Caveman
Token-efficient development stack for AI agents with 75% compression primitive and spec-driven workflows
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How the 7.2 was reached
- Quality of output7/10
108k stars, JetBrains tested on 86 tasks with no measurable quality loss, but real-world savings measured at 8.5% versus the 65% claim. Source: github.com
- Ease of use9/10
One command install with no account or API key. Source: getcaveman.dev
- Pricing value9/10
MIT licensed and free with no premium tiers or accounts required. Source: coddykit.com
- API and integration quality5/10
Ships as a Skill, extension, proxy and middleware for coding agents; no formal API documented. Source: getcaveman.dev
- Solves the problem it claims to solve6/10
Reduces tokens and keeps code intact, but independent testing found 8.5% real savings, well under the advertised figure. Source: pub.towardsai.net
7.2/10 is the average of the five criterion scores above. The evidence comes from 5 different sources.Scores come from public evidence (the tool's own pages and documentation, and independent reviews); we do not test every tool hands-on. Scored 29 September 2026, on version 1 of our five-criteria scoring method.
Pricing
- MIT licensed
- Full ecosystem access
- Compression primitive
- Spec-driven workflows
For the full list of what each plan includes, and current prices, see coddykit.com.
Key Features
- 75% token compression without losing task fidelity
- Spec-driven development workflow
- Cross-agent persistent memory system
- Coding agent CLI integration
- Modular ecosystem design
Pros & Cons
Pros
- Significant token cost reduction
- Open source and MIT licensed
- Modular architecture allows selective use
- Strong GitHub community (37k+ stars)
- Spec-over-vibes approach
Cons
- Still in preview/development phase
- Limited documentation for newcomers
- Requires understanding of token optimization concepts
- Caveman Code CLI not yet shipped
Caveman offers a compelling approach to token-efficient AI development with impressive compression claims and a thoughtful modular design. While still early-stage, the strong community backing and practical focus on cost optimization make it worth watching for developers building AI agents.
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