wandb/weave

What it solves

Weave simplifies the experimental and often messy process of developing Generative AI applications. It provides a toolset to log, debug, and evaluate language model workflows, ensuring that developers can move from experimentation to production with more rigor and better organization.

How it works

Weave integrates into Python code via a simple decorator (@weave.op). By decorating functions—including API calls to LLM providers like OpenAI or Anthropic and internal data transformations—Weave automatically generates a trace tree of all inputs and outputs. This allows developers to visualize the flow of data and pinpoint exactly where a model's output failed or succeeded.

Who it’s for

Developers building Generative AI applications who need to track their LLM calls, debug complex chains of functions, and perform consistent, "apples-to-apples" evaluations of different model versions or prompts.

Highlights

• Automatic Tracing: Easily track any function call, from LLM APIs to custom validation logic, using a single decorator.
• Rigorous Evaluation: Build consistent evaluation frameworks to compare different iterations of your AI application.
• Workflow Organization: Centralizes all information generated across the LLM lifecycle, from initial experimentation to production deployment.