      "api-reference/entities/delete-user"
+                    ]
+                  },
+                  {
+                    "group": "Organizations APIs",
+                    "icon": "building",
+                    "pages": [
+                      "api-reference/organization/create-org",
+                      "api-reference/organization/get-orgs",
+                      "api-reference/organization/get-org",
+                      "api-reference/organization/get-org-members",
+                      "api-reference/organization/add-org-member",
+                      "api-reference/organization/delete-org"
+                    ]
+                  },
+                  {
+                    "group": "Webhook APIs",
+                    "icon": "webhook",
+                    "pages": [
+                      "api-reference/webhook/create-webhook",
+                      "api-reference/webhook/get-webhook",
+                      "api-reference/webhook/update-webhook",
+                      "api-reference/webhook/delete-webhook"
+                    ]
+                  }
+                ]
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "anchor": "Your Dashboard",
+        "href": "https://app.mem0.ai",
+        "icon": "chart-simple"
+      },
+      {
+        "anchor": "Demo",
+        "href": "https://demo.mem0.ai",
+        "icon": "play"
+      },
+      {
+        "anchor": "Discord",
+        "href": "https://mem0.dev/DiD",
+        "icon": "discord"
+      },
+      {
+        "anchor": "GitHub",
+        "href": "https://github.com/mem0ai/mem0",
+        "icon": "github"
+      },
+      {
+        "anchor": "Support",
+        "href": "mailto:founders@mem0.ai",
+        "icon": "envelope"
+      }
+    ]
+  },
+  "logo": {
+    "light": "/logo/light.svg",
+    "dark": "/logo/dark.svg",
+    "href": "https://github.com/mem0ai/mem0"
+  },
+  "background": {
+    "color": {
+      "light": "#fff",
+      "dark": "#0f1117"
+    }
+  },
+  "navbar": {
+    "primary": {
+      "type": "button",
+      "label": "Your Dashboard",
+      "href": "https://app.mem0.ai"
+    }
+  },
+  "footer": {
+    "socials": {
+      "discord": "https://mem0.dev/DiD",
+      "x": "https://x.com/mem0ai",
+      "github": "https://github.com/mem0ai",
+      "linkedin": "https://www.linkedin.com/company/mem0/"
+    }
+  },
+  "integrations": {
+    "posthog": {
+      "apiKey": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX",
+      "apiHost": "https://mango.mem0.ai"
+    }
+  }
+}
@@ -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.
@@ -0,0 +1,66 @@
+---
+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>
+
+## 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!
+
@@ -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
        },
    },
@@ -17,6 +17,9 @@ Here are some examples of how Mem0 can be integrated into various applications:
 ## Examples

 <CardGroup cols={2}>
+  <Card title="AI Companion in Node.js" icon="square-6" href="/examples/ai_companion_js">
+    Create a Personalized AI Companion using Mem0 in Node.js.
+  </Card>
  <Card title="Mem0 with Ollama" icon="square-1" href="/examples/mem0-with-ollama">
    Run Mem0 locally with Ollama.
  </Card>
@@ -0,0 +1,92 @@
+---
+title: FAQs
+icon: "question"
+iconType: "solid"
+---
+
+
+<AccordionGroup>
+    <Accordion title="How does Mem0 work?">
+        Mem0 utilizes a sophisticated hybrid database system to efficiently manage and retrieve memories for AI agents and assistants. Each memory is linked to a unique identifier, such as a user ID or agent ID, enabling Mem0 to organize and access memories tailored to specific individuals or contexts.
+
+        When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them across various data stores: a vector database and a graph database. This hybrid strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
+
+        When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
+
+        The retrieved memories can be seamlessly integrated into the LLM's prompt as required, enhancing the personalization and relevance of responses.
+  </Accordion>
+
+    <Accordion title="What are the key features of Mem0?">
+        - **User, Session, and AI Agent Memory**: Retains information across sessions and interactions for users and AI agents, ensuring continuity and context.
+        - **Adaptive Personalization**: Continuously updates memories based on user interactions and feedback.
+        - **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
+        - **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
+        - **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
+        - **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
+    </Accordion>
+
+    <Accordion title="How Mem0 is different from traditional RAG?">
+        Mem0's memory implementation for Large Language Models (LLMs) offers several advantages over Retrieval-Augmented Generation (RAG):
+
+        - **Entity Relationships**: Mem0 can understand and relate entities across different interactions, unlike RAG which retrieves information from static documents. This leads to a deeper understanding of context and relationships.
+
+        - **Contextual Continuity**: Mem0 retains information across sessions, maintaining continuity in conversations and interactions, which is essential for long-term engagement applications like virtual companions or personalized learning assistants.
+
+        - **Adaptive Learning**: Mem0 improves its personalization based on user interactions and feedback, making the memory more accurate and tailored to individual users over time.
+
+        - **Dynamic Updates**: Mem0 can dynamically update its memory with new information and interactions, unlike RAG which relies on static data. This allows for real-time adjustments and improvements, enhancing the user experience.
+
+        These advanced memory capabilities make Mem0 a powerful tool for developers aiming to create personalized and context-aware AI applications.
+    </Accordion>
+
+
+    <Accordion title="What are the common use-cases of Mem0?">
+        - **Personalized Learning Assistants**: Long-term memory allows learning assistants to remember user preferences, strengths and weaknesses, and progress, providing a more tailored and effective learning experience.
+
+        - **Customer Support AI Agents**: By retaining information from previous interactions, customer support bots can offer more accurate and context-aware assistance, improving customer satisfaction and reducing resolution times.
+
+        - **Healthcare Assistants**: Long-term memory enables healthcare assistants to keep track of patient history, medication schedules, and treatment plans, ensuring personalized and consistent care.
+
+        - **Virtual Companions**: Virtual companions can use long-term memory to build deeper relationships with users by remembering personal details, preferences, and past conversations, making interactions more delightful.
+
+        - **Productivity Tools**: Long-term memory helps productivity tools remember user habits, frequently used documents, and task history, streamlining workflows and enhancing efficiency.
+
+        - **Gaming AI**: In gaming, AI with long-term memory can create more immersive experiences by remembering player choices, strategies, and progress, adapting the game environment accordingly.
+
+    </Accordion>
+
+    <Accordion title="Why aren't my memories being created?">
+        Mem0 uses a sophisticated classification system to determine which parts of text should be extracted as memories. Not all text content will generate memories, as the system is designed to identify specific types of memorable information.
+        There are several scenarios where mem0 may return an empty list of memories:
+
+        - When users input definitional questions (e.g., "What is backpropagation?")
+        - For general concept explanations that don't contain personal or experiential information
+        - Technical definitions and theoretical explanations
+        - General knowledge statements without personal context
+        - Abstract or theoretical content
+
+        Example Scenarios
+
+        ```
+        Input: "What is machine learning?"
+        No memories extracted - Content is definitional and does not meet memory classification criteria.
+
+        Input: "Yesterday I learned about machine learning in class"
+        Memory extracted - Contains personal experience and temporal context.
+        ```
+
+        Best Practices
+
+        To ensure successful memory extraction:
+        - Include temporal markers (when events occurred)
+        - Add personal context or experiences
+        - Frame information in terms of real-world applications or experiences
+        - Include specific examples or cases rather than general definitions
+    </Accordion>
+
+</AccordionGroup>
+
+
+
+
+
@@ -1,5 +1,7 @@
 ---
 title: Features
+icon: "wrench"
+iconType: "solid"
 ---

 ## Core features
@@ -13,46 +15,6 @@ title: Features



-## How does Mem0 work?
-
-Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
-
-When a message is added to the Mem0 using add()  method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
-
-When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
-
-The retrieved memories can then be appended to the LLM's prompt as needed, making responses personalized and relevant.
-
-
-## Common Use Cases
-
- **Personalized Learning Assistants**: Long-term memory allows learning assistants to remember user preferences, strengths and weaknesses, and progress, providing a more tailored and effective learning experience.
-
- **Customer Support AI Agents**: By retaining information from previous interactions, customer support bots can offer more accurate and context-aware assistance, improving customer satisfaction and reducing resolution times.
-
- **Healthcare Assistants**: Long-term memory enables healthcare assistants to keep track of patient history, medication schedules, and treatment plans, ensuring personalized and consistent care.
-
- **Virtual Companions**: Virtual companions can use long-term memory to build deeper relationships with users by remembering personal details, preferences, and past conversations, making interactions more delightful.
-
- **Productivity Tools**: Long-term memory helps productivity tools remember user habits, frequently used documents, and task history, streamlining workflows and enhancing efficiency.
-
- **Gaming AI**: In gaming, AI with long-term memory can create more immersive experiences by remembering player choices, strategies, and progress, adapting the game environment accordingly.
-
-## How is Mem0 different from RAG?
-
-Mem0's memory implementation for Large Language Models (LLMs) offers several advantages over Retrieval-Augmented Generation (RAG):
-
- **Entity Relationships**: Mem0 can understand and relate entities across different interactions, unlike RAG which retrieves information from static documents. This leads to a deeper understanding of context and relationships.
-
- **Recency, Relevancy, and Decay**: Mem0 uses custom search algorithms to prioritize recent interactions and gradually forgets outdated information, ensuring the memory remains relevant and up-to-date for more accurate responses.
-
- **Contextual Continuity**: Mem0 retains information across sessions, maintaining continuity in conversations and interactions, which is essential for long-term engagement applications like virtual companions or personalized learning assistants.
-
- **Adaptive Learning**: Mem0 improves its personalization based on user interactions and feedback, making the memory more accurate and tailored to individual users over time.
-
- **Dynamic Updates**: Mem0 can dynamically update its memory with new information and interactions, unlike RAG which relies on static data. This allows for real-time adjustments and improvements, enhancing the user experience.
-
-These advanced memory capabilities make Mem0 a powerful tool for developers aiming to create personalized and context-aware AI applications.

 If you have any questions, please feel free to reach out to us using one of the following methods:

@@ -0,0 +1,49 @@
+---
+title: Advanced Retrieval
+icon: "magnifying-glass"
+iconType: "solid"
+---
+
+Mem0's **Advanced Retrieval** feature delivers superior search results by leveraging state-of-the-art search algorithms. Beyond the default search functionality, Mem0 offers the following advanced retrieval modes:
+
+1. **Keyword Search**
+
+   This mode emphasizes keywords within the query, returning memories that contain the most relevant keywords alongside those from the default search. By default, this parameter is set to `false`. Enabling it enhances search recall, though it may slightly impact precision.
+
+   ```python
+   client.search(query, keyword_search=True, user_id='alex')
+   ```
+
+2. **Reranking**
+
+   Reranking allows you to reorder the memories returned by the default search based on relevance. This parameter is set to `false` by default. When enabled, it reorders the memories based on the relevance score.
+
+   ```python
+   client.search(query, rerank=True, user_id='alex')
+   ```
+
+3. **Filtering**
+
+   Filtering enables you to narrow down the search results by applying specific criteria. This parameter is set to `false` by default. Activating it enhances search precision, potentially reducing recall by a small margin.
+
+   ```python
+   client.search(query, filter_memories=True, user_id='alex')
+   ```
+
+**Note:** You can enable or disable these search modes by passing the respective parameters to the `search` method. There is no required sequence for these modes, and any combination can be used based on your needs.
+
+
+### Latency Numbers
+
+Here are the typical latency ranges for each search mode:
+
+| **Mode**            | **Latency**      |
+|---------------------|------------------|
+| **Keyword Search**  | **&lt;10ms**        |
+| **Reranking**       | **150-200ms**    |
+| **Filtering**       | **200-300ms**    |
+
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -1,6 +1,8 @@
 ---
 title: Async Client
 description: 'Asynchronous client for Mem0'
+icon: "bolt"
+iconType: "solid"
 ---

 The `AsyncMemoryClient` is an asynchronous client for interacting with the Mem0 API. It provides similar functionality to the synchronous `MemoryClient` but allows for non-blocking operations, which can be beneficial in applications that require high concurrency.
@@ -12,13 +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>
@@ -58,7 +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
@@ -1,61 +1,145 @@
 ---
 title: Custom Categories
 description: 'Enhance your product experience by adding custom categories tailored to your needs'
+icon: "tags"
+iconType: "solid"
 ---

 ## How to set custom categories?

-Users can now create custom categories tailored to their specific needs, in addition to the default categories such as travel, sports, music, and more. When custom categories are provided, they will override the default categories.
-To setup the custom categories, user has to specify the category name and a description of what that category signifies.
-Here’s how you can do it:
+You can now create custom categories tailored to your specific needs, instead of using the default categories such as travel, sports, music, and more (see [default categories](#default-categories) below). **When custom categories are provided, they will override the default categories.**

-```python
-from mem0 import MemoryClient
+There are two ways to set custom categories:

-m = MemoryClient(api_key="xxx")
+### 1. Project Level

-custom_categories = [
-    {"cooking": "For users interested in cooking, including recipes, cooking tips, and culinary experiences."},
-    {"fitness": "Includes content related to fitness, such as workouts, exercises, and fitness tips."}
-]
+You can set custom categories at the project level, which will be applied to all memories added within that project. Mem0 will automatically assign relevant categories from your custom set to new memories based on their content. Setting custom categories at the project level will override the default categories.

-messages = [
-    {"role" : "user", "content" : "Hi, my name is Alice. I love to play badminton."},
-    {"role" : "assistant", "content" : "Hello Alice! It's nice to meet you. Badminton is such an amazing sport. How can I assist you today?"},
-    {"role" : "user", "content" : "I am a fitness freak, I go to gym daily."},
-    {"role" : "assistant", "content" : "That's great! Regular exercise is very beneficial for health."},
-    {"role" : "user", "content" : "Because of my gym plan, I mostly cook at home."},
-    {"role" : "assistant", "content" : "Cooking at home is a good way to ensure you have a balanced diet."}
-]
-```
+Here's how to set custom categories:

 <CodeGroup>
 ```python Code
-client.add(messages, user_id="alice", custom_categories=custom_categories)
+import os
+from mem0 import MemoryClient
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
+
+# Update custom categories
+new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+response = client.update_project(custom_categories = new_categories)
+print(response)
 ```

-```markdown Memories with categories
-User's name is Alice (personal_details)
-Loves playing badminton (sports)
-User is a fitness freak. (fitness)
-Likes to go to gym daily. (fitness)
-Mostly cook at home because of gym plan. (fitness, cooking)
+```json Output
+{
+    "message": "Updated custom categories"
+}
 ```
 </CodeGroup>

-<Note> The more detailed the description of categories is, the better output the user will receive. When custom categories are provided in the `add` API call, they will completely replace the default categories and will be directly assigned to the memory, so make sure to include all categories you want to use. </Note>
+This is how you will use these custom categories during the `add` API call:
+
+<CodeGroup>
+```python Code
+messages = [
+    {"role": "user", "content": "My name is Alice. I need help organizing my daily schedule better. I feel overwhelmed trying to balance work, exercise, and social life."},
+    {"role": "assistant", "content": "I understand how overwhelming that can feel. Let's break this down together. What specific areas of your schedule feel most challenging to manage?"},
+    {"role": "user", "content": "I want to be more productive at work, maintain a consistent workout routine, and still have energy for friends and hobbies."},
+    {"role": "assistant", "content": "Those are great goals for better time management. What's one small change you could make to start improving your daily routine?"},
+]
+
+# Add memories with custom categories
+client.add(messages, user_id="alice")
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Wants to have energy for friends and hobbies (lifestyle_management_concerns)
+Wants to maintain a consistent workout routine (seeking_structure, lifestyle_management_concerns)
+Wants to be more productive at work (lifestyle_management_concerns, seeking_structure)
+Name is Alice (personal_information)
+```
+</CodeGroup>
+
+You can also retrieve the current custom categories:
+
+<CodeGroup>
+```python Code
+# Get current custom categories
+categories = client.get_project(fields=["custom_categories"])
+print(categories)
+```
+
+```json Output
+{
+  "custom_categories": [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+  ]
+}
+
+```
+</CodeGroup>
+
+These project-level categories will be automatically applied to all new memories added to the project.
+
+
+
+### 2. During the `add` API call
+You can also set custom categories during the `add` API call. This will override any project-level custom categories for that specific memory addition. For example, if you want to use different categories for food-related memories, you can provide custom categories like "food" and "user_preferences" in the `add` call. These custom categories will be used instead of the project-level categories when categorizing those specific memories.
+
+<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 = [
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
+messages = [
+    {"role": "user", "content": "My name is Alice. I need help organizing my daily schedule better. I feel overwhelmed trying to balance work, exercise, and social life."},
+    {"role": "assistant", "content": "I understand how overwhelming that can feel. Let's break this down together. What specific areas of your schedule feel most challenging to manage?"},
+    {"role": "user", "content": "I want to be more productive at work, maintain a consistent workout routine, and still have energy for friends and hobbies."},
+    {"role": "assistant", "content": "Those are great goals for better time management. What's one small change you could make to start improving your daily routine?"},
+]
+
+client.add(messages, user_id="alice", custom_categories=custom_categories)
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Wants to have energy for friends and hobbies (seeking_structure)
+Wants to maintain a consistent workout routine (seeking_structure)
+Wants to be more productive at work (seeking_structure)
+Name is Alice (personal_information)
+```
+</CodeGroup>
+
+<Note>Providing more detailed and specific category descriptions will lead to more accurate and relevant memory categorization.</Note>

-<Note> We will soon release a feature that allows users to set custom categories in `project`. This will allow the functionality where relevant categories are automatically assigned to the memory based on the input text provided. </Note>

 ## Default Categories
-Here is the list of **default categories**. Ensure you review these before creating custom categories to prevent duplication.
-
+Here is the list of **default categories**. If you don't specify any custom categories using the above methods, these will be used as default categories.
 ```
 - personal_details
 - family
 - professional_details
 - sports
- travel 
+- travel
 - food
 - music
 - health
@@ -68,6 +152,51 @@ Here is the list of **default categories**. Ensure you review these before creat
 - misc
 ```

+<CodeGroup>
+```python Code
+import os
+from mem0 import MemoryClient
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
+
+messages = [
+    {"role": "user", "content": "Hi, my name is Alice."},
+    {"role": "assistant", "content": "Hi Alice, what sports do you like to play?"},
+    {"role": "user", "content": "I love playing badminton, football, and basketball. I'm quite athletic!"},
+    {"role": "assistant", "content": "That's great! Alice seems to enjoy both individual sports like badminton and team sports like football and basketball."},
+    {"role": "user", "content": "Sometimes, I also draw and sketch in my free time."},
+    {"role": "assistant", "content": "That's cool! I'm sure you're good at it."}
+]
+
+# Add memories with default categories
+client.add(messages, user_id='alice')
+```
+
+```python Memories with categories
+# Following categories will be created for the memories added
+Sometimes draws and sketches in free time (hobbies)
+Is quite athletic (sports)
+Loves playing badminton, football, and basketball (sports)
+Name is Alice (personal_details)
+```
+</CodeGroup>
+
+You can check whether default categories are being used by calling `get_project()`. If `custom_categories` returns `None`, it means the default categories are being used.
+
+<CodeGroup>
+```python Code
+client.get_project(["custom_categories"])
+```
+
+```json Output
+{
+    'custom_categories': None
+}
+```
+</CodeGroup>
+
 If you have any questions, please feel free to reach out to us using one of the following methods:

 <Snippet file="get-help.mdx" />
@@ -0,0 +1,76 @@
+---
+title: Custom Instructions
+description: 'Enhance your product experience by adding custom instructions tailored to your needs'
+icon: "pencil"
+iconType: "solid"
+---
+
+## Introduction to Custom Instructions
+
+Custom instructions allow you to define specific guidelines for your project. This feature helps ensure consistency and provides clear direction for handling project-specific requirements.
+
+Custom instructions are particularly useful when you want to:
+- Define how information should be extracted from conversations
+- Specify what types of data should be captured or ignored
+- Set rules for categorizing and organizing memories
+- Maintain consistent handling of project-specific requirements
+
+When custom instructions are set at the project level, they will be applied to all new memories added within that project. This ensures that your data is processed according to your defined guidelines across your entire project.
+
+## Setting Custom Instructions
+
+You can set custom instructions for your project using the following method:
+
+<CodeGroup>
+```python Code
+# Update custom instructions
+prompt ="""
+Your Task: Extract ONLY health-related information from conversations, focusing on the following areas:
+
+1. Medical Conditions, Symptoms, and Diagnoses:
+   - Illnesses, disorders, or symptoms (e.g., fever, diabetes).
+   - Confirmed or suspected diagnoses.
+
+2. Medications, Treatments, and Procedures:
+   - Prescription or OTC medications (names, dosages).
+   - Treatments, therapies, or medical procedures.
+
+3. Diet, Exercise, and Sleep:
+   - Dietary habits, fitness routines, and sleep patterns.
+
+4. Doctor Visits and Appointments:
+   - Past, upcoming, or regular medical visits.
+
+5. Health Metrics:
+   - Data like weight, BP, cholesterol, or sugar levels.
+
+Guidelines:
+- Focus solely on health-related content.
+- Maintain clarity and context accuracy while recording.
+"""
+response = client.update_project(custom_instructions=prompt)
+print(response)
+```
+
+```json Output
+{
+    "message": "Updated custom instructions"
+}
+```
+</CodeGroup>
+
+You can also retrieve the current custom instructions:
+
+<CodeGroup>
+```python Code
+# Retrieve current custom instructions
+response = client.get_project(fields=["custom_instructions"])
+print(response)
+```
+
+```json Output
+{
+    "custom_instructions": "Your Task: Extract ONLY health-related information from conversations, focusing on the following areas:\n1. Medical Conditions, Symptoms, and Diagnoses - illnesses, disorders, or symptoms (e.g., fever, diabetes), confirmed or suspected diagnoses.\n2. Medications, Treatments, and Procedures - prescription or OTC medications (names, dosages), treatments, therapies, or medical procedures.\n3. Diet, Exercise, and Sleep - dietary habits, fitness routines, and sleep patterns.\n4. Doctor Visits and Appointments - past, upcoming, or regular medical visits.\n5. Health Metrics - data like weight, BP, cholesterol, or sugar levels.\n\nGuidelines: Focus solely on health-related content. Maintain clarity and context accuracy while recording."
+}
+```
+</CodeGroup>
@@ -1,6 +1,8 @@
 ---
 title: Custom Prompts
 description: 'Enhance your product experience by adding custom prompts tailored to your needs'
+icon: "pencil"
+iconType: "solid"
 ---

 ## Introduction to Custom Prompts
@@ -15,7 +17,8 @@ To create an effective custom prompt:

 Example of a custom prompt:

-```python
+<CodeGroup>
+```python Python
 custom_prompt = """
 Please only extract entities containing customer support information, order details, and user information. 
 Here are some few shot examples:
@@ -37,12 +40,37 @@ Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt inst

 Return the facts and customer information in a json format as shown above.
 """
-
 ```

-Here we initialize the custom prompt in the config.
+```typescript TypeScript
+const customPrompt = `
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:

-```python
+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 prompt in the config:
+
+<CodeGroup>
+```python Python
 from mem0 import Memory

 config = {
@@ -51,7 +79,7 @@ config = {
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
        }
    },
    "custom_prompt": custom_prompt,
@@ -61,15 +89,40 @@ config = {
 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 Code
+```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": [
@@ -95,11 +148,16 @@ m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")

 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
+```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": [],
@@ -107,3 +165,5 @@ m.add("I like going to hikes", user_id="alice")
 }
 ```
 </CodeGroup>
+
+The custom prompt will process both the user and assistant messages to extract relevant information according to the defined format.
@@ -1,6 +1,8 @@
 ---
 title: Direct Import
 description: 'Bypass the memory deduction phase and directly store pre-defined memories for efficient retrieval'
+icon: "arrow-right"
+iconType: "solid"
 ---

 ## How to use Direct Import?
@@ -39,7 +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
@@ -0,0 +1,147 @@
+---
+title: Memory Export
+description: 'Export memories in a structured format using customizable Pydantic schemas'
+icon: "file-export"
+iconType: "solid"
+---
+
+## Overview
+
+The Memory Export feature allows you to create structured exports of memories using customizable Pydantic schemas. This process enables you to transform your stored memories into specific data formats that match your needs. You can apply various filters to narrow down which memories to export and define exactly how the data should be structured.
+
+## Creating a Memory Export
+
+To create a memory export, you'll need to:
+1. Define your schema structure
+2. Submit an export job
+3. Retrieve the exported data
+
+### Define Schema
+
+Here's an example schema for extracting professional profile information:
+
+```json
+{
+    "$defs": {
+        "EducationLevel": {
+            "enum": ["high_school", "bachelors", "masters"],
+            "title": "EducationLevel",
+            "type": "string"
+        },
+        "EmploymentStatus": {
+            "enum": ["full_time", "part_time", "student"],
+            "title": "EmploymentStatus", 
+            "type": "string"
+        }
+    },
+    "properties": {
+        "full_name": {
+            "anyOf": [
+                {
+                    "maxLength": 100,
+                    "minLength": 2,
+                    "type": "string"
+                },
+                {
+                    "type": "null"
+                }
+            ],
+            "default": null,
+            "description": "The professional's full name",
+            "title": "Full Name"
+        },
+        "current_role": {
+            "anyOf": [
+                {
+                    "type": "string"
+                },
+                {
+                    "type": "null"
+                }
+            ],
+            "default": null,
+            "description": "Current job title or role",
+            "title": "Current Role"
+        }
+    },
+    "title": "ProfessionalProfile",
+    "type": "object"
+}
+```
+
+### Submit Export Job
+
+<CodeGroup>
+
+```python Python
+response = client.create_memory_export(
+    schema=json_schema,
+    user_id="user123"
+)
+print(response)
+```
+
+```bash cURL
+curl -X POST "https://api.mem0.ai/v1/memories/export/" \
+     -H "Authorization: Token your-api-key" \
+     -H "Content-Type: application/json" \
+     -d '{
+         "schema": {json_schema},
+         "user_id": "user123"
+     }'
+```
+
+```json Output
+{
+    "message": "Memory export request received. The export will be ready in a few seconds.",
+    "id": "550e8400-e29b-41d4-a716-446655440000"
+}
+```
+
+</CodeGroup>
+
+### Retrieve Export
+
+Once the export job is complete, you can retrieve the structured data:
+
+<CodeGroup>
+
+```python Python
+response = client.get_memory_export(user_id="user123")
+print(response)
+```
+
+```bash cURL
+curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=user123" \
+     -H "Authorization: Token your-api-key"
+```
+
+```json Output
+{
+    "full_name": "John Doe",
+    "current_role": "Senior Software Engineer",
+    "years_experience": 8,
+    "employment_status": "full_time",
+    "education_level": "masters",
+    "skills": ["Python", "AWS", "Machine Learning"]
+}
+```
+
+</CodeGroup>
+
+## Available Filters
+
+You can apply various filters to customize which memories are included in the export:
+
+- `user_id`: Filter memories by specific user
+- `agent_id`: Filter memories by specific agent
+- `run_id`: Filter memories by specific run
+- `session_id`: Filter memories by specific session
+
+<Note>
+The export process may take some time to complete, especially when dealing with a large number of memories or complex schemas.
+</Note>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,120 @@
+---
+title: Multimodal Support
+description: Integrate images into your interactions with Mem0
+icon: "image"
+iconType: "solid"
+---
+
+Mem0 extends its capabilities beyond text by supporting multimodal data, including images. With this feature, users can seamlessly integrate images into their interactions—allowing Mem0 to extract relevant information from visual content and enrich the memory system.
+
+## How It Works
+
+When a user submits an image, Mem0 processes it to extract textual information and other pertinent details. These details are then added to the user's memory, enhancing the system's ability to understand and recall visual inputs.
+
+<CodeGroup>
+```python Code
+import os
+from mem0 import MemoryClient
+
+os.environ["MEM0_API_KEY"] = "your-api-key"
+
+client = MemoryClient()
+
+messages = [
+    {
+        "role": "user",
+        "content": "Hi, my name is Alice."
+    },
+    {
+        "role": "assistant",
+        "content": "Nice to meet you, Alice! What do you like to eat?"
+    },
+    {
+        "role": "user",
+        "content": {
+            "type": "image_url",
+            "image_url": {
+                "url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+            }
+        }
+    },
+]
+
+# Calling the add method to ingest messages into the memory system
+client.add(messages, user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Name is Alice",
+      "event": "ADD",
+      "id": "7ae113a3-3cb5-46e9-b6f7-486c36391847"
+    },
+    {
+      "memory": "Likes large pizza with toppings including cherry tomatoes, black olives, green spinach, yellow bell peppers, diced ham, and sliced mushrooms",
+      "event": "ADD",
+      "id": "56545065-7dee-4acf-8bf2-a5b2535aabb3"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+## Image Integration Methods
+
+Mem0 supports incorporating images into user interactions using two primary methods: by providing an image URL or by using a Base64-encoded image. The examples below demonstrate both approaches.
+
+## 1. Using an Image URL (Recommended)
+
+You can include an image by providing its direct URL. This method is simple and efficient for online images.
+
+```python {2, 5-13}
+# Define the image URL
+image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+
+# Create the message dictionary with the image URL
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {
+            "url": image_url
+        }
+    }
+}
+client.add([image_message], user_id="alice")
+```
+
+## 2. Using Base64 Image Encoding for Local Files
+
+For local images—or when embedding the image directly is preferable—you can use a Base64-encoded string.
+```python
+import base64
+
+# Path to the image file
+image_path = "path/to/your/image.jpg"
+
+# Encode the image in Base64
+with open(image_path, "rb") as image_file:
+    base64_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+# Create the message dictionary with the Base64-encoded image
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {
+            "url": f"data:image/jpeg;base64,{base64_image}"
+        }
+    }
+}
+client.add([image_message], user_id="alice")
+```
+
+Using these methods, you can seamlessly incorporate images into your interactions, further enhancing Mem0's multimodal capabilities.
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -1,5 +1,7 @@
 ---
 title: OpenAI Compatibility
+icon: "code"
+iconType: "solid"
 ---

 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.
@@ -0,0 +1,42 @@
+---
+title: Overview
+icon: "info"
+iconType: "solid"
+---
+
+Learn about the key features and capabilities that make Mem0 a powerful platform for memory management and retrieval.
+
+## Core Features
+
+<CardGroup>
+  <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="Multimodal Support" icon="photo-film" href="/features/multimodal-support">
+    Process and analyze various types of content including images.
+  </Card>
+  <Card title="Memory Customization" icon="filter" href="/features/selective-memory">
+    Customize and curate stored memories to focus on relevant information while excluding unnecessary data, enabling improved accuracy, privacy control, and resource efficiency.
+  </Card>
+  <Card title="Custom Categories" icon="tags" href="/features/custom-categories">
+    Create and manage custom categories to organize memories based on your specific needs and requirements.
+  </Card>
+  <Card title="Custom Instructions" icon="list-check" href="/features/custom-instructions">
+    Define specific guidelines for your project to ensure consistent handling of information and requirements.
+  </Card>
+  <Card title="Direct Import" icon="message-bot" href="/features/direct-import">
+    Tailor the behavior of your Mem0 instance with custom prompts for specific use cases or domains.
+  </Card>
+  <Card title="Async Client" icon="bolt" href="/features/async-client">
+    Asynchronous client for non-blocking operations and high concurrency applications.
+  </Card>
+  <Card title="Memory Export" icon="file-export" href="/features/memory-export">
+    Export memories in structured formats using customizable Pydantic schemas.
+  </Card>
+</CardGroup>
+
+## Getting Help
+
+If you have any questions about these features or need assistance, our team is here to help:
+
+<Snippet file="get-help.mdx" />
@@ -1,6 +1,8 @@
 ---
 title: Memory Customization
 description: 'Mem0 supports customizing the memories you store, allowing you to focus on pertinent information while omitting irrelevant data.'
+icon: "filter"
+iconType: "solid"
 ---

 ## Benefits of Memory Customization
@@ -27,9 +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"
@@ -0,0 +1,201 @@
+---
+title: Webhooks
+description: 'Configure and manage webhooks to receive real-time notifications about memory events'
+icon: "webhook"
+iconType: "solid"
+---
+
+## Overview
+
+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 webhook for your project; it will receive events only from that project:
+<CodeGroup>
+
+```python Python
+import os
+from mem0 import MemoryClient
+
+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"]
+)
+print(webhook)
+```
+
+```javascript JavaScript
+const { MemoryClient } = require('mem0ai');
+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"]
+});
+console.log(webhook);
+```
+
+```json Output
+{
+  "webhook_id": "wh_123",
+  "name": "Memory Logger",
+  "url": "https://your-app.com/webhook",
+  "event_types": ["memory_add"],
+  "project": "default-project",
+  "is_active": true,
+  "created_at": "2025-02-18T22:59:56.804993-08:00",
+  "updated_at": "2025-02-18T23:06:41.479361-08:00"
+}
+```
+
+</CodeGroup>
+
+### Get Webhooks
+
+Retrieve all webhooks for your project:
+
+<CodeGroup>
+
+```python Python
+# Get webhooks for a specific project
+webhooks = client.get_webhooks(project_id="proj_123")
+print(webhooks)
+```
+
+```javascript JavaScript
+// Get webhooks for a specific project
+const webhooks = await client.getWebhooks({projectId: "proj_123"});
+console.log(webhooks);
+```
+
+```json Output
+[
+    {
+        "webhook_id": "wh_123",
+        "url": "https://mem0.ai",
+        "name": "mem0",
+        "owner": "john",
+        "event_types": ["memory_add"],
+        "project": "default-project",
+        "is_active": true,
+        "created_at": "2025-02-18T22:59:56.804993-08:00",
+        "updated_at": "2025-02-18T23:06:41.479361-08:00"
+    }
+]
+
+```
+
+</CodeGroup>
+
+### Update Webhook
+
+Update an existing webhook’s configuration by specifying its `webhook_id`:
+
+<CodeGroup>
+
+```python Python
+# Update webhook for a specific project
+updated_webhook = client.update_webhook(
+    name="Updated Logger",
+    url="https://your-app.com/new-webhook",
+    event_types=["memory_update", "memory_add"],
+    webhook_id="wh_123"
+)
+print(updated_webhook)
+```
+
+```javascript JavaScript
+// Update webhook for a specific project
+const updatedWebhook = await client.updateWebhook({
+    name: "Updated Logger",
+    url: "https://your-app.com/new-webhook",
+    eventTypes: ["memory_update", "memory_add"],
+    webhookId: "wh_123"
+});
+console.log(updatedWebhook);
+```
+
+```json Output
+{
+  "message": "Webhook updated successfully"
+}
+```
+
+</CodeGroup>
+
+### Delete Webhook
+
+Delete a webhook by providing its `webhook_id`:
+
+<CodeGroup>
+
+```python Python
+# Delete webhook from a specific project
+response = client.delete_webhook(webhook_id="wh_123")
+print(response)
+```
+
+```javascript JavaScript
+// Delete webhook from a specific project
+const response = await client.deleteWebhook({webhookId: "wh_123"});
+console.log(response);
+```
+
+```json Output
+{
+  "message": "Webhook deleted successfully"
+}
+```
+
+</CodeGroup>
+
+## Event Types
+
+Mem0 supports the following event types for webhooks:
+
+- `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, Mem0 sends an HTTP POST request to your webhook URL with the following payload:
+
+```json
+{
+    "event_details": {
+        "id": "a1b2c3d4-e5f6-4g7h-8i9j-k0l1m2n3o4p5",
+            "data": {
+            "memory": "Name is Alex"
+            },
+        "event": "ADD"
+    }
+}
+```
+
+## Best Practices
+
+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 originate from Mem0.
+
+3. **Process Events Asynchronously**: Process webhook events asynchronously to avoid timeouts and ensure reliable handling.
+
+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:
+
+<Snippet file="get-help.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}]},
@@ -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" />
@@ -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)
@@ -25,11 +25,13 @@ 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(
-    "---",
-    org_id="---",
-    project_id="---"
+    org_id=your_org_id,
+    project_id=your_project_id
 )
 ```

@@ -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" />
@@ -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
@@ -80,7 +80,7 @@ config = {
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
        },
    },
    "embedder": {
@@ -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" />
@@ -5,7 +5,7 @@ title: Vercel AI SDK
 The [**Mem0 AI SDK Provider**](https://www.npmjs.com/package/@mem0/vercel-ai-provider) is a library developed by **Mem0** to integrate with the Vercel AI SDK. This library brings enhanced AI interaction capabilities to your applications by introducing persistent memory functionality.

 <Note type="info">
-  🎉 Exciting news! Mem0 AI SDK now supports **OpenAI**, **Anthropic**, **Cohere**, and **Groq** providers.
+  🎉 Exciting news! Mem0 AI SDK now supports <strong>Tools Call</strong>.
 </Note>

 ## Overview
@@ -43,11 +43,19 @@ npm install @mem0/vercel-ai-provider
      config: {
        compatibility: "strict",
      },
+      // Optional Mem0 Global Config
+      mem0Config: {
+        user_id: "mem0-user-id",
+        org_id: "mem0-org-id",
+        project_id: "mem0-project-id",
+      },
    });
    ```

    > **Note**: The `openai` provider is set as default. Consider using `MEM0_API_KEY` and `OPENAI_API_KEY` as environment variables for security.

+    > **Note**: The `mem0Config` is optional. It is used to set the global config for the Mem0 Client (eg. `user_id`, `agent_id`, `app_id`, `run_id`, `org_id`, `project_id` etc).
+
 3. Add Memories to Enhance Context:

    ```typescript
@@ -145,6 +153,44 @@ npm install @mem0/vercel-ai-provider
    }
    ```

+### 4. Generate Responses with Tools Call
+
+    ```typescript
+    import { generateText } from "ai";
+    import { createMem0 } from "@mem0/vercel-ai-provider";
+    import { z } from "zod";
+
+    const mem0 = createMem0({
+      provider: "anthropic",
+      apiKey: "anthropic-api-key",
+      mem0Config: {
+        // Global User ID
+        user_id: "borat"
+      }
+    });
+
+    const prompt = "What the temperature in the city that I live in?"
+
+    const result = await generateText({
+      model: mem0('claude-3-5-sonnet-20240620'),
+      tools: {
+        weather: tool({
+          description: 'Get the weather in a location',
+          parameters: z.object({
+            location: z.string().describe('The location to get the weather for'),
+          }),
+          execute: async ({ location }) => ({
+            location,
+            temperature: 72 + Math.floor(Math.random() * 21) - 10,
+          }),
+        }),
+      },
+      prompt: prompt,
+    });
+
+    console.log(result);
+    ```
+
 ## Key Features

 - `createMem0()`: Initializes a new Mem0 provider instance.
@@ -1,247 +0,0 @@
-{
-  "$schema": "https://mintlify.com/schema.json",
-  "name": "Mem0.ai",
-  "favicon": "/logo/favicon.png",
-  "colors": {
-    "primary": "#6c60f0",
-    "light": "#E6FFA2",
-    "dark": "#a3df02",
-    "background": {
-      "dark": "#0f1117",
-      "light": "#fff"
-    }
-  },
-  "logo": {
-    "dark": "/logo/dark.svg",
-    "light": "/logo/light.svg",
-    "href": "https://github.com/mem0ai/mem0"
-  },
-  "topbarCtaButton": {
-    "name": "Your Dashboard",
-    "url": "https://app.mem0.ai"
-  },
-  "anchors": [
-    {
-      "name": "Your Dashboard",
-      "icon": "chart-simple",
-      "url": "https://app.mem0.ai"
-    },
-    {
-      "name": "API Reference",
-      "url": "api-reference",
-      "icon": "square-terminal"
-    },
-    {
-      "name": "Discord",
-      "icon": "discord",
-      "url": "https://mem0.dev/DiD"
-    },
-    {
-      "name": "GitHub",
-      "icon": "github",
-      "url": "https://github.com/mem0ai/mem0"
-    },
-    {
-      "name": "Support",
-      "icon": "envelope",
-      "url": "mailto:taranjeet@mem0.ai"
-    }
-  ],
-  "navigation": [
-    {
-      "group": "Get Started",
-      "pages": [
-        "overview",
-        "quickstart",
-        "playground",
-        "features"
-      ]
-    },
-    {
-      "group": "Platform",
-      "pages": [
-        "platform/overview",
-        "platform/quickstart",
-        {
-          "group": "Features",
-          "pages": ["features/selective-memory", "features/custom-categories", "features/direct-import", "features/async-client"]
-        },
-        "features/langchain-tools"
-      ]
-    },
-    {
-      "group": "Open Source",
-      "pages": [
-        "open-source/quickstart",
-        {
-          "group": "Graph Memory",
-          "pages": ["open-source/graph_memory/overview", "open-source/graph_memory/features"]
-        },
-        {
-          "group": "LLMs",
-          "pages": [
-            "components/llms/overview",
-            "components/llms/config",
-            {
-              "group": "Supported LLMs",
-              "pages": [
-                "components/llms/models/openai",
-                "components/llms/models/anthropic",
-                "components/llms/models/azure_openai",
-                "components/llms/models/ollama",
-                "components/llms/models/together",
-                "components/llms/models/groq",
-                "components/llms/models/litellm",
-                "components/llms/models/mistral_AI",
-                "components/llms/models/google_AI",
-                "components/llms/models/aws_bedrock",
-                "components/llms/models/gemini"
-              ]
-            }
-          ]
-        },
-        {
-          "group": "Vector Databases",
-          "pages": [
-            "components/vectordbs/overview",
-            "components/vectordbs/config",
-            {
-              "group": "Supported Vector Databases",
-              "pages": [
-                "components/vectordbs/dbs/qdrant",
-                "components/vectordbs/dbs/chroma",
-                "components/vectordbs/dbs/pgvector",
-                "components/vectordbs/dbs/milvus",
-                "components/vectordbs/dbs/azure_ai_search",
-                "components/vectordbs/dbs/redis"
-              ]
-            }
-          ]
-        },
-        {
-          "group": "Embedding Models",
-          "pages": [
-            "components/embedders/overview",
-            "components/embedders/config",
-            {
-              "group": "Supported Embedding Models",
-              "pages": [
-                "components/embedders/models/openai",
-                "components/embedders/models/azure_openai",
-                "components/embedders/models/ollama",
-                "components/embedders/models/huggingface",
-                "components/embedders/models/vertexai",
-                "components/embedders/models/gemini"
-              ]
-            }
-          ]
-        },
-        {
-          "group": "Features",
-          "pages": ["features/openai_compatibility", "features/custom-prompts"]
-        }
-      ]
-    },
-    {
-      "group": "API Reference",
-      "pages": [
-        "api-reference/overview",
-        {
-          "group": "Memory APIs",
-          "pages": [
-            "api-reference/memory/v1-get-memories",
-            "api-reference/memory/v2-get-memories",
-            "api-reference/memory/add-memories",
-            "api-reference/memory/delete-memories",
-            "api-reference/memory/get-memory",
-            "api-reference/memory/update-memory",
-            "api-reference/memory/delete-memory",
-            "api-reference/memory/v1-search-memories",
-            "api-reference/memory/v2-search-memories",
-            "api-reference/memory/history-memory",
-            "api-reference/memory/batch-update",
-            "api-reference/memory/batch-delete"
-          ]
-        },
-        {
-          "group": "Entities APIs",
-          "pages": [
-            "api-reference/entities/get-users",
-            "api-reference/entities/delete-user"
-          ]
-        },
-        {
-          "group": "Organizations APIs",
-          "pages": [
-            "api-reference/organization/get-orgs",
-            "api-reference/organization/get-org",
-            "api-reference/organization/create-org",
-            "api-reference/organization/delete-org",
-            {
-              "group": "Members APIs",
-              "pages": [
-                "api-reference/organization/get-org-members",
-                "api-reference/organization/add-org-member",
-                "api-reference/organization/update-org-member",
-                "api-reference/organization/delete-org-member"
-              ]
-            }
-          ]
-        },
-        {
-          "group": "Projects APIs",
-          "pages": [
-            "api-reference/project/get-projects",
-            "api-reference/project/get-project",
-            "api-reference/project/create-project",
-            "api-reference/project/delete-project",
-            {
-              "group": "Members APIs",
-              "pages":[
-                "api-reference/project/get-project-members",
-                "api-reference/project/add-project-member",
-                "api-reference/project/update-project-member",
-                "api-reference/project/delete-project-member"
-              ]
-            }
-          ]
-        }
-      ]
-    },
-    {
-      "group": "Integrations",
-      "pages": [
-        "integrations/vercel-ai-sdk",
-        "integrations/crewai",
-        "integrations/multion",
-        "integrations/autogen",
-        "integrations/langchain",
-        "integrations/langgraph",
-        "integrations/llama-index"
-      ]
-    },
-    {
-      "group": "💡 Examples",
-      "pages": [
-        "examples/overview",
-        "examples/mem0-with-ollama",
-        "examples/personal-ai-tutor",
-        "examples/customer-support-agent",
-        "examples/personal-travel-assistant",
-        "examples/llama-index-mem0"
-      ]
-    }
-  ],
-  "footerSocials": {
-    "discord": "https://mem0.dev/DiD",
-    "x": "https://x.com/mem0ai",
-    "github": "https://github.com/mem0ai",
-    "linkedin": "https://www.linkedin.com/company/mem0/"
-  },
-  "analytics": {
-    "posthog": {
-      "apiKey": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX",
-      "apiHost": "https://mango.mem0.ai"
-    }
-  }
-}
@@ -0,0 +1,91 @@
+---
+title: REST API Server
+icon: "server"
+iconType: "solid"
+---
+
+Mem0 provides a REST API server (written using FastAPI). Users can perform all operations through REST endpoints. The API also includes OpenAPI documentation, accessible at `/docs` when the server is running.
+
+<Frame caption="APIs supported by Mem0 REST API Server">
+  <img src="/images/rest-api-server.png" />
+</Frame>
+
+## Features
+
+- **Create memories:** Create memories based on messages for a user, agent, or run.
+- **Retrieve memories:** Get all memories for a given user, agent, or run.
+- **Search memories:** Search stored memories based on a query.
+- **Update memories:** Update an existing memory.
+- **Delete memories:** Delete a specific memory or all memories for a user, agent, or run.
+- **Reset memories:** Reset all memories for a user, agent, or run.
+- **OpenAPI Documentation:** Accessible via `/docs` endpoint.
+
+## Running Locally
+
+<Tabs>
+<Tab title="With Docker">
+
+1. Create a `.env` file in the current directory and set your environment variables. For example:
+
+```txt
+OPENAI_API_KEY=your-openai-api-key
+```
+
+2. Either pull the docker image from docker hub or build the docker image locally.
+
+<Tabs>
+  <Tab title="Pull from Docker Hub">
+
+```bash
+docker pull mem0/mem0-api-server
+```
+
+  </Tab>
+
+  <Tab title="Build Locally">
+
+```bash
+docker build -t mem0-api-server .
+```
+
+  </Tab>
+</Tabs>
+
+3. Run the Docker container:
+
+``` bash
+docker run -p 8000:8000 mem0-api-server --env-file .env
+```
+
+4. Access the API at http://localhost:8000.
+
+</Tab>
+
+<Tab title="Without Docker">
+
+1. Create a `.env` file in the current directory and set your environment variables. For example:
+
+```txt
+OPENAI_API_KEY=your-openai-api-key
+```
+
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Start the FastAPI server:
+
+```bash
+uvicorn main:app --reload
+```
+
+4. Access the API at http://localhost:8000.
+
+</Tab>
+</Tabs>
+
+## Usage
+
+Once the server is running (locally or via Docker), you can interact with it using any REST client or through your preferred programming language (e.g., Go, Java, etc.). You can test out the APIs using the OpenAPI documentation at [http://localhost:8000/docs](http://localhost:8000/docs) endpoint.
@@ -1,6 +1,8 @@
 ---
 title: Features
 description: 'Graph Memory features'
+icon: "list-check"
+iconType: "solid"
 ---

 Graph Memory is a powerful feature that allows users to create and utilize complex relationships between pieces of information.
@@ -26,8 +28,7 @@ config = {
            "password": "xxx"
        },
        "custom_prompt": "Please only extract entities containing sports related relationships and nothing else.",
-    },
-    "version": "v1.1"
+    }
 }

 m = Memory.from_config(config_dict=config)
@@ -1,6 +1,8 @@
 ---
 title: Overview
 description: 'Enhance your memory system with graph-based knowledge representation and retrieval'
+icon: "database"
+iconType: "solid"
 ---

 Mem0 now supports **Graph Memory**.
@@ -12,7 +14,7 @@ This integration enables users to leverage the strengths of both vector-based an
 To use Mem0 with Graph Memory support, install it using pip:

 ```bash
-pip install mem0ai[graph]
+pip install "mem0ai[graph]"
 ```

 This command installs Mem0 along with the necessary dependencies for graph functionality.
@@ -36,8 +38,7 @@ 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>

@@ -63,8 +64,7 @@ config = {
            "username": "neo4j",
            "password": "xxx"
        }
-    },
-    "version": "v1.1"
+    }
 }

 m = Memory.from_config(config_dict=config)
@@ -79,7 +79,7 @@ config = {
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
-            "max_tokens": 1500,
+            "max_tokens": 2000,
        }
    },
    "graph_store": {
@@ -96,8 +96,7 @@ config = {
                "temperature": 0.0,
            }
        }
-    },
-    "version": "v1.1"
+    }
 }

 m = Memory.from_config(config_dict=config)
@@ -109,6 +108,10 @@ 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`.
+</Note>
+
 <CodeGroup>
 ```python Code
 m.add("I like pizza", user_id="alice")
@@ -0,0 +1,119 @@
+---
+title: Multimodal Support
+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.
+
+## 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.
+
+<CodeGroup>
+```python Code
+from mem0 import Memory
+
+client = Memory()
+
+messages = [
+    {
+        "role": "user",
+        "content": "Hi, my name is Alice."
+    },
+    {
+        "role": "assistant",
+        "content": "Nice to meet you, Alice! What do you like to eat?"
+    },
+    {
+        "role": "user",
+        "content": {
+            "type": "image_url",
+            "image_url": {
+                "url": "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+            }
+        }
+    },
+]
+
+# Calling the add method to ingest messages into the memory system
+client.add(messages, user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Name is Alice",
+      "event": "ADD",
+      "id": "7ae113a3-3cb5-46e9-b6f7-486c36391847"
+    },
+    {
+      "memory": "Likes large pizza with toppings including cherry tomatoes, black olives, green spinach, yellow bell peppers, diced ham, and sliced mushrooms",
+      "event": "ADD",
+      "id": "56545065-7dee-4acf-8bf2-a5b2535aabb3"
+    }
+  ]
+}
+```
+</CodeGroup>
+
+## Image Integration Methods
+
+Mem0 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.
+
+## 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.
+
+```python
+# Define the image URL
+image_url = "https://www.superhealthykids.com/wp-content/uploads/2021/10/best-veggie-pizza-featured-image-square-2.jpg"
+
+# Create the message dictionary with the image URL
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {
+            "url": image_url
+        }
+    }
+}
+```
+
+## 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
+import base64
+
+# Path to the image file
+image_path = "path/to/your/image.jpg"
+
+# Encode the image in Base64
+with open(image_path, "rb") as image_file:
+    base64_image = base64.b64encode(image_file.read()).decode("utf-8")
+
+# Create the message dictionary with the Base64-encoded image
+image_message = {
+    "role": "user",
+    "content": {
+        "type": "image_url",
+        "image_url": {
+            "url": f"data:image/jpeg;base64,{base64_image}"
+        }
+    }
+}
+```
+
+By utilizing these methods, you can effectively incorporate images into user interactions, enhancing the multimodal capabilities of your Mem0 instance.
+
+<Note>
+Currently, we support only OpenAI models for image description.
+</Note>
+
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,380 @@
+---
+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: "user123", metadata: { category: "movie_recommendations" } });
+```
+
+```json Output
+{
+  "results": [
+    {
+      "id": "c03c9045-df76-4949-bbc5-d5dc1932aa5c",
+      "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: "user123" });
+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": "user123"
+    },
+    {
+      "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": "user123"
+    },
+    {
+      "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": "user123"
+    }
+  ]
+}
+```
+</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": "user123"
+}
+```
+</CodeGroup>
+
+### Search Memories
+
+<CodeGroup>
+```typescript Code
+const result = await memory.search('What do you know about me?', { userId: "user123" });
+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": "user123"
+    },
+    {
+      "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": "user123"
+    },
+    {
+      "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": "user123"
+    }
+  ]
+}
+```
+</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,
+    "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
+
+```typescript
+// Delete a memory by id
+await memory.delete('892db2ae-06d9-49e5-8b3e-585ef9b85b8e');
+
+// Delete all memories for a user
+await memory.deleteAll({ userId: "user123" });
+```
+
+### 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="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" />
@@ -0,0 +1,480 @@
+---
+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
+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": "---"
+        }
+    }
+}
+
+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"}
+    ]
+}
+```
+</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"
+        }
+    ]
+}
+```
+</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"
+        }
+    ]
+}
+```
+</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                          | "v1.1"                     |
+| `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
+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" />
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Deshraj Yadav	7c8628eadc	Update pyproject.toml (#2301 )	2025-03-04 14:22:12 -08:00
Deshraj Yadav	20c03eaa92	[Misc] Clean up unnecessary checks in chromadb vector store integration (#2284 )	2025-03-04 14:21:27 -08:00
Saket Aryan	aa7ab9736d	Add Mem0 Demo (#2291 )	2025-03-04 10:04:59 -08:00
Dev Khant	f7500c925e	fix multimodal functionality and version bump -> 0.1.62 (#2296 )	2025-03-04 17:51:27 +05:30
Dev Khant	8b53b1473a	Add contribution docs (#2294 )	2025-03-04 15:47:38 +05:30
Dev Khant	c611e3e0e7	Docs: Add dify integration (#2293 )	2025-03-04 14:29:25 +05:30
Taranjeet Singh	bc4c15962a	Fix: improve url of node js sdk (#2292 )	2025-03-03 23:08:44 -08:00
Dev-Khant	6e65730b0e	version bump -> 0.1.61	2025-03-03 23:32:41 +05:30
Dev Khant	8452dd598f	Integrate Supabase VectorDB (#2290 )	2025-03-03 23:16:24 +05:30
Dev Khant	2556c5fe88	Doc: Update examples in LLMs, VectorDBs and Embedding models pages (#2288 )	2025-03-03 13:21:19 +05:30
Dev Khant	a4340b2336	Fix Qdrant Tests (#2287 )	2025-03-03 10:46:56 +05:30
Dev Khant	f4dc5f6c71	version bump -> 0.1.60 (#2280 )	2025-03-01 13:11:38 +05:30
Deshraj Yadav	32ebdaef2f	[Bug Fix] Fix issue with chromadb not working with 0.6.0 and onwards (#2279 )	2025-03-01 13:09:59 +05:30
Dev Khant	4318663697	Make api_version=v1.1 default and version bump -> 0.1.59 (#2278 ) Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>	2025-03-01 11:36:20 +05:30
Saket Aryan	5606c3ffb8	Update Docs (#2277 )	2025-02-28 16:37:05 -08:00
Saket Aryan	c1aba35884	Mem0 TS Spec/Docs Update (#2275 )	2025-02-28 21:43:51 +05:30
Dev Khant	d9b48191de	Doc: Update embeddings config page (#2276 )	2025-02-28 21:43:00 +05:30
Dev Khant	e06f95cea4	fix deprecation warning: qdrant and version bump -> 0.1.58 (#2274 )	2025-02-28 16:41:45 +05:30
Dev Khant	b131c4bfc4	Update max_token and formatting (#2273 )	2025-02-28 15:59:34 +05:30
Wonbin Kim	6acb00731d	Add config option for vertex embedding tasks (#2266 )	2025-02-28 15:20:05 +05:30
Dev Khant	8143f86be6	Fix proxy pytests (#2272 )	2025-02-28 15:05:26 +05:30
Dev-Khant	8d07469ba7	bump version -> 0.1.57	2025-02-28 10:55:12 +05:30
Saket Aryan	434b555a29	Updated docs for typescript package (#2269 )	2025-02-27 18:22:28 -08:00
Saket Aryan	f8071a753b	Update AI SDK Example (#2271 )	2025-02-27 18:11:59 -08:00
Saket Aryan	d200691e9b	Added Mem0 TS Library (#2270 )	2025-02-27 15:19:17 -08:00
Dev Khant	ecff6315e7	User_id creation for client and formatting (#2264 )	2025-02-28 00:00:11 +05:30
Dev Khant	ff4510f83d	Doc: add param fields in v2 get_all (#2268 )	2025-02-27 15:49:57 +05:30
Dev Khant	308e79bb68	Docs: Update v2 GET ALL endpoint (#2267 )	2025-02-27 02:11:28 -08:00
Dev-Khant	48176bd194	doc: fix xai tile	2025-02-26 13:56:09 +05:30
Dev Khant	5cebe9ab52	Rename xai.mdx to xAI.mdx	2025-02-26 13:45:32 +05:30
Dev Khant	371848cfbc	Doc: fix xai (#2263 )	2025-02-26 13:43:38 +05:30
Dev Khant	8e3ed634d1	version bump -> 0.1.56 (#2262 )	2025-02-26 13:35:25 +05:30
Dev Khant	e9bc4cdc95	Add Grok Support (#2260 )	2025-02-26 13:34:01 +05:30
Dev Khant	a236aa2315	Doc: fix integrations page (#2261 )	2025-02-26 13:18:52 +05:30
Dev Khant	5660fffa96	Doc: update example on quickstart (#2255 )	2025-02-26 00:04:38 +05:30
Dev Khant	eba6f77330	Doc: update anthropic model (#2254 )	2025-02-25 00:59:14 +05:30
Dev Khant	b5d00e9b6c	Docs: set api_key to env (#2252 )	2025-02-24 13:42:39 +05:30
Dev Khant	1be0d70d02	Doc: api_key changes (#2251 )	2025-02-24 10:23:28 +05:30
Taranjeet Singh	edb53209ef	improvement: Update multimodal docs. (#2250 )	2025-02-23 16:43:23 -08:00
Taranjeet Singh	7443e58a9d	improvement: Update webhook docs. (#2249 )	2025-02-23 16:10:21 -08:00
Dev Khant	7f25caba47	version bump -> 0.1.55 (#2248 )	2025-02-23 18:07:11 +05:30
Dev Khant	c42934b7fb	Formatting and Client changes (#2247 )	2025-02-23 00:39:26 +05:30
Dev Khant	17887b5959	Docs: Add immutable param to ADD (#2246 )	2025-02-22 23:37:30 +05:30
Dev Khant	5d47f4f060	Doc: Update quickstart and Webhook page (#2245 )	2025-02-22 01:24:10 +05:30
Dev-Khant	600c9fae63	update webhook js doc	2025-02-21 21:32:54 +05:30
Dev Khant	2d0c8fe94e	embedchain: version bump -> 0.1.127 (#2244 )	2025-02-21 17:51:47 +05:30
Dev Khant	369d5325f9	version bump -> 0.1.54 (#2243 )	2025-02-21 16:03:18 +05:30
Dev Khant	29d63306a4	Webhook API reference and update/delete function change (#2242 )	2025-02-21 16:01:53 +05:30
Deshraj Yadav	96628d7791	Update docs for running REST API Server (#2241 )	2025-02-21 01:26:57 -08:00
Deshraj Yadav	244fd2231d	Add support for Mem0 REST API Server in OSS package (#2240 )	2025-02-21 01:05:55 -08:00
Dev Khant	3db028c719	Docs: update webhook (#2238 )	2025-02-21 00:48:59 +05:30
Dev Khant	c86b1e4d4c	Docs: update webhooks (#2237 )	2025-02-21 00:42:02 +05:30
Dev Khant	5f5b738745	version bump -> 0.1.53 (#2236 )	2025-02-20 23:58:54 +05:30
Dev Khant	acaf47ed54	Project_id mandatory for Webhooks (#2232 ) Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>	2025-02-20 23:57:29 +05:30
cola	9734b2db7e	delete same vector in retrieved_old_memory (#2201 )	2025-02-20 10:10:09 -08:00
Saket Aryan	3fe31e1c31	Fix Build Errors (#2235 )	2025-02-20 22:47:15 +05:30
Seetha Rama Guptha	f4c0f98fde	Adding Native OpenSearch support for Mem0 (#2211 )	2025-02-20 11:42:12 +05:30
Dev Khant	6e781f616c	Doc: Update Redis config (#2233 )	2025-02-20 11:24:00 +05:30
Prateek Chhikara	dcba83186a	Updated docs (#2231 )	2025-02-19 18:37:01 -08:00
Saket Aryan	a6d305f8d0	Update Docs for Mem0 AI SDK (#2230 )	2025-02-19 14:39:49 -08:00
Lennex Zinyando	db512950b9	Docs updates (#2229 )	2025-02-19 13:48:48 -08:00
Dev-Khant	d4df9f6dfe	fix webhook doc	2025-02-20 01:27:26 +05:30
Dev Khant	0e6b20982a	Docs: webhook announcement (#2228 )	2025-02-19 15:52:13 +05:30
Dev Khant	92e1a9b433	Docs: Update webhook (#2227 )	2025-02-19 14:06:46 +05:30
Dev-Khant	3be356a0e9	Webhook doc update	2025-02-19 13:46:00 +05:30
Dev Khant	760cd54ddf	Webhook Support (#2225 )	2025-02-19 00:04:01 -08:00
Deshraj Yadav	1436da18b1	Update docs (#2222 )	2025-02-18 17:52:32 -08:00
Prateek Chhikara	cc9acb7493	Added support of vision input	2025-02-18 11:47:13 -08:00
Dev Khant	cbee71a63e	Proper error msg for API Key validation (#2220 )	2025-02-18 23:59:59 +05:30
Dev Khant	b052a86424	Update API reference to remove Org/Proj (#2221 )	2025-02-18 22:34:58 +05:30
Saket Aryan	95f5fb3ab4	Fix Vercel AI SDK Build Errors (#2219 ) Co-authored-by: Dev Khant <devkhant24@gmail.com> Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>	2025-02-18 16:03:36 +05:30
Dev Khant	04d7f2e48c	Fix deprecation warning of output_format for ADD and Version Bump (#2216 )	2025-02-18 10:50:56 +05:30
Lennex Zinyando	be46f4eb40	[Docs] Docs update (#2199 )	2025-02-17 10:30:40 -08:00
Dev Khant	f16a580ef6	Fix Azure OpenAI test (#2217 )	2025-02-17 19:21:12 +05:30
Dev Khant	095189d39a	Update client in README (#2215 )	2025-02-17 00:14:23 +05:30
Dev Khant	064d28626b	Doc: Update search output (#2208 )	2025-02-14 06:34:47 +05:30
Dev Khant	cd31c5897a	increase timeout and version bump (#2205 )	2025-02-14 06:00:40 +05:30
Prateek Chhikara	0bb6137877	updated custom categories docs (#2207 )	2025-02-13 11:55:04 -08:00
Saket Aryan	2c63f4d866	Added Typescript Docs and fixed a broken url (#2204 )	2025-02-12 09:48:23 -08:00
Prateek Chhikara	3984b90f62	docs update (#2196 )	2025-02-06 11:59:18 -08:00
Prateek Chhikara	b08b50cbc6	added enhanced search params (#2195 )	2025-02-05 12:34:52 -08:00
Dev Khant	90096d6954	Doc: Add config params for Memory() (#2193 )	2025-02-05 12:44:41 +05:30
Prateek Chhikara	7581974805	updated docs and resolved some bugs (#2186 )	2025-02-01 12:29:46 -08:00
Prateek Chhikara	a8fafb9368	updated docs for mem0 architecture diagram (#2185 )	2025-02-01 11:43:17 -08:00
Dev Khant	75a4a253f7	Doc: Fix full stack link (#2184 )	2025-02-01 10:53:37 +05:30
Dev Khant	f9995d144f	Update README.md	2025-02-01 00:37:08 +05:30
junmo1215	8d3c8c695d	Fix query filter in azure ai search (#2171 )	2025-01-31 15:38:06 +05:30
Dev Khant	9f27b88843	Update README.md	2025-01-31 12:00:24 +05:30
Dev Khant	d2f0e23dc8	Update README.md	2025-01-31 10:42:41 +05:30
Dev Khant	6e23a6f00e	Update README.md	2025-01-30 23:51:28 +05:30
Dev Khant	a06c9a99ae	Doc changes and Storage fix (#2181 )	2025-01-30 10:16:33 -08:00
Dev Khant	63fbd2dc2c	Doc: update banner link (#2180 )	2025-01-28 12:41:44 +05:30
Dev Khant	203943919b	version bump - 0.1.48 (#2174 )	2025-01-23 17:47:24 +05:30
Dev Khant	04bbad67ac	DeepSeek Integration (#2173 )	2025-01-23 17:45:03 +05:30
Dev Khant	e1b527b73f	Doc: update delete_users (#2169 )	2025-01-22 13:28:12 +05:30
Dev Khant	625846caf8	Doc: update delete_users (#2168 )	2025-01-22 13:25:13 +05:30
Dev Khant	c1bd4e19fe	version bump -> 0.1.47 (#2167 )	2025-01-22 13:20:20 +05:30
Dev Khant	8d172d6139	Update delete_users (#2166 )	2025-01-22 13:18:26 +05:30
Dev Khant	a5355f7488	version bump -> 0.1.46 (#2164 )	2025-01-21 10:07:34 +05:30
Yunsung Lee	f13f3b9283	Fix/es query filter (🚨 URGENT) (#2162 )	2025-01-21 10:03:50 +05:30
Deshraj Yadav	56351d1f8d	Fix async client `update_project` method (#2155 )	2025-01-19 09:05:59 +05:30
Dev Khant	a9d1383909	Fix pytests (#2157 )	2025-01-18 15:06:49 -08:00
Dev Khant	80c9c6a577	Doc: Update V2 Search/GetAll docs (#2158 )	2025-01-18 10:43:03 +05:30
Dev Khant	e4e5511642	Doc: Update API reference (#2154 )	2025-01-18 01:06:22 +05:30
Dev Khant	a4b085553a	Code formatting (#2153 )	2025-01-16 12:33:56 +05:30
Prateek Chhikara	e12273c7cb	changes to docs for custom categories (#2146 )	2025-01-15 12:43:15 -08:00
Saket Aryan	ee2b5adfc0	Fix lib/utils issue (#2151 )	2025-01-15 09:49:38 -08:00
Dev Khant	205a03a5f2	Doc: Add update_project API (#2148 )	2025-01-15 08:52:20 +05:30
Dev Khant	7be029a26f	Doc: Custom instructions/Categories (#2147 )	2025-01-15 07:51:16 +05:30
Dev-Khant	0bd177b30c	version bump -> 0.1.44	2025-01-15 05:55:26 +05:30
Dev Khant	82359774b7	Custom instructions API improvements (#2140 ) Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>	2025-01-15 05:54:23 +05:30
Dev Khant	3fa4b80deb	Doc: Update ES and version bump (#2142 )	2025-01-13 20:14:31 +05:30
Dev-Khant	e96fd5d269	update makefile	2025-01-13 20:07:48 +05:30
Yunsung Lee	927644d712	Feat/mem0 support es (#2125 )	2025-01-13 19:35:38 +05:30
Dev Khant	7397279872	HNSW support for pgvector (#2139 )	2025-01-11 10:16:42 -08:00
Dev Khant	6851fac327	update api-reference for get_all (#2138 )	2025-01-11 15:30:54 +05:30
Dev-Khant	254524a624	version bump -> 0.1.42	2025-01-11 13:42:17 +05:30
Dev Khant	7f0d766c09	Add support: Custom instruction/categories for projects (#2134 )	2025-01-11 13:38:20 +05:30
spike-spiegel-21	ac8cf59473	entities added in proxy (#2135 )	2025-01-11 01:47:42 +05:30
Dev Khant	9c4acdcba7	Doc: MemoryExport update (#2132 )	2025-01-10 00:00:18 +05:30
Dev Khant	a6b9721ede	version bump -> 0.1.41 (#2131 )	2025-01-09 20:50:47 +05:30
Dev Khant	a8f3ec25b7	Code formatting and doc update (#2130 )	2025-01-09 20:48:18 +05:30
Dev Khant	21854c6a24	Add support: MemoryExport API (#2129 ) Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>	2025-01-09 20:43:01 +05:30
Dev-Khant	09bf7ad916	update doc	2025-01-09 18:05:14 +05:30
haarishmk26	0cc528f3b1	Commit tracking (#2127 )	2025-01-09 17:40:11 +05:30
AkisAya	cbd845fe41	fix VectorStoreBase abstract methods params (#2068 ) Co-authored-by: Dev Khant <devkhant24@gmail.com>	2025-01-09 17:30:16 +05:30
gmdorfman	2e782b0963	feature/fixed-where-clause-default (#2042 )	2025-01-09 17:21:27 +05:30
Hieu Lam	4c31c65649	Fix not working with Gemini models (#2021 )	2025-01-09 17:19:26 +05:30
Mike	c90f87e657	feat: allow boto3 to use its native credential finding functionality (#1536 )	2025-01-09 16:59:55 +05:30
Dev Khant	c63c0aca9d	version bump -> 0.1.40 (#2122 )	2025-01-06 16:18:41 +05:30
非法操作	d4dbed9dbd	fix request mem0 without org_id raise error (#2121 )	2025-01-06 16:16:13 +05:30
Dev-Khant	e9188a51fe	update README	2025-01-06 11:38:57 +05:30
Dev Khant	d893033dcf	version bump -> 0.1.39 (#2120 )	2025-01-03 22:29:20 +05:30
Mayank	78a2ef41d7	[graph_memory]: improve delete/add graph memory (#2073 )	2025-01-03 22:21:05 +05:30