Caveman is designed to help AI agents perform tasks with fewer tokens, thereby reducing operational costs. It achieves this through several core mechanisms that can be applied to both pre-existing agents and custom-built applications.
How Caveman Works
- Reads Less: The tool compresses logs, test output, JSON, and diffs before they are processed by the AI model. The original, uncompressed data remains available for recovery if the model requires it. This significantly reduces input token usage.
- Says Less: Caveman trims unnecessary filler from agent responses, ensuring that only essential information, such as code, commands, and error messages, is returned. This leads to a reduction in output tokens.
- Picks the Model: Through adaptive model selection, Caveman routes each task to the most cost-effective model that can successfully complete it. This ensures that more complex, expensive models are only used when truly necessary.
The platform also offers Caveman Cloud, which provides comprehensive analytics to track agent costs, identify waste, and implement improvements automatically. It includes features for monitoring every request, seeing who uses what, setting budgets, and maintaining an audit log for all changes.