LogoLogo
Python SDKSlack
  • Documentation
  • Cookbooks
  • Self-Hosting
  • Release Notes
  • Reference
  • Arize AI
  • Quickstarts
  • ✨Arize Copilot
  • Concepts
    • Agent Evaluation
    • Tracing
      • What is OpenTelemetry?
      • What is OpenInference?
      • Openinference Semantic Conventions
    • Evaluation
  • 🧪Develop
    • Quickstart: Experiments
    • Datasets
      • Create a dataset
      • Update a dataset
      • Export a dataset
    • Experiments
      • Run experiments
      • Run experiments with code
        • Experiments SDK differences in AX vs Phoenix
        • Log experiment results via SDK
      • Evaluate experiments
      • Evaluate experiment with code
      • CI/CD with experiments
        • Github Action Basics
        • Gitlab CI/CD Basics
      • Download experiment
    • Prompt Playground
      • Use tool calling
      • Use image inputs
    • Playground Integrations
      • OpenAI
      • Azure OpenAI
      • AWS Bedrock
      • VertexAI
      • Custom LLM Models
    • Prompt Hub
  • 🧠Evaluate
    • Online Evals
      • Run evaluations in the UI
      • Run evaluations with code
      • Test LLM evaluator in playground
      • View task details & logs
      • ✨Copilot: Eval Builder
      • ✨Copilot: Eval Analysis
      • ✨Copilot: RAG Analysis
    • Experiment Evals
    • LLM as a Judge
      • Custom Eval Templates
      • Arize Templates
        • Agent Tool Calling
        • Agent Tool Selection
        • Agent Parameter Extraction
        • Agent Path Convergence
        • Agent Planning
        • Agent Reflection
        • Hallucinations
        • Q&A on Retrieved Data
        • Summarization
        • Code Generation
        • Toxicity
        • AI vs Human (Groundtruth)
        • Citation
        • User Frustration
        • SQL Generation
    • Code Evaluations
    • Human Annotations
  • 🔭Observe
    • Quickstart: Tracing
    • Tracing
      • Setup tracing
      • Trace manually
        • Trace inputs and outputs
        • Trace function calls
        • Trace LLM, Retriever and Tool Spans
        • Trace prompt templates & variables
        • Trace as Inferences
        • Send Traces from Phoenix -> Arize
        • Advanced Tracing (OTEL) Examples
      • Add metadata
        • Add events, exceptions and status
        • Add attributes, metadata and tags
        • Send data to a specific project
        • Get the current span context and tracer
      • Configure tracing options
        • Configure OTEL tracer
        • Mask span attributes
        • Redact sensitive data from traces
        • Instrument with OpenInference helpers
      • Query traces
        • Filter Traces
          • Time Filtering
        • Export Traces
        • ✨AI Powered Search & Filter
        • ✨AI Powered Trace Analysis
        • ✨AI Span Analysis & Evaluation
    • Tracing Integrations
      • OpenAI
      • OpenAI Agents SDK
      • LlamaIndex
      • LlamaIndex Workflows
      • LangChain
      • LangGraph
      • Hugging Face smolagents
      • Autogen
      • Google GenAI (Gemini)
      • Model Context Protocol (MCP)
      • Vertex AI
      • Amazon Bedrock
      • Amazon Bedrock Agents
      • MistralAI
      • Anthropic
      • LangFlow
      • Haystack
      • LiteLLM
      • CrewAI
      • Groq
      • DSPy
      • Guardrails AI
      • Prompt flow
      • Vercel AI SDK
      • Llama
      • Together AI
      • OpenTelemetry (arize-otel)
      • BeeAI
    • Evals on Traces
    • Guardrails
    • Sessions
    • Dashboards
      • Dashboard Widgets
      • Tracking Token Usage
      • ✨Copilot: Dashboard Widget Creation
    • Monitors
      • Integrations: Monitors
        • Slack
          • Manual Setup
        • OpsGenie
        • PagerDuty
      • LLM Red Teaming
    • Custom Metrics & Analytics
      • Arize Query Language Syntax
        • Conditionals and Filters
        • All Operators
        • All Functions
      • Custom Metric Examples
      • ✨Copilot: ArizeQL Generator
  • 📈Machine Learning
    • Machine Learning
      • User Guide: ML
      • Quickstart: ML
      • Concepts: ML
        • What Is A Model Schema
        • Delayed Actuals and Tags
        • ML Glossary
      • How To: ML
        • Upload Data to Arize
          • Pandas SDK Example
          • Local File Upload
            • File Upload FAQ
          • Table Ingestion Tuning
          • Wildcard Paths for Cloud Storage
          • Troubleshoot Data Upload
          • Sending Data FAQ
        • Monitors
          • ML Monitor Types
          • Configure Monitors
            • Notifications Providers
          • Programmatically Create Monitors
          • Best Practices for Monitors
        • Dashboards
          • Dashboard Widgets
          • Dashboard Templates
            • Model Performance
            • Pre-Production Performance
            • Feature Analysis
            • Drift
          • Programmatically Create Dashboards
        • Performance Tracing
          • Time Filtering
          • ✨Copilot: Performance Insights
        • Drift Tracing
          • ✨Copilot: Drift Insights
          • Data Distribution Visualization
          • Embeddings for Tabular Data (Multivariate Drift)
        • Custom Metrics
          • Arize Query Language Syntax
            • Conditionals and Filters
            • All Operators
            • All Functions
          • Custom Metric Examples
          • Custom Metrics Query Language
          • ✨Copilot: ArizeQL Generator
        • Troubleshoot Data Quality
          • ✨Copilot: Data Quality Insights
        • Explainability
          • Interpreting & Analyzing Feature Importance Values
          • SHAP
          • Surrogate Model
          • Explainability FAQ
          • Model Explainability
        • Bias Tracing (Fairness)
        • Export Data to Notebook
        • Automate Model Retraining
        • ML FAQ
      • Use Cases: ML
        • Binary Classification
          • Fraud
          • Insurance
        • Multi-Class Classification
        • Regression
          • Lending
          • Customer Lifetime Value
          • Click-Through Rate
        • Timeseries Forecasting
          • Demand Forecasting
          • Churn Forecasting
        • Ranking
          • Collaborative Filtering
          • Search Ranking
        • Natural Language Processing (NLP)
        • Common Industry Use Cases
      • Integrations: ML
        • Google BigQuery
          • GBQ Views
          • Google BigQuery FAQ
        • Snowflake
          • Snowflake Permissions Configuration
        • Databricks
        • Google Cloud Storage (GCS)
        • Azure Blob Storage
        • AWS S3
          • Private Image Link Access Via AWS S3
        • Kafka
        • Airflow Retrain
        • Amazon EventBridge Retrain
        • MLOps Partners
          • Algorithmia
          • Anyscale
          • Azure & Databricks
          • BentoML
          • CML (DVC)
          • Deepnote
          • Feast
          • Google Cloud ML
          • Hugging Face
          • LangChain 🦜🔗
          • MLflow
          • Neptune
          • Paperspace
          • PySpark
          • Ray Serve (Anyscale)
          • SageMaker
            • Batch
            • RealTime
            • Notebook Instance with Greater than 20GB of Data
          • Spell
          • UbiOps
          • Weights & Biases
      • API Reference: ML
        • Python SDK
          • Pandas Batch Logging
            • Client
            • log
            • Schema
            • TypedColumns
            • EmbeddingColumnNames
            • ObjectDetectionColumnNames
            • PromptTemplateColumnNames
            • LLMConfigColumnNames
            • LLMRunMetadataColumnNames
            • NLP_Metrics
            • AutoEmbeddings
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
          • Single Record Logging
            • Client
            • log
            • TypedValue
            • Ranking
            • Multi-Class
            • Object Detection
            • Embedding
            • LLMRunMetadata
            • utils.types.ModelTypes
            • utils.types.Metrics
            • utils.types.Environments
        • Java SDK
          • Constructor
          • log
          • bulkLog
          • logValidationRecords
          • logTrainingRecords
        • R SDK
          • Client$new()
          • Client$log()
        • Rest API
    • Computer Vision
      • How to: CV
        • Generate Embeddings
          • How to Generate Your Own Embedding
          • Let Arize Generate Your Embeddings
        • Embedding & Cluster Analyzer
        • ✨Copilot: Embedding Summarization
        • Similarity Search
        • Embedding Drift
        • Embeddings FAQ
      • Integrations: CV
      • Use Cases: CV
        • Image Classification
        • Image Segmentation
        • Object Detection
      • API Reference: CV
Powered by GitBook

Support

  • Chat Us On Slack
  • support@arize.com

Get Started

  • Signup For Free
  • Book A Demo

Copyright © 2025 Arize AI, Inc

On this page
  • Install our tracing packages
  • Get your API keys
  • Add our tracing code
  • Run your LLM application
  • Next steps

Was this helpful?

  1. Observe

Quickstart: Tracing

Learn how to trace your LLM application and run evaluations in Arize

Last updated 14 days ago

Was this helpful?

To trace your LLM app and start troubleshooting your LLM calls, you'll need to do the following:

You can also dive right into examples below.

  • Python:

  • JS/TS:

Install our tracing packages

Run the following commands below to install our open source tracing packages, which works on top of . This example below uses openai, and we support many LLM providers ().

Using pip

pip install arize-otel openai openinference-instrumentation-openai opentelemetry-exporter-otlp

Using conda

conda install -c conda-forge openai openinference-instrumentation-openai opentelemetry-exporter-otlp

Install with npm

npm install @arizeai/openinference-instrumentation-openai @opentelemetry/exporter-trace-otlp-grpc @grpc/grpc-js

Get your API keys

Go to your space settings in the left navigation, and create a key using the button below.

Add our tracing code

Python and JS/TS examples are shown below.

The following code snippet showcases how to automatically instrument your OpenAI application.

# Import open-telemetry dependencies
from arize.otel import register

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)

# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.openai import OpenAIInstrumentor

# Finish automatic instrumentation
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

Set OpenAI Key:

import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass("OpenAI API key")

To test, let's send a chat request to OpenAI:

import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Write a haiku."}],
    max_tokens=20,
)
print(response.choices[0].message.content)

Now start asking questions to your LLM app and watch the traces being collected by Arize.

The following code snippet implements instrumentation for an OpenAI client in typescript.

/*instrumentation.ts */

import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { 
  OpenAIInstrumentation
} from "@arizeai/openinference-instrumentation-openai";
import { ConsoleSpanExporter } from "@opentelemetry/sdk-trace-base";
import {
  NodeTracerProvider,
  BatchSpanProcessor,
} from "@opentelemetry/sdk-trace-node";
import { Resource } from "@opentelemetry/resources";
import { 
  OTLPTraceExporter as GrpcOTLPTraceExporter 
} from "@opentelemetry/exporter-trace-otlp-grpc"; // Arize specific
import { diag, DiagConsoleLogger, DiagLogLevel } from "@opentelemetry/api";
import { Metadata } from "@grpc/grpc-js"

// For troubleshooting, set the log level to DiagLogLevel.DEBUG
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.DEBUG);

// Arize specific - Create metadata and add your headers
const metadata = new Metadata();

// Your Arize Space and API Keys, which can be found in the UI
metadata.set('space_id', 'your-space-id');
metadata.set('api_key', 'your-api-key');

const provider = new NodeTracerProvider({
  resource: new Resource({
    // Arize specific - The name of a new or preexisting model you 
    // want to export spans to
    "model_id": "your-model-id",
    "model_version": "your-model-version"
  }),
});

provider.addSpanProcessor(new BatchSpanProcessor(new ConsoleSpanExporter()));
provider.addSpanProcessor(
  new BatchSpanProcessor(
    new GrpcOTLPTraceExporter({
      url: "https://otlp.arize.com/v1",
      metadata,
    }),
  ),
);

registerInstrumentations({
  instrumentations: [new OpenAIInstrumentation({})],
});

provider.register();

You can also follow our example application at the OpenInference github.

The following code snippet showcases how to automatically instrument your LLM application.

import os

# Import open-telemetry dependencies
from arize.otel import register

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

# Finish automatic instrumentation
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

To test, you can create a simple RAG application using LlamaIndex.

from gcsfs import GCSFileSystem
from llama_index.core import (
    Settings,
    StorageContext,
    load_index_from_storage,
)
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

file_system = GCSFileSystem(project="public-assets-275721")
index_path = "arize-phoenix-assets/datasets/unstructured/llm/llama-index/arize-docs/index/"
storage_context = StorageContext.from_defaults(
    fs=file_system,
    persist_dir=index_path,
)

Settings.llm = OpenAI(model="gpt-4-turbo-preview")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
index = load_index_from_storage(
    storage_context,
)
query_engine = index.as_query_engine()

response = query_engine.query("What is Arize and how can it help me as an AI Engineer?")     

Now start asking questions to your LLM app and watch the traces being collected by Arize.🦙

The following code snippet showcases how to automatically instrument your LLM application.

import os

# Import open-telemetry dependencies
from arize.otel import register

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.langchain import LangChainInstrumentor

# Finish automatic instrumentation
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)

To test, you can create a simple RAG application using Langchain.

from langchain.chains import RetrievalQA
from langchain.retrievers import KNNRetriever
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

knn_retriever = KNNRetriever(
    index=np.stack(df["text_vector"]),
    texts=df["text"].tolist(),
    embeddings=OpenAIEmbeddings(),
)
chain_type = "stuff"  # stuff, refine, map_reduce, and map_rerank
chat_model_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=chat_model_name)
chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type=chain_type,
    retriever=knn_retriever,
    metadata={"application_type": "question_answering"},
)

response = chain.invoke("What is Arize and how can it help me as an AI Engineer?")     

Now start asking questions to your LLM app and watch the traces being collected by Arize.

In this example we will instrument an LLM application built using Groq

pip install openinference-instrumentation-groq groq arize-otel

Set up GroqInstrumentor to trace calls to Groq LLM in the application and sends the traces to an Arize model endpoint as defined below.

from openinference.instrumentation.groq import GroqInstrumentor
# Import open-telemetry dependencies
from arize.otel import register

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)

GroqInstrumentor().instrument(tracer_provider=tracer_provider)

Run a simple Chat Completion via Groq and see it instrumented

import os
from groq import Groq

# get your groq api key by visiting https://groq.com/
os.environ["GROQ_API_KEY"] = "your-groq-api-key" 

client = Groq()

# send a request to the groq client
chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Explain the importance of low latency LLMs",
        }
    ],
    model="mixtral-8x7b-32768",
)
print(chat_completion.choices[0].message.content)

Run your LLM application

Once you've executed a sufficient number of queries (or chats) to your application, you can view the details on the LLM Tracing page.

Next steps

Dive deeper into the following topics to keep improving your LLM application!

Arize is an OpenTelemetry collector, which means you can configure your tracer and span processor. For more OTEL configurability, .

The package we are using is , which is a lightweight convenience package to set up OpenTelemetry and send traces to Arize.

Are you coding with Javascript instead of Python? See our on auto-instrumentation or manual instrumentation with Javascript examples.

A detailed view of a trace of a RAG application using LlamaIndex

To continue with this guide, go to the to add evaluation labels to your traces!

🔭
see how to set your tracer for auto instrumentors
arize-otel
detailed guide
trace evaluations guide
Colab Link
Github
OpenTelemetry
see full list
Install our tracing packages
Get your API keys
Add our tracing code
Run your LLM application

Deep dive on tracing
Iterate with prompts
Setup monitors and dashboards
Evaluate your traces
openinference/js/examples/openai at main · Arize-ai/openinferenceGitHub
Logo
Where to find your API Keys