Introduction

Weave is a lightweight toolkit for tracking and evaluating LLM applications, built by Weights & Biases.

Our goal is to bring rigor, best-practices, and composability to the inherently experimental process of developing AI applications, without introducing cognitive overhead.

Get started by decorating Python functions with @weave.op().

Seriously, try the 🍪 quickstart 🍪 first.

You can use Weave to:

• Log and debug language model inputs, outputs, and traces
• Build rigorous, apples-to-apples evaluations for language model use cases
• Organize all the information generated across the LLM workflow, from experimentation to evaluations to production

Key concepts

Weave's core types layer contains everything you need for organizing Generative AI projects, with built-in lineage, tracking, and reproducibility.

• Datasets: Version, store, and share rich tabular data.
• Models: Version, store, and share parameterized functions.
• Evaluations: Test suites for AI models.
• [soon] Agents: ...

Weave's tracking layer brings immutable tracing and versioning to your programs and experiments.

• Objects: Weave's extensible serialization lets you easily version, track, and share Python objects.
• Ops: Versioned, reproducible functions, with automatic tracing.
• Tracing: Automatic organization of function calls and data lineage.

Weave's ecosystem is batteries included for other libraries, systems, and best practices.

• OpenAI: automatic tracking for openai api calls
• [soon] Langchain auto-logging
• [soon] llama-index auto-logging

Weave's tools layer contains utilities for making use of Weave objects.

• Serve: FastAPI server for Weave Ops and Models
• Deploy: Deploy Weave Ops and Models to various targets

What's next?

Try the Quickstart to see Weave in action.