mem0/docs/integrations/agentops.mdx

---
title: AgentOps
description: "Integrate Mem0 with AgentOps for automatic monitoring, analytics, and real-time tracking of memory operations."
---

Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [AgentOps](https://agentops.ai), a comprehensive monitoring and analytics platform for AI agents. This integration enables automatic tracking and analysis of memory operations, providing insights into agent performance and memory usage patterns.

## Overview

1. Automatic monitoring of Mem0 operations and performance metrics
2. Real-time tracking of memory add, search, and retrieval operations
3. Analytics dashboard with memory usage patterns and insights
4. Error tracking and debugging capabilities for memory operations

## Prerequisites

Before setting up Mem0 with AgentOps, ensure you have:

1. Installed the required packages:
```bash
pip install mem0ai agentops python-dotenv
```

2. Valid API keys:
   - [AgentOps API Key](https://app.agentops.ai/dashboard/api-keys)
   - OpenAI API Key (for LLM operations)
   - [Mem0 API Key](https://app.mem0.ai/dashboard/api-keys) (optional, for cloud operations)

## Basic Integration Example

The following example demonstrates how to integrate Mem0 with AgentOps monitoring for comprehensive memory operation tracking:

```python
#Import the required libraries for local memory management with Mem0
from mem0 import Memory, AsyncMemory
import os
import asyncio
import logging
from dotenv import load_dotenv
import agentops
import openai

load_dotenv()
#Set up environment variables for API keys
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

#Set up the configuration for local memory storage and define sample user data. 
local_config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4.1-nano-2025-04-14",
            "temperature": 0.1,
            "max_tokens": 2000,
        },
    }
}
user_id = "alice_demo"
agent_id = "assistant_demo"
run_id = "session_001"

sample_messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about a thriller? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {
        "role": "assistant",
        "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
    },
]

sample_preferences = [
    "I prefer dark roast coffee over light roast",
    "I exercise every morning at 6 AM",
    "I'm vegetarian and avoid all meat products",
    "I love reading science fiction novels",
    "I work in software engineering",
]

#This function demonstrates sequential memory operations using the synchronous Memory class
def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id):
    """
    Demonstrate synchronous Memory class operations.
    """

    agentops.start_trace("mem0_memory_example", tags=["mem0_memory_example"])
    try:
        
        memory = Memory.from_config(local_config)

        result = memory.add(
            sample_messages, user_id=user_id, metadata={"category": "movie_preferences", "session": "demo"}
        )

        for i, preference in enumerate(sample_preferences):
            result = memory.add(preference, user_id=user_id, metadata={"type": "preference", "index": i})
       
        search_queries = [
            "What movies does the user like?",
            "What are the user's food preferences?",
            "When does the user exercise?",
        ]

        for query in search_queries:
            results = memory.search(query, user_id=user_id)
        
            if results and "results" in results:
                for j, result in enumerate(results['results']): 
                    print(f"Result {j+1}: {result.get('memory', 'N/A')}")
            else:
                print("No results found")

        all_memories = memory.get_all(user_id=user_id)
        if all_memories and "results" in all_memories:
            print(f"Total memories: {len(all_memories['results'])}")

        delete_all_result = memory.delete_all(user_id=user_id)
        print(f"Delete all result: {delete_all_result}")

        agentops.end_trace(end_state="success")
    except Exception as e:
        agentops.end_trace(end_state="error")

# Execute sync demonstrations
demonstrate_sync_memory(local_config, sample_messages, sample_preferences, user_id)

```

For detailed information on this integration, refer to the official [Agentops Mem0 integration documentation](https://docs.agentops.ai/v2/integrations/mem0).


## Key Features

### 1. Automatic Operation Tracking

AgentOps automatically monitors all Mem0 operations:

- **Memory Operations**: Track add, search, get_all, delete operations and much more
- **Performance Metrics**: Monitor response times and success rates
- **Error Tracking**: Capture and analyze operation failures

### 2. Real-time Analytics Dashboard

Access comprehensive analytics through the AgentOps dashboard:

- **Usage Patterns**: Visualize memory usage trends over time
- **User Behavior**: Analyze how different users interact with memory
- **Performance Insights**: Identify bottlenecks and optimization opportunities

### 3. Session Management

Organize your monitoring with structured sessions:

- **Session Tracking**: Group related operations into logical sessions
- **Success/Failure Rates**: Track session outcomes for reliability monitoring
- **Custom Metadata**: Add context to sessions for better analysis

## Best Practices

1. **Initialize Early**: Always initialize AgentOps before importing Mem0 classes
2. **Session Management**: Use meaningful session names and end sessions appropriately
3. **Error Handling**: Wrap operations in try-catch blocks and report failures
4. **Tagging**: Use tags to organize different types of memory operations
5. **Environment Separation**: Use different projects or tags for dev/staging/prod

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