---
title: 'Mem0'
description: 'Track and monitor Mem0 memory operations with AgentOps'
---
[Mem0](https://mem0.ai/) provides a smart memory layer for AI applications, enabling personalized interactions by remembering user preferences, conversation history, and context across sessions.
## Why Track Mem0 with AgentOps?
When building memory-powered AI applications, you need visibility into:
- **Memory Operations**: Track when memories are created, updated, or retrieved
- **Search Performance**: Monitor how effectively your AI finds relevant memories
- **Memory Usage Patterns**: Understand what information is being stored and accessed
- **Error Tracking**: Identify issues with memory storage or retrieval
- **Cost Analysis**: Track API calls to both Mem0 and your LLM provider
AgentOps automatically instruments Mem0 to provide complete observability of your memory operations.
## Installation
```bash pip
pip install agentops mem0ai python-dotenv
```
```bash poetry
poetry add agentops mem0ai python-dotenv
```
```bash uv
uv pip install agentops mem0ai python-dotenv
```
## Environment Configuration
Load environment variables and set up API keys. The MEM0_API_KEY is only required if you're using the cloud-based MemoryClient.
```bash Export to CLI
export AGENTOPS_API_KEY="your_agentops_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
```
```txt Set in .env file
AGENTOPS_API_KEY="your_agentops_api_key_here"
OPENAI_API_KEY="your_openai_api_key_here"
```
## Tracking Memory Operations
```python Local Memory
import agentops
from mem0 import Memory
# Start a trace to group related operations
agentops.start_trace("user_preference_learning",tags=["mem0_memory_example"])
try:
# Initialize Memory - AgentOps tracks the configuration
memory = Memory.from_config({
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4o-mini",
"temperature": 0.1
}
}
})
# Add memories - AgentOps tracks each operation
memory.add(
"I prefer morning meetings and dark roast coffee",
user_id="user_123",
metadata={"category": "preferences"}
)
# Search memories - AgentOps tracks search queries and results
results = memory.search(
"What are the user's meeting preferences?",
user_id="user_123"
)
# End trace - AgentOps aggregates all operations
agentops.end_trace(end_state="success")
except Exception as e:
agentops.end_trace(end_state="error")
```
```python Cloud Memory
import agentops
from mem0 import MemoryClient
# Start trace for cloud operations
agentops.start_trace("cloud_memory_sync",tags=["mem0_memoryclient_example"])
try:
# Initialize MemoryClient - AgentOps tracks API authentication
client = MemoryClient(api_key="your_mem0_api_key")
# Batch add memories - AgentOps tracks bulk operations
messages = [
{"role": "user", "content": "I work in software engineering"},
{"role": "user", "content": "I prefer Python over Java"},
]
client.add(messages, user_id="user_123")
# Search with filters - AgentOps tracks complex queries
filters = {"AND": [{"user_id": "user_123"}]}
results = client.search(
query="What programming languages does the user know?",
filters=filters,
version="v2"
)
# End trace - AgentOps aggregates all operations
agentops.end_trace(end_state="success")
except Exception as e:
agentops.end_trace(end_state="error")
```
## What You'll See in AgentOps
When using Mem0 with AgentOps, your dashboard will show:
1. **Memory Operation Timeline**: Visual flow of all memory operations
2. **Search Analytics**: Query patterns and retrieval effectiveness
3. **Memory Growth**: Track how user memories accumulate over time
4. **Performance Metrics**: Latency for adds, searches, and retrievals
5. **Error Tracking**: Failed operations with full error context
6. **Cost Attribution**: Token usage for memory extraction and searches
## Examples
Simple example showing memory storage and retrieval with AgentOps tracking
Track concurrent memory operations with async/await patterns