Getting Observability for Streamlit with OpenAI: Challenges & Solutions

Aditya A. · 2025-12-03 07:43:40.560764+00

Hey, I’ve Arize Phoenix running locally in docker. I’ve Streamlit based app which uses OpenAI for LLM based chats. I am trying to get observability working in this app. I followed OpenAi getting started guide, that worked for me in notebook. But same doesn’t work in Streamlit app. Inside app, do I need to do something different? I tried DSPy based example from your GitHub. But I’ve no luck getting it up and running.

Getting Observability for Streamlit with OpenAI: Challenges & Solutions | Arize AI Community

RunLLM
APP
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To get observability working in your Streamlit app using OpenAI, you need to ensure that the tracing setup is correctly integrated within the Streamlit app's main thread. The issue might be due to asynchronous execution not being supported in non-main threads, which is common in Streamlit apps.1. Ensure Main Thread Execution: Make sure the tracing setup is executed in the main thread of your Streamlit app. 2. Use nest_asyncio: Apply nest_asyncio to allow nested event loops, which can help with asynchronous execution in Streamlit.Here's a basic example to integrate OpenAI tracing in a Streamlit app: LBKSimport streamlit as st import nest_asyncio import os from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry import trace as trace_api from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk import trace as trace_sdk from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace.export import SimpleSpanProcessorLBKS# Apply nest_asyncio nest_asyncio.apply()LBKS# Set up tracing endpoint = "[http://localhost:6006/v1/traces](http://localhost:6006/v1/traces)" resource = Resource(attributes={}) tracer_provider = trace_sdk.TracerProvider(resource=resource) tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint))) trace_api.set_tracer_provider(tracer_provider=tracer_provider)LBKS# Instrument OpenAI OpenAIInstrumentor().instrument()LBKS# Streamlit app code st.title("OpenAI LLM Chat") query = st.text_input("Enter your query:") if query: # Your OpenAI call here response = openai.Completion.create( model="text-davinci-003", prompt=query, max_tokens=50 ) st.write(response.choices[0].text)LBKSWould you like more detail on any specific part of this setup?Sources: - Slack Discussion on Async Execution - OpenAI Integration Guide
💡 Hint: Mention RunLLM in the thread for followups.
Aman K.
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from Mikyo in the other thread We’ve got a bunch of examples with streamlit here! https://github.com/Arize-ai/openinference/tree/main/python/examples
Aditya A.
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I followed the examples in this git. I also tried creating manual spans. These manual test spans are going thru. But OpenAI.chat.completions.create traces are not getting logged.
Dustin N.
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Can you please show how you're instrumenting OpenAI?
Aditya A.
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I’m using the following code. from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry import trace as trace_api from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk import trace as trace_sdk from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace.export import SimpleSpanProcessor collector_endpoint = "http://localhost:6006/v1/traces" def instrument(): print("Instrumenting OpenAI") resource = Resource(attributes={"project_name": "ragpile"}) tracer_provider = trace_sdk.TracerProvider(resource=resource) span_exporter = OTLPSpanExporter(endpoint=collector_endpoint) span_processor = SimpleSpanProcessor(span_exporter=span_exporter) tracer_provider.add_span_processor(span_processor=span_processor) trace_api.set_tracer_provider(tracer_provider=tracer_provider) OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
Mikyo
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Hey Aditya A. when you docker compose phoenix will not be on localhost. It will be using docker’s internal dns https://github.com/Arize-ai/phoenix/blob/main/examples/manually-instrumented-chatbot/compose.yml#L12
Mikyo
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https://docs.docker.com/compose/how-tos/networking/
Aditya A.
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Hmm. But with this setup I’m able to get following trace logged. Also, I can get traces working from Jupyter notebook using localhost endpoint with tracer.start_as_current_span("manual-span") as span: print("This is a test span.")
Aditya A.
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And my Streamlit app is not running in docker container
Mikyo
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Where is streamlet running?
Aditya A.
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Streamlit app is running locally using Streamlit run
Mikyo
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RunLLM can you show how to mount a consolespanexporter to troubleshoot OTEL exports?
RunLLM
APP
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To troubleshoot OTEL exports, you can add a ConsoleSpanExporter to your tracer provider. This will print the spans to the console, allowing you to verify if the spans are being created and exported correctly. Here's how you can modify your instrument function to include a ConsoleSpanExporter: ```from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry import trace as trace_api from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk import trace as trace_sdk from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExportercollector_endpoint = "[http://localhost::6006/v1/traces](http:://localhost:6006/v1/traces)"def instrument(): print("Instrumenting OpenAI") resource = Resource(attributes={"project_name": "ragpile"}) tracer_provider = trace_sdk.TracerProvider(resource=resource)
# OTLP Span Exporter span_exporter = OTLPSpanExporter(endpoint=collector_endpoint) span_processor = SimpleSpanProcessor(span_exporter=span_exporter) tracer_provider.add_span_processor(span_processor) # Console Span Exporter for debugging console_exporter = ConsoleSpanExporter() console_processor = SimpleSpanProcessor(console_exporter) tracer_provider.add_span_processor(console_processor) trace_api.set_tracer_provider(tracer_provider=tracer_provider) OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)LBKS# Call the instrument function
instrument()```This setup will print the spans to the console, which can help you verify if the spans are being created and exported correctly. If the manual spans are being logged but the OpenAI spans are not, it might indicate an issue with the OpenAI instrumentation.Would you like more detail on any specific part of this setup?Sources: - https://github.com/arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-langchain/examples/openai_chat_stream.py - https://github.com/arize-ai/openinference/blob/main/python/instrumentation/openinference-instrumentation-openai/examples/chat_completions_stream.py
💡 Hint: Mention RunLLM in the thread for followups.
Aditya A.
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I checked docker logs for phoenix. Here all the rest calls are logged. I’m seeing traces endpoint is invoked in following cases
1.
From Jupyter notebook for OpenAI traces
2.
From Streamlit app for manual traces
Mikyo
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Hmm, it’s a bit hard to tell what might be an issue. If there is a minimum working example. I’d be happy to take a look. Sorry about that

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