Input Tagging
Input Tagging is a categorization method within the OpenClaw architecture framework that assigns structured labels to input data to facilitate organization and downstream processing. Tags function as metadata descriptors that capture relevant characteristics of inputs, including their source, data type, content classification, or intended use case. This systematic approach enables AI agents and processing pipelines to efficiently filter, route, and process information based on predefined organizational schemes.
Function and Implementation
Within OpenClaw, tags serve as a lightweight mechanism for marking inputs without requiring modification to the underlying data. Each tag typically consists of a key-value pair or categorical label that can be applied at ingestion time or during preprocessing stages. This allows downstream components to quickly identify relevant inputs for specific tasks without performing expensive content analysis operations.
Use Cases in Agent Systems
Input Tagging supports multiple operational patterns in agentic AI systems. Agents can use tags to prioritize certain inputs, apply context-specific processing rules, or route information to specialized handlers. Tags also enable filtering mechanisms that help agents ignore irrelevant inputs and focus computational resources on data matching specified criteria, improving both efficiency and response quality in multi-step workflows.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”