lar

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  <img src="https://raw.githubusercontent.com/snath-ai/.github/main/assets/lar-logo.png" width="80" alt="Lár Logo" />
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<p align="center"><em>Lár: The Pytorch for Agents</em></p>
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# Lár: The PyTorch for Agents



**Lár** (Irish for "core" or "center") is the open source standard for **Deterministic, Auditable, and Air-Gap Capable** AI agents.

It is a **"define-by-run"** framework that acts as a **Flight Recorder** for your agent, creating a complete audit trail for every single step.

> [!NOTE]
> **Lár is NOT a wrapper.**
> It is a standalone, ground-up engine designed for reliability. It does not wrap LangChain, OpenAI Swarm, or any other library. It is pure, dependency-lite Python code optimized for "Code-as-Graph" execution.

## The "Black Box" Problem

You are a developer launching a **mission-critical AI agent**. It works on your machine, but in production, it fails.
You don't know **why**, **where**, or **how much** it cost. You just get a 100-line stack trace from a "magic" framework.

## The "Glass Box" Solution

**Lár removes the magic.**

It is a simple engine that runs **one node at a time**, logging every single step to a forensic **Flight Recorder**.

This means you get:
1.  **Instant Debugging**: See the exact node and error that caused the crash.
2.  **Free Auditing**: A complete history of every decision and token cost, built-in by default.
3.  **Total Control**: Build deterministic "assembly