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Installation

Install the required OpenTelemetry and OpenInference dependencies: 
pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp opentelemetry-instrumentation
Tested with:
  • opentelemetry-api==1.23.0
  • opentelemetry-sdk==1.23.0
  • openinference-instrumentation-langchain==0.3.1

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Authentication

  1. Generate an API key from your Galileo account.
  2. Copy your project name and Log stream name from the Galileo Console.
Set the authentication headers in your environment: 
import os

headers = {
    "Galileo-API-Key": "<YOUR_API_KEY>",           # Replace with your actual API key
    "project": "<YOUR_PROJECT_NAME>",         # Replace with your project name
    "logstream": "<YOUR_RUN_NAME>",          # Must match the Log stream name in Galileo
}

# Format headers as a comma-separated key=value string
# Example: Galileo-API-Key=abc123,project=my_project,logstream=my_run
os.environ['OTEL_EXPORTER_OTLP_TRACES_HEADERS'] = ",".join([f"{k}={v}" for k, v in headers.items()])

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OpenTelemetry setup

Place this setup at the top of your script, before initializing your LLM or pipeline framework: 
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry import trace as trace_api
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import ConsoleSpanExporter
from openinference.instrumentation.langchain import LangChainInstrumentor

# OTel exporter endpoint
endpoint = "https://app.galileo.ai/api/galileo/otel/traces"

# Configure OTel tracer provider
tracer_provider = trace_sdk.TracerProvider()

# Sends traces to Galileo
tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(endpoint)))

# Optional: Prints traces to console for local debugging
tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter()))

trace_api.set_tracer_provider(tracer_provider=tracer_provider)

# Instrument LangChain (or replace with your framework's instrumentor)
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
Note: Apply instrumentation before importing or initializing your framework (e.g., LangChain, OpenAI).

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Full working example

Check out a complete working example on Colab: 🔗 OpenTelemetry + Galileo Integration Notebook

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Summary

To enable OTel logging with Galileo:
  1. Set the OTel headers using your API key, project name, and Log stream name.
  2. Set the exporter endpoint: https://app.galileo.ai/api/galileo/otel/traces
  3. Register your tracer provider and processors.
  4. Import and apply the OpenInference instrumentor(s) before initializing your application.

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Supported instrumentors

These can be used individually or in combination, depending on your framework:

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FAQ

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How do I create a project?

Log in to the Galileo Console and create a new logging project. After creation, copy the project name and Log stream name from the interface.

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Which instrumentors should I import?

Choose the instrumentor(s) matching your framework: 
# OpenAI
from openinference.instrumentation.openai import OpenAIInstrumentor

# LlamaIndex
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

# DSPy
from openinference.instrumentation.dspy import DSPyInstrumentor

# Bedrock
from openinference.instrumentation.bedrock import BedrockInstrumentor

# LangChain
from openinference.instrumentation.langchain import LangChainInstrumentor

# Mistral AI
from openinference.instrumentation.mistralai import MistralAIInstrumentor

# Guardrails
from openinference.instrumentation.guardrails import GuardrailsInstrumentor

# Vertex AI
from openinference.instrumentation.vertexai import VertexAIInstrumentor

# CrewAI
from openinference.instrumentation.crewai import CrewAIInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor

# Haystack
from openinference.instrumentation.haystack import HaystackInstrumentor

# LiteLLM
from openinference.instrumentation.litellm import LiteLLMInstrumentor

# Groq
from openinference.instrumentation.groq import GroqInstrumentor

# Instructor
from openinference.instrumentation.instructor import InstructorInstrumentor

# Anthropic
from openinference.instrumentation.anthropic import AnthropicInstrumentor

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Instructor example with client:

from openinference.instrumentation.instructor import InstructorInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
from openai import OpenAI
import instructor

client = instructor.from_openai(OpenAI())

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What is the correct OTel endpoint?

Use the Galileo API base URL with the /otel/traces path: 
https://app.galileo.ai/api/galileo/otel/traces
or cluster your custom cluster
http://api.YOUR_CLUSTER_URL/otel/traces
If your console URL is https://console.your_cluster_url.com, the endpoint becomes http://api.your_cluster_url.com/otel/traces.

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How do I verify it’s working?

  • Use ConsoleSpanExporter to see local trace output.
  • Check the Galileo dashboard for incoming traces.
  • Add a test span like this:
from opentelemetry import trace

tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("test-span"):
    print("Tracing is active")

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Troubleshooting

  • ✅ Confirm your API key, project name, and Log stream name are correct.
  • ✅ Make sure the environment variable is correctly formatted.
  • ✅ Ensure the endpoint is accessible from your environment.
  • ✅ Instrument your framework before using it.
  • ✅ Restart environment after changing environment variables.
  • ✅ Use print(os.environ['OTEL_EXPORTER_OTLP_TRACES_HEADERS']) to debug your header string.
  • ✅ Make sure to use BatchSpanProcessor(OTLPSpanExporter(endpoint)) not SingleSpanProcessor

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Resources

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Notebooks

🔗 Anthropic + Galileo + OTel Notebook 🔗 Crew AI + Galileo + OTel Notebook 🔗 DSPy + Galileo + OTel Notebook 🔗 Groq + Galileo + OTel Notebook 🔗 LLamaIndex + Galileo + OTel Notebook 🔗 LangChain + Galileo + OTel Notebook 🔗 LiteLLM + Galileo + OTel Notebook 🔗 Mistral + Galileo + OTel Notebook 🔗 OpenAI + Galileo + OTel Notebook 🔗 Vertex AI + Galileo + OTel Notebook