Weave
ActiveDescription
A toolkit by Weights & Biases for developing AI-powered applications, providing LLM call tracing, evaluation experiment management, and versioning from prototype to production.
Key Features
- LLM Call Tracing: Automatically trace function inputs and outputs with @weave.op decorator, generating complete call chains
- Evaluation Experiment Management: Build rigorous, apples-to-apples evaluations for LLM use cases with systematic experimentation
- Workflow Information Consolidation: Unify management of all information generated across LLM workflows from experimentation to production
- Multi-Model Compatibility: Supports tracing for OpenAI, Anthropic, Google AI Studio, Hugging Face and other major LLM providers
- Versioning and Reproducibility: Provide versioning capabilities for LLM applications to ensure reproducible experiment and evaluation results
- W&B Platform Integration: Deep integration with Weights & Biases platform, leveraging its mature experiment tracking and visualization infrastructure
Use Cases
💡 LLM Application Debugging: Trace model call chains to locate performance bottlenecks and anomalous outputs
💡 Model Evaluation Comparison: Systematically evaluate performance differences of different LLMs on specific tasks
💡 AI Product Production Monitoring: Continuously monitor LLM call quality and costs in production environments
💡 Team Collaborative Development: Share LLM experiment results and evaluation metrics across teams to unify development workflows
Categories
Quick Start
1. Install: pip install weave
2. Initialize: import weave; weave.init("my-project")
3. Trace functions: Mark functions with @weave.op decorator
4. Run code and view trace results on the W&B platform