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Telegram is the default. Everyone recommends it. Every tutorial starts with it. And that’s fine if you haven’t thought very hard about what you actually need. I have, and this post documents that thinking. If you’re a social scientist considering OpenClaw for research work (or honestly, anyone who cares about where their data ends up), this might save you a few weeks of going in circles.

Here’s what I need. First, topic-specific channels. I want a separate channel for each research project, one for admin, one for MethodsNET social media research, one to keep track of MethodsNET people’s publications, one that keeps track of all cool methods publications in general social science journals, one for data wrangling, one for scraping needs, and I want different agents (potentially running different LLMs) bound to each of them. This is the whole point of a multi-agent OpenClaw setup, and the communication layer needs to support it. Second, I need privacy appropriate for social science work. This isn’t about state secrets, but it is about GDPR, which we need to be taking seriously. Survey data is usually anonymized, but panel data can be personalized. Open-ended responses can contain identifying information. Transcribed interviews almost certainly do. Sending any of this through Discord’s or Telegram’s servers is, at minimum, a conversation you don’t want to have with your university’s data protection officer. (And if you’ve read my rant about academic ethics administration, you know I don’t love that conversation even when I’m clearly in the right. And I want to make sure that ethically I want to be in the right, even if procedures do not make sense.) Third, I want a casual channel for just talking to my Claw day to day. “Hey Bruce, what’s on my calendar?” “Summarize that paper I dropped in your folder.” That kind of thing. These are two different use cases. They probably need two different solutions.

Before diving into specific platforms, there’s an important structural distinction to make. OpenClaw ships with (an ever-increasing number of) native communication channels bundled into the core install: WhatsApp, Telegram, Discord, Signal, iMessage, IRC, and, etc. Others, like Mattermost, Nextcloud Talk, and so on, ship as plugins you install separately. The plugin install is one command (openclaw plugins install @openclaw/mattermost) and the plugin updates independently from OpenClaw’s release cycle, which is actually nice. But it’s still a dependency. One more thing that can break when you update. One more thing to debug when it does. If you’re building infrastructure you intend to rely on daily, bundled is safer. Not a dealbreaker, but a factor I weighed.

Discord: The Configurability King

Among bundled channels, Discord wins on organizational depth, and it’s not even close. You get categories containing channels containing threads containing forum posts, and every one of these levels can be independently bound to a different OpenClaw agent running a different LLM. Each agent gets its own Discord bot with its own avatar, name, and presence, so your coding agent looks different from your research agent. Discord also has the richest interactive component model: buttons, select menus, forms, media galleries, all routed back to the agent as inbound messages. And the thread binding system is particularly clever: the /focus command lets you dynamically attach a thread to a specific agent or subagent session, and /unfocus releases it. For managing multi-agent workflows, this is exactly the kind of tooling you want.

But Discord is cloud-hosted on Discord’s infrastructure. Your messages transit their servers. Your data sits on their servers. If all you work on is publicly available macro-comparative economic data from Eurostat or the World Bank, use Discord to your heart’s content. Honestly, it’s great. If your agent might ever touch GDPR-relevant data, keep reading.

Telegram: The Pragmatic Default

Telegram is where most people start, and for good reason. It has first-class support in OpenClaw (via the grammY library), the best mobile notification experience of any channel, and the simplest setup. Supergroup topics (introduced in 2022, now mature) give you a flat list of topic threads within a single group, providing some channel-like organization. You can bind different agents to different topics. It works.

The organizational model is shallower than Discord, though. Topics are one level deep: all siblings, no hierarchy, no categories. And the cloud-trust-model is the same as Discord: your messages transit Telegram’s servers. The track record is arguably slightly better (Telegram has been more aggressively privacy-positioned in its marketing, whether you believe them is another conversation), but the fundamental architecture is the same. Third-party servers see your data.

Nothing Else Bundled Comes Close

I looked at everything. Slack matches Discord’s organizational depth (channels, threads, distinct bot identities, Block Kit interactive components), and its thread model is arguably better for agent work because threads are first-class conversation containers. But Slack is cloud-only on Salesforce’s infrastructure. That’s worse for privacy, not better. Microsoft Teams has a comparable structure (teams → channels → threads) and is bundled, but the bot framework is heavy, the developer experience is worse, and you’re on Microsoft’s infrastructure. Google Chat has spaces and threads, but the organizational model is shallow, and the bot API is limited. IRC is the dark horse. It is fully self-hostable, channels map perfectly to topics, has zero third-party dependencies, but there’s no encryption, no modern UX, no mobile push without extra infrastructure, and no media sharing. You’d need a modern IRCv3 server plus a bouncer plus a decent web client (The Lounge, maybe), and at that point, you’ve assembled a lot of glue to get something worse than what a plugin gives you out of the box.

The bottom line is this: if you want Discord-level flexibility with self-hosting, you’re looking at a plugin. There’s no way around it with the current bundled set.

Mattermost: Self-Hosted Discord, Basically

Mattermost is what I landed on for structured work. It gives you channels, threads, DMs, multiple bot accounts with distinct identities, interactive buttons, and a familiar Slack-like UX. It’s a single Go binary plus PostgreSQL, a reverse proxy a reverse proxy (e.g., Nginx, Caddy, or Traefik), Docker Compose it up, and you’re done. Significantly easier to deploy and maintain than the alternatives. If you’ve ever administered a web app, you can run Mattermost.

On the OpenClaw side, Mattermost is a plugin (yes, I know what I said above about bundled being safer). The integration connects via bot token and WebSocket events, supports channels, groups, DMs, threads, reactions, and interactive components. It supports multiple bot accounts, so you can bind different agents to different channels with distinct identities. The setup is straightforward: install the plugin, create a Mattermost bot account, point OpenClaw at your server’s URL, and you’re running.

Now, the encryption story. Mattermost does not have native end-to-end encryption. The server sees plaintext. There is a third-party E2EE plugin from Quarkslab that uses WebCrypto with non-extractable keys, but it has real limitations: no mobile support, can’t edit encrypted messages, and the webapp integrity problem means an attacker with server control could theoretically deliver malicious JavaScript to extract keys. Mattermost and Qrypt announced a quantum-secure E2EE joint development program in mid-2025, but it’s not generally available yet.

Here’s why I don’t think this matters for my use case. You probably don’t encrypt every file on your own computer. (Unless Apple does it for you, which, fine, but you didn’t make that decision consciously.) The question that matters isn’t “is my data encrypted at rest on my own machine?” It’s “is my data leaving my infrastructure?” With self-hosted Mattermost behind your firewall, it isn’t. You control your data. No third-party platform touches the message content. The only data that leaves is whatever you send to the LLM API, and I am self-hosting that, too. For GDPR purposes, this is a defensible position. Certainly more defensible than routing everything through Discord’s or Telegram’s US-based infrastructure.

Matrix: The Security Maximalist Option (That I’m Not Using Yet)

I should mention Matrix because it’s the answer to the question I didn’t quite need to ask. Matrix has protocol-level end-to-end encryption (Olm and Megolm, the same Double Ratchet family as Signal), rooms and spaces for Discord-like organizational hierarchy, federation if you want it, and the OpenClaw Matrix adapter already supports E2EE through the Rust crypto SDK. It’s also a plugin, also well-supported.

But it’s heavier to deploy. Synapse (the reference homeserver) is Python and is known for being resource-hungry. Conduit (Rust alternative) is lighter but less mature. Federation adds complexity even if you don’t use it. Setup takes meaningfully longer than Mattermost, especially if your homeserver sits behind a VPN or requires certificate configuration.

Signal: The Casual Secure Channel

For the second use case (just talking to my claw or having a native backup in case something breaks), Signal is the obvious bundled answer. It has the strongest encryption of any bundled channel, full stop. The Signal Protocol (Double Ratchet + X3DH key agreement) with decryption happening locally inside the signal-cli process on your server. No data passes through any third-party AI provider’s infrastructure in transit. OpenClaw never sees plaintext messages on the wire; everything is decrypted locally.

Signal has no topic channels, no threading, no rich UI. It’s flat DMs and group chats. That’s it. But for casual daily interaction, that’s exactly what you want. “What’s on my calendar today?” “Summarize the email from the dean.” “Remind me to submit that review by Friday.” Quick questions, quick approvals, morning briefings. Signal does this well, and it does it with real encryption, not “trust us, we’re a platform” encryption of WhatsApp, Telegram, and etc.

It’s bundled, native, and well-supported via signal-cli (a Java-based command-line client). You’ll need a dedicated phone number for the bot. Don’t use your personal one; Signal’s self-message protection will cause routing conflicts. Link a second SIM or register a new number, and you’re set.

The Plan (For Now)

So here’s what I’m going with. Mattermost for structured work: topic channels bound to specialized agents with different LLMs, task management, sub-agent coordination, anything involving data or research workflows. Signal for casual daily interaction: the “hey Bruce” channel. Two channels, two purposes. Neither leaks data to a third-party platform. Both run on infrastructure I control.

This is the plan for now. It will almost certainly change as we actually build this thing. Maybe someone will build a native OpenClaw channel that does everything I want, and this whole post will be obsolete. That’s fine. The point isn’t to pick the perfect answer forever. The point is to think through what you need, make a defensible choice, and document why, so that when it changes (and it will), you know what you were optimizing for and what tradeoffs you’re willing to revisit. Let’s see how it goes.

I set up a Google account for my claw: Bruce. Bruce is named after the Bruce Nuclear Generating Station and my second data analysis server I had at CEU. (When talking about the first server with CEU IT, we first started calling it the power plant and then subsequently Paks. When I got an upgrade, I just found a bigger nuclear station, one with an easy name, to name it after. Current analysis machines have very different naming schemes. One’s called Monster, the Sparks are called Frodo, Bilbo, with Sam most likely incoming.)

I am neck deep in Google services: Drive, Google Docs, Gmail. So, I figured it just makes sense for Bruce to be on a Google account. I actually have a Google Workspace so I just did it there. Regular gmail would likely work as well, though I was worried about two-factor authentication and my SMS verification failed. (I do have too many gmail accounts, they probably limit how many you can verify with a single phone number.)

Then I started making an avatar. Meet Bruce, my molted alter ego.

OK. Just a quick one today. I am going to go and scare my children with this photo…

We will need a computer to run OpenClaw. Here are our options with the pros and cons.

Your computer (DON’T!!) – And the advice is that you REALLY should not do this. Giving OpenClaw unrestricted access to your entire computer can quickly turn into a nightmare. So don’t do it. You’ll need a machine where you can better control what OpenClaw has access to and what it doesn’t. While on your regular computer, OpenClaw will be the most capable; the advice is to build up. Treat OpenClaw like an employee. Give it its own hardware. Give it its own accounts. Provide additional access through sharing, forwarding, etc., as you build trust and get to know its capabilities. So we are not doing this. (I am not even sure it would make sense. I am mostly a Chromebook user who organized his life so he can pick up any Chromebook and just use it. I do have a few lying around at the in-laws’, the office, etc. I have no idea how OpenClaw could even run on a Chromebook. It can use its Linux container, of course, but I digress…)

Mac Mini – This is the favorite. And as such, there seems to be a shortage, though it hasn’t reached Hungary just yet, I don’t think. I could have a base model delivered tomorrow, even a bit under list price. But ever since my family’s iCloud accounts got jumbled up (over a decade or so ago), I have been hating Macs hard. (They even mixed all my parents’ photos with mine in Google through some autosync. And my mother photographs like a stereotypical Japanese tourist.) Even though I use an iPad and an M2 Mac Mini (with a lot of storage to locally sync everything I have in various clouds) on my home office desk, I have been doing everything I can to avoid becoming any more of a Mac person than I already am. 

But the advantage of using an Apple Silicon Mac is undeniable. You could keep it at home. Home is the best place to access the Internet from. OpenClaw creator Peter Steinberger said, (and yes, I listened to that 3+ hour interview, and you should too) If you access any website from inside a data center, you are going to get more CAPTCHAs and more blocks than if you access it from a residential network. You even get more such things from your University network. I am sure you noticed too. The Internet is getting locked down, and the inevitable reaction to the proliferation of AI agents will be further lockdowns. And while most agents who can use your desktop click through a CAPTCHAs like a human, my (playwright) browser simulator on Linux today got trapped by CAPTCHAs and asked me to log in somewhere and do one part of the job myself as it waited. That’s not ideal when you expect your agents to work while you sleep (or not looking). 

At the same time, most new tools are now invariably developed for Macs. Whenever a new AI app comes out, new features are rolled out to older apps; they are invariably first available on Macs. (It is just like how new apps always come to iPhones first, and later, maybe there will be an Android version.) It is also very easy to think through what an AI agent could do on a computer. It is the same thing you can do yourself. You want your agent to be using the various AI tools. You can have your agents use the various apps. Let’s say the agent is living on a Linux server; its computer use will be more limited, and the apps it has access to will be more limited as well. Its browser use will be more curtailed. On a Mac, of course, you have access to the terminal. You have Unix (BSD) running in there. So you have the advantages of both a Unix machine and a Mac. Running OpenClaw on Mac is the clear winner here.

Other Apple Silicon? There’s, of course, the question, if a Mac, which Mac. Most people buy the M4 base model. One of the reasons I could not get myself to do this was that it is quite outdated. There’s an M5 coming soon for sure. Does it matter? No. But I hate paying full price for outdated hardware. So I guess I could go with an older model, buy a used one. But Macs hold their resale value well, and the M4 upgrade included 16GB of RAM by default, whereas that was at a premium on earlier generations. Looking at older model prices, sometimes they made no sense. They were not even discounts spec-per-spec. I tried deal hunting. Nothing made sense. Of course, you could be like my young Russian colleague (who could easily live on the smell of an oily rag) and find a well-used M1 MacBook Air with a broken screen, but honestly…?

There’s also the option of spec-ing up. Macs are great at running LLMs locally. Unfortunately, the models you’d want to use in an OpenClaw setup need a LOT of hardware. Realistically, you can maybe run some routine tasks with the small models (the 20b-35b range), and that is it. Anything usable, like MiniMax M2.5, you will need at least 192GB of RAM, but you are paying with fire under 256GB, and the Mac Studios that come with this much are pricey. Maybe one can make some lower-level tasks work with ~120b models (like Qwen 3.5 122b a10b or Mistral 4 Small 123b), but you’d need at least 96GB of RAM for them. So the bottom line is that (new or used), we are looking at a higher-end Mac Studio before you can run anything worthwhile locally. If you max out the Mac Mini’s RAM (and now we are talking $2000 minimum, probably closer to $2500 to make sense), you are still only at 64GB. MacBooks, Mac Studios, we are talking substantially more. With 64GB, you can run Qwen 3 Next 80b at best. That’s probably not good enough for most things most of the time, and QWEN 3.5 is out already, but not with a model in this size range. Most models are over 100b, which is too big for 64GB, and are under 35b, which require 32GB max, but they won’t be good for much in the world of OpenClaw. Maybe if the M5 Pro Mac Mini will have 96GB of RAM. The M4 Pro MacBooks had a max of 48GB, while the Mac Mini went up to 64GB. Now, the M5 Pro MacBook has a max of 64GB. Maybe the M5 Pro Mac Mini will have more. But I doubt the price will be below $2000. At minimum, I am gonna wait for the M5 Mac Minis.

Whatever hardware – Alex Finn recommends that you run your OpenClaw locally on whatever hardware you have lying around unused. Your last machine is fine. Unfortunately, my last four laptops were all Chromebooks. I have a 2018-ish Surface base model, which I got when COVID lockdowns drove us to use new tools that weren’t immediately available on Chromebooks. But that thing is useless at this point. I also have a 2015 MacBook that can’t even run a current version of Chrome (or anything else, for that matter). But your luck may vary. A Mac that still gets updates or a functional Windows device (in one ever existed – I certainly don’t think so), may be your ticket.

Virtual Private Server – On YouTube, everybody (and their dog, too) pushes Hostinger (and Alex Finn swears none of them use it). I guess it is not a bad solution if you are comfortable running your own server and highly proficient in Linux. While looking for a solution, I searched for cheap VPS options (because I am often too cheap to pay a few bucks a month for Hostinger) and found an Always Free tier at Oracle Cloud Infrastructure for impressively powerful Oracle Ampere servers. You can run Ubuntu on these and get 4 CPUs, 24GB of RAM, and 200GB of storage for absolutely free. With Ollama, you can even run models at an impressive speed without a GPU. My testing suggests that I should just use a locally run GPT-OSS-20b as the OpenClaw heartbeat. There are a few catches. You need to have a credit card to create an account. (Debit cards, temp cards, etc., need not apply.) It is a serious pain in the ass to set up (though a better AI Chat will walk you through it). Only one account allowed per person (and if they catch you cheating thye may delete your machine.) The always-free plan has very few available servers at each location. (And you cannot change locations once you have your account.) I literally had to have Claude Code write a script, try to get one every 10-15 minutes, and it still took over a week. (Yes, that script ran nonstop.) Once you get it, you REALLY have to use it, otherwise they take it away from you. (This is not gonna be a problem if you are running OpenClaw on it, but better be quick to set it up.) You can upgrade your account to pay-as-you-go. Running this server will still be free, and I hope there are no surprise charges. But it is easier to get a slot. (This blog runs on such a server. And my OpenClaw will run on the one “my wife set up”.) My advice is maximum patience if you go this route. If you are in the US, set up in Chicago. Most machines. Multiple sites at one location (us-chicago-1, us-chicago-2, and us-chicago-3). And if you want to do this in the EU, go with Frankfurt. Same story. And if you are outside the US or the EU, set one up near you. Those are usually less oversubscribed. If there are several near you, do some research on which location is bigger with multiple sites.)

In Sum, I have my Oracle Virtual Private Server, and I am a loyal 25-year Linux user who is very comfortable with Linux systems. (Windows Millennium Edition I thank you for pushing me away from Microsoft forever.) Now, with the help of Claude Code, I can even administer my own web server. (Let’s hope my incompetence here will not show as some attacker takes this website down.)  I will give this approach a month or two. In the meantime, we may get M5 Mac Minis, and my mother should be getting a new laptop, freeing up an M1 MacBook Air in the family. This is my plan. Take all these considerations and make the wisest choice for yourself.

A little back story. Over Christmas break, like so many people, I was given some extra usage of Claude Code by Anthropic. So I started playing with it. And after a few days of use, I was impressed and wanted to build something difficult.

I was always fascinated by social simulations. (Little-known fact: my MA thesis (2004, Political Science, I also have one in Survey Research from 2010) was an agent-based model simulating societal outcomes of various game-theoretic cooperation strategies.) The idea of having LLMs simulate the agents in simulations seemed intriguing. They can surely act like people. But all examples I have come across were quite rigid with a fixed number of players, very rigid worlds, and very old-school turn-based timing structures. I wanted something a little more free-flowing and a little more flexible. By this time, I have been following The Nerdy Novelist on YouTube and learning about the rhythm of LLM-assisted fiction writing, and I wondered if Sudowrite’s “beats” are possibly a better way to more fluidly handle turns in a social simulation. LLMs can invent dialogue. All we need to know is who is where, who they are with, and what they are doing. At the back of my mind, of course, were social scientific applications: simulations of societal disruptions like a natural disaster, the death of a society member, etc. But the idea could easily have been an art project too, where we just let agents live their lives and see if any interesting story emerges.

At the time, my AI server wasn’t running anything. So it also seemed like a good technical exercise. We have three NVIDIA Quadro RTX 8000 GPUs. I can definitely do this well with a smaller (Ministral 3 14b) model that fits into one of these (even unquantized with a decent context window). Let’s make sure they run parallel, simulating the beats taking place simultaneously. So, early January, right around the release of Claude Code 2.1, I started building.

It was insane. I only had the 17 EUR version of Claude, so for every 45 minutes of use, I waited 4 hours. Still, a few days later, I was simulating space-fantasy stories, urban interactions, and small-village life. I realized I needed to stop this vanity project and start building something for my current research. I had one use case where I needed to do quite slow but repetitive AI synthetic data simulation tasks. I conducted many experiments comparing the performance of various models, quantizations, and simulation approaches. After a quick upgrade to the (cheaper) Claude Max account, the next thing I knew, I was building general tools that went beyond my immediate use cases. (I am still on the cheaper accounts and have not hit a limit yet, though today I got to 99% building this blog.) Soon, I had more data than I knew what to do with, and I still wanted to run more and more case studies. The world felt like it had changed. And I had no idea how much.

Still in January, this was around the time OpenClaw (or ClawdBot, as it was called at the time – definitely not to be confused with Claude or Claude Code) exploded. The little open source AI assistant that stitched together a few AI functionalities and produced magic. (Dangrous magic, but magic nevertheless.) And I wondered if a ClawdBot / Moltbot / OpenClaw could do what Claude Code did for me, supervised, but do it autonomously, handle data generation, run analyses, and organize and visualize the results based on a few already-working examples. If it could, that would be amazing.

Around this time, I had to make a cross-continent move. And I quickly grew wary of all the OpenClaw horror stories: API accounts running up into the 1000s overnight, Anthropic accounts being blocked for terms-of-service violations, and OpenClaw randomly deleting their “watcher’s” emails or accessing their APIs and credit cards without permission. I figured it is better to wait, watch, and learn a little bit, understand the problems, and see if the security of OpenClaw improves. I have done this, and I feel now that it is time. A few days ago, I searched for academic (social-scientific) applications of OpenClaw. I found nothing. So I decided to document the journey.

The question is, can OpenClaw also become a useful research (and academic admin) assistant? Let’s find out. Join me for the journey.