---
title: "Observability"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Observability

To effectively implement observability and monitoring in CrewAI, you can integrate specialized tools that provide real-time insights into your agents' performance and operations. Two prominent solutions are **AgentOps** and **Langtrace**.

**1. AgentOps Integration:**

[Official Docs](https://docs.crewai.com/how-to/agentops-observability)

AgentOps is a monitoring and observability platform tailored for AI agents and Large Language Model (LLM) applications. It offers features such as session replays, performance metrics, and cost tracking. To integrate AgentOps with CrewAI:

- **Installation:**
  Install the AgentOps SDK and the CrewAI package with AgentOps support:

  ```bash
  pip install agentops
  pip install 'crewai[agentops]'
  ```

- **Initialization:**
  Initialize AgentOps in your code before invoking any models:

  ```python
  import agentops
  agentops.init('<YOUR_API_KEY>')
  ```

  Ensure your API key is set as an environment variable for secure access.

- **Execution:**
  Run your CrewAI application. After execution, AgentOps will provide a direct link to your session in the dashboard for detailed analysis.

This integration allows you to monitor your CrewAI agents seamlessly, providing valuable insights into their performance and interactions. 

**2. Langtrace Integration:**

[Official Docs](https://docs.crewai.com/how-to/langtrace-observability)

[Step-by-Step Langtrace + CrewAI Tutorial - Production Agent Stack](https://www.youtube.com/watch?v=dh9zv8EUwBA)

Langtrace is an open-source observability tool designed for monitoring the performance of LLMs, LLM frameworks, and VectorDBs. To integrate Langtrace with CrewAI:

- **Installation:**
  Install the Langtrace Python SDK:

  ```bash
  pip install langtrace-python-sdk
  ```

- **Initialization:**
  Initialize Langtrace in your project before importing CrewAI modules:

  ```python
  from langtrace_python_sdk import langtrace
  langtrace.init(api_key='<LANGTRACE_API_KEY>')
  ```

- **Monitoring:**
  Wrap your CrewAI operations with Langtrace's tracing functionality to monitor task execution and agent performance:

  ```python
  with langtrace.trace("CrewAI Task Execution"):
      result = crew.kickoff()
  ```

By integrating Langtrace, you can gain insights into cost, latency, and overall efficiency, ensuring your agents operate optimally. 

Incorporating these observability tools into your CrewAI development process is essential for maintaining high performance and user satisfaction. They provide the necessary visibility to monitor, analyze, and enhance your AI agents effectively. 

## Which is Better?

When selecting an observability tool for CrewAI, both **AgentOps** and **Langtrace** offer valuable features tailored to AI agent monitoring. Here's a comparative overview to assist in your decision:

**AgentOps:**
- **Focus:** Provides comprehensive observability and traceability for AI agents, emphasizing performance monitoring, debugging, and optimization.
- **Features:**
  - Session replays for analyzing agent interactions.
  - LLM cost tracking to monitor expenses associated with language model usage.
  - Compliance monitoring to ensure adherence to regulatory standards.
- **Integration:** Seamlessly integrates with frameworks like CrewAI, AutoGen, and LangChain, simplifying implementation and enhancing agent performance with minimal setup. 

**Langtrace:**
- **Focus:** Specializes in tracing language model interactions, offering insights into model behavior and performance.
- **Features:**
  - Detailed tracing of language model operations.
  - Insights into model behavior and performance metrics.
- **Integration:** Designed to work with various language models, providing a focused approach to tracing and monitoring. 

**Considerations:**
- **Comprehensive Monitoring:** If your priority is a holistic observability solution encompassing performance monitoring, debugging, cost analysis, and compliance, **AgentOps** is a robust choice.
- **Language Model Tracing:** If your focus is specifically on tracing and understanding language model interactions, **Langtrace** offers specialized capabilities.

In summary, for a more extensive observability framework that integrates seamlessly with CrewAI and provides a wide range of monitoring features, **AgentOps** is the preferable option. However, if your needs are centered around detailed tracing of language model operations, **Langtrace** may be more suitable. 
However, Langfuse's transparent pricing structure and feature-rich plans may offer a more economical solution for projects needing extensive LLM engineering tools.
