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Gas Town vs Swarm-Tools: Multi-Agent AI Orchestration Compared

Two ambitious open-source projects tackling the same fundamental problem: how do you coordinate multiple AI coding agents to work together effectively?

TL;DR

Aspect	Gas Town	Swarm-Tools
Author	Steve Yegge	Joel Hooks
Language	Go	TypeScript/Bun

OpenClaw Prompts - Build Your Own AI Assistant

Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.

1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."

2. Meeting Action Items (Fathom)

OpenClaw: System Prompt File Templates

Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).

Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.

AGENTS.md

OpenClaw: Extracted Prompts (Generalized)

22 copy/paste-ready prompts for building your own AI agent system. Each prompt builds a functional system or implements a proven best practice you can hand to an AI coding assistant.

Replace placeholders like <your-workspace>, <your-messaging-platform>, and <your-model> with your own values.

	#!/usr/bin/env bash

	#
	# Reads AirTag data from the FindMy.app cache and converts it to a daily GPX file
	#
	# Rsyncs the data to a web accessible folder that can be displayed with e.g.
	# https://gist.github.com/henrik242/84ad80dd2170385fe819df1d40224cc4
	#
	# This should typically be run as a cron job
	#

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	#!/bin/sh
	#
	# festivaltts-install.sh
	# -----
	#
	# Installation and setup of a Festival based TTS system.
	# Following instructions from:
	# - [http://ubuntuforums.org/showthread.php?t=751169]
	#