Weave is a Python toolkit designed for developing and evaluating Generative AI applications, targeting AI engineers and researchers. It aims to bring rigor and best practices to the LLM workflow by providing tools for logging, debugging, and rigorous evaluation of language model inputs, outputs, and traces.

How It Works

Weave operates by instrumenting Python functions with @weave.op() decorators. This allows it to capture and trace function calls, including their inputs, outputs, and execution flow, creating a trace tree. This approach facilitates detailed debugging and comparison of different model runs or configurations without altering the core logic of the application.

Quick Start & Requirements

• Primary install: pip install weave
• Prerequisites: Python 3.x.
• Documentation: https://docs.wandb.ai/guides/weave

Highlighted Details

• Enables tracing of various AI calls, including OpenAI, Anthropic, Hugging Face models, and custom validation functions.
• Supports structured output generation from LLMs, as demonstrated with OpenAI's response_format.
• Focuses on tracing and evaluations, with engine and boards features currently on pause.

Maintenance & Community

• Developed by Weights & Biases.
• Tracing code is primarily located in weave/trace and weave/trace_server.
• Evaluations code is in weave/flow.

Licensing & Compatibility

• License: Not explicitly stated in the provided README.

Limitations & Caveats

The README indicates that the "Weave engine" and "Weave boards" components are on pause, with current development focused on Tracing and Evaluations.