Ante — A Self-Contained Terminal Agent for Coding
A ~15MB Rust binary that runs coding agents in your terminal with far lower footprint and native offline inference.
- Source repo
- AntigmaLabs/ante
- Stars
- ★ 2k
- Last updated
- 1d ago
- License
- Apache-2.0
- Primary language
- Rust
- FA score
- Insufficient evidence
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code · OpenAI API · Claude API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A developer wanting to quickly review a diff for security issues:
git diff | ante -p "review this for security issues". - Not a fit if
- Teams needing native Windows support; macOS and Linux only
- Users who require a fully open-source, auditable core; only prebuilt binaries are provided
- Production teams that cannot tolerate breaking changes in a beta preview
- Source review
- Insufficient evidence 8 safety controls not found
What does this agent do, and when should you use it?
Ante is a terminal-based coding agent from Antigma Labs, distributed as a single Rust executable (~15MB compressed, ~34.1 MiB unpacked) with zero runtime dependencies. It offers an interactive TUI, headless mode (-p), a server mode (ante serve) with JSONL protocol, and a gateway mode to run as a Slack/Discord bot. The harness embeds a managed llama.cpp engine to run GGUF models fully offline, while also providing 17 maintained provider presets and a flexible config layer for any OpenAI-compatible endpoint. On Terminal-Bench 2.1 it scored 82.7% with a DeepSeek V4 Flash model, ~$68 inference cost, and uses ~7x less peak memory, ~9x less average CPU, and ~5x less disk I/O than Claude Code across 20 parallel Docker tasks. The repo open-sources documentation, protocol crates, and the Harbor eval adapter; the core harness is currently distributed as a prebuilt binary under Apache-2.0.
Ante operates as a coding agent: it reads context from files and git, runs commands, and iterates on tasks in a terminal loop. Headless mode (ante -p) executes one-shot prompts, e.g., fixing bugs, reviewing diffs, or writing tests. Server mode (ante serve) exposes a JSONL protocol for integrations. Gateway mode connects to Slack/Discord for bot interactions. It can use a profile system (--profile) to swap the entire agent configuration (system prompt, tools, skills). Built-in tools include a custom Grep, git, and Bash. For local inference, Ante uses its internal llama.cpp engine to load GGUF models. It can connect to remote providers via presets (OpenAI, Anthropic, etc.) or custom catalog.json definitions.
- A developer wanting to quickly review a diff for security issues:
git diff | ante -p "review this for security issues". - A team operating in an air-gapped environment that needs an agent running fully offline with a local GGUF model.
- An infrastructure team that needs to run hundreds of parallel agent tasks in CI with minimal resource usage.
- A developer building an editor plugin that calls
ante serveover JSONL to get agent assistance. - A community manager wanting a Slack bot that can answer coding questions or automate repo tasks via
ante gateway. - A power user who wants a minimal, deterministic agent by using a curated profile like
pithat only has four tools.
How do you install or deploy this agent?
On macOS or Linux, run curl -fsSL https://ante.run/install.sh | bash. To install a specific release channel or directory, use bash -s -- nightly or set ANTE_INSTALL_DIR=/usr/local/bin. No other dependencies are required. On Windows, use WSL.
How do you use this agent?
Launch the interactive TUI with ante, or run one-shot tasks with ante -p "describe the task". To use cloud models, you must provide your own API key, e.g., ante --provider openai --model gpt-5.5 -p "..." or log in via OAuth. For offline use, point to a GGUF model: ante --offline-model ~/.ante/models/Qwen3.5-9B-Q4_K_M.gguf -p "...". Update with ante update, and switch channels with ante update --channel nightly.
What are this agent's strengths and limitations?
- Very low resource footprint: in a 20-task Docker benchmark, it uses ~7x less peak memory, ~9x less average CPU, and ~5x less disk I/O than Claude Code.
- Supports native offline inference via a built-in llama.cpp engine; no API keys or internet required for local models.
- Truly model-agnostic: supports many providers out of the box and any OpenAI-compatible endpoint via custom catalog.
- Self-contained single binary with no runtime dependencies; heavy components are embedded and run in-process.
- Public, reproducible benchmark results with exact build versions and raw run logs.
- Beta preview: breaking changes and incomplete functionality; only macOS and Linux are natively supported (Windows requires WSL).
- The core harness is not open source; only prebuilt binaries are provided, limiting community auditing and contributions.
- Offline mode requires users to source and manage GGUF model files themselves.
- Cloud model use requires bringing your own API keys or subscriptions; Ante does not include model access.
- Some features like skills, MCP servers, and session saving are disabled in stripped-down profiles like
bare, requiring trade-offs. - The documentation and protocol are open, but some parts of the architecture (client-daemon) might be complex for newcomers.
How does this agent compare with similar options?
Ante explicitly positions itself against Claude Code and Codex, claiming to work like them without their dependencies or model constraints. Benchmark data shows much lower resource usage than Claude Code in a parallel Docker scenario.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Ante — A Self-Contained Terminal Agent for Coding This agent | Insufficient evidence | CLIFree + model costs | ★ 2k | 1d ago | Rust | Codex · Claude Code · OpenAI API · Claude API |
| Coven | 79 · Good | CLIFree | ★ 73 | today | Rust | Codex · Claude Code |
| chartr | 67 · Some gaps | Desktop appFree + model costs | ★ 349 | 1d ago | Rust | — |
| DeepTide - AI Coding Agent by DeepSeek | 44 · Major gaps | CLIFree + model costs | ★ 1.1k | 2mo ago | Rust | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Insufficient evidence: repository contains only documentation, protocol crates, evaluation workflows, and curated configs; core binary is closed-source. No evidence of permission model, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Hence all trust criteria scored 0.
Insufficient evidence: no core code or tests. Self-consistency, dependency availability, and failure messages cannot be assessed. Hence all reliability criteria scored 0.
Insufficient evidence: README describes various modes and scenarios, but core implementation is absent, so capability boundaries, trigger precision, and environment fit cannot be verified. Hence all adaptability criteria scored 0.
Insufficient evidence: README, LICENSE, CHANGELOG, and CI workflows exist, but core code is missing, so information architecture, install notes, naming stability, examples, known limitations, versioning, or maintenance responsibility cannot be evaluated. Hence all convention criteria scored 0.
Insufficient evidence: no executable code or tests to assess output usability, marginal value, or cost-benefit. Hence all effectiveness criteria scored 0.
Insufficient evidence: claims in README cannot be traced because no core code or tests. Hence all verifiability criteria scored 0.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- Core binary is closed-source, so permissions, security, and behavior cannot be audited.
- Repository contains only documentation and peripheral components, insufficient to evaluate the Agent product.
- Publisher identity unverified and core code absent, so benchmark claims cannot be verified.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
v0.2.14 - 2026-10-21 Added - Native MiniMax provider: set MINIMAX_API_KEY to use MiniMax M3.1 Flash directly (1M context, vision, low–max reasoning, max by default); MINIMAX_BASE_URL overrides the endpoint - MiniMax M3.1 Flash in the OpenRouter catalog Changed - Offline mode defaults to llama.cpp's port 9931 instead of 8080: managed launches try 9931–9940, and user-started servers are discovered on 9931 and 8080 only (previously 8080–8179). A router already running keeps its recorded port, and a server on any other port can still be attached by endpoint …View on GitHub ↗ -
Release notes excerpt
v0.2.13 - 2026-10-18 Added - Jump-to-bottom pill in fullscreen: while the conversation is scrolled up, its last row shows N new messages (ctrl+l) ↓, or Jump to bottom (ctrl+l) ↓ when nothing new has arrived; Ctrl+L or a click on that row jumps to the latest output, and sending a prompt or a !command follows new output again …View on GitHub ↗ -
Release notes excerpt
v0.2.12 - 2026-10-15 Added - Remote MCP servers over Streamable HTTP: an mcpServers entry takes url in place of command, with an optional transport of stdio or streamable-http (http is a synonym). An entry that mixes command and url, or names the legacy sse transport, fails that server at warm-up with a message saying what to change. /mcp shows the URL, and rage redacts a URL's query string …View on GitHub ↗
FAQ
Is Ante free to use? Do I need an account?
Can I run it on Windows?
How are tool permissions handled?
Can I use my own custom model or provider?
~/.ante/catalog.json to add custom providers, including any OpenAI-compatible API, custom headers, and auth methods.