Building a Repository-Aware AI Coding Loop in Rust

What the engine does

A normal run performs the following steps:

• Inspects the target repository.
• Creates an implementation plan.
• Lets the implementation agent read and modify files.
• Runs the repository's verification commands.
• Sends the actual changes and test output to a reviewer.
• Reflects on the result.
• Repeats when verification or review fails.

The loop only reports completion when:

• The repository contains real file changes.
• The implementation agent explicitly finishes.
• No tool error remains unresolved.
• Every configured verification command passes.
• The reviewer approves the implementation.
• The reflector agrees that the objective is complete.

A model cannot complete the workflow merely by returning the word COMPLETE.

Why Rust?

The engine executes file operations and local verification commands, so predictable behavior matters. Rust gives the project:

• Strong types for loop state and tool actions.
• Explicit error handling.
• Safe path validation.
• Good support for asynchronous HTTP and subprocess execution.
• A single installable CLI binary.

The engine also uses optimistic concurrency for file updates. An existing file must be read before it can be written. Immediately before writing, the engine confirms that the file still matches the version the agent read. This prevents the agent from silently overwriting a change made by the developer during the run.

Installing Loop Engine

git clone https://github.com/anggadb/loop-engine.git
cd loop-engine
cargo install --path . --locked

Create your local settings: