Caveman vs Osaurus: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Caveman and Osaurus — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Caveman

Julius Brussee

Freemium

Efficiency stack that caches, compresses, and routes AI traffic to cut LLM output tokens by up to 65% with verified savings.

Caveman Skill: MIT-licensed Claude Code skill that teaches 30+ agents (Claude Code, Codex, Cursor, and more) to answer in a compressed dialect, cutting output tokens ~65% while keeping code and errors byte-exact.
Local Proxy Wrap: One command (`caveman claude`) launches your agent with recoverable local context compression — no account required, BYOK, engine stores original bytes before lossy replacement.
Recoverable Context Compression: Engine recognizes logs, JSON, code, diffs, and tables, then sends smaller eligible context to the model and can restore the originals on demand.
Agent SDK: `@caveman-ai/agent` TypeScript SDK adds catalog-price guards, per-request token bills, and eval-gated context plans to production agents.
Cave Score & Ledger: Inferred local savings score and a verified 'causal-cache' ledger on paid tiers so you can prove cut tokens and cut dollars.
Managed Cloud Gateway: Point traffic at one URL and caching / compression / routing run eval-gated on autopilot, with a synced savings dashboard.
Browser Extension: Ships for ChatGPT, Claude, and Gemini so end-user chats benefit from the same output compression without any code changes.
Enterprise & OEM: Same stack self-hosted in your cloud or datacenter with signed savings receipts, zero data retention, and OEM embed options.

LLM Bill Reduction: Cap OpenAI, Anthropic, or Google spend without changing model choice by cutting output tokens per response across your agent fleet.
Coding Agent Efficiency: Install the skill to make Claude Code, Codex, Cursor, and other CLI agents produce terse, byte-exact answers so long tasks fit in context.
Provider Wrap for Production Agents: Use the SDK to add per-call token bills, catalog-price guards, and eval-gated context plans to LangChain / custom agents.
Central Cost Gateway: Point every agent in the org at Caveman Cloud so caching and routing are enforced from one URL with a shared dashboard.
On-Prem or OEM Embed: Ship the Enterprise stack inside a regulated network or embed it in your own AI product with signed savings receipts and zero data retention.
Chat-App Compression: Install the browser extension for ChatGPT, Claude, or Gemini to keep casual chats short, cheaper, and inside the context window.

Osaurus, Inc.

Free

Native macOS harness for AI agents that runs any local model on Apple Silicon with persistent memory and offline execution.

Native Apple Silicon App: Built in Swift and optimized for M-series chips so inference runs locally with millisecond round trips.
One-Click Model Runtimes: Connect Ollama, MLX, or LM Studio in a single click and switch between them from the UI.
Fully Offline Mode: Turn Wi-Fi off and Osaurus keeps working — no server calls, no telemetry, no data leaves the Mac.
Cloud Fallback: Add ChatGPT, Claude, or Gemini for tasks that demand a frontier model without losing the shared memory context.
Persistent Shared Memory: One memory layer spans local and cloud models so agents remember prior sessions across providers.
Autonomous Agents: Build agents driven by voice control, folder watchers, browser plugins, or parallel jobs that keep working in the background.
File and Tool Execution: Drop in a folder and Osaurus can read, write, and run tools against local files like a resident assistant.
MIT-Licensed and Free: Open source under MIT with no subscription, usage caps, or billing — fork it and ship it.

Privacy-First Work: Run an assistant over sensitive code, contracts, or medical notes without any data leaving your Mac.
Offline Field Use: Keep an AI assistant available on flights, in remote locations, or on air-gapped machines.
Local Development Copilot: Point Osaurus at a repo and let a local model refactor, review, or generate code without cloud costs.
Personal Agent Automation: Set up folder-watcher or voice-controlled agents to file downloads, transcribe recordings, or summarize new emails.
Multi-Model Comparison: Route the same prompt through local and cloud models to compare outputs while reusing one memory context.
Open-Source Base for Products: Fork the MIT-licensed harness to build a branded desktop AI app on top of Apple Silicon inference.