> ## Documentation Index
> Fetch the complete documentation index at: https://braintrust.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Perplexity

> Perplexity AI model provider configuration and integration guide

If you are a coding agent, prefer the Braintrust [`bt` CLI](/docs/reference/cli/quickstart) for repeatable, scriptable work: running evals, instrumenting code, querying logs, syncing data, managing functions, and configuring coding agents. Use the MCP server for reasoning over Braintrust data in conversation, and for capabilities the CLI doesn't cover, such as monitor views, alerts, and authoring evaluators, preprocessors, and facets.

Perplexity AI provides access to advanced language models designed for reasoning and search-augmented generation. Braintrust integrates seamlessly with Perplexity through direct API access, wrapper functions for automatic tracing, and proxy support.

## Setup

To use Perplexity models, configure your Perplexity API key in Braintrust.

1. Get a Perplexity API key from [Perplexity Console](https://www.perplexity.ai/settings/api)
2. Add the Perplexity API key as an [organization or project AI provider](/docs/admin/ai-providers).
3. Set the Perplexity API key and your Braintrust API key as environment variables

```bash title=".env" theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
PERPLEXITY_API_KEY=<your-perplexity-api-key>
BRAINTRUST_API_KEY=<your-braintrust-api-key>

# For organizations on the EU data plane, use https://api-eu.braintrust.dev
# For self-hosted deployments, use your data plane URL
# BRAINTRUST_API_URL=<your-braintrust-api-url>
```

<Note>
  API keys are stored as one-way cryptographic hashes, never in plaintext.
</Note>

## Use Perplexity with Braintrust gateway

The [Braintrust gateway](/docs/deploy/gateway) allows you to access Perplexity models through a unified interface. Use any [supported provider's SDK](/docs/integrations/ai-providers) to call Perplexity models.

Install the `braintrust` and `openai` packages.

<CodeGroup>
  ```bash Typescript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  # pnpm
  pnpm add braintrust openai
  # npm
  npm install braintrust openai
  ```

  ```bash Python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  pip install braintrust openai
  ```
</CodeGroup>

Then, initialize the client and make a request to a Perplexity model via the Braintrust gateway.

<CodeGroup dropdown>
  ```typescript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import { OpenAI } from "openai";

  const client = new OpenAI({
    baseURL: "https://gateway.braintrust.dev/v1",
    apiKey: process.env.BRAINTRUST_API_KEY,
  });

  const response = await client.chat.completions.create({
    model: "sonar",
    messages: [{ role: "user", content: "Hello, world!" }],
  });
  ```

  ```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import os

  from openai import OpenAI

  client = OpenAI(
      base_url="https://gateway.braintrust.dev/v1",
      api_key=os.environ["BRAINTRUST_API_KEY"],
  )

  response = client.chat.completions.create(
      model="sonar",
      messages=[{"role": "user", "content": "Hello, world!"}],
  )
  ```
</CodeGroup>

## Trace logs with Perplexity

Trace your Perplexity LLM calls for observability and monitoring.

When using the Braintrust gateway, API calls are automatically logged to the specified project.

<CodeGroup dropdown>
  ```typescript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import { OpenAI } from "openai";
  import { initLogger } from "braintrust";

  initLogger({
    projectName: "My Project",
    apiKey: process.env.BRAINTRUST_API_KEY,
  });

  const client = new OpenAI({
    baseURL: "https://gateway.braintrust.dev/v1",
    apiKey: process.env.BRAINTRUST_API_KEY,
  });

  // All API calls are automatically logged
  const result = await client.chat.completions.create({
    model: "sonar",
    messages: [{ role: "user", content: "What is machine learning?" }],
  });
  ```

  ```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import os

  from braintrust import init_logger
  from openai import OpenAI

  init_logger(project="My Project")

  client = OpenAI(
      base_url="https://gateway.braintrust.dev/v1",
      api_key=os.environ["BRAINTRUST_API_KEY"],
  )

  # All API calls are automatically logged
  result = client.chat.completions.create(
      model="sonar",
      messages=[{"role": "user", "content": "What is machine learning?"}],
  )
  ```
</CodeGroup>

<Tip>
  The Braintrust gateway is not required. For more control, learn how to [customize traces](/docs/instrument/advanced-tracing).
</Tip>

## Evaluate with Perplexity

Evaluations distill the non-deterministic outputs of Perplexity models into an effective feedback loop that enables you to ship more reliable, higher quality products. Braintrust `Eval` is a simple function composed of a dataset of user inputs, a task, and a set of scorers. To learn more about evaluations, see the [Experiments](/docs/evaluate/run-evaluations) guide.

<CodeGroup dropdown>
  ```typescript theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import { Eval } from "braintrust";
  import { OpenAI } from "openai";

  const client = new OpenAI({
    baseURL: "https://gateway.braintrust.dev/v1",
    apiKey: process.env.BRAINTRUST_API_KEY,
  });

  Eval("Perplexity Evaluation", {
    data: () => [
      { input: "What is 2+2?", expected: "4" },
      { input: "What is the capital of France?", expected: "Paris" },
    ],
    task: async (input) => {
      const response = await client.chat.completions.create({
        model: "sonar",
        messages: [{ role: "user", content: input }],
      });
      return response.choices[0].message.content;
    },
    scores: [
      {
        name: "accuracy",
        scorer: (args) => (args.output === args.expected ? 1 : 0),
      },
    ],
  });
  ```

  ```python theme={"theme":{"light":"github-light","dark":"github-dark-dimmed"}}
  import os

  from braintrust import Eval
  from openai import OpenAI

  client = OpenAI(
      base_url="https://gateway.braintrust.dev/v1",
      api_key=os.environ["BRAINTRUST_API_KEY"],
  )


  def task(input):
      response = client.chat.completions.create(
          model="sonar",
          messages=[{"role": "user", "content": input}],
      )
      return response.choices[0].message.content


  def accuracy_scorer(output, expected, **kwargs):
      return 1 if output == expected else 0


  Eval(
      "Perplexity Evaluation",
      data=[
          {"input": "What is 2+2?", "expected": "4"},
          {"input": "What is the capital of France?", "expected": "Paris"},
      ],
      task=task,
      scores=[accuracy_scorer],
  )
  ```
</CodeGroup>

<Tip>
  To learn more about tool use, multimodal support, attachments, and masking sensitive data with Perplexity, visit the [customize traces](/docs/instrument/advanced-tracing) guide.
</Tip>