Agentloop FAQs

Question 1

How does Agentloop ensure reliable and verifiable agent execution?

Accepted Answer

It implements a deterministic orchestrator-worker-reviewer pattern with a closed feedback loop, where tasks are decomposed, executed by workers, reviewed against success criteria, and refined until met. It also supports configurable verification commands like `pytest` to enforce quality.

Question 2

Can Agentloop be used with existing coding agents for development tasks?

Accepted Answer

Yes, Agentloop is highly pluggable. Coding agents can serve as workers through its command-line interface (CLI), allowing them to perform actions in isolated git worktrees. Alternatively, any MCP-aware agent can invoke Agentloop as a tool via its MCP server.

Question 3

Which LLM backends can Agentloop integrate with?

Accepted Answer

Agentloop is backend-agnostic, designed to drive any LLM through a single `Agent` interface. This includes popular models like Anthropic Claude, Claude Code, Codex, OpenCode, and Aider.

Question 4

What is Agentloop and what problem does it solve?

Accepted Answer

Agentloop is a lightweight Python framework for orchestrating AI agents using a deterministic orchestrator-worker-reviewer pattern. It solves the challenge of reliably guiding LLMs through complex tasks by providing a structured, verifiable, and iterative feedback loop.

Question 5

What features does Agentloop offer for safe and recoverable coding?

Accepted Answer

Agentloop supports isolated Git worktrees for safe, temporary coding environments. It also automatically creates checkpoint commits after every iteration, ensuring that partial work is never lost and is fully recoverable, even if a run fails.

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