How to Use OpenClaw Node Audio

OpenClaw node audio covers voice notes and inbound audio attachments that need to become usable text before the agent replies. The docs describe a pipeline that finds the first audio attachment, downloads it when needed, enforces a size limit, picks the first eligible model entry, and falls back when a model fails, skips, or times out. On success, the inbound body is replaced with an [Audio] block and the transcript is exposed as {{Transcript}}.

Start with the default auto-detection

If you do not configure audio models and you have not set tools.media.audio.enabled to false, OpenClaw auto-detects options. The documented order starts with the active reply model when it supports audio understanding, then local CLIs such as sherpa-onnx-offline, whisper-cli, and Python whisper, then Gemini CLI, then provider auth fallback. Provider fallback order in the docs includes OpenAI, Groq, xAI, Deepgram, Google, SenseAudio, ElevenLabs, and Mistral.

Configure explicit models when reliability matters

Auto-detection is convenient, but production workflows should pin the path. Use tools.media.audio.models when you know which transcription route you want. A provider entry can specify provider and model, while a CLI entry can specify command, args, and timeout. Keep maxBytes conservative for your channels. The docs show a provider-plus-CLI fallback pattern where OpenAI transcribes first and a local Whisper command is available if provider access fails.

Remember command parsing

The useful part of audio understanding is not only a pretty transcript. When transcription succeeds, OpenClaw sets CommandBody and RawBody to the transcript. That means slash commands and directive-style inputs can work from voice notes instead of being treated as opaque attachments. If a user sends a voice note that says a command, the command parser can see the text after transcription. If transcription fails, the reply flow continues with the original body and attachment rather than crashing the channel.

Scope audio before enabling it everywhere

Audio is more sensitive than plain text. The docs support scope gating, including denying group chats while allowing direct messages. That is a strong default for teams: enable audio where users expect it, and avoid surprising transcriptions in noisy group channels. Also use verbose logs during setup. They show when transcription runs and when it replaces the body, which makes debugging much easier than guessing whether a provider, local binary, or size check skipped the attachment.

Operational checklist

Send one short voice note, confirm the transcript appears in the agent-visible body, then test a large or unsupported file and confirm the system skips understanding cleanly. Write down the selected provider or CLI, size cap, timeout, and scope rules. The OpenClaw Playbook is useful here because audio looks simple from the outside, but reliable operations require a known fallback chain and a privacy boundary everyone understands.

Handle transcripts as user input

Once a voice note becomes a transcript, it should be treated like any other user message: useful, but not automatically trusted. Accents, noise, and model mistakes can change commands. For high-impact workflows, ask the assistant to confirm the interpreted action before executing it. If audio is used for customer support, keep transcript echo behavior and retention policy clear. If audio is used for personal reminders, decide whether transcripts should appear in the final reply. The docs give you the technical pipeline; the operating rule is to make transcription visible enough to audit without exposing private audio more broadly than needed.

Final verification

Before calling How to Use OpenClaw Node Audio finished, perform one direct test, one failure test, and one rollback check. The direct test proves the happy path works. The failure test proves the documented guardrail is real, not just assumed. The rollback check tells the next operator how to undo the change without improvising. Save those notes beside the channel, node, or gateway config you changed. OpenClaw gets powerful when agents can act, but it stays trustworthy when every new surface has a small, repeatable verification habit attached to it.