Fireworks

Fireworks AI provides fast inference for open-source language models including Llama, Mixtral, Code Llama, and other state-of-the-art models. Braintrust integrates seamlessly with Fireworks through direct API access, wrapper functions for automatic tracing, and proxy support.

Setup

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

Get a Fireworks API key from Fireworks AI Console
Add the Fireworks API key as an organization or project AI provider.
Set the Fireworks API key and your Braintrust API key as environment variables

.env

FIREWORKS_API_KEY=<your-fireworks-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-here>

API keys are stored as one-way cryptographic hashes, never in plaintext.

Use Fireworks with Braintrust gateway

The Braintrust gateway allows you to access Fireworks models through a unified interface. Use any supported provider’s SDK to call Fireworks models. Install the braintrust and openai packages.

# pnpm
pnpm add braintrust openai
# npm
npm install braintrust openai

pip install braintrust openai

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

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: "fireworks/llama-3.1-70b-instruct",
  messages: [{ role: "user", content: "Hello, world!" }],
});

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="fireworks/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "Hello, world!"}],
)

Trace logs with Fireworks

Trace your Fireworks LLM calls for observability and monitoring. When using the Braintrust gateway, API calls are automatically logged to the specified project.

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: "fireworks/llama-3.1-70b-instruct",
  messages: [{ role: "user", content: "What is machine learning?" }],
});

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="fireworks/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "What is machine learning?"}],
)

The Braintrust gateway is not required. For more control, learn how to customize traces.

Evaluate with Fireworks

Evaluations distill the non-deterministic outputs of Fireworks 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 guide.

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("Fireworks 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: "fireworks/llama-3.1-70b-instruct",
      messages: [{ role: "user", content: input }],
    });
    return response.choices[0].message.content;
  },
  scores: [
    {
      name: "accuracy",
      scorer: (args) => (args.output === args.expected ? 1 : 0),
    },
  ],
});

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="fireworks/llama-3.1-70b-instruct",
        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(
    "Fireworks Evaluation",
    data=[
        {"input": "What is 2+2?", "expected": "4"},
        {"input": "What is the capital of France?", "expected": "Paris"},
    ],
    task=task,
    scores=[accuracy_scorer],
)

To learn more about tool use, multimodal support, attachments, and masking sensitive data with Fireworks, visit the customize traces guide.

AI providers

SDKs

Developer tools

Setup

Use Fireworks with Braintrust gateway

Trace logs with Fireworks

Evaluate with Fireworks

​Setup

​Use Fireworks with Braintrust gateway

​Trace logs with Fireworks

​Evaluate with Fireworks

Setup

Use Fireworks with Braintrust gateway

Trace logs with Fireworks

Evaluate with Fireworks