Traces
Log everything for production monitoring and debugging
Weave automatically logs all inputs, outputs, code, and metadata in your application at a granular level, organizing the data so you can easily visualize traces of your LLM calls. Using these traces, you can debug issues during development and monitor your agents and AI applications when they are deployed in production.
Trace trees
Weave organizes logs from various levels of the call stack into a trace tree, which you can use to quickly detect, analyze, and solve issues. Metrics such as latency and cost are automatically aggregated at every level of the tree, making it easy to pinpoint root causes of problems.
Production monitoring
Monitor live traces from your application in production to identify edge cases missed during evaluation and testing. Continuously improve your application’s quality and performance. Use online evals (preview) to score live, incoming traces for monitoring without impacting the production environment.
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Multimodality
Weave logs text, datasets, code, images, and audio with support for video and other modalities coming soon.
Built for long text
Weave is built from the ground up for LLM applications. It makes it easy to visualize and examine large strings like documents and code in traces. When you click on a cell, a popout launches where you can change the display format—you can choose text, markdown, or code.
Integrated chat view
When analyzing LLM responses, you can use the chat view to visualize user requests, system prompts, and LLM outputs for a given conversation thread.
Capture user feedback
To thoroughly test AI applications, you need real-world end-user feedback. When you incorporate a feedback mechanism into your application, Weave logs this user feedback as well. This helps you combine calculated scores with user input for a more holistic evaluation of your application’s performance.
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Traces
Explore and
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Rigorous evaluations of GenAI applications