diff --git a/CODEOWNERS b/CODEOWNERS
index 9862e91e5c..f88a47de90 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -6,6 +6,7 @@ data/text/ @Marty-D
 databases/ @monsanto
 server/chat-plugins/hosts.ts @AnnikaCodes
 server/chat-plugins/mafia.ts @HoeenCoder
+server/chat-plugins/net-filters.ts @mia-pi-git
 server/chat-plugins/othermetas.ts @KrisXV @TheImmortal
 server/chat-plugins/quotes.ts @mia-pi-git @KrisXV
 server/chat-plugins/random-battles.ts @KrisXV @TheImmortal
diff --git a/package.json b/package.json
index 9a83e060ee..cc65a573ab 100644
--- a/package.json
+++ b/package.json
@@ -10,6 +10,7 @@
     "sucrase": "^3.15.0"
   },
   "optionalDependencies": {
+    "brain.js": "^2.0.0-beta.2",
     "better-sqlite3": "^7.1.0",
     "cloud-env": "^0.2.3",
     "node-static": "^0.7.11",
diff --git a/server/chat-plugins/net-filters.ts b/server/chat-plugins/net-filters.ts
new file mode 100644
index 0000000000..6e6138f0da
--- /dev/null
+++ b/server/chat-plugins/net-filters.ts
@@ -0,0 +1,246 @@
+/**
+ * Neural net chatfilters.
+ * These are in a separate file so that they don't crash the other filters.
+ * (issues with globals, etc)
+ * by Mia.
+ * @author mia-pi-git
+ */
+
+import {QueryProcessManager} from '../../lib/process-manager';
+import {FS} from '../../lib/fs';
+import {Utils} from '../../lib/utils';
+import {Config} from '../config-loader';
+import {Repl} from '../../lib/repl';
+
+const PATH = "config/chat-plugins/net.json";
+const NUM_PROCESSES = Config.netfilterprocesses || 1;
+const PM_TIMEOUT = 2 * 60 * 60 * 1000; // training can be _really_ slow
+const WHITELIST = ["mia"];
+
+interface NetQuery {
+	data: string | TrainingLine[];
+	type: "run" | "train" | "save" | "load";
+	options?: AnyObject;
+}
+
+interface TrainingLine {
+	input: string;
+	// "|flag" (bad) "|ok" (good)
+	output: string;
+}
+
+function modelExists() {
+	try {
+		require.resolve('brain.js');
+	} catch (e) {
+		return false;
+	}
+	return true;
+}
+
+export class NeuralNetChecker {
+	model: import('brain.js').recurrent.LSTM | null;
+	constructor(path?: string) {
+		try {
+			this.model = new (require('brain.js').recurrent.LSTM)();
+		} catch (e) {
+			this.model = null;
+		}
+		if (path) this.load(path);
+	}
+	async train(data: TrainingLine[], iterations?: number) {
+		// 100 has good perf but is still effective
+		if (!iterations) iterations = 100;
+		const now = Date.now();
+		await FS(PATH).copyFile(PATH + '.backup');
+		if (!this.model) throw new Error(`Attempting to train with no model installed`);
+		this.model.train(data, {iterations});
+		this.save();
+		return Date.now() - now; // time data is helpful for training
+	}
+	save(path = PATH) {
+		if (!this.model) return {};
+		const state = this.model.toJSON();
+		FS(path).writeUpdate(() => JSON.stringify(state));
+		return state;
+	}
+	load(path: string) {
+		if (!FS(path).existsSync()) return;
+		const data = JSON.parse(FS(path).readSync());
+		this.model?.fromJSON(data);
+	}
+	run(data: string) {
+		let result = '';
+		if (!this.model) return result;
+		try {
+			result = this.model.run(data);
+		} catch (e) {}
+		// usually means someone didn't train it, carry on
+		// acceptable to drop since training is very slow
+		return result;
+	}
+	static async train(data: TrainingLine[]) {
+		// do the training in its own process
+		const result = await PM.queryTemporaryProcess({type: 'train', data});
+		// load it into the main process that we're querying
+		for (const process of PM.processes) {
+			await process.query({type: 'load', data: PATH});
+		}
+		return result;
+	}
+}
+
+function checkAllowed(context: CommandContext) {
+	if (!modelExists()) throw new Chat.ErrorMessage(`Net filters are disabled - install brain.js to use them.`);
+	const user = context.user;
+	if (WHITELIST.includes(user.id)) return true;
+	return context.canUseConsole();
+}
+
+export let net: NeuralNetChecker | null = null;
+export let disabled = false;
+
+export const hits: {[roomid: string]: {[userid: string]: number}} = (() => {
+	const cache = Object.create(null);
+	if (global.Chat) {
+		if (Chat.plugins['net-filters']?.hits) {
+			Object.assign(cache, Chat.plugins['net-filters'].hits);
+		}
+	}
+	return cache;
+})();
+
+export const chatfilter: ChatFilter = function (message, user, room) {
+	if (disabled || !modelExists()) return;
+	// not awaited as so to not hold up the filters (additionally we can wait on this)
+	void (async () => {
+		if (!room || room.persist || room.roomid.startsWith('help-')) return;
+		const result = await PM.query({type: "run", data: message});
+		if (result?.endsWith("|flag")) {
+			if (!hits[room.roomid]) hits[room.roomid] = {};
+			if (!hits[room.roomid][user.id]) hits[room.roomid][user.id] = 0;
+			hits[room.roomid][user.id]++;
+			const minCount = Config.netfilterlimit || 20;
+			if (hits[room.roomid][user.id] >= minCount) {
+				Rooms.get('upperstaff')?.add(
+					`|c|&|/log [ERPMonitor] Suspicious messages detected in <<${room.roomid}>>`
+				).update();
+				hits[room.roomid][user.id] = 0; // so they can't spam messages
+			}
+		}
+	})();
+	return undefined;
+};
+
+export const PM = new QueryProcessManager<NetQuery, any>(module, query => {
+	if (!net) throw new Error("Neural net not intialized");
+	const {data, type, options} = query;
+	switch (type) {
+	case 'run':
+		let response = '';
+		try {
+			response = net.run(data as string);
+		} catch (e) {} // uninitialized (usually means intializing, which can be slow) - drop it for now
+		return response;
+	case 'train':
+		return net.train(data as TrainingLine[], options?.iterations);
+	case 'save':
+		return net.save();
+	case 'load':
+		try {
+			net.load(data as string);
+		} catch (e) {
+			return e.message;
+		}
+		return 'success';
+	}
+}, PM_TIMEOUT);
+
+if (!PM.isParentProcess) {
+	global.Config = Config;
+	// we only want to spawn one network, when it's the subprocess
+	// otherwise, we use the PM for interfacing with the network
+	net = new NeuralNetChecker(PATH);
+	// eslint-disable-next-line no-eval
+	Repl.start('net-filters', cmd => eval(cmd));
+} else {
+	PM.spawn(NUM_PROCESSES);
+}
+
+export const commands: ChatCommands = {
+	netfilter: {
+		limit(target, room, user) {
+			checkAllowed(this);
+			const int = parseInt(target);
+			if (isNaN(int)) {
+				return this.errorReply("Invalid number");
+			}
+			if (int < 20) {
+				return this.errorReply("Too low.");
+			}
+			Config.netfilterlimit = int;
+			this.privateGlobalModAction(`${user.name} temporarily set the net filter trigger limit to ${int}`);
+			this.globalModlog(`NETFILTER LIMIT`, null, int.toString());
+		},
+		async train(target, room, user) {
+			checkAllowed(this);
+			const data: TrainingLine[] = [];
+			const parts = target.split('\n');
+			for (const line of parts) {
+				const [input, output] = Utils.splitFirst(line, '|');
+				if (!['ok', 'flag'].some(i => output === i)) {
+					return this.errorReply(`Malformed line: ${line} - output must be 'ok' or 'flag'.`);
+				}
+				if (!input.trim()) {
+					return this.errorReply(`Malformed line: ${line} - input must be a string`);
+				}
+				data.push({input, output});
+			}
+			if (!data.length) {
+				return this.errorReply(`You need to provide some sort of data`);
+			}
+			this.sendReply(`Initiating training...`);
+			const results = await NeuralNetChecker.train(data);
+			this.sendReply(`Training completed in ${Chat.toDurationString(results)}`);
+			this.privateGlobalModAction(`${user.name} trained the net filters on ${Chat.count(data.length, 'lines')}`);
+			this.stafflog(`${data.map(i => `(lines: '${i.input}' => '${i.output}'`).join('; ')})`);
+		},
+		async rollback(target, room, user) {
+			checkAllowed(this);
+			const backup = FS(PATH + '.backup');
+			if (!backup.existsSync()) return this.errorReply(`No backup exists.`);
+			await backup.copyFile(PATH);
+			const result = await PM.query({type: "load", data: PATH});
+			if (result && result !== 'success') {
+				return this.errorReply(`Rollback failed: ${result}`);
+			}
+			this.privateGlobalModAction(`${user.name} rolled the net filters back to last backup`);
+		},
+		async test(target, room, user) {
+			checkAllowed(this);
+			const result = await PM.query({type: 'run', data: target});
+			return this.sendReply(`Result for '${target}': ${result}`);
+		},
+		enable: 'disable',
+		disable(target, room, user, connection, cmd) {
+			checkAllowed(this);
+			let logMessage;
+			if (cmd === 'disable') {
+				if (disabled) return this.errorReply(`Net filters are already disabled.`);
+				disabled = true;
+				this.globalModlog(`NETFILTER DISABLE`, null);
+				logMessage = `${user.name} disabled the net filters`;
+			} else {
+				if (!disabled) return this.errorReply(`The net filters are already enabled`);
+				disabled = false;
+				this.globalModlog(`NETFILTER ENABLE`, null);
+				logMessage = `${user.name} enabled the net filters`;
+			}
+			this.privateGlobalModAction(logMessage);
+		},
+	},
+};
+
+if (global.Chat) {
+	process.nextTick(() => Chat.multiLinePattern.register('/netfilter train '));
+}