> ## Documentation Index
> Fetch the complete documentation index at: https://enrolla-nk-hub-guardrails.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Observability with LangSmith and OpenLLMetry

<Frame>
  <img src="https://mintcdn.com/enrolla-nk-hub-guardrails/MnHRGhi6rxtiPuOU/img/integrations/langsmith.png?fit=max&auto=format&n=MnHRGhi6rxtiPuOU&q=85&s=ebb50b72c83868decf2616166f55701c" width="2814" height="1796" data-path="img/integrations/langsmith.png" />
</Frame>

LangSmith is an [all-in-one developer platform](https://www.langchain.com/langsmith) for every step of the LLM-powered application lifecycle.

LangSmith supports ingesting traces using OpenTelemetry / OpenLLMetry format. For more details, see [LangSmith's OpenTelemetry documentation](https://docs.smith.langchain.com/observability/how_to_guides/tracing/trace_with_opentelemetry).

### To Log Traces to Langsmith

Signup for LangSmith and create an API Key. Then setup your environment variables:

```bash theme={null}
TRACELOOP_BASE_URL=https://api.smith.langchain.com/otel
TRACELOOP_HEADERS="x-api-key=<LANGSMITH_API_KEY>"
```

You can then log traces with OpenLLMetry to LangSmith, here is an example:

```python theme={null}
from openai import OpenAI
from traceloop.sdk import Traceloop

client = OpenAI()
Traceloop.init()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "Write a haiku about recursion in programming."
        }
    ]
)

print(completion.choices[0].message)
```