---
title: Agent Monitoring with OpenLIT
description: Quickly start monitoring your Agents in just a single line of code with OpenTelemetry.
icon: magnifying-glass-chart
---

# OpenLIT Overview

[OpenLIT](https://github.com/openlit/openlit?src=crewai-docs) is an open-source tool that makes it simple to monitor the performance of AI agents, LLMs, VectorDBs, and GPUs with just **one** line of code. 

It provides OpenTelemetry-native tracing and metrics to track important parameters like cost, latency, interactions and task sequences. 
This setup enables you to track hyperparameters and monitor for performance issues, helping you find ways to enhance and fine-tune your agents over time.

<Frame caption="OpenLIT Dashboard">
  <img src="/images/openlit1.png" alt="Overview Agent usage including cost and tokens" />
  <img src="/images/openlit2.png" alt="Overview of agent otel traces and metrics" />
  <img src="/images/openlit3.png" alt="Overview of agent traces in details" />
</Frame>

### Features

- **Analytics Dashboard**: Monitor your Agents health and performance with detailed dashboards that track metrics, costs, and user interactions.
- **OpenTelemetry-native Observability SDK**: Vendor-neutral SDKs to send traces and metrics to your existing observability tools like Grafana, DataDog and more.
- **Cost Tracking for Custom and Fine-Tuned Models**: Tailor cost estimations for specific models using custom pricing files for precise budgeting.
- **Exceptions Monitoring Dashboard**: Quickly spot and resolve issues by tracking common exceptions and errors with a monitoring dashboard.
- **Compliance and Security**: Detect potential threats such as profanity and PII leaks.
- **Prompt Injection Detection**: Identify potential code injection and secret leaks.
- **API Keys and Secrets Management**: Securely handle your LLM API keys and secrets centrally, avoiding insecure practices.
- **Prompt Management**: Manage and version Agent prompts using PromptHub for consistent and easy access across Agents.
- **Model Playground** Test and compare different models for your CrewAI agents before deployment.

## Setup Instructions

<Steps>
    <Step title="Deploy OpenLIT">
      <Steps>
        <Step title="Git Clone OpenLIT Repository">
          ```shell
          git clone git@github.com:openlit/openlit.git
          ```
        </Step>
        <Step title="Start Docker Compose">
          From the root directory of the [OpenLIT Repo](https://github.com/openlit/openlit), Run the below command:
          ```shell
          docker compose up -d
          ```
        </Step>
      </Steps>
    </Step>
    <Step title="Install OpenLIT SDK">
      ```shell
      pip install openlit
      ```
    </Step>
    <Step title="Initialize OpenLIT in Your Application">
      Add the following two lines to your application code:
      <Tabs>
        <Tab title="Setup using function arguments">
          ```python
          import openlit
          openlit.init(otlp_endpoint="http://127.0.0.1:4318")
          ```
          
          Example Usage for monitoring a CrewAI Agent:
    
          ```python
          from crewai import Agent, Task, Crew, Process
          import openlit

          openlit.init(disable_metrics=True)
          # Define your agents
          researcher = Agent(
              role="Researcher",
              goal="Conduct thorough research and analysis on AI and AI agents",
              backstory="You're an expert researcher, specialized in technology, software engineering, AI, and startups. You work as a freelancer and are currently researching for a new client.",
              allow_delegation=False,
              llm='command-r'
          )


          # Define your task
          task = Task(
              description="Generate a list of 5 interesting ideas for an article, then write one captivating paragraph for each idea that showcases the potential of a full article on this topic. Return the list of ideas with their paragraphs and your notes.",
              expected_output="5 bullet points, each with a paragraph and accompanying notes.",
          )

          # Define the manager agent
          manager = Agent(
              role="Project Manager",
              goal="Efficiently manage the crew and ensure high-quality task completion",
              backstory="You're an experienced project manager, skilled in overseeing complex projects and guiding teams to success. Your role is to coordinate the efforts of the crew members, ensuring that each task is completed on time and to the highest standard.",
              allow_delegation=True,
              llm='command-r'
          )

          # Instantiate your crew with a custom manager
          crew = Crew(
              agents=[researcher],
              tasks=[task],
              manager_agent=manager,
              process=Process.hierarchical,
          )

          # Start the crew's work
          result = crew.kickoff()

          print(result)
          ```
        </Tab>
        <Tab title="Setup using Environment Variables">

          Add the following two lines to your application code:
          ```python
          import openlit

          openlit.init()
          ```

          Run the following command to configure the OTEL export endpoint:
          ```shell
          export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318"
          ```

          Example Usage for monitoring a CrewAI Async Agent:

          ```python
          import asyncio
          from crewai import Crew, Agent, Task
          import openlit

          openlit.init(otlp_endpoint="http://127.0.0.1:4318")

          # Create an agent with code execution enabled
          coding_agent = Agent(
            role="Python Data Analyst",
            goal="Analyze data and provide insights using Python",
            backstory="You are an experienced data analyst with strong Python skills.",
            allow_code_execution=True,
            llm="command-r"
          )

          # Create a task that requires code execution
          data_analysis_task = Task(
            description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
            agent=coding_agent,
            expected_output="5 bullet points, each with a paragraph and accompanying notes.",
          )

          # Create a crew and add the task
          analysis_crew = Crew(
            agents=[coding_agent],
            tasks=[data_analysis_task]
          )

          # Async function to kickoff the crew asynchronously
          async def async_crew_execution():
              result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
              print("Crew Result:", result)

          # Run the async function
          asyncio.run(async_crew_execution())
          ```
        </Tab>
      </Tabs>
      Refer to OpenLIT [Python SDK repository](https://github.com/openlit/openlit/tree/main/sdk/python) for more advanced configurations and use cases.
    </Step>
    <Step title="Visualize and Analyze">
      With the Agent Observability data now being collected and sent to OpenLIT, the next step is to visualize and analyze this data to get insights into your Agent's performance, behavior, and identify areas of improvement.

      Just head over to OpenLIT at `127.0.0.1:3000` on your browser to start exploring. You can login using the default credentials
      - **Email**: `user@openlit.io`
      - **Password**: `openlituser`
      
      <Frame caption="OpenLIT Dashboard">
        <img src="/images/openlit1.png" alt="Overview Agent usage including cost and tokens" />
        <img src="/images/openlit2.png" alt="Overview of agent otel traces and metrics" />
      </Frame>

    </Step>
</Steps> 

