Inside OpenClaw: How Agentic Assistants Work

Brajesh Kumar — Fri, 17 Apr 2026 10:09:56 +0000

Over the last couple of weeks, I’ve been digging into OpenClaw from an engineering perspective.

What pulled me in was a report that an OpenClaw agent had deleted emails before confirming with the user. I installed it locally to understand the architecture for myself and to answer a simple question: where does control actually live in the stack?

First thing I learned is OpenClaw is purely an engineering infrastructure stack project which provides components and tools to facilitate LLM intelligence to achieve autonomous work. The stack has building blocks to support common agentic tasks like memory, tool execution etc. This is different from chat agents or workflows which are built around context (previously prompt) engineering to achieve a single goal. OpenClaw stack allows users to connect different models(Claude, OpenAI etc), channels(Slack) to make it more versatile.

OpenClaw Stack Overview

Stack architecture is based on a central component, the Gateway which officiates connections between interaction points (input messages) and intelligence layer (Agent). Gateway is simple WebSocket server works like broker between input and the intelligence layer.

Let’s dive little deeper into these building block layers.

Input Layer

Input layer offers different ways to connect multiple channels to the agent. OpenClaw supports many native channels like WhatsApp, Telegram, CLI, and Web UI. Each channel plugin defines its own contract for message formatting and authentication, making the stack extensible without changing the core. For example, admin inputs are authenticated using tokens, while WhatsApp uses QR code pairing.

Gateway Layer

Gateway layer is the centralised control plane. It resolves sessions and ensures each input is mapped to the right agent execution and that agent output is routed back to the correct channel. Sessions are broadly scoped to main, DM, or group based on configuration. The Gateway also handles channel routing deterministically, the model never chooses where to respond.

Intelligence Layer

This is where the magic happens. Once the Gateway dispatches an input, the agent runtime builds context from conversation history and memory to achieve the user's goal. It reasons over the input, decides whether to respond directly or invoke tools, and loops until the task is complete. OpenClaw ships with ready-made tools for memory (SQLite), cron jobs, browser automation, and file access — enough to get started with personal assistant workflows. Skills can optionally enhance how the agent combines these tools for more complex tasks.

Example Flow

Let's take a simple WhatsApp message "What's on my calendar today?" through the stack layers.

The message hits the WhatsApp channel plugin, which has already authenticated via QR code pairing. The plugin normalises the payload into OpenClaw's common format and passes it to the Gateway.

The Gateway layer resolves the session eg. a direct message on the main scope and dispatches to the agent.

The agent loads conversation history and memory, then sends everything to the LLM. The model calls the calendar tool, gets back three meetings, and the agent formats a response. The reply travels back through the Gateway to the WhatsApp plugin and onto the user's phone.

Why OpenClaw Is Special

What makes OpenClaw special is the simplicity of its architecture. The stack is deliberately minimal: Gateway, channels, agent, tools—yet flexible enough to support almost any use case. Anyone can build custom tools and skills on top of it without touching the core.

This extensibility has created a agentic ecosystem. Individuals and companies are publishing niche skills and integrations to expand their businesses through OpenClaw. The ClawHub marketplace has grown to over 35K skills (+8K in a single week), ranging from personal self-help assistants to self-evolving autonomous agents.

Security for OpenClaw

Deploying autonomous agents in production safely is a different problem from running them locally. OpenClaw's power, tool access, persistent memory, multi-channel reach, is also its attack surface.

The challenges are both static and dynamic. Static risks include unsafe infrastructure configurations and unchecked skill distribution through ClawHub. Dynamic risks are harder to catch, an agent performing lateral movement to execute unauthorised tasks, or leaking sensitive data mid-conversation.

OpenClaw agents are getting more capable, but most teams have no visibility into what their agents can access, trigger, or leak. I built a tool that analyzes configurations and OpenClaw skills to understand the security posture of a deployment. The tool is in our open source repository trnt-ai/trent-openclaw-security-assessment.

DEV Community: Brajesh Kumar