Totem FAQs

Question 1

What is Totem and its main purpose?

Accepted Answer

Totem is a suite of CLI tools that provides a persistent memory and context layer for AI coding agents. Its main purpose is to enforce architectural integrity and code quality through deterministic rules, preventing agents from repeating mistakes and ensuring consistency across projects.

Question 2

How does Totem enforce code quality and architectural integrity?

Accepted Answer

Totem allows you to compile plain-English 'lessons' into AST or regex rules. These rules are then enforced deterministically and offline via sub-second linting commands and Git hooks (like `pre-push`), blocking code that violates established architectural patterns before it's committed.

Question 3

Does Totem use LLMs for real-time code quality enforcement?

Accepted Answer

No. `totem lint` runs entirely offline and completes in under two seconds, with zero LLMs in the loop for rule enforcement. While Totem leverages LLMs for higher-level planning tasks like generating implementation specs or performing architectural reviews, its core enforcement layer is deterministic and fast.

Question 4

How does Totem provide 'persistent memory' for AI agents?

Accepted Answer

Totem creates a local semantic knowledge base (using Tree-sitter + LanceDB) by indexing your lessons and architectural decision records (ADRs). This allows any MCP-compatible AI agent to query project-specific context, past mistakes, and shared helpers, preventing them from 'forgetting' crucial information.

Question 5

What are the key benefits of integrating Totem into a development workflow?

Accepted Answer

Totem helps maintain high code quality and architectural consistency, prevents AI agents from making repetitive errors, accelerates development by providing agents with grounded project context, and offers fast, deterministic rule enforcement with no cloud dependency for core linting.

Totem

Totem

Características Principales

Casos de Uso

Características Principales

Casos de Uso