OpenClaw Broadcast Groups: Send Operator Updates Without Turning Channels Into Spam

Broadcasting sounds dangerous until you define the boundary. The bad version is one message detonating across every channel, every agent, and every human who was unlucky enough to be nearby. The useful version is narrower: one eligible WhatsApp peer, a short list of focused agents, and enough routing discipline that each agent does one job instead of turning the group into noise.

That is what OpenClaw Broadcast Groups are for. The official docs describe them as an experimental WhatsApp-only feature that lets multiple agents process the same message simultaneously. They do not replace group allowlists. They do not skip mention gating. They do not make the model choose a channel. They change which agents run after OpenClaw has already decided that the incoming WhatsApp message is allowed and should receive a reply.

If you operate agents around customer support, incident response, content operations, or founder workflows, that distinction matters. A broadcast group should feel like calling the right mini-team into the room, not like enabling a loudspeaker.

The routing model is still deterministic

OpenClaw routes replies back to the channel where the message came from. The model does not pick the outbound channel. The host configuration decides the route using channel, account, peer, thread, team, guild, and agent binding rules. Direct messages usually collapse into the agent's main session, while groups and channels get isolated session keys such as agent:<agentId>:<channel>:group:<id>.

Broadcast Groups sit on top of that routing model. In normal routing, one inbound message resolves to one agent. In broadcast routing, a configured peer resolves to a list of agents. The docs say broadcast takes priority over bindings for the broadcast peer, so the safest mental model is simple: if a WhatsApp peer is in the top-level broadcast map, the broadcast list owns that peer's agent selection.

That still leaves the admission rules in place. WhatsApp groups can be allowlisted. Group senders can be restricted. Mention gating can require a direct mention before OpenClaw replies. Broadcast only answers the "which agents should process this eligible message?" question.

Start with one peer and two jobs

The config surface is intentionally small. Add a top-level broadcast section next to bindings. Keys are WhatsApp peer ids. Group chats use a group JID such as 120363403215116621@g.us. DMs can use an E.164 phone number such as +15555550123. The value is an array of agent IDs.

{
  "broadcast": {
    "120363403215116621@g.us": ["support-agent", "ops-reviewer", "logger"]
  }
}

That tiny block is enough to change the operational shape of a group. When OpenClaw would reply in that WhatsApp peer, it runs the listed agents. Each agent gets its own session key, its own conversation history, its own workspace, and its own tool access. The group context buffer is shared per peer, so the agents see the same recent group context when the broadcast is triggered, but they do not see each other's responses as shared memory.

I would not start with six agents. Start with two: the agent that answers and the agent that reviews. If the second agent never adds value, remove it. If the review role catches real mistakes, then maybe add a logger, translator, or escalation agent.

Parallel is fast; sequential is deliberate

Broadcast Groups support two processing strategies. parallel is the default. All listed agents process at the same time, which is what you want for independent perspectives: one support answer, one security review, one task logger, one documentation note.

{
  "broadcast": {
    "strategy": "parallel",
    "120363403215116621@g.us": ["support-agent", "ops-reviewer"]
  }
}

sequential runs agents in order, with each one waiting for the previous agent to finish. That is better when the workflow has a dependency chain. A language detector can run before translators. A triage agent can classify before a follow-up agent drafts the reply.

{
  "broadcast": {
    "strategy": "sequential",
    "+15555550123": ["detect-language", "translator-en", "translator-de"]
  }
}

Do not use sequential just because it feels more controlled. It is slower by design. Use it when the second agent genuinely depends on the first agent's result. For a support room where independent specialists need to react to the same customer message, parallel is usually the cleaner default.

Mid-run buyer note: Broadcast Groups are powerful when the operating rules are written down before the agents join the room. The OpenClaw Playbook is the short path for that: role boundaries, channel policy, session isolation, and proof checks in one operator guide. Get ClawKit — $9.99.

Put allowlists before broadcast

Broadcast Groups are currently scoped to WhatsApp. In WhatsApp groups, they are evaluated after channel allowlists and group activation rules. That is exactly how it should be. The broadcast list should never be your first safety control.

Use groupPolicy, group allowlists, and mention gating to decide when a group message is eligible. The Groups docs describe the common flow: disabled groups drop, allowlisted groups must match, mention-gated groups store non-mentions as context only, and eligible messages can trigger a reply.

{
  channels: {
    whatsapp: {
      groupPolicy: "allowlist",
      groupAllowFrom: ["+15551234567"],
      groups: {
        "120363403215116621@g.us": { requireMention: true },
      },
    },
  },
}

In practice, I want three gates before any broadcast team responds:

• Peer gate: only the intended WhatsApp group JID appears in channels.whatsapp.groups or the broadcast map.
• Sender gate: groupAllowFrom limits who can trigger replies in the group.
• Mention gate: requireMention keeps ambient chatter as context instead of work.

That last point is the difference between useful automation and channel spam. If a group is busy, agents should not respond to every line. They should respond when the owner or the team intentionally calls them in.

Make every agent boringly specific

The Broadcast Groups docs recommend focused agents with clear responsibilities. I agree, with extra force. The worst broadcast setup is three general assistants all trying to be helpful in the same voice. That produces duplicates, contradictions, and the very human feeling that the software is talking over everyone.

A better setup names each agent by job. One support agent drafts the practical answer. One ops reviewer checks whether the answer touches a risky workflow. One logger records the decision or creates a task. Those jobs should be obvious from config before anyone reads the prompt.

{
  "agents": {
    "list": [
      {
        "id": "support-agent",
        "name": "Support Agent",
        "workspace": "~/agents/support",
        "tools": { "allow": ["read"] }
      },
      {
        "id": "ops-reviewer",
        "name": "Ops Reviewer",
        "workspace": "~/agents/ops-reviewer",
        "tools": { "allow": ["read", "exec"] }
      },
      {
        "id": "logger",
        "name": "Logger",
        "workspace": "~/agents/logger",
        "tools": { "allow": ["read", "write"] }
      }
    ]
  },
  "broadcast": {
    "strategy": "parallel",
    "120363403215116621@g.us": ["support-agent", "ops-reviewer", "logger"]
  }
}

Tool access should match the job. A reviewer may only need read access and maybe a safe command surface. A logger may need write access to a task file or external system. A support answer agent may need no write tools at all. OpenClaw's group and tool policy layers still apply, and deny rules still win over allow rules, so keep the group channel more restrictive than your private DM setup.

Session isolation is the hidden win

The best part of Broadcast Groups is not that several agents can speak. It is that several agents can speak without sharing the same session history. The docs call out separate session keys, separate conversation history, separate workspaces, separate tool access, separate memory context, and a shared per-peer group context buffer.

That design keeps roles from melting together. A security reviewer does not become the support agent after three turns. A logger does not inherit a personality meant for customer replies. A test generator does not accidentally remember that another agent approved something unless that fact is present in the group context it receives.

For operators, this makes audits easier. If the wrong agent responded, inspect that agent's session. If the right agent responded with the wrong tool access, inspect that agent's tool policy. If everyone responded to the wrong group, inspect the peer ID and allowlist. You are debugging named surfaces instead of a blended conversation soup.

Keep the agent count small

The docs mention no hard maximum, but warn that 10 or more agents may be slow. I would be stricter in production. Two to four agents is the useful range for most rooms. Five to ten is an advanced setup that needs real proof. More than that is usually a sign that you are using a group chat as a workflow engine.

There is also a rate-limit angle. All agents count toward WhatsApp rate limits. Parallel responses can arrive in any order. Failures are independent, which is good, but it also means one broken role should not be allowed to create a confusing partial operation every time the room speaks.

Before adding an agent, ask what new action or judgment it owns. If the answer is "another perspective," skip it. If the answer is "translate to German after language detection" or "log customer escalation when a support answer is produced," it may belong in the broadcast list.

Smoke-test before people depend on it

I would test Broadcast Groups like an operator surface, not like a toy demo. First, send one mentioned message in the target WhatsApp group. Confirm every configured agent responds from its intended role. Then send a non-mentioned message and confirm it does not trigger a reply when requireMention is enabled. Finally, review the Gateway log for broadcast routing before you add more agents.

# Send one @openclaw message in the WhatsApp group.
# Confirm each configured agent responds from its own role.
# Check that messages without a mention stay quiet when requireMention is true.
# Review gateway logs for broadcast routing before adding more agents.

The troubleshooting path in the docs is direct. If no agents respond, check that the agent IDs exist in agents.list, the peer ID format is correct, and the agents are not denied by policy. If only one agent responds, the peer may still be in normal bindings instead of broadcast. If performance is rough, reduce the agent count, use lighter models for simpler roles, and check sandbox startup time.

What I would ship first

My first production Broadcast Group would be a customer support WhatsApp group with three roles: answer, QA, and log. The answer agent writes the customer-facing reply. The QA agent only responds if it spots a gap. The logger records the decision or follow-up. The group requires a mention, only trusted operators can trigger it, and every agent has narrower tools than the private main session.

That setup is boring in the right way. It gives the human team a visible operating surface, keeps casual chat quiet, and creates enough session separation to debug mistakes later. It also avoids pretending that multi-agent means many agents talking all the time. The best broadcast group is the one that knows when not to broadcast.

Want the complete guide? Get ClawKit — $9.99