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How OpenClaw / ClawBot works behind the scenes - and why agent observability matter

Rogerio Chaves

Feb 3, 2026

OpenClaw (formerly Clawdbot) exploded across the developer internet almost overnight. Within 24 hours of launch, engineers were running it locally, wiring it into their personal chat apps, and letting AI take real actions—sending messages, deleting emails, triggering workflows—without writing custom integrations.

The appeal was obvious:

Local-first, self-hosted AI
Works across popular chat platforms
Open source and hackable
Agents that actually do things, not just talk

But once you go beyond the demo magic, Clawbot, OpenClaw is a great real-world example of something bigger: agentic systems that connect user inputs to real-world side effects.

In this article, we’ll break down how Moltbot works under the hood, based on its open-source architecture and highlight where agent critical as systems like this move from experiments to production.

What Clawbot actually is

At its core, Clawbot is not a chat UI, nor a thin wrapper around an LLM.

It’s a self-hosted agent runtime and message router.

Concretely, Claw is a long-running Node.js service that:

Connects to multiple chat platforms
Normalizes incoming messages into a shared format
Routes those messages to an AI agent
Executes tools when the agent decides to act
Sends results back to the original chat app

You can run it locally, connect it to WhatsApp, Telegram, or Discord, describe who you are and what you want done—and the agent handles the rest, without ever leaving the chat interface.

This design makes Moltbot a textbook example of a cross-channel AI agent system.

High-Level architecture

At a high level, the system looks like this:

Chat App → Channel Adapter → Gateway → Agent Runtime → Tools → Response

Each step is intentionally separated. That separation is what makes Moltbot flexible—but also what introduces new testing and observability challenges.

The Gateway: The core of Moltbot

The Gateway is the heart of the system.

It’s a continuously running Node.js process that:

Exposes a local WebSocket API
Receives messages from all connected channels
Tracks sessions and routing state
Forwards messages to the agent runtime
Sends agent responses back to the correct channel

The key thing to understand: the Gateway does not think.

It doesn’t decide what to do—it decides where messages go.

From an agent-engineering perspective, this is a clean separation of concerns:

Routing logic lives in the Gateway
Decision-making lives in the agent

Example Flow

You send: “Delete all emails from last week” on WhatsApp
WhatsApp adapter → Gateway → Agent Runtime
Agent decides it needs the email tool
Tool executes and deletes the messages
Response flows back: “Deleted 47 emails”
Gateway routes it back to WhatsApp

From the user’s perspective, it feels seamless.

From an engineering perspective, this is a multi-step agent execution with side effects.

Channel Adapters: How Moltbot Talks to Apps

Each chat platform connects through a channel adapter—a thin integration layer that speaks the platform’s native API.

Each adapter:

Connects via official APIs or CLI bridges
Listens for incoming messages or events
Converts them into Moltbot’s internal message format
Sends them to the Gateway
Converts responses back into platform-specific formats

This adapter pattern allows Moltbot to support many platforms without touching the agent logic.

Example: Telegram Message Normalization

Telegram adapter receives a Telegram-specific payload
Converts it into something like:
{ "text":"Hello", "sender":"user123", "platform":"telegram" }
{ "text":"Hello", "sender":"user123", "platform":"telegram" }
{ "text":"Hello", "sender":"user123", "platform":"telegram" }
Sends it to the Gateway
Agent responds
Message is converted back into Telegram’s API format

For agent builders, this highlights a key challenge: the same agent behavior must remain consistent across very different input sources.

Where the AI Actually Lives

Once a message reaches the Gateway, it’s forwarded to the agent runtime.

This layer is responsible for:

Constructing the prompt and context
Calling the configured LLM provider
Interpreting the model’s output
Deciding whether a tool needs to be invoked

The agent’s output typically falls into two categories:

Plain text responses
Structured instructions to execute a tool

This is where Moltbot becomes powerful—and where things can go wrong without proper evaluation.

Tools: Where Side Effects Happen

Tools are what turn Moltbot from a chatbot into an action-taking system.

From the repository, tools can:

Run shell commands
Read and write local files
Interact with external services
Trigger automations
Perform system-level operations

The agent decides when to use a tool, and the system executes it.

In other words: natural language input can directly map to real system actions.

This is exactly the class of system where traditional prompt testing breaks down—and where agent-level testing, simulations, and monitoring become essential.

Why Security and Safety Matter So Much

Moltbot’s architecture is powerful, but it comes with real risks if not handled carefully.

Risk 1: Exposing the Gateway

The Gateway binds to 127.0.0.1 by default.

Expose it publicly, and anyone who can reach that port may be able to interact with your agent.

Risk 2: Untrusted Senders

Messages originate from external platforms.

Without proper pairing or allow-listing, anyone who can message the bot can attempt to trigger actions.

Risk 3: Tool Permissions

Tools execute with the permissions of the host machine.

A poorly scoped tool plus a malicious or misinterpreted prompt is a real security risk.

From a LangWatch perspective, this is where pre-deployment simulations, continuous evaluation, and runtime guardrails become non-negotiable—not optional.

Hosting Moltbot

While many developers run OpenClaw on local machines or Mac minis. Regardless of where it runs, the architectural risks, and the need for observability, remain the same.