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docs/integrations/agentops.mdx
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title: AgentOps
---
<Snippet file="blank-notif.mdx" />
Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.
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title: Agno
---
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This integration of [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.com/agno-agi/agno, enables persistent, multimodal memory for Agno-based agents - improving personalization, context awareness, and continuity across conversations.
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Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
<Snippet file="blank-notif.mdx" />
## Overview
In this guide, we'll explore an example of creating a conversational AI system with memory:
docs/integrations/aws-bedrock.mdx
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title: AWS Bedrock
---
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This integration demonstrates how to use **Mem0** with **AWS Bedrock** and **Amazon OpenSearch Service (AOSS)** to enable persistent, semantic memory in intelligent agents.
## Overview
docs/integrations/crewai.mdx
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title: CrewAI
---
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Build an AI system that combines CrewAI's agent-based architecture with Mem0's memory capabilities. This integration enables persistent memory across agent interactions and personalized task execution based on user history.
## Overview
docs/integrations/dify.mdx
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title: Dify
---
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# Integrating Mem0 with Dify AI
Mem0 brings a robust memory layer to Dify AI, empowering your AI agents with persistent conversation storage and retrieval capabilities. With Mem0, your Dify applications gain the ability to recall past interactions and maintain context, ensuring more natural and insightful conversations.
docs/integrations/elevenlabs.mdx
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title: ElevenLabs
---
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Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables persistent, context-aware voice interactions that remember past conversations.
## Overview
docs/integrations/flowise.mdx
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title: Flowise
---
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The [**Mem0 Memory**](https://github.com/mem0ai/mem0) integration with [Flowise](https://github.com/FlowiseAI/Flowise) enables persistent memory capabilities for your AI chatflows. [Flowise](https://flowiseai.com/) is an open-source low-code tool for developers to build customized LLM orchestration flows & AI agents using a drag & drop interface.
## Overview
docs/integrations/google-ai-adk.mdx
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title: Google Agent Development Kit
---
<Snippet file="blank-notif.mdx" />
Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Google Agent Development Kit (ADK)](https://github.com/google/adk-python), an open-source framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
## Overview
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title: Keywords AI
---
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Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Keywords AI.
## Overview
docs/integrations/langchain-tools.mdx
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description: 'Integrate Mem0 with LangChain tools to enable AI agents to store, search, and manage memories through structured interfaces'
---
<Snippet file="blank-notif.mdx" />
## Overview
Mem0 provides a suite of tools for storing, searching, and retrieving memories, enabling agents to maintain context and learn from past interactions. The tools are built as Langchain tools, making them easily integrable with any AI agent implementation.
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title: Langchain
---
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Build a personalized Travel Agent AI using LangChain for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient travel planning experiences.
## Overview
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title: LangGraph
---
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Build a personalized Customer Support AI Agent using LangGraph for conversation flow and Mem0 for memory retention. This integration enables context-aware and efficient support experiences.
## Overview
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title: Livekit
---
<Snippet file="blank-notif.mdx" />
This guide demonstrates how to create a memory-enabled voice assistant using LiveKit, Deepgram, OpenAI, and Mem0, focusing on creating an intelligent, context-aware travel planning agent.
## Prerequisites
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title: LlamaIndex
---
<Snippet file="blank-notif.mdx" />
LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-index-memory-mem0). In this guide, we'll show you how to use it.
<Note type="info">
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title: Mastra
---
<Snippet file="blank-notif.mdx" />
The [**Mastra**](https://mastra.ai/) integration demonstrates how to use Mastra's agent system with Mem0 as the memory backend through custom tools. This enables agents to remember and recall information across conversations.
## Overview
docs/integrations/mcp-server.mdx
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title: MCP Server
---
<Snippet file="blank-notif.mdx" />
## Integrating mem0 as an MCP Server in Cursor
[mem0](https://github.com/mem0ai/mem0-mcp) is a powerful tool designed to enhance AI-driven workflows, particularly in code generation and contextual memory. In this guide, we'll walk through integrating mem0 as an **MCP (Model Context Protocol) server** within [Cursor](https://cursor.sh/), an AI-powered coding editor.
## Prerequisites
Before proceeding, ensure you have the following installed:
- Cursor IDE
- Python (>=3.8)
- Git
- [mem0-mcp](https://github.com/mem0ai/mem0-mcp) (Clone the repository and set up as per the instructions in the README)
## Configuring Cursor to use mem0 as an MCP Server
1. **Open Cursor.**
2. **Navigate to `Settings` > `Cursor Settings` > `Features` > `MCP Servers`.**
3. **Add a new provider using the MCP server:**
- Click on **`Add new MCP server`**
- Provide a name for the server, e.g. `mem0` and select type as `sse`
- Enter the **SSE Endpoint**: `http://0.0.0.0:8080/sse`
4. **Save and Restart Cursor** to apply changes.
## Demo
<iframe width="560" height="315" src="https://www.youtube.com/embed/fWa6KX7cpG8?si=cmJDz2sQevGnItSI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## Using mem0 in Cursor
Once integrated, mem0 can assist with contextual memory and AI-driven coding enhancements. Some key functionalities include:
### 1. Storing Coding Preferences
Mem0 can store and manage coding preferences, including:
- Complete code snippets with dependencies
- Language/framework versions
- Documentation and comments
- Best practices and example usage
### 2. Retrieving Stored Preferences
Access all stored coding references to:
- Review implementations
- Maintain consistency in coding practices
### 3. Semantic Search for Preferences
Use natural language queries to find:
- Code snippets
- Technical documentation
- Best practices
- Setup guides
## Benefits of Using mem0 in Cursor
- **Persistent Context Storage**: Retain and reuse coding insights across sessions.
- **Seamless Integration**: Works directly within Cursor as an MCP server.
- **Efficient Search**: Retrieve relevant coding insights using semantic search.
## Conclusion
By integrating mem0 as an MCP server within Cursor, you enhance your development workflow with AI-powered memory and context-aware assistance. Follow the steps above to set up and start leveraging mem0 in your coding environment.
For more details on MCP integration, refer to Cursor's [Model Context Protocol documentation](https://docs.cursor.com/context/model-context-protocol).
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---
title: MultiOn
---
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Build a personal browser agent that remembers user preferences and automates web tasks. It integrates Mem0 for memory management with MultiOn for executing browser actions, enabling personalized and efficient web interactions.
## Overview
In this guide, we'll explore two examples of creating Browser-based AI Agents:
1. An agent that searches [arxiv.org](https://arxiv.org) for research papers relevant to user's research interests.
2. A travel agent that provides personalized travel information based on user preferences. Refer to the [notebook](https://github.com/MULTI-ON/cookbook/blob/main/personalized-travel-agent/mem0_travel_agent.ipynb) for detailed code.
## Setup and Configuration
Install necessary libraries:
```bash
pip install mem0ai multion openai
```
First, we'll import the necessary libraries and set up our configurations.
```python
import os
from mem0 import Memory, MemoryClient
from multion.client import MultiOn
from openai import OpenAI
# Configuration
OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
MULTION_API_KEY = 'your-multion-key' # Replace with your actual MultiOn API key
MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
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() # For API usage
multion = MultiOn(api_key=MULTION_API_KEY)
```
## Example 1: Research Paper Search Agent
### Add memories to Mem0
Define user data and add it to Mem0.
```python
USER_DATA = """
About me
- I'm Deshraj Yadav, Co-founder and CTO at Mem0, interested in AI and ML Infrastructure.
- Previously, I was a Senior Autopilot Engineer at Tesla, leading the AI Platform for Autopilot.
- I built EvalAI at Georgia Tech, an open-source platform for evaluating ML algorithms.
- Outside of work, I enjoy playing cricket in two leagues in the San Francisco.
"""
memory.add(USER_DATA, user_id=USER_ID)
print("User data added to memory.")
```
### Retrieving Relevant Memories
Define search command and retrieve relevant memories from Mem0.
```python
command = "Find papers on arxiv that I should read based on my interests."
relevant_memories = memory.search(command, user_id=USER_ID, limit=3)
relevant_memories_text = '\n'.join(mem['memory'] for mem in relevant_memories['results'])
print(f"Relevant memories:")
print(relevant_memories_text)
```
### Browsing arXiv
Use MultiOn to browse arXiv based on the command and relevant memories.
```python
prompt = f"{command}\n My past memories: {relevant_memories_text}"
browse_result = multion.browse(cmd=prompt, url="https://arxiv.org/")
print(browse_result)
```
## Example 2: Travel Agent
### Get Travel Information
Add conversation to Mem0 and create a function to get travel information based on user's question and optionally their preferences from memory.
<CodeGroup>
```python Code
def get_travel_info(question, use_memory=True):
if use_memory:
previous_memories = memory_client.search(question, user_id=USER_ID)
relevant_memories_text = ""
if previous_memories and previous_memories.get('results'):
print("Using previous memories to enhance the search...")
relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
command = "Find travel information based on my interests:"
prompt = f"{command}\n Question: {question} \n My preferences: {relevant_memories_text}"
else:
command = "Find travel information based on my interests:"
prompt = f"{command}\n Question: {question}"
print("Searching for travel information...")
browse_result = multion.browse(cmd=prompt)
return browse_result.message
# Example usage
question = "Show me flight details for it."
answer_without_memory = get_travel_info(question, use_memory=False)
answer_with_memory = get_travel_info(question, use_memory=True)
print("Answer without memory:", answer_without_memory)
print("Answer with memory:", answer_with_memory)
# Another example
question = "What is the best place to eat there?"
answer_without_memory = get_travel_info(question, use_memory=False)
answer_with_memory = get_travel_info(question, use_memory=True)
print("Answer without memory:", answer_without_memory)
print("Answer with memory:", answer_with_memory)
```
```json Conversation
# Add conversation to Mem0
conversation = [
{
"role": "user",
"content": "What are the best travel destinations in the world?"
},
{
"role": "assistant",
"content": "Could you please specify your interests or the type of travel information you are looking for? This will help me find the most relevant information for you."
},
{
"role": "user",
"content": "Sure, I want to travel to San Francisco."
},
{
"role": "assistant",
"content": """
Based on the information gathered from TripAdvisor, here are some popular attractions, activities, and travel tips for San Francisco: \
1. **Golden Gate Bridge**: A must-see iconic landmark. \
2. **Alcatraz Island**: Famous former prison offering tours. \
3. **Fisherman's Wharf**: Popular tourist area with shops, restaurants, and sea lions. \
4. **Chinatown**: The largest Chinatown outside of Asia. \
5. **Golden Gate Park**: Large urban park with gardens, museums, and recreational activities. \
6. **Cable Cars**: Historic streetcars offering a unique way to see the city. \
7. **Exploratorium**: Interactive science museum. \
8. **San Francisco Museum of Modern Art (SFMOMA)**: Modern and contemporary art museum. \
9. **Lombard Street**: Known for its steep, one-block section with eight hairpin turns. \
10. **Union Square**: Major shopping and cultural hub. \
Travel Tips: \
- **Weather**: San Francisco has a mild climate, but it can be foggy and windy. Dress in layers. \
- **Transportation**: Use public transportation like BART, Muni, and cable cars to get around. \
- **Safety**: Be aware of your surroundings, especially in crowded tourist areas. \
- **Dining**: Try local specialities like sourdough bread, seafood, and Mission-style burritos. \
"""
},
{
"role": "user",
"content": "Show me hotels around Golden Gate Bridge."
},
{
"role": "assistant",
"content": """The search results for hotels around Golden Gate Bridge in San Francisco include: \
1. Hilton Hotels In San Francisco - Hotel Near Fishermans Wharf (hilton.com) \
2. The 10 Closest Hotels to Golden Gate Bridge (tripadvisor.com) \
3. Hotels near Golden Gate Bridge (expedia.com) \
4. Hotels near Golden Gate Bridge (hotels.com) \
5. Holiday Inn Express & Suites San Francisco Fishermans Wharf, an IHG Hotel $146 (1.8K) 3-star hotel Golden Gate Bridge • 3.5 mi DEAL 19% less than usual \
6. Holiday Inn San Francisco-Golden Gateway, an IHG Hotel $151 (3.5K) 3-star hotel Golden Gate Bridge • 3.7 mi Casual hotel with dining, a bar & a pool \
7. Hotel Zephyr San Francisco $159 (3.8K) 4-star hotel Golden Gate Bridge • 3.7 mi Nautical-themed lodging with bay views \
8. Lodge at the Presidio \
9. The Inn Above Tide \
10. Cavallo Point \
11. Casa Madrona Hotel and Spa \
12. Cow Hollow Inn and Suites \
13. Samesun San Francisco \
14. Inn on Broadway \
15. Coventry Motor Inn \
16. HI San Francisco Fisherman's Wharf Hostel \
17. Loews Regency San Francisco Hotel \
18. Fairmont Heritage Place Ghirardelli Square \
19. Hotel Drisco Pacific Heights \
20. Travelodge by Wyndham Presidio San Francisco \
"""
}
]
```
</CodeGroup>
## Conclusion
By integrating Mem0 with MultiOn, you've created personalized browser agents that remember user preferences and automate web tasks. The first example demonstrates a research-focused agent, while the second example shows a travel agent capable of providing personalized recommendations.
These examples illustrate how combining memory management with web browsing capabilities can create powerful, context-aware AI agents for various applications.
## Help
- 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" />
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title: OpenAI Agents SDK
---
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Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), a lightweight framework for building multi-agent workflows. This integration enables agents to access persistent memory across conversations, enhancing context retention and personalization.
## Overview
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description: 'Integrate Mem0 with Pipecat for conversational memory in AI agents'
---
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# Pipecat Integration
Mem0 seamlessly integrates with [Pipecat](https://pipecat.ai), providing long-term memory capabilities for conversational AI agents. This integration allows your Pipecat-powered applications to remember past conversations and provide personalized responses based on user history.
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description: "Mem0 Raycast extension for intelligent memory management"
---
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Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. This extension lets you store and retrieve text snippets using Mem0's intelligent memory system. Find Mem0 in [Raycast Store](https://www.raycast.com/dev_khant/mem0) for using it.
## Getting Started
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title: Vercel AI SDK
---
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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">