---
source: ⚠️ Jupyter Notebook
title: Integrate X.ai (Grok) with Langfuse
sidebarTitle: xAI Grok
logo: /images/integrations/xai_icon.svg
logoAppearance: dark
description: Guide on integrating xAI's Grok models with Langfuse for observability.
category: Integrations
---
# Observability for xAI / Grok with Langfuse
This guide shows you how to integrate Grok with Langfuse using the OpenAI SDK.
> **What is Grok?** Grok is X.ai’s advanced AI platform that streamlines natural language processing for intelligent application integration. Learn more at [Grok Documentation](https://docs.x.ai/docs).
> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open source AI engineering platform that helps teams trace API calls, monitor performance, and debug issues in their LLM applications.
## Step 1: Install Dependencies
Make sure you have installed the necessary Python packages:
```python
%pip install openai langfuse
```
## Step 2: Set Up Environment Variables
```python
import os
# Get keys for your project from the project settings page
# https://cloud.langfuse.com
os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
# Get your Grok API key from your Grok account settings
os.environ.setdefault("GROK_API_KEY", "xai-...");
```
## Step 3: Use Grok with the OpenAI SDK
To utilize Grok through the OpenAI SDK, we use the Langfuse drop-in replacement for OpenAI. Replace the base URL with Grok’s endpoint.
```python
# Instead of importing openai directly:
from langfuse.openai import openai
client = openai.OpenAI(
api_key=os.environ.get("GROK_API_KEY"),
base_url="https://api.x.ai/v1" # Grok's endpoint
)
```
## Step 4: Run an Example
The following example demonstrates how to make a simple request using Grok's API. All API calls will be automatically traced by Langfuse.
```python
response = client.chat.completions.create(
model="grok-2-latest",
messages=[
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "What is Langfuse?"}
],
name = "Grok-2-Trace"
)
print(response.choices[0].message.content)
```
## Step 5: Enhance Tracing (Optional)
You can enhance your Grok traces:
- Add [metadata](https://langfuse.com/docs/tracing-features/metadata), [tags](https://langfuse.com/docs/tracing-features/tags), [log levels](https://langfuse.com/docs/tracing-features/log-levels) and [user IDs](https://langfuse.com/docs/tracing-features/users) to traces
- Group traces by [sessions](https://langfuse.com/docs/tracing-features/sessions)
- [`@observe()` decorator](https://langfuse.com/docs/sdk/python/decorators) to trace additional application logic
- Use [Langfuse Prompt Management](https://langfuse.com/docs/prompts/get-started) and link prompts to traces
- Add [score](https://langfuse.com/docs/scores/custom) to traces
Visit the [OpenAI SDK integration docs](/integrations/model-providers/openai-py#custom-trace-properties) to see more examples on passing additional parameters.
Find out more about Langfuse Evaluations and Prompt Management in the [Langfuse documentation](https://langfuse.com/docs).
## Step 6: See Traces in Langfuse
After running the example, log in to Langfuse to view the detailed traces, including:
- Request parameters
- Response content
- Token usage and latency metrics
_[Public example trace link in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/9178205e-2cb4-4952-8ec0-5244a1105263?timestamp=2025-03-05T11%3A07%3A46.600Z)_
## Interoperability with the Python SDK
You can use this integration together with the Langfuse [SDKs](/docs/observability/sdk/overview) to add additional attributes to the observation.
The [`@observe()` decorator](/docs/observability/sdk/instrumentation#custom-instrumentation) provides a convenient way to automatically wrap your instrumented code and add additional attributes to the observation.
```python
from langfuse import observe, propagate_attributes, get_client
langfuse = get_client()
@observe()
def my_llm_pipeline(input):
# Add additional attributes (user_id, session_id, metadata, version, tags) to all spans created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
tags=["agent", "my-observation"],
metadata={"email": "user@langfuse.com"},
version="1.0.0"
):
# YOUR APPLICATION CODE HERE
result = call_llm(input)
return result
# Run the function
my_llm_pipeline("Hi")
```
Learn more about using the Decorator in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
The [Context Manager](/docs/observability/sdk/instrumentation#custom-instrumentation) allows you to wrap your instrumented code using context managers (with `with` statements), which allows you to add additional attributes to the observation.
```python
from langfuse import get_client, propagate_attributes
langfuse = get_client()
with langfuse.start_as_current_observation(
as_type="span",
name="my-observation",
trace_context={"trace_id": "abcdef1234567890abcdef1234567890"}, # Must be 32 hex chars
) as observation:
# Add additional attributes (user_id, session_id, metadata, version, tags)
# to all observations created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
metadata={"experiment": "variant_a", "env": "prod"},
version="1.0",
):
# YOUR APPLICATION CODE HERE
result = call_llm("some input")
# Flush events in short-lived applications
langfuse.flush()
```
Learn more about using the Context Manager in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
## Troubleshooting
No observations appearing
First, enable [debug mode](/docs/observability/sdk/advanced-features#logging--debugging) in the Python SDK:
```bash
export LANGFUSE_DEBUG="True"
```
Then run your application and check the debug logs:
- **OTel observations appear in the logs:** Your application is instrumented correctly but observations are not reaching Langfuse. To resolve this:
1. Call [`langfuse.flush()`](/docs/observability/sdk/instrumentation#client-lifecycle--flushing) at the end of your application to ensure all observations are exported.
2. Verify that you are using the correct API keys and base URL.
- **No OTel spans in the logs:** Your application is not instrumented correctly. Make sure the instrumentation runs before your application code.
Unwanted observations in Langfuse
The Langfuse SDK is based on OpenTelemetry. Other libraries in your application may emit OTel spans that are not relevant to you. These still count toward your [billable units](/docs/administration/billable-units), so you should filter them out. See [Unwanted spans in Langfuse](/faq/all/unwanted-http-database-spans) for details.
Missing attributes
Some attributes may be stored in the metadata object of the observation rather than being mapped to the Langfuse data model. If a mapping or integration does not work as expected, please [raise an issue on GitHub](/issues).
## Next Steps
Once you have instrumented your code, you can manage, evaluate and debug your application:
- [Manage prompts in Langfuse](/docs/prompts/get-started)
- [Add evaluation scores](/docs/evaluation/features/evaluation-methods/custom-scores)
- [Run LLM-as-a-judge Evaluators](/docs/scores/model-based-evals)
- [Create datasets](/docs/datasets/overview)
- [Create custom dashboards](/docs/analytics/custom-dashboards)
- [Test queries in the Playground](/docs/playground)
---
## Agent Instructions
This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/integrations/model-providers/xai-grok.md`.
### Querying these docs
If the answer is not on this page, query the documentation instead of guessing:
- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:
```bash
curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
```
- **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).
### Before writing Langfuse code
- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal.
Found an error in these docs? Please open an issue at .