OpenLIT Integration

OpenLIT Overview

OpenLIT 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.

OpenLIT Dashboard

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

1. Deploy OpenLIT
   1. Git Clone OpenLIT Repository

      git clone git@github.com:openlit/openlit.git

   2. Start Docker Compose

      From the root directory of the OpenLIT Repo, Run the below command:

      docker compose up -d

2. Install OpenLIT SDK

   pip install openlit

3. Initialize OpenLIT in Your Application

   Add the following two lines to your application code:

   Setup using function arguments

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

   Example Usage for monitoring a CrewAI Agent:

   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)

   Setup using Environment Variables

   Add the following two lines to your application code:

   import openlit
   
   openlit.init()

   Run the following command to configure the OTEL export endpoint:

   export OTEL_EXPORTER_OTLP_ENDPOINT = "http://127.0.0.1:4318"

   Example Usage for monitoring a CrewAI Async Agent:

   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())

   Refer to OpenLIT Python SDK repository for more advanced configurations and use cases.

4. 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

   

   OpenLIT Dashboard