TARS
A self-hosted AI agent with a visible, auditable brain. Every decision is traceable through a hash-chained action ledger. Every personality trait is a Bayesian probability, not a hardcoded prompt. Every model call comes with a cost receipt.
The Problem
AI agents are black boxes. You can't see why they made a decision, you can't verify their reasoning, and you can't tune their personality without rewriting prompts. Most agent frameworks optimize for capability at the expense of transparency.
TARS takes the opposite approach: every action is logged in an immutable ledger, every personality trait is a tunable probability distribution, and every model call is tracked with cost receipts.
Key Features
Bayesian Genome System
Personality traits are probability distributions, not static strings. The agent's behavior evolves based on interaction feedback — measurably and predictably.
Hash-Chained Action Ledger
Every action, decision, and model call is logged in a tamper-evident chain. Full audit trail from input to output. Immutable history.
Multi-Tier Model Router
Intelligent routing between model tiers based on task complexity. Simple queries go to fast/cheap models, complex reasoning goes to capable ones. Cost receipts on every call.
Self-Hosted
Runs entirely on your infrastructure. No data leaves your network unless you route a model call externally. Privacy by architecture, not policy.
37 Test Files
Comprehensive test coverage across all 9 implementation phases. Each system component is independently testable and verified.
Phased Architecture
Built in 9 deliberate phases — from core runtime to memory to model routing to audit. Each phase is stable before the next begins.