LangSmith

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Integrating Agno with LangSmith

LangSmith offers a comprehensive platform for tracing and monitoring AI model calls. By integrating Agno with LangSmith, you can utilize OpenInference to send traces and gain insights into your agent’s performance.

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Prerequisites

1. Create a LangSmith Account

   • Sign up for an account at LangSmith.
   • Obtain your API key from the LangSmith dashboard.

2. Set Environment Variables

   Configure your environment with the LangSmith API key and other necessary settings:

   export LANGSMITH_API_KEY=<your-key>
   export LANGSMITH_TRACING=true
   export LANGSMITH_ENDPOINT=https://eu.api.smith.langchain.com  # or https://api.smith.langchain.com for US
   export LANGSMITH_PROJECT=<your-project-name>

3. Install Dependencies

   Ensure you have the necessary packages installed:

   pip install openai openinference-instrumentation-agno opentelemetry-sdk opentelemetry-exporter-otlp

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Sending Traces to LangSmith

This example demonstrates how to instrument your Agno agent with OpenInference and send traces to LangSmith.

import os

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from openinference.instrumentation.agno import AgnoInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

# Set the endpoint and headers for LangSmith
endpoint = "https://eu.api.smith.langchain.com/otel/v1/traces"
headers = {
    "x-api-key": os.getenv("LANGSMITH_API_KEY"),
    "Langsmith-Project": os.getenv("LANGSMITH_PROJECT"),
}

# Configure the tracer provider
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    SimpleSpanProcessor(OTLPSpanExporter(endpoint=endpoint, headers=headers))
)
trace_api.set_tracer_provider(tracer_provider=tracer_provider)

# Start instrumenting agno
AgnoInstrumentor().instrument()

# Create and configure the agent
agent = Agent(
    name="Stock Market Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
    markdown=True,
    debug_mode=True,
)

# Use the agent
agent.print_response("What is news on the stock market?")

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Notes

• Environment Variables: Ensure your environment variables are correctly set for the API key, endpoint, and project name.
• Data Regions: Choose the appropriate LANGSMITH_ENDPOINT based on your data region.

By following these steps, you can effectively integrate Agno with LangSmith, enabling comprehensive observability and monitoring of your AI agents.