luckeyfaraday/athena-loops

Athena Loops — backend-agnostic AI agent orchestration loop for Python

agentloop is a lightweight Python framework for multi-agent orchestration. It implements the orchestrator → worker → reviewer pattern (the AI agent orchestration loop) as a deterministic harness with a closed feedback loop: a goal is decomposed into subtasks, fanned out to worker subagents, aggregated, and run through a review gate that loops until the work meets its success criteria. One loop drives any LLM backend — Anthropic Claude, Claude Code, Codex, opencode, or aider — through a single Agent interface, and it ships as both an MCP server and a plain CLI so any coding agent can call it.

The design principle: the loop is a harness (deterministic code), not a skill. A prompt can describe "decompose, review, loop until done" but can't guarantee it. So the control flow lives in code, and the model-facing judgement (how to decompose, the review rubric) lives in swappable prompts. One harness drives any backend through a single Agent interface.

The tweet that started it all — Peter Steinberger (@steipete): "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." agentloop is that idea as a reusable harness.

            ┌──────────── harness (this package) ────────────┐
goal ─▶ decompose ─▶ fan-out to subagents ─▶ aggregate ─▶ review gate ─▶ done?
            ▲                                                          │ no
            └──────────────── feedback: refine plan ◀──────────────────┘

Quick start

python3 -m examples.run_demo        # zero-dependency MockAgent
python3 -m pytest                   # full test suite, no deps

from agentloop import Orchestr