126 lines
4.4 KiB
Plaintext
126 lines
4.4 KiB
Plaintext
---
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title: 'Memori'
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description: 'Track and monitor Memori memory operations with AgentOps'
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---
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[Memori](https://github.com/GibsonAI/memori) provides automatic short-term and long-term memory for AI applications and agents, seamlessly recording conversations and adding context to LLM interactions without requiring explicit memory management.
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## Why Track Memori with AgentOps?
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- **Memory Recording**: Track when conversations are automatically captured and stored
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- **Context Injection**: Monitor how memory is automatically added to LLM context
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- **Conversation Flow**: Understand the complete dialogue history across sessions
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- **Memory Effectiveness**: Analyze how historical context improves response quality
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- **Performance Impact**: Track latency and token usage from memory operations
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- **Error Tracking**: Identify issues with memory recording or context retrieval
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AgentOps automatically instruments Memori to provide complete observability of your memory operations.
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## Installation
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<CodeGroup>
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```bash pip
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pip install agentops memorisdk openai python-dotenv
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```
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```bash poetry
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poetry add agentops memorisdk openai python-dotenv
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```
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```bash uv
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uv pip install agentops memorisdk openai python-dotenv
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```
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</CodeGroup>
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## Environment Configuration
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Load environment variables and set up API keys.
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<CodeGroup>
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```bash Export to CLI
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export AGENTOPS_API_KEY="your_agentops_api_key_here"
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export OPENAI_API_KEY="your_openai_api_key_here"
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```
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```txt Set in .env file
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AGENTOPS_API_KEY="your_agentops_api_key_here"
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OPENAI_API_KEY="your_openai_api_key_here"
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```
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</CodeGroup>
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## Tracking Automatic Memory Operations
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<CodeGroup>
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```python Basic Memory Tracking
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import agentops
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from memori import Memori
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from openai import OpenAI
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# Start a trace to group related operations
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agentops.start_trace("memori_conversation_flow", tags=["memori_memory_example"])
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try:
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# Initialize OpenAI client
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openai_client = OpenAI()
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# Initialize Memori with conscious ingestion enabled
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# AgentOps tracks the memory configuration
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memori = Memori(
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database_connect="sqlite:///agentops_example.db",
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conscious_ingest=True,
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auto_ingest=True,
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)
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memori.enable()
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# First conversation - AgentOps tracks LLM call and memory recording
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response1 = openai_client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[
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{"role": "user", "content": "I'm working on a Python FastAPI project"}
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],
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)
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print("Assistant:", response1.choices[0].message.content)
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# Second conversation - AgentOps tracks memory retrieval and context injection
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response2 = openai_client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": "Help me add user authentication"}],
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)
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print("Assistant:", response2.choices[0].message.content)
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print("💡 Notice: Memori automatically provided FastAPI project context!")
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# End trace - AgentOps aggregates all operations
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agentops.end_trace(end_state="success")
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except Exception as e:
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agentops.end_trace(end_state="error")
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```
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</CodeGroup>
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## What You'll See in AgentOps
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When using Memori with AgentOps, your dashboard will show:
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1. **Conversation Timeline**: Complete flow of all conversations with memory context
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2. **Memory Injection Analytics**: Track when and how much context is automatically added
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3. **Context Relevance**: Monitor the effectiveness of automatic memory retrieval
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4. **Performance Metrics**: Latency impact of memory operations on LLM calls
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5. **Token Usage**: Track additional tokens consumed by memory context
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6. **Memory Growth**: Visualize how conversation history accumulates over time
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7. **Error Tracking**: Failed memory operations with full error context
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## Key Benefits of Memori + AgentOps
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- **Zero-Effort Memory**: Memori automatically handles conversation recording
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- **Intelligent Context**: Only relevant memory is injected into LLM context
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- **Complete Visibility**: AgentOps tracks all automatic memory operations
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- **Performance Monitoring**: Understand the cost/benefit of automatic memory
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- **Debugging Support**: Full traceability of memory decisions and context injection
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