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Quick start One install command. Pick a model. Start coding. No accounts, no configuration files, no setup guides. Step 01 Go offline in one command Type /offline-mode — Ante installs a local inference engine. No API keys, no internet. Step 02 Configure the model Choose your model, set context window, enable thinking mode. Tuned to your hardware. Step 03 Agent does the work Give it a task. The agent reads your codebase and produces working output — fully offline. MEET ANTE AI-native, cloud-native, local-first agent runtime Built from the ground up in native Rust — a single self-contained binary with no external dependencies. Designed for cellular-native agents: lightweight enough to run by the thousands and reliable enough that the system self-heals when any one fails. Lightweight agent core A single lightweight binary with zero runtime dependencies. Built for minimal overhead and maximum throughput — the ideal runtime for orchestrating agents at cellular scale. Native local models Run models entirely on your machine with built-in llama.cpp integration. No API keys, no internet, no data leaving your device. Zero vendor lock-in Bring your own API key, subscription, or local model. Switch between providers freely — Anthropic, OpenAI, Gemini, Grok, Open Router, and more. No account required. Peak memory7×less than Claude Code Avg CPU9×less than Claude Code Disk I/O5×less total I/O generated Binary~15 MBSingle Rust binary, zero deps Engineering principles Built on first principles Ante is designed for cellular-native agents — like cells in a living organism, tiny and expendable, massively replicated. Everything we build serves this thesis. Lightweight Hundreds of agent replicas can't each cost gigabytes. Every byte per instance matters at scale — so we maintain a tight, tiny core. Reliable The return on reliability is non-linear. There's a phase transition — and you need to be on the right side of it. Closed-loop Declarative intent, automatic reconciliation. Individual agents are expendable; the organism persists. Minimal cognitive load Fewer concepts to learn, fewer knobs to turn. If a feature needs a paragraph of explanation, it's probably too complex. WHAT'S NEW Aug 4, 2026 How Much Does the Agent Harness Matter? Jul 25, 2026 Claude Code Cut Their System Prompt by 80%. Does That Work for Small Models Too? Apr 24, 2026 From Arcade to Living Room: Offline Coding Models Hit Their Console Moment Mar 31, 2026 Introduce Ante: Self-Contained Agent That Self-Organize View all posts Star Github Visit our github Blog See our blog Documentation Read our docs Discord Join our discord Hugging Face Follow us on Hugging Face Twitter Follow us on Twitter