Ante Review: A 15MB Rust Coding Agent That Runs Offline

Key highlights:

• 1,791 GitHub stars, 58 forks, repo opened December 23, 2025; currently at v0.preview.81 (August 15, 2026)

• ~15MB single binary: TUI, a rebuilt `Grep`, `git`, local PDF/OCR, and a managed llama.cpp all live in one process

• Terminal-Bench 2.1: 82.7% with the open-weight DeepSeek V4 Flash 0731 model (368/445 trials, ~$68 of inference), run under official leaderboard constraints with publishable raw runs

• ~7× less peak memory, ~9× less average CPU, ~5× less disk I/O than Claude Code across 20 parallel Dockerized tasks

• Four modes: interactive TUI, headless (`ante -p`), server (`ante serve`, JSONL protocol), and gateway (Slack/Discord bot)

Ante is Antigma Labs' answer to a specific complaint about the current generation of terminal coding agents: they're heavy. Claude Code and Codex are excellent, but they arrive as Node.js applications with a node_modules tree, shell out to external binaries for search and version control, and assume a live connection to a hosted model.

Ante inverts all three assumptions. It's hand-written Rust compiled to a single self-contained executable. The parts other agents shell out to — ripgrep-style search, git, PDF and OCR handling — are compiled into the binary and run in the same process. The inference engine is a pinned, checksum-verified official [llama.cpp](https://github.com/ggml-org/llama.cpp) build that Ante installs and manages for you, matched to your hardware (Metal on Apple silicon; CUDA, Vulkan, or CPU on Linux).

The name is a double pun the team owns up to in their FAQ: Another Terminal agent, and ante, the stake you put on the table to play. Installation is a one-liner:

Ante makes two quantitative claims. Both are checkable, which is more than most agent projects offer — and both need context.

Ante runs Terminal-Bench 2.1 continuously under official leaderboard constraints — 89 tasks, 5 trials each — pinning each result to the exact downloadable build and linking the raw Harbor run for audit. Latest full run: 82.7% (368/445 trials) on Ante 0.preview.71 with DeepSeek V4 Flash 0731, at roughly $68 of inference.

The number that makes this credible rather than promotional: DeepSeek independently reports the same 82.7% for that model using its own unreleased harness in minimal mode. Ante isn't inflating the model's score — but it isn't beating it either. The parity demonstrates that Ante's harness doesn't lose points relative to the model vendor's own scaffolding, which is a reasonable thing for a harness to prove.

This is the feature nothing else in the category has natively. Point Ante at a GGUF file and the loop is entirely local:

ante --offline-model ~/.ante/models/Qwen3.5-9B-Q4_K_M.gguf \

No API key, no account, no network. Ante handles engine installation, model discovery, and memory management itself. For regulated environments, air-gapped machines, or anyone who doesn't want their codebase leaving the laptop, this is the whole reason to look at Ante.

This is a condensed version. The full article includes architecture diagrams, code examples, comparison tables, and practical use cases.

👉 Read the complete analysis on andrew.ooo

Topics: ante, coding agent, rust, llama.cpp, offline ai