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Try our W&B Inference quickstart to get started without external API keys.
W&B Weave makes it easy to track and evaluate your LLM applications. Follow these steps to track your first call or Open In Colab

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1. Install W&B Weave and create an API Key

Install weave First install the weave library:
  • Python
  • TypeScript
pip install weave
Get your API key Then, create a Weights & Biases (W&B) account and copy your API key.

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2. Log a trace to a new project

To get started with tracking your first project with Weave:
  1. Import the weave library
  2. Call weave.init('project-name') to start tracking
    • When you run your code, Weave prompts you to log in with your API key if you are not yet logged in on your machine.
    • To log to a specific W&B Team name, replace project-name with team-name/project-name. If you don’t specify a W&B team, your default entity is used. To find or update your default entity, refer to User Settings in the W&B Models documentation. NOTE: In automated environments, you can define the environment variable WANDB_API_KEY with your API key to login without prompting.
  3. Add the @weave.op() decorator to the Python functions you want to track
The following example trace calls to OpenAI and requires an OpenAI API key.
  • Python
  • TypeScript
# highlight-next-line
import weave
from openai import OpenAI

client = OpenAI()

# Weave will track the inputs, outputs and code of this function
# highlight-next-line
@weave.op()
def extract_dinos(sentence: str) -> dict:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": """In JSON format extract a list of `dinosaurs`, with their `name`,
their `common_name`, and whether its `diet` is a herbivore or carnivore"""
            },
            {
                "role": "user",
                "content": sentence
            }
            ],
            response_format={ "type": "json_object" }
        )
    return response.choices[0].message.content


# Initialise the weave project
# highlight-next-line
weave.init('jurassic-park')

sentence = """I watched as a Tyrannosaurus rex (T. rex) chased after a Triceratops (Trike), \
both carnivore and herbivore locked in an ancient dance. Meanwhile, a gentle giant \
Brachiosaurus (Brachi) calmly munched on treetops, blissfully unaware of the chaos below."""

result = extract_dinos(sentence)
print(result)
When you call the extract_dinos function Weave will output a link to view your trace.

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3. Automated LLM library logging

Weave automatically tracks calls made to OpenAI, Anthropic and many more LLM libraries and logs their LLM metadata, token usage and cost. If your LLM library isn’t currently one of our integrations you can track calls to other LLMs libraries or frameworks easily by wrapping them with @weave.op().

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4. See traces of your application in your project

Once you’ve configured your Weave in your project, Weave automatically captures the input & output data, and logs any changes made to the code. Weave Trace Outputs 1

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Next steps

Now that you’ve seen Weave in action, try the W&B Inference service for easier experimentation. Get Started with W&B Inference - no need to manage multiple API keys, and free credits are included. Continue learning:
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