// Agent profile
Caveman is a token-reduction toolkit that works across 30+ AI coding agents including Claude Code, Codex, Gemini CLI, Cursor, and Windsurf. It operates at three levels: a skill that makes agents respond in terse abbreviated style (reducing output tokens), a local proxy that compresses logs, test output, JSON, and diffs before the model reads them (reducing input tokens with byte-exact recovery), and middleware for custom applications. Independent benchmarks show 33% input token reduction and up to 3x cost reduction in best cases, with no measurable quality loss. Apache 2.0 licensed, no accounts or API keys required.
Caveman tackles one of the most practical pain points in AI-assisted development: token cost. Rather than improving what an AI coding agent can do, Caveman reduces how many tokens it takes to do it. The system works at both ends of the conversation — compressing the context that goes into the model (logs, test output, diffs, JSON) and making the model’s responses more concise — while preserving the actual information content. With 109K+ GitHub stars, it has become one of the most widely adopted developer tools in the AI coding ecosystem.
Caveman operates at three levels. The skill installs into your agent’s system prompt and teaches it to respond in abbreviated “caveman speak,” cutting output tokens without losing meaning. The proxy sits between your agent and its tools, compressing verbose tool results (stack traces, test output, large JSON payloads) before they reach the model, with byte-exact recovery of the originals when needed. The middleware mode lets developers integrate the same compression into custom agent applications. Independent testing by JetBrains showed 8.5% output token reduction with no quality loss, while proxy benchmarks demonstrate 33.2% input token reduction across standard test cases.
Caveman is most valuable for developers running AI coding agents at scale — teams where token costs add up across dozens of developers and hundreds of daily sessions. It is particularly effective for workflows that involve large context windows: debugging with verbose stack traces, reviewing lengthy diffs, or working with large configuration files. The A/B testing feature lets teams validate that compression does not degrade the quality of agent responses before rolling it out broadly.
Caveman is a cost-optimization layer, not a coding agent itself. It requires an existing AI coding agent to wrap around. The abbreviated output style may take some getting used to when reading agent responses directly. The proxy mode adds a local process to your development setup, though it runs entirely on your machine with no external dependencies.
Caveman is built for developers and engineering teams who use AI coding agents daily and want to reduce token costs without switching tools or sacrificing output quality. It appeals to cost-conscious teams running Claude Code, Cursor, or similar agents at scale, and to individual developers who want to stretch their API budgets further.
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