OpenClaw Channel Layer: Platform-Specific to Unified Format

Introduction

The OpenClaw Channel Layer translates platform-specific data formats into a common internal structure. Telegram uses update objects; WhatsApp uses webhooks; Slack uses events. The agent doesn't care — it receives a unified message format. Mentions, reactions, media attachments: all normalized.

This abstraction is what makes OpenClaw's multi-platform story possible. Without it, the agent would need separate logic for every platform: "if Telegram, parse Update; if Slack, parse Event; if WhatsApp, parse Webhook." The Channel Layer eliminates that complexity. One agent, six platforms, one code path. This article explains how it works and why it matters.

Purpose

Single agent logic for all platforms. No "if Telegram do X, if Slack do Y" in the agent. The Channel Layer handles it. Add new platform = add new adapter. Agent code unchanged.

The benefits extend beyond simplicity. When you add a new messaging platform — say, Signal or Microsoft Teams — you implement one adapter. The agent immediately works on that platform. No changes to SOUL.md, HEARTBEAT.md, or skill logic. The adapter is a thin translation layer: platform format in, unified format out.

This design also simplifies testing. You can test the agent with mock messages in the unified format. No need to spin up Telegram or WhatsApp for unit tests. The Channel Layer is the boundary between "messaging world" and "agent world."

Normalization

Common format: sender_id, platform, message_text, attachments[], mentions[], timestamp. Each platform adapter maps native format to this. Agent receives consistent structure. Response goes back through adapter to platform-specific format.

Example: A Telegram user sends "Check my calendar @openclaw" with a photo. The adapter produces:

{
  sender_id: "tg_12345",
  platform: "telegram",
  message_text: "Check my calendar @openclaw",
  attachments: [{ type: "photo", url: "https://..." }],
  mentions: ["openclaw"],
  timestamp: "2026-02-18T10:30:00Z"
}

A Slack user sends the same request in a thread. The adapter produces the same structure — different sender_id and platform, but identical shape. The agent's calendar skill doesn't need to know the difference.

Outbound: The agent returns a response. The adapter translates it to the platform's format. Telegram gets a sendMessage call. Slack gets a chat.postMessage. WhatsApp gets a template or interactive message. The agent says "here's your calendar" — the adapter handles the rest.

Platform Adapters in Practice

Each platform has quirks. Telegram supports inline keyboards; Slack has blocks; WhatsApp has templates and quick replies. The Channel Layer doesn't hide these entirely — it provides a common baseline. Rich features (buttons, carousels) may require platform-specific extensions. But for 90% of use cases, the unified format is sufficient.

Adapters also handle platform-specific auth and connection management. Telegram uses long polling or webhooks. WhatsApp uses webhooks. Slack uses the Events API. The adapter abstracts "how we receive messages" so the rest of the system sees a single, consistent stream.

When a platform changes its API — and they do — only the adapter needs updating. The January 2026 WhatsApp API changes required adapter updates; the agent logic was untouched. This isolation is a major maintainability win.

Media and Attachments

Media normalization is trickier. Telegram sends file_id; WhatsApp sends URL; Slack sends file permalink. The Channel Layer normalizes to a common attachment format: type (image, document, audio), url or local path, and optional metadata (filename, size).

For agent skills that process attachments (e.g., "summarize this PDF"), the skill receives a URL or path. It doesn't care whether the file came from Telegram or Slack. The adapter ensures the file is accessible — downloaded if necessary, stored temporarily, passed to the skill.

Reactions and read receipts are similarly normalized. "User reacted with 👍" becomes a structured event. The agent can use this for feedback loops (e.g., "user liked this response, reinforce that pattern") without platform-specific code.

Adding New Platforms

To add a new platform, implement the adapter interface: connect, receive (translate to unified format), send (translate from agent response to platform format), disconnect. The Gateway discovers adapters and routes messages accordingly.

Community adapters exist for lesser-supported platforms. The core distribution includes WhatsApp, Telegram, Slack, Discord, iMessage, Signal. Third-party adapters extend to Teams, Mattermost, and others. See Gateway architecture for the full picture.

Wrapping Up

Channel Layer is the abstraction that makes multi-platform possible. One agent, many platforms, one logic path. See Gateway and messaging apps for integration details.