Caveman
why use many token when few do trick
About Caveman
Caveman is an innovative open-source tool designed for developers and AI practitioners seeking to optimize their interaction with large language models and AI providers. By wrapping calls to models like Claude Code, Codex, Hermes, and others within a local proxy, Caveman significantly reduces token usage through intelligent compression of logs, tool outputs, and files before each provider call. Its unique approach enables users to achieve around 33% fewer input tokens in benchmark tests, all while maintaining high correctness checks. Additionally, Caveman can run existing agent skills with approximately 70% fewer tokens by loading text as images, making it a versatile and efficient solution for token-conscious AI workflows. Built on a robust open-source ecosystem with over 97,000 GitHub stars, Caveman appeals to developers looking to streamline and cost-optimize their AI integrations.
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Pros
- ✓Reduces token consumption by up to 33% in benchmarks
- ✓Supports multiple AI providers like Claude, Codex, and Hermes
- ✓Open-source with a large community backing
- ✓Can run existing agent skills with 70% fewer tokens
- ✓Flexible local proxy architecture for customization
Cons
- ✗May require technical expertise to implement and customize
- ✗Limited information on formal support or enterprise features
- ✗Performance depends on local hardware and setup
Use Cases
Pricing
As an open-source project, Caveman is free to use. Additional costs may stem from the need for local infrastructure or hardware, but there are no subscription fees or paid plans associated with the tool itself.
Quick Info
Topics
Makers
Julius Brussee
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