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The lightweight toolkit for tracking, evaluating, and iterating on LLM applications in production.

Weave, developed by Weights & Biases, represents the next generation of LLM application development platforms, specifically engineered for the 2026 enterprise landscape where 'Black Box' AI is no longer acceptable. Its technical architecture is built around the concept of 'Traces' and 'Evals,' providing a low-latency layer that captures every LLM interaction without significant performance overhead. Unlike traditional logging, Weave Studio focuses on structured data flow, allowing Lead AI Architects to visualize complex multi-step chains (like RAG or Agentic workflows) as hierarchical waterfall diagrams. The platform's 2026 market positioning is centered on the 'Evaluation-First' development cycle, where developers define success metrics before writing code. It seamlessly integrates with the broader W&B ecosystem, providing a bridge between experimental research and production-grade reliability. By offering programmatic evaluation frameworks and version-controlled prompt management, Weave enables teams to move from anecdotal 'vibe-checks' to rigorous, data-driven performance benchmarks across diverse model providers including OpenAI, Anthropic, and local Llama instances.
Weave, developed by Weights & Biases, represents the next generation of LLM application development platforms, specifically engineered for the 2026 enterprise landscape where 'Black Box' AI is no longer acceptable.
Explore all tools that specialize in manage prompt versions. This domain focus ensures Weave (by Weights & Biases) delivers optimized results for this specific requirement.
Explore all tools that specialize in hallucination detection. This domain focus ensures Weave (by Weights & Biases) delivers optimized results for this specific requirement.
Define scoring logic in Python to automatically grade LLM outputs against ground truth data.
Nested UI view of multi-agent interactions, showing timing and cost for every sub-call.
A web interface to tweak system prompts and see immediate effects across multiple test cases.
Every dataset used for evaluation is hashed and stored as a W&B Artifact.
Native handling of streamed LLM responses to capture final output without breaking UX.
Integrated hooks for scanning traces for sensitive information or harmful content.
Lightweight client-side library that minimizes network overhead during data capture.
Install the Weave library using 'pip install weave'.
Authenticate with Weights & Biases using 'wandb login'.
Initialize a project in your script with 'weave.init('project_name')'.
Use the @weave.op() decorator on functions to automatically capture inputs and outputs.
Run your LLM application to populate the Weave Studio dashboard with initial traces.
Define a 'Model' class in Weave to version-control your prompts and parameters.
Create an 'Evaluation' object by defining a dataset and a list of scoring functions.
Execute programmatic evals to generate a leaderboard of model performance.
Review waterfall traces in the Weave UI to identify bottlenecks or high-latency steps.
Deploy the versioned model to production and monitor live traces for drift.
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"Users praise the seamless transition from experimentation to production and the UI's ability to handle complex nested traces."
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