Ready to build Auditable AI Agents
Lár (Irish for "core" or "center") is the open source standard for Deterministic, Auditable, and Air-Gap Capable AI agents, powered by LiteLLM.
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.
Not a Wrapper
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 lines," not chaotic chat rooms.
"This demonstrates that for a graph without randomness or external model variability, Lár executes deterministically and produces identical state traces."
Stop guessing. Start building agents you can trust.
Demos & Examples
Learn by building with our ready-made demos:
- DMN: The Showcase: A Cognitive Architecture that sleeps, dreams, and remembers.
- RAG Agent Demo: A self-correcting RAG agent with local vector search.
- Customer Support Swarm: A multi-agent orchestration pattern.
Power Your IDE (Cursor / Windsurf)
Make your IDE an expert Lár Architect with this 2-Step Workflow:
- Reference The Rules: In your chat, type
@lar/IDE_MASTER_PROMPT.md. This loads the strict typing rules. - Use The Template: Fill out
@lar/IDE_PROMPT_TEMPLATE.mdwith your agent requirements.
Ready for Production?
Lár is designed to be deployed as a standard Python library. Read our Deployment Guide to learn how to wrap your graph in FastAPI and deploy to AWS/Heroku.
Get Started in 3 Minutes https://docs.snath.ai/getting-started/
Author
Lár was created by Aadithya Vishnu Sajeev.