Clawdbot AKA OpenClaw for Enterprise
Let's get the important thing out of the way first: Clawery is not OpenClaw. It doesn't share a codebase with OpenClaw, it doesn't fork OpenClaw, and it is not subject to the security vulnerabilities that have turned OpenClaw into a recurring headline for every major cybersecurity firm from CrowdStrike to Palo Alto Networks to Cisco.
What Clawery does share with OpenClaw is a conviction that the basic architecture is right. The idea that you should be able to talk to an AI agent through the messaging tools you already use — Telegram, Slack, Teams, WhatsApp, Discord — and have that agent remember you, learn your preferences, and actually do things on your behalf? That's a good idea. Peter Steinberger and the OpenClaw community proved that. Tens of thousands of GitHub stars don't lie.
The problem is that good ideas and production-grade enterprise software are separated by a very long bridge, and most of it is paved with security engineering.
What went wrong with OpenClaw
OpenClaw's security posture has been well-documented at this point. Plaintext API keys in Markdown files. An authentication bypass through localhost trust when sitting behind Nginx. Hundreds of malicious skills on ClawHub distributing credential-stealing malware. Persistent memory as an attack surface for slow-burn prompt injection. Palo Alto Networks called it a "lethal trifecta" — private data access, untrusted content exposure, and external communication capability, all wrapped in an agent with a long memory. Cisco was less diplomatic about it.
To his credit, Steinberger has been candid: OpenClaw is a hobby project, it requires careful configuration, and it's not meant for non-technical users. That's a perfectly reasonable position for an open-source project. It's not a reasonable position for something your compliance team needs to sign off on.
What Clawery actually is
Clawery is a managed agentic service. You don't self-host it. There's no Docker Compose file to babysit, no reverse proxy to misconfigure, no filesystem full of plaintext secrets waiting for a prompt injection to exfiltrate them.
Your agents run on Clawery's infrastructure, and the security model is designed around the assumption that agents will be exposed to adversarial input — because in an enterprise, they will be.
Here's what that looks like in practice:
Channels, not servers. Clawery agents connect to Telegram, Microsoft Teams, WhatsApp, Slack, Discord, and generic webhooks. You configure a channel, point it at your agent, and your users talk to it wherever they already are. You don't run a gateway process on a VPS somewhere.
Memory that's actually managed. Like OpenClaw, Clawery agents have persistent memory. Unlike OpenClaw, that memory isn't a Markdown file on disk. It's stored in a managed data layer with retention policies, legal hold support, and tamper-evident audit logging. Your compliance team can tell an auditor exactly what an agent remembered, when it remembered it, and when that memory was disposed of.
Skills in JS, executed in isolation. Clawery agents can create and run skills written in JavaScript, similar to OpenClaw's skill system. The difference is execution context. Skills run in a secure, isolated environment — not in the same process that has access to your secrets and filesystem. There's no equivalent of the ClawHub malware problem here because skills don't get ambient access to everything the agent can touch.
Secrets in a secrets store. This one shouldn't need to be a differentiator, but here we are. API keys, tokens, and credentials live in a dedicated secrets store. They're not in JSON files. They're not in the agent's reachable filesystem. A prompt injection that tricks your agent into reading its own config doesn't get your Stripe key.
Native MCP support. Clawery has first-class support for the Model Context Protocol. Not bolted on, not through a plugin — it's a core part of the agent architecture. This means your agents can connect to MCP-compatible tools and data sources with a standardized interface rather than bespoke integrations.
Workspace files. Like OpenClaw, Clawery supports Workspace files — structured data and configuration that agents can reference during interactions. The implementation sits behind the same access controls and audit logging as everything else.
Hundreds of LLMs, not just one
Clawery agents aren't locked to a single model provider. They can use hundreds of LLMs — swap models based on task requirements, cost constraints, or compliance needs. Need Opus for complex reasoning and Haiku for quick classification? Configure it per agent or per task.
More interesting is what happens when a single agent isn't enough. Clawery agents can spawn sub-agents, and those sub-agents can communicate with each other to coordinate work. This isn't a demo feature — it's how you build agents that handle multi-step enterprise workflows where different parts of the task require different capabilities, different access levels, or different models entirely.
Who this is for
Clawery is built for enterprise IT teams that looked at OpenClaw and thought: "Yes, but how do we actually deploy this without our CISO losing sleep?"
If you need AI personal assistants that connect to the channels your organization already uses, that remember context across interactions, that can execute custom logic safely, and that produce the audit trail your compliance framework requires — that's the gap Clawery fills.
It's the OpenClaw vision, rebuilt from the ground up with the assumption that security isn't a configuration exercise. It's an architectural one.
Get started at clawery.com.