Lár: The Pytorch for Agents

# 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

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