{"categories":[{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/2"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"},{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"},{"label":"Widget Sets","url":"https://skillfed.io/packages/category/software-development-widget-sets"},{"label":"Spreadsheet","url":"https://skillfed.io/packages/category/office-business-financial-spreadsheet"}],"enrichment":{"capability":"Weave is a toolkit for tracing, logging, and evaluating generative AI applications, letting you instrument functions to capture inputs, outputs, and execution traces for debugging and analysis.","skillfed_tags":["llm-observability","generative-ai","tracing"],"use_cases":["Instrument LLM API calls (OpenAI, Anthropic, etc.) and downstream processing to debug multi-step generative workflows.","Build reproducible evaluations comparing model outputs across different prompts, parameters, or model versions.","Log and organize experimental runs during development to track which configurations and data transformations produced which results.","Monitor production LLM applications by capturing request traces and flagging anomalies or errors in real time.","Validate data transformations and intermediate outputs in complex AI pipelines to catch silent failures early."],"what_it_does":"Weave is a tracing and evaluation framework for generative AI development built by Weights & Biases. It lets you decorate Python functions with @weave.op to automatically capture their inputs, outputs, and execution flow, creating a queryable trace tree. This is useful for debugging LLM pipelines, understanding how data flows through your application, and building systematic evaluations of model behavior. The toolkit integrates with OpenTelemetry for observability and requires a Weights & Biases account to store and visualize traces.\n\nThe package depends on 15 runtime libraries including pydantic for schema validation, opentelemetry for instrumentation, sentry-sdk for error tracking, and tenacity for retry logic. It is actively maintained, supports current Python versions (3.10+), and carries no known security vulnerabilities. The Apache 2.0 license permits commercial use with standard attribution requirements.","worth_installing":"Yes, if you are actively developing or operating generative AI applications and want structured tracing without manual instrumentation. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. The requirement for a Weights & Biases account and Python 3.10+ are minor barriers; the 15 runtime dependencies are a consideration only if you have strict dependency budgets."},"id":"weave","links":{"html":"https://skillfed.io/packages/weave","md":"https://skillfed.io/packages/weave.md","pypi":"https://pypi.org/project/weave/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"permissive","name":"weave","python_support":"supports_current","summary":"A toolkit for building composable interactive data driven applications."},"popularity":{"monthly_downloads":1007512,"position":4523,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.53.6"}