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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?

  • Memory Recording: Track when conversations are automatically captured and stored
  • Context Injection: Monitor how memory is automatically added to LLM context
  • Conversation Flow: Understand the complete dialogue history across sessions
  • Memory Effectiveness: Analyze how historical context improves response quality
  • Performance Impact: Track latency and token usage from memory operations
  • Error Tracking: Identify issues with memory recording or context retrieval
AgentOps automatically instruments Memori to provide complete observability of your memory operations.

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Installation

pip install agentops memorisdk openai python-dotenv

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Environment Configuration

Load environment variables and set up API keys. 
export AGENTOPS_API_KEY="your_agentops_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"

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Tracking Automatic Memory Operations

import agentops
from memori import Memori
from openai import OpenAI

# Start a trace to group related operations
agentops.start_trace("memori_conversation_flow", tags=["memori_memory_example"])

try:
    # Initialize OpenAI client
    openai_client = OpenAI()

    # Initialize Memori with conscious ingestion enabled
    # AgentOps tracks the memory configuration
    memori = Memori(
        database_connect="sqlite:///agentops_example.db",
        conscious_ingest=True,
        auto_ingest=True,
    )

    memori.enable()

    # First conversation - AgentOps tracks LLM call and memory recording
    response1 = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "user", "content": "I'm working on a Python FastAPI project"}
        ],
    )

    print("Assistant:", response1.choices[0].message.content)

    # Second conversation - AgentOps tracks memory retrieval and context injection
    response2 = openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Help me add user authentication"}],
    )

    print("Assistant:", response2.choices[0].message.content)
    print("💡 Notice: Memori automatically provided FastAPI project context!")

    # End trace - AgentOps aggregates all operations
    agentops.end_trace(end_state="success")

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

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What You’ll See in AgentOps

When using Memori with AgentOps, your dashboard will show:
  1. Conversation Timeline: Complete flow of all conversations with memory context
  2. Memory Injection Analytics: Track when and how much context is automatically added
  3. Context Relevance: Monitor the effectiveness of automatic memory retrieval
  4. Performance Metrics: Latency impact of memory operations on LLM calls
  5. Token Usage: Track additional tokens consumed by memory context
  6. Memory Growth: Visualize how conversation history accumulates over time
  7. Error Tracking: Failed memory operations with full error context

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Key Benefits of Memori + AgentOps

  • Zero-Effort Memory: Memori automatically handles conversation recording
  • Intelligent Context: Only relevant memory is injected into LLM context
  • Complete Visibility: AgentOps tracks all automatic memory operations
  • Performance Monitoring: Understand the cost/benefit of automatic memory
  • Debugging Support: Full traceability of memory decisions and context injection
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