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 according to predefined criteria.
Purpose and Function
The primary purpose of Input Tagging is to create a standardized way to describe and classify incoming data before it enters the main processing pipeline. By applying consistent tags at the point of ingestion, the system establishes a common vocabulary for understanding input properties. This allows downstream components—whether AI agents, data processors, or decision-making modules—to quickly identify relevant inputs and apply appropriate handling logic without needing to re-analyze the raw data.
Integration with OpenClaw
Within the OpenClaw framework, Input Tagging operates as part of the data preparation and routing layer. Tags can be applied manually, automatically through detection algorithms, or through a combination of both approaches. The tagged inputs then flow through the system where various agents and modules can use these labels to determine their actions, making the overall architecture more modular and efficient.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”