Caveman

why use many token when few do trick

Developer ToolsArtificial IntelligenceGitHubOpen Source

▲ 140 votes9 commentsLaunched Aug 13, 2026

Daily #4Weekly #29

One command wraps Claude Code, Codex, Hermes, and more with a local proxy that compresses logs, tool output, and files before every provider call. In a pinned 54-run benchmark: 33.2% fewer input tokens with 18/18 correctness checks. Caveman can also run any existing agent skill with ~70% fewer tokens by loading text as images. Built on an open-source ecosystem with 97K+ GitHub stars.

AI Analysis

📝 Summary

Caveman is a local proxy tool that wraps AI models like Claude Code, Codex, and Hermes. It compresses logs, tool outputs, and files before provider calls, achieving 33.2% fewer input tokens with perfect correctness in benchmarks. It further reduces tokens by ~70% for agent skills via text-as-image loading. Built on open-source with 97K+ GitHub stars, it solves high token costs, inefficiency, and context bloat in AI coding workflows. The value proposition is simple one-command efficiency that cuts expenses while preserving performance for developers.

📈 Market Timing

The current market timing is favorable for 2025-2026. With surging adoption of AI coding agents and LLMs, token costs and efficiency have become critical pain points amid maturing technology and economic pressures to reduce AI spend. User demand is shifting toward optimization tools that integrate seamlessly with existing workflows. Open-source solutions align with community trends. This is Excellent Timing.

✅ Feasibility

Overall feasibility is High. The local proxy architecture has moderate technical difficulty leveraging existing compression methods. Development and operation costs are low due to open-source foundation and community support (97K+ stars). Minimal supply chain risks; scalability is strong for dev adoption. Main considerations are maintaining compatibility with evolving AI providers and data privacy compliance.

🎯 Target Market

Main target segments: Software developers, AI engineers, and technical teams building with LLMs for coding and agents. Industries: Tech and software development. Geographic: Global with strong presence in US and Europe. The AI developer tools market has large TAM with strong demand. Core pain points are escalating token costs and context inefficiencies. Potential willingness to pay is high for proven cost-saving tools, especially in professional workflows.

⚔️ Competition

Competition level is Medium. Direct competitors: LiteLLM (litellm.ai), LLMLingua (github.com/microsoft/LLMLingua), Guidance (github.com/guidance-ai/guidance), Outlines (github.com/outlines-dev/outlines). Advantages: Specialized coding benchmarks showing 33%+ savings with full correctness, unique text-to-image token reduction for agents, simple one-command proxy. Disadvantages: Newer entrant, relies on local setup which may limit some users compared to cloud alternatives, less established brand.

Upgrade Pro to unlock full AI analysis