Setting up OpenClaw with Venice for private autonomous AI agents

What OpenClaw is and why it blew up

OpenClaw, previously known as MoltBot and Clawdbot, is a persistent AI agent framework. Unlike a chatbot you prompt one message at a time, it runs on its own and takes actions without being asked. It ships with over 50 integrations and you talk to it through everyday messaging apps like WhatsApp or Telegram.

It gained traction fast, passing 150,000 GitHub stars in roughly a week. People are pushing it hard: one user built phone-call infrastructure so the agent could ring him, and another funded a crypto wallet and told the agent to make money, which it used to raise $10,000 for hardware in under 12 hours.

The important thing to understand is what is actually new here. The models powering these agents have existed for months. OpenClaw is the framework that gives that intelligence agency, the ability to act, not a leap in raw intelligence.

The real costs and risks

Autonomous agents fail in expensive and dangerous ways. Because the agent acts on its own, it can burn API credits doing trivial things on a loop. The video cites a user whose agent spent $20 overnight essentially checking whether it was daytime yet.

On the security side, this is open-source hobby software that was never built to be enterprise ready. It is exposed to prompt injection attacks and system prompt leaks, and if you do not understand what is running under the hood, something can go wrong badly. Proper guardrails are not optional. The takeaway is to treat OpenClaw as powerful but unproven, and to plan for both spending limits and security from the start.

Why Venice: private inference and the DM token

Venice addresses the privacy and cost concerns directly. Your API inference can run fully private on open-source models. You can also use proprietary models like Opus, but remember those providers retain your data somewhere. When you route through Venice, that data is anonymized, so you can work with sensitive material without it being logged under your identity on a corporate AI server.

Venice also offers the DM token. Holding DM gives you $1 in Venice API credit every day, and those credits renew every 24 hours. You buy it once, and over time the credits effectively turn into recurring free inference for your agent.

Set Venice as your provider so your OpenClaw agent runs on private, open-source models with daily-renewing API credit.

Security and privacy prep before you install

Install OpenClaw on a separate machine. Neither Venice nor the presenter recommends running an agent framework with full machine access on your main computer where your work, business, and sensitive files live. Use a spare device such as a Mac Mini, a Raspberry Pi, or any extra computer.

The video also points to a more advanced privacy guide linked in its description. That guide goes further than this quick start, recommending tools like Tailscale and Matrix for stronger isolation. If ultra privacy matters to you, read it before going deeper. This guide covers the simpler path of installing directly on a spare machine.

Installing OpenClaw and setting Venice as your provider

Grab the one-line install command from the OpenClaw GitHub repo or quick start page, paste it into your terminal, and press enter. This launches the OpenClaw doctor, which generates and configures a gateway token. Accept the recommended answers, which are sensible defaults for beginners. When it finishes it opens a dashboard, but you can keep working from the command line.

Next, run the onboarding wizard:

openclaw onboard

Confirm that you understand the framework is inherently risky, then choose the quick start. Select Venice AI as your model auth provider, choose API key, and paste your API key from the Venice dashboard. When prompted for a default model, the video selects a private open-source option, GLM 4.7.

Connecting and pairing a Telegram bot

The wizard asks which channel you want to use. Telegram is the easiest to set up, though it runs through Telegram's servers; the advanced guide recommends Matrix, and Nostr is another option. Pick whatever you are comfortable with.

For Telegram, create a bot through BotFather. Open BotFather, send /newbot, and give it a name (the video uses "Venice Claus" with the username "Venice Claus Bot"). BotFather returns a bot token. Paste that token into the OpenClaw Telegram configuration. You can then select skills with the space bar, confirm any API keys, optionally skip hooks, and restart the gateway service if asked.

To pair the bot, click the link BotFather gave you, open the chat, and press start. The bot replies that your access is not configured and shows a pairing command. Copy that command into your terminal, replacing the placeholder with the code the bot displayed. Once paired, send a test message to confirm everything is connected.

Switching models and running live tasks

You can change the default model at any time from the terminal. On the Venice models page in the docs, click a model to get the exact command. The pattern is:

openclaw model set venice/<model-name>

For example, openclaw model set venice/claude-opus-4.5 switches the agent to Opus running through Venice. The gateway restarts and messages you when it is ready; asking it what company powers it confirms the model.

From there you drive everything through chat. When you ask the agent to scrape X, it first requests your X cookies. Open your browser, right-click and inspect, go to the Application tab, copy the auth_token and ct0 values, and paste them into the chat so the agent can pull posts. You can then ask follow-ups like requesting tweet URLs, and set up recurring jobs such as "every hour send me the hottest new AI posts." The agent can also learn and be taught new skills and workflows over time.

Final warnings and next steps

Be deliberate with this power. The agent can act on its own, spend money, and touch external services, so the separate-machine rule and the privacy guidance are not formalities. Reviewing the linked privacy tutorial and similar resources is the best way to understand what you are running and to build up your own privacy toolkit.

With Venice behind it, your OpenClaw setup runs on private inference and recurring API credits from the DM token. Share what you build in the Venice Discord.

Key takeaways

• OpenClaw gives existing models agency to act on their own; the framework is new, the intelligence is not.
• Run it on a spare machine, never your main computer, because of prompt injection, prompt leaks, and runaway costs.
• Venice provides anonymized inference and the DM token's daily-renewing $1 credit to power the agent privately.
• Setup is a one-line install, the `openclaw onboard` wizard for Venice and Telegram, and `openclaw model set venice/<model>` to switch models anytime.