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Show HN: Ante, a coding agent in a single binary that runs offline

Ante — substrate for self-organizing intelligence

Alpha preview: expect breaking changes and incomplete functionality. macOS and Linux only; on Windows we suggest WSL.

Two things many people ask about:

Where is the source? The core harness currently ships as a prebuilt binary; this repo holds the docs, protocol, SDK, and eval pipeline (details). We are working out a way to ship the source code along with the binary, to address security and privacy concerns first, while taking the time to figure out how open source should work in the agentic era. If you have concerns today, run Ante in a sandbox: it is a single binary with minimal runtime dependencies, built to be easy to deploy in a container or on a remote machine.

Is there telemetry? Yes, and it is opt-out: set ANTE_TELEMETRY=off to disable export entirely. The RUST_LOG filter also applies to exported logs, a convenience carried over from the Rust ecosystem. A better UX is in the works. Details →

A ghost in your shell. Ante is a self-contained coding agent that lives in your terminal and self-organizes. One ~15MB Rust binary from Antigma Labs, zero runtime dependencies, built to get the most out of any model.

It works like Claude Code or Codex, with none of their dependencies or model constraints. It can also be the optimized core for building your own harness and high-performing assistants.

curl -fsSL https://ante.run/install.sh | bash
ante

Every agent claims to be good. Here are numbers you can check:

🥇 Continuously evaled and evolved, in public

Ante runs Terminal-Bench 2.1 continuously under official leaderboard constraints: 89 tasks, 5 trials each. Each result pins the exact build you can download and links the raw Harbor run for independent audit. Latest full run: 82.7% with open-weight DeepSeek V4 Flash 0731 (368/445 trials, Ante 0.preview.71, about $68 of inference). DeepSeek reports the same 82.7 for this model, measured with its unreleased DeepSeek Harness in minimal mode.

Live results → · Methodology →

🪶 A fraction of the footprint

Ante is hand-written Rust with the heavy parts (Grep, git) embedded in one binary, one process, and local inference handled by a pinned, managed llama.cpp. Across the same 20 parallel tasks in Docker, Ante uses ~7× less peak memory, ~9× less average CPU, and ~5× less disk I/O than Claude Code.

Resource Usage Comparison

Raw numbers → · Benchmark details →

Ante's inference engine is a pinned, managed version of llama.cpp. Point it at a GGUF file and the whole loop runs on your machine: no API key, no account, no internet.

ante --offline-model ~/.ante/models/Qwen3.5-9B-Q4_K_M.gguf \
  -p "add error handling to src/main.rs"

Offline mode → · nanochat-rs, a toy engine for study →

The three are one design decision. An agent you can verify, afford, and run anywhere is light enough to run by the thousands: the substrate for self-organizing intelligence.

See all cookbook guides

Ante is a single, self-contained binary with no external dependencies: download and run.

curl -fsSL https://ante.run/install.sh | bash

# Install a specific release channel
curl -fsSL https://ante.run/install.sh | bash -s -- nightly

# Install into a directory already on PATH
curl -fsSL https://ante.run/install.sh | ANTE_INSTALL_DIR=/usr/local/bin bash
Mode Command Use it for
Interactive TUI ante day-to-day work in the terminal
Headless ante -p "..." one-shot tasks, scripts, CI
Server ante serve editor plugins and integrations, over a JSONL protocol
Gateway ante gateway running Ante as a Slack or Discord bot 
# Fix a bug
ante -p "find and fix the failing test in src/auth"

# Review a diff
git diff | ante -p "review this for security issues"

# Use a different provider
ante --provider openai --model gpt-5.5 -p "refactor the database module"

# Resume a saved session
ante --resume ses_01ARZ3NDEKTSV4RRFFQ69G5FAV -p "now add tests"

# Run fully offline with a local GGUF model
ante --offline-model ~/.ante/models/Qwen3.5-9B-Q4_K_M.gguf \
  -p "add error handling to src/main.rs"
ante update

# One-off update from a different channel
ante update --channel nightly

# Roll back or pin to an exact release
ante update --version v0.preview.71

Beyond the headline numbers

  • Zero vendor lock-in: bring your own API key, subscription, or local model. Switch between 12+ providers freely. No account required, not even with us.
  • Multi-agent orchestration: spawn sub-agents and coordinate complex tasks across independent, decentralized, and centralized architectures. See the patterns →
  • Channel integrations: run Ante as a Slack or Discord bot with ante gateway.
  • Extensible: custom skills, sub-agents, MCP, and persistent memory across sessions.

Ante works with 12+ providers out of the box:

Provider Example Models
Anthropic Claude Sonnet 4.5, Opus 4.6
OpenAI GPT-5 family
Google Gemini Gemini 3 family
Grok (xAI) Grok 4
Open Router Multiple providers
Local (GGUF) Any GGUF model via built-in llama.cpp
...and more Vertex AI, Zai, Antix, OpenAI-compatible

Configure providers via environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.) or OAuth. Add custom providers in ~/.ante/catalog.json.

We open sourced what really matters in the age of agentic coding, all under Apache 2.0:

  1. Detailed documentation, the descriptive truth. docs-site/ is the source for docs.antigma.ai: a precise description of what the harness does and how to drive it.
  2. The protocol, the algorithm of the core. crates/protocol-shape defines the schema and wire messages spoken by ante serve; crates/agent-sdk is the Rust SDK and client for building against agent runtimes.
  3. The eval pipeline, constraint and continuous improvement. ante-harbor/ is the Harbor agent adapter behind our Terminal-Bench results: use it to reproduce any run at antigma.ai/eval. CHANGELOG.md records the improvement, release by release.

The core harness itself is developed in a private repository during the alpha and ships as a prebuilt binary via releases. Core libraries from it are included here progressively as they stabilize; crates/exec, standalone process execution, is the first.

The protocol surface maps to Ante's client-daemon architecture:

┌─────────────────────────────────────────────────────────────┐
│                         Clients                             │
│                                                             │
│   ┌───────────┐    ┌───────────┐    ┌────────────────────┐  │
│   │    TUI    │    │ Headless  │    │    ante serve      │  │
│   │  (ante)   │    │ (ante -p) │    │  (stdio / ws)      │  │
│   └─────┬─────┘    └─────┬─────┘    └─────────┬──────────┘  │
└─────────┼────────────────┼─────────────────────┼────────────┘
          │                │                     │
          ▼                ▼                     ▼
┌─────────────────────────────────────────────────────────────┐
│                         Daemon                              │
│                                                             │
│   Session ──▶ Turn ──▶ Step                                │
│                                                             │
│   ┌──────────┐  ┌──────────────┐  ┌───────────────────┐     │
│   │  Tools   │  │  Permission  │  │  Skills / Agents  │     │
│   └──────────┘  └──────────────┘  └───────────────────┘     │
└────────────────────────┬────────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────────┐
│                     LLM Providers                           │
│                                                             │
│   Anthropic · OpenAI · Gemini · Grok · Open Router · Local  │
└─────────────────────────────────────────────────────────────┘

We care about the harness, not the model or the prompts.

Documentation is the new source code.

Ante is designed for cellular-native agents: like cells in an organism, tiny, expendable, massively replicated. That thesis is why the three headline claims exist. A cell-scale agent must be verified (reliability compounds at scale), tiny (every byte is multiplied by thousands), and self-contained (no runtime to install, no service to phone home to). Read more in our philosophy and agent organization patterns.

Why another terminal agent?

The name is the answer: Another Terminal agent, and ante, the stake you put on the table to play. Ante is fast, lightweight, and the only terminal agent with native local inference built in. We believe a self-contained agent core that self-organizes is the foundation of the coming agent economy.

How is Ante different from other agents?

Ante has most of the features you expect from agents like Claude Code or Codex: multi-agents, skills, MCP, persistent memory. The difference is the build philosophy.

  • Built from scratch in Rust. Core components like Grep (fully rebuilt and customized) and git are embedded in the same ~15MB binary and run in the same process at runtime, so nothing is shelled out and no resources leak. Most similar projects ship on Node.js or CPython and carry an order-of-magnitude larger footprint.
  • Local inference is built in: the engine is a pinned, managed version of llama.cpp, so a local GGUF model is all Ante needs to run without any provider. To study how such an engine works, see nanochat-rs, our toy version.
  • No vendor lock-in, not even to ourselves: no account needed, reuse your existing API credentials. An opt-in, fully integrated server-side experience lives at antix.antigma.ai.
  • Every claim is backed by public, reproducible benchmarks of the exact builds we ship: antigma.ai/eval.

Beyond the footprint it comes down to agent architecture, and ultimately to who is building it and with what philosophy. Anyone can fork a binary; taste and engineering rigor don't copy. Those differences leak into every detail of the product.

Why care about runtime optimization like memory and I/O if model inference is usually the biggest bottleneck?

For one-on-one agent interactions, runtime overhead like memory usage and I/O is often less important than model inference.

But our vision is much bigger: millions of agents self-organizing and communicating at massive scale. At that point, even small inefficiencies get multiplied millions or billions of times, so runtime optimization becomes economically significant.

Can I run Ante completely offline?

Yes. Ante has a built-in llama.cpp engine that runs GGUF models locally. It handles engine installation, model discovery, and memory management automatically. No API keys or internet connection required.

Can I use my own custom models or providers?

Yes. Create a ~/.ante/catalog.json file to add or override providers and models with custom endpoints, API keys, and configurations. Any OpenAI-compatible API works.

What is the ante serve mode for?

Server mode runs Ante as a long-lived daemon that communicates over a structured JSONL protocol. It's ideal for building editor plugins, web UIs, and custom integrations on top of Ante.

Full documentation is available at docs.antigma.ai.

Source code in this repository (including the SDK and protocol crates) is licensed under the Apache License 2.0.

The prebuilt ante binary is free to use — including commercially — during the alpha preview under the Binary Preview Terms. The core harness is currently developed in a private repository and shipped as a binary; the SDK and protocol surface you build against here will remain permissively licensed.