OpenLIT

Integrate Agno with OpenLIT for OpenTelemetry-native observability, tracing, and monitoring of your AI agents.

Integrating Agno with OpenLIT

OpenLIT is an open-source, self-hosted, OpenTelemetry-native platform for a continuous feedback loop for testing, tracing, and fixing AI agents. By integrating Agno with OpenLIT, you can automatically instrument your agents to gain full visibility into LLM calls, tool usage, costs, performance metrics, and errors.

Prerequisites

1. Install Dependencies

   Ensure you have the necessary packages installed:

   uv pip install agno openai openlit
   

2. Deploy OpenLIT

   OpenLIT is open-source and self-hosted. Quick start with Docker:

   git clone https://github.com/openlit/openlit
   cd openlit
   docker-compose up -d
   

   Access the dashboard at http://127.0.0.1:3000 with default credentials (username: [e-mail], password: openlituser).

   Other Deployment Options:

   For production deployments, Kubernetes with Helm, or other infrastructure setups, see the OpenLIT Installation Guide for detailed instructions on:

   • Kubernetes deployment with Helm charts
   • Custom Docker configurations
   • Reusing existing ClickHouse or OpenTelemetry Collector infrastructure
   • OpenLIT Operator for zero-code instrumentation in Kubernetes

3. Set Environment Variables (Optional)

   Configure the OTLP endpoint based on your deployment:

   # Local deployment
   export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
   
   # Self-hosted on your infrastructure
   export OTEL_EXPORTER_OTLP_ENDPOINT="http://your-openlit-host:4318"
   

Sending Traces to OpenLIT

Example: Basic Agent Setup

This example demonstrates how to instrument your Agno agent with OpenLIT for automatic tracing.

import openlit
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.yfinance import YFinanceTools

# Initialize OpenLIT instrumentation
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318"  # Your OpenLIT OTLP endpoint
)

# Create and configure the agent
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True)],
    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
    show_tool_calls=True,
)

# Use the agent - all calls are automatically traced
agent.print_response("What is the current price of Tesla and what do analysts recommend?")

Example: Development Mode (Console Output)

For local development without a collector, OpenLIT can output traces directly to the console:

import openlit
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools

# Initialize OpenLIT without OTLP endpoint for console output
openlit.init()

# Create and configure the agent
agent = Agent(
    name="Web Search Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    instructions="Search the web and provide comprehensive answers.",
    markdown=True,
)

# Use the agent - traces will be printed to console
agent.print_response("What are the latest developments in AI agents?")

Example: Multi-Agent Team Tracing

OpenLIT automatically traces complex multi-agent workflows:

import openlit
from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools

# Initialize OpenLIT instrumentation
openlit.init(otlp_endpoint="http://127.0.0.1:4318")

# Research Agent
research_agent = Agent(
    name="Market Research Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    instructions="Research current market conditions and news",
)

# Financial Analysis Agent
finance_agent = Agent(
    name="Financial Analyst",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools(stock_price=True, company_info=True)],
    instructions="Perform quantitative financial analysis",
)

# Coordinated Team
finance_team = Team(
    name="Finance Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[research_agent, finance_agent],
    instructions=[
        "Collaborate to provide comprehensive financial insights",
        "Consider both fundamental analysis and market sentiment",
    ],
)

# Execute team workflow - all agent interactions are traced
finance_team.print_response("Analyze Apple (AAPL) investment potential")

Example: Custom Tracer Configuration

For advanced use cases with custom OpenTelemetry configuration:

import openlit
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools

# Configure custom tracer provider
trace_provider = TracerProvider()
trace_provider.add_span_processor(
    SimpleSpanProcessor(
        OTLPSpanExporter(endpoint="http://127.0.0.1:4318/v1/traces")
    )
)
trace.set_tracer_provider(trace_provider)

# Initialize OpenLIT with custom tracer
openlit.init(
    tracer=trace.get_tracer(__name__),
    disable_batch=True
)

# Create and configure the agent
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    markdown=True,
)

# Use the agent
agent.print_response("What is currently trending on Twitter?")

OpenLIT Dashboard Features

Once your agents are instrumented, you can access the OpenLIT dashboard to:

• View Traces: Visualize complete execution flows including agent runs, tool calls, and LLM requests
• Monitor Performance: Track latency, token usage, and throughput metrics
• Analyze Costs: Monitor API costs across different models and providers
• Track Errors: Identify and debug exceptions with detailed stack traces
• Compare Models: Evaluate different LLM providers based on performance and cost

Configuration Options

The openlit.init() function accepts several parameters:

openlit.init(
    otlp_endpoint="http://127.0.0.1:4318",  # OTLP collector endpoint
    tracer=None,  # Custom OpenTelemetry tracer
    disable_batch=False,  # Disable batch span processing
    environment="production",  # Environment name for filtering
    application_name="my-agent",  # Application identifier
)

CLI-Based Instrumentation

For true zero-code instrumentation, you can use the openlit-instrument CLI command to run your application without modifying any code:

openlit-instrument \
  --service-name my-ai-app \
  --environment production \
  --otlp-endpoint http://127.0.0.1:4318 \
  python your_app.py

This approach is particularly useful for:

• Adding observability to existing applications without code changes
• CI/CD pipelines where you want to instrument automatically
• Testing observability before committing to code modifications

Notes

• Automatic Instrumentation: OpenLIT automatically instruments supported LLM providers (OpenAI, Anthropic, etc.) and frameworks
• Zero Code Changes: Use either openlit.init() in your code or the openlit-instrument CLI to trace all LLM calls without modifications
• OpenTelemetry Native: OpenLIT uses standard OpenTelemetry protocols, ensuring compatibility with other observability tools
• Open-Source & Self-Hosted: OpenLIT is fully open-source and runs on your own infrastructure for complete data privacy and control

Integration with Other Platforms

OpenLIT can export traces to other observability platforms like Grafana Cloud, New Relic and more. See the Langfuse integration guide for an example of using OpenLIT with Langfuse.