OpenLIT Integration via OpenTelemetry
Langfuse is an OpenTelemetry backend, allowing trace ingestion from various OpenTelemetry instrumentation libraries. This guide demonstrates how to use the OpenLit instrumentation library to instrument a compatible framework or LLM provider.
Step 1: Install Dependencies
Install the necessary Python packages: openai, langfuse, and openlit. These will allow you to interact with OpenAI as well as setup the instrumentation for tracing.
%pip install openai langfuse openlit --upgradeStep 2: Configure Environment Variables
Before sending any requests, you need to configure your credentials and endpoints. First, set up the Langfuse authentication by providing your public and secret keys. Then, configure the OpenTelemetry exporter endpoint and headers to point to Langfuse’s backend. You should also specify your OpenAI API key.
import os
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
# Your openai key
os.environ["OPENAI_API_KEY"] = "sk-proj-..."With the environment variables set, we can now initialize the Langfuse client. get_client() initializes the Langfuse client using the credentials provided in the environment variables.
from langfuse import get_client
langfuse = get_client()
# Verify connection
if langfuse.auth_check():
print("Langfuse client is authenticated and ready!")
else:
print("Authentication failed. Please check your credentials and host.")Langfuse client is authenticated and ready!Step 3: Initialize Instrumentation
With the environment set up, import the needed libraries and initialize OpenLIT instrumentation. We set tracer=tracer to use the tracer we created in the previous step.
import openlit
# Initialize OpenLIT instrumentation. The disable_batch flag is set to true to process traces immediately.
openlit.init(disable_batch=True)Overriding of current TracerProvider is not allowed
/Users/jannik/Documents/GitHub/langfuse-docs/.venv/lib/python3.13/site-packages/pydantic/_internal/_config.py:345: UserWarning: Valid config keys have changed in V2:
* 'fields' has been removed
warnings.warn(message, UserWarning)
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170103458290000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": [
{
"filtered_attributes": {},
"value": 370,
"time_unix_nano": 1750170052815487000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170103458290000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": [
{
"filtered_attributes": {},
"value": 6.08782696723938,
"time_unix_nano": 1750170052815772000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170103458290000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": [
{
"filtered_attributes": {},
"value": 6.08782696723938,
"time_unix_nano": 1750170052815795000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170103458290000,
"value": 1,
"exemplars": [
{
"filtered_attributes": {},
"value": 1,
"time_unix_nano": 1750170052815808000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170103458290000,
"value": 356,
"exemplars": [
{
"filtered_attributes": {},
"value": 356,
"time_unix_nano": 1750170052815854000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170103458290000,
"value": 14,
"exemplars": [
{
"filtered_attributes": {},
"value": 14,
"time_unix_nano": 1750170052815865000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170103458290000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": [
{
"filtered_attributes": {},
"value": 0.0005409999999999999,
"time_unix_nano": 1750170052815876000,
"span_id": 16196589366933882483,
"trace_id": 60747386605375572337608415382303199835
}
]
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170163463164000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170163463164000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170163463164000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170163463164000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170163463164000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170163463164000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170163463164000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170223471008000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170223471008000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170223471008000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170223471008000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170223471008000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170223471008000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170223471008000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170283485878000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170283485878000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170283485878000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170283485878000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170283485878000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170283485878000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170283485878000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170343507759000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170343507759000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170343507759000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170343507759000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170343507759000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170343507759000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170343507759000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170403523327000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170403523327000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170403523327000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170403523327000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170403523327000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170403523327000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170403523327000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170463532154000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170463532154000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170463532154000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170463532154000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170463532154000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170463532154000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170463532154000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170523543109000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170523543109000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170523543109000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170523543109000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170523543109000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170523543109000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170523543109000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170583567364000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170583567364000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170583567364000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170583567364000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170583567364000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170583567364000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170583567364000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170643572617000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170643572617000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170643572617000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170643572617000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170643572617000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170643572617000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170643572617000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170703611152000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170703611152000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170703611152000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170703611152000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170703611152000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170703611152000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170703611152000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170763618434000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170763618434000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170763618434000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170763618434000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170763618434000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170763618434000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170763618434000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}
{
"resource_metrics": [
{
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "openlit",
"telemetry.sdk.version": "1.34.1",
"service.name": "default",
"deployment.environment": "default"
},
"schema_url": ""
},
"scope_metrics": [
{
"scope": {
"name": "openlit.otel.metrics",
"version": "0.1.0",
"schema_url": "",
"attributes": null
},
"metrics": [
{
"name": "gen_ai.client.token.usage",
"description": "Measures number of input and output tokens used",
"unit": "{token}",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815727000,
"time_unix_nano": 1750170823628772000,
"count": 1,
"sum": 370,
"bucket_counts": [
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
1,
4,
16,
64,
256,
1024,
4096,
16384,
65536,
262144,
1048576,
4194304,
16777216,
67108864
],
"min": 370,
"max": 370,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.client.operation.duration",
"description": "GenAI operation duration",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815787000,
"time_unix_nano": 1750170823628772000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0
],
"explicit_bounds": [
0.01,
0.02,
0.04,
0.08,
0.16,
0.32,
0.64,
1.28,
2.56,
5.12,
10.24,
20.48,
40.96,
81.92
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.server.time_to_first_token",
"description": "Time to generate first token for successful responses",
"unit": "s",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815801000,
"time_unix_nano": 1750170823628772000,
"count": 1,
"sum": 6.08782696723938,
"bucket_counts": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0
],
"explicit_bounds": [
0.001,
0.005,
0.01,
0.02,
0.04,
0.06,
0.08,
0.1,
0.25,
0.5,
0.75,
1.0,
2.5,
5.0,
7.5,
10.0
],
"min": 6.08782696723938,
"max": 6.08782696723938,
"exemplars": []
}
],
"aggregation_temporality": 2
}
},
{
"name": "gen_ai.total.requests",
"description": "Number of requests to GenAI",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815823000,
"time_unix_nano": 1750170823628772000,
"value": 1,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.output_tokens",
"description": "Number of completion tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815861000,
"time_unix_nano": 1750170823628772000,
"value": 356,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.input_tokens",
"description": "Number of prompt tokens processed.",
"unit": "1",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815871000,
"time_unix_nano": 1750170823628772000,
"value": 14,
"exemplars": []
}
],
"aggregation_temporality": 2,
"is_monotonic": true
}
},
{
"name": "gen_ai.usage.cost",
"description": "The distribution of GenAI request costs.",
"unit": "USD",
"data": {
"data_points": [
{
"attributes": {
"telemetry.sdk.name": "openlit",
"service.name": "default",
"deployment.environment": "default",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"server.address": "api.openai.com",
"server.port": 443,
"gen_ai.response.model": "gpt-4o-2024-08-06"
},
"start_time_unix_nano": 1750170052815881000,
"time_unix_nano": 1750170823628772000,
"count": 1,
"sum": 0.0005409999999999999,
"bucket_counts": [
0,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"explicit_bounds": [
0.0,
5.0,
10.0,
25.0,
50.0,
75.0,
100.0,
250.0,
500.0,
750.0,
1000.0,
2500.0,
5000.0,
7500.0,
10000.0
],
"min": 0.0005409999999999999,
"max": 0.0005409999999999999,
"exemplars": []
}
],
"aggregation_temporality": 2
}
}
],
"schema_url": ""
}
],
"schema_url": ""
}
]
}Step 4: Make a Chat Completion Request
For this example, we will make a simple chat completion request to the OpenAI Chat API. This will generate trace data that you can later view in the Langfuse dashboard.
from openai import OpenAI
# Create an instance of the OpenAI client.
openai_client = OpenAI()
# Make a sample chat completion request. This request will be traced by OpenLIT and sent to Langfuse.
chat_completion = openai_client.chat.completions.create(
messages=[
{
"role": "user",
"content": "What is LLM Observability?",
}
],
model="gpt-4o",
)
print(chat_completion){
"name": "chat gpt-4o",
"context": {
"trace_id": "0x2db38566327e2785c5cc38577934d25b",
"span_id": "0xe0c5d8374cf68673",
"trace_state": "[]"
},
"kind": "SpanKind.CLIENT",
"parent_id": null,
"start_time": "2025-06-17T14:20:46.725103Z",
"end_time": "2025-06-17T14:20:52.815914Z",
"status": {
"status_code": "OK"
},
"attributes": {
"telemetry.sdk.name": "openlit",
"gen_ai.operation.name": "chat",
"gen_ai.system": "openai",
"gen_ai.request.model": "gpt-4o",
"gen_ai.request.seed": "",
"server.port": 443,
"gen_ai.request.frequency_penalty": 0.0,
"gen_ai.request.max_tokens": -1,
"gen_ai.request.presence_penalty": 0.0,
"gen_ai.request.stop_sequences": [],
"gen_ai.request.temperature": 1.0,
"gen_ai.request.top_p": 1.0,
"gen_ai.response.id": "chatcmpl-BjRT54iJi0Xc59gTjyZNycAOtkE56",
"gen_ai.response.model": "gpt-4o-2024-08-06",
"gen_ai.usage.input_tokens": 14,
"gen_ai.usage.output_tokens": 356,
"server.address": "api.openai.com",
"gen_ai.request.service_tier": "auto",
"gen_ai.response.service_tier": "default",
"gen_ai.response.system_fingerprint": "fp_07871e2ad8",
"deployment.environment": "default",
"service.name": "default",
"gen_ai.request.user": "",
"gen_ai.request.is_stream": false,
"gen_ai.usage.total_tokens": 370,
"gen_ai.usage.cost": 0.0005409999999999999,
"gen_ai.server.time_to_first_token": 6.08782696723938,
"gen_ai.sdk.version": "1.88.0",
"gen_ai.response.finish_reasons": [
"stop"
],
"gen_ai.output.type": "text"
},
"events": [
{
"name": "gen_ai.content.prompt",
"timestamp": "2025-06-17T14:20:52.815372Z",
"attributes": {
"gen_ai.prompt": "user: What is LLM Observability?"
}
},
{
"name": "gen_ai.content.completion",
"timestamp": "2025-06-17T14:20:52.815397Z",
"attributes": {
"gen_ai.completion": "LLM Observability refers to the monitoring, understanding, and optimizing of large language models (LLMs) and their behavior in real-world applications. As LLMs are deployed in various contexts, it becomes crucial to ensure they perform as expected, remain reliable, and provide transparency in their operation. Observability in this context involves several key aspects:\n\n1. **Monitoring Performance**: Continuously tracking the performance of LLMs through metrics like response time, accuracy, relevance, and user satisfaction. This helps in identifying any degradation in performance or emerging issues.\n\n2. **Behavior Analysis**: Understanding how the model makes decisions and produces outputs. This involves interpreting the patterns of interactions, examining decision pathways, and ensuring the model behaves as intended.\n\n3. **Bias and Fairness Checking**: Observability includes assessing the model for any biases or unfair behavior, ensuring that outputs are equitable and do not inadvertently disadvantage any group or individual.\n\n4. **Anomaly Detection**: Identifying unusual or unexpected behaviors in LLM outputs that could indicate problems, such as out-of-distribution responses or errors in reasoning.\n\n5. **Transparency and Explainability**: Providing insights into the inner workings of LLMs, which can help build trust by allowing users and stakeholders to understand how decisions are being made.\n\n6. **Compliance and Safety Monitoring**: Ensuring that LLMs are used in a manner compliant with data protection laws, ethical guidelines, and safety standards.\n\n7. **Feedback and Iteration**: Implementing mechanisms for collecting user feedback to continuously improve the model and adapt to changing requirements or environments.\n\nOverall, LLM Observability is about creating a framework to ensure that large language models remain effective, trustworthy, and aligned with human values and goals through systematic monitoring and evaluation."
}
}
],
"links": [],
"resource": {
"attributes": {
"telemetry.sdk.language": "python",
"telemetry.sdk.name": "opentelemetry",
"telemetry.sdk.version": "1.34.1",
"service.name": "unknown_service"
},
"schema_url": ""
}
}
ChatCompletion(id='chatcmpl-BjRT54iJi0Xc59gTjyZNycAOtkE56', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='LLM Observability refers to the monitoring, understanding, and optimizing of large language models (LLMs) and their behavior in real-world applications. As LLMs are deployed in various contexts, it becomes crucial to ensure they perform as expected, remain reliable, and provide transparency in their operation. Observability in this context involves several key aspects:\n\n1. **Monitoring Performance**: Continuously tracking the performance of LLMs through metrics like response time, accuracy, relevance, and user satisfaction. This helps in identifying any degradation in performance or emerging issues.\n\n2. **Behavior Analysis**: Understanding how the model makes decisions and produces outputs. This involves interpreting the patterns of interactions, examining decision pathways, and ensuring the model behaves as intended.\n\n3. **Bias and Fairness Checking**: Observability includes assessing the model for any biases or unfair behavior, ensuring that outputs are equitable and do not inadvertently disadvantage any group or individual.\n\n4. **Anomaly Detection**: Identifying unusual or unexpected behaviors in LLM outputs that could indicate problems, such as out-of-distribution responses or errors in reasoning.\n\n5. **Transparency and Explainability**: Providing insights into the inner workings of LLMs, which can help build trust by allowing users and stakeholders to understand how decisions are being made.\n\n6. **Compliance and Safety Monitoring**: Ensuring that LLMs are used in a manner compliant with data protection laws, ethical guidelines, and safety standards.\n\n7. **Feedback and Iteration**: Implementing mechanisms for collecting user feedback to continuously improve the model and adapt to changing requirements or environments.\n\nOverall, LLM Observability is about creating a framework to ensure that large language models remain effective, trustworthy, and aligned with human values and goals through systematic monitoring and evaluation.', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1750170047, model='gpt-4o-2024-08-06', object='chat.completion', service_tier='default', system_fingerprint='fp_07871e2ad8', usage=CompletionUsage(completion_tokens=356, prompt_tokens=14, total_tokens=370, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))Step 5: See Traces in Langfuse
You can view the generated trace data in Langfuse. You can view this example trace in the Langfuse UI.
Using Dataset Experiments with the OpenLit Instrumentation
With Dataset Experiments, you can test your application on a dataset before deploying it to production.
First, set up the helper function (otel_helper_function) that will be used to run the application. This function returns the application output as well as the Langfuse trace to link to dataset run with the trace.
from opentelemetry.trace import format_trace_id
def otel_helper_function(input):
with tracer.start_as_current_span("Otel-Trace") as span:
# Your gen ai application logic here: (make sure this function is sending traces to Langfuse)
response = openai.OpenAI().chat.completions.create(
messages=[{"role": "user", "content": input}],
model="gpt-4o-mini",
)
print(response.choices[0].message.content)
# Fetch the current span and trace id
current_span = trace.get_current_span()
span_context = current_span.get_span_context()
trace_id = span_context.trace_id
formatted_trace_id = format_trace_id(trace_id)
langfuse_trace = langfuse.trace(
id=formatted_trace_id,
input=input,
output=response.choices[0].message.content
)
return langfuse_trace, response.choices[0].message.contentThen loop over the dataset items and run the application.
from langfuse import Langfuse
langfuse = Langfuse()
dataset = langfuse.get_dataset("<langfuse_dataset_name>")
# Run our application against each dataset item
for item in dataset.items:
langfuse_trace, output = otel_helper_function(item.input["text"])
# Link the trace to the dataset item for analysis
item.link(
langfuse_trace,
run_name="run-01",
run_metadata={ "model": "gpt-4o-mini" }
)
# Optionally, store a quick evaluation score for demonstration
langfuse_trace.score(
name="<example_eval>",
value= your_evaluation_function(output),
comment="This is a comment"
)
# Flush data to ensure all telemetry is sent
langfuse.flush()You can repeat this process with different:
- Models (OpenAI GPT, local LLM, etc.)
- Prompts (different system messages)
Then compare them side-by-side in Langfuse:
Was this page helpful?