# Mi

> Mi is a 30-line agentic loop with two tools and an LLM plug-in-compact, optimized for local LLMs, token-efficient, and featuring sandbox mode to run in a container for safer, isolated execution.

- Type: AI tool
- Page: https://completeaitraining.com/ai-tools/mi/
- Published: 2026-05-12
- Updated: 2026-05-14
- Author: Jeroen Erne
- Jobs: IT and development
- Topics: Developer Tools, Open Source
- AI tools: GitHub
- Reading time: 2 min

### About Mi

Mi is a compact, single-file CLI agent for code fixes and refactoring. It provides a zero-configuration, low-footprint way to run an LLM-driven loop against a codebase without adding frameworks or external dependencies beyond the runtime.

### Review

Mi compresses the agent pattern into a very small script that repeatedly queries an LLM, determines whether to call tools, executes them, and feeds results back into the loop. Despite its size, it includes built-in tools that let it read repositories, modify code, run tests, and iterate on failures.

#### Key Features

- 30-line, single-file CLI agent that runs with no extra setup.
- Works with any compatible LLM API or local model endpoints.
- Two built-in tools: a system shell tool for repository access and a skills loader for markdown playbooks.
- Sandbox mode to run the agent in a container for safer experimentation.
- Simple iterative loop architecture for LLM + tool interactions.

#### Pricing and Value

Mi is available for free and is open source. Its main value is in low barrier experimentation: you can grab a tiny agent, connect it to a model endpoint you control, and start automating lightweight coding tasks without committing to a large framework. For teams or users seeking a production-grade, feature-rich agent platform, additional tooling and customization will likely be required.

#### Pros

- Extremely small and easy to inspect - good for learning how an agent loop works.
- No external framework or heavy dependencies; quick to drop into a project for testing.
- Supports local model endpoints and common APIs, which is useful for privacy-focused workflows.
- Sandbox mode offers an extra layer of safety when running agents against untrusted code.
- Built-in abilities to read repos, edit code, run tests, and retry on failures make it practical for many simple tasks.

#### Cons

- Feature set is intentionally minimal; larger agent frameworks provide more integrations and governance tools.
- Single-file simplicity can require manual extension or orchestration for complex, multi-step pipelines.
- Reliance on external model endpoints means overall behavior and cost depend on the models you choose to connect.

Mi is best suited for developers who want a tiny, transparent agent to prototype code automation, experiment with local models, or learn agent design. It is less ideal as-is for organizations that need extensive integrations, access controls, or enterprise-grade orchestration without further engineering. For small-scale debugging, refactoring tasks, or educational use, Mi is a very practical starting point.

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