> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Anthropic
> Learn how Openlayer integrates with Anthropic
Openlayer integrates with [Anthropic](https://www.anthropic.com/) in two different ways:
* If you are building an AI system with Anthropic LLMs and want to evaluate it,
you can use the [SDKs](/api-reference/sdk/overview) to make Openlayer part of your
workflow.
* Some tests on Openlayer are based on a score produced by an LLM judge. You can
set any of Anthropic's LLMs as the LLM judge for these tests.
This integration guide explores each of these paths.
## Evaluating Anthropic LLMs
You can set up Openlayer tests to evaluate your Anthropic LLMs
in [monitoring](/monitoring/overview) and [development](/development/overview).
### Monitoring
To use the [monitoring mode](/monitoring/overview), you must instrument your code to publish
the requests your AI system receives to the Openlayer platform. This process
is streamlined for Anthropic LLMs.
To set it up, you must follow the steps in the code snippet below:
```python Python theme={null}
# 1. Set the environment variables
import anthropic
import os
os.environ["ANTHROPIC_API_KEY"] = "YOUR_ANTHROPIC_API_KEY_HERE"
os.environ["OPENLAYER_API_KEY"] = "YOUR_OPENLAYER_API_KEY_HERE"
os.environ["OPENLAYER_INFERENCE_PIPELINE_ID"] = "YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE"
# 2. Import the `trace_anthropic` function and wrap the Anthropic client with it
from openlayer.lib import trace_anthropic
anthropic_client = trace_anthropic(anthropic.Anthropic())
# 3. From now on, every message creation call with
# the `anthropic_client`is traced by Openlayer. E.g.,
completion = anthropic_client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1024,
messages=[
{"role": "user", "content": "How are you doing today?"}
],
)
```
Once the code is instrumented, all your Anthropic LLM calls are automatically published to Openlayer,
along with metadata, such as latency, number of tokens, cost estimate, and more.
If you navigate to the "Data" page of your Openlayer data source, you can see
the traces for each request.
If the Anthropic LLM call is just one of the steps of your AI system, you can
use the code snippets above together with [tracing](/monitoring/tracing). In
this case, your Anthropic LLM calls get added as a step of a larger trace.
Refer to the [Tracing guide](/monitoring/tracing) for details.
After your AI system requests are continuously published and logged by Openlayer, you can
[create tests](/tests/overview) that run at a regular cadence on top of them.
Refer to the [Monitoring overview](/monitoring/overview), for details on Openlayer's
monitoring mode, to the [Publishing data guide](/monitoring/publishing-data), for more
information on setting it up, or to the [Tracing guide](/monitoring/tracing), to
understand how to trace more complex systems.
### Development
In [development mode](/development/overview), Openlayer becomes a step in
your CI/CD pipeline, and your tests get automatically evaluated after being triggered
by some events.
Openlayer tests often rely on your AI system's outputs on a validation
dataset. As discussed in the
[Configuring output generation guide](/development/configuring-output-generation),
you have two options:
1. either provide a way for Openlayer to run your AI system on your datasets, or
2. before pushing, generate the model outputs yourself and push them alongside your
artifacts.
For AI systems built with Anthropic LLMs, if you are **not** computing
your system's outputs yourself, you must provide your **API credentials**.
To do so, navigate to "**Workspace settings**" -> "**Environment variables**," and add the `ANTHROPIC_API_KEY`
secret.
If you don't add the required Anthropic API key, you'll encounter a "Missing API key"
error when Openlayer tries to run your AI system to get its outputs.
## Using Anthropic LLMs as the LLM judge
Some tests on Openlayer rely on scores produced by an LLM judge. For example,
tests that use [Ragas metrics](/integrations/ragas)
and the custom [LLM evaluator test](/tests/performance/l-l-m-rubric-threshold).
You can use any of Anthropic's LLMs as the underlying LLM evaluator for these tests.
You can change the default LLM evaluator for a project in the project settings page. To
do so, navigate to "Settings" > Select your project in the left sidebar > click on "Metrics" to
go to the metric settings page. Under "LLM evaluator," choose the Anthropic LLM you
want to use.
Furthermore, make sure to add your `ANTHROPIC_API_KEY` as an environment variable.
