Guide
OpenClaw Agent-to-Agent Communication
Deploy multiple OpenClaw agents that talk to each other, delegate work, and collaborate across channels — building AI teams instead of isolated bots.
What Is Agent-to-Agent Communication?
Agent-to-agent communication means multiple AI agents can send messages to each other, share results, and coordinate on tasks — just as people on a team would. Instead of one monolithic bot trying to handle everything, you deploy several specialized agents and let them work together.
In OpenClaw, a single gateway hosts several named agents. Each one keeps its own workspace, memory, model and system prompt, so they stay genuinely isolated — but any agent can run a turn on another and read back the result. Tasks flow between them automatically, each contributing what it does best, without a second deployment.
Why Connect Multiple OpenClaw Agents?
A single agent is powerful, but multi-agent systems unlock capabilities that one bot cannot achieve alone:
- Specialization — One agent can be configured for deep coding assistance with a code-optimized model, while another is tuned for research and web retrieval. Each performs better in its own lane than a generalist would across both.
- Context isolation — Each agent keeps its own workspace and memory, so a long research thread never pollutes the coding agent's context. This is usually the biggest quality win: a scoped agent with a clean history outperforms a generalist carrying everything.
- Different models per role — You can run a fast, inexpensive model on your front-facing agent for quick routing decisions, and reserve a more capable model on a backend agent for heavy reasoning tasks.
- Independent channel presence — Each agent can be connected to its own Telegram bot, Discord server, or web channel. Users interact with the right specialist directly, without needing to specify what they need.
- Blast-radius control — A bad prompt or a runaway conversation is contained to the agent it happened in; the others keep their own state. Note the limit: agents on one instance share a gateway, so this isolates context and behaviour, not infrastructure.
How It Works in OpenClaw
You do not need a separate deployment per agent. A single OpenClaw gateway hosts multiple named, isolated agents — each with its own workspace, agent directory, model, and session history. They live side by side in one instance and can consult each other directly.
- Multiple agents in one gateway — Agents are registered in the
agents.listconfig array. Add one withopenclaw agents add <id>, and confirm the roster withopenclaw agents list. Each agent gets its own isolated workspace and memory, so context never bleeds between them. - One agent consulting another — Any agent can run a turn on a sibling and read back its answer:
openclaw agent --agent <id> --message "…" --json
Because agents already have shell execution, this needs no extra tool, no gateway URL, and no shared credentials. - Routing bindings — Inbound messages can be routed to the right agent by channel or account with
--bind, so a Telegram account and a Discord account can each land on a different specialist automatically.
Common Patterns
Most multi-agent setups follow one of three patterns. You can mix them as your workflow grows:
| Pattern | How It Works | Example |
|---|---|---|
| Supervisor + Worker | One coordinator agent breaks down tasks and delegates to specialist workers. | A manager agent receives a request, routes coding questions to a dev agent and research questions to a search agent. |
| Specialist Team | Each agent owns a specific domain. Users or other agents route requests by topic. | Separate agents for billing, technical support, and onboarding — each with its own model and skills. |
| Pipeline | Output from one agent feeds directly into the next as structured input. | A search agent retrieves raw data, passes it to a summarizer agent, which sends the digest to a writer agent. |
Setting It Up on OpenClaw Launch
All of this happens inside a single deployed instance, so a specialist team does not consume extra instances from your plan. Deploy once on OpenClaw Launch, then add agents to it.
1. Deploy your instance
Use the visual configurator to deploy an OpenClaw instance. The agent it ships with is main, and it stays the default that inbound messages route to. Give it a system prompt describing its coordinating role: it receives requests, decides which specialist should handle them, and relays the answer back.
2. Add specialist agents
From the instance terminal, register each specialist. Every agent gets an isolated workspace and its own memory:
openclaw agents add research --non-interactive --workspace ~/.openclaw/workspace/research
Add --model <id> to give a specialist its own model — a code model for a dev agent, a cheaper model for a triage agent. Confirm the roster with openclaw agents list.
3. Get each new agent out of bootstrap mode
This step is easy to miss and it looks like a broken feature if you skip it. A newly added agent starts in bootstrap mode: its workspace contains a BOOTSTRAP.md, and until that is resolved it answers every request with an identity interview rather than doing the work — asking what it should call itself, what its vibe is, and what your name and timezone are.
Two ways through it. Either message the agent once and answer its questions, which is worth doing for a specialist you want to have a personality; or, for a purely task-focused worker, delete the file:
rm ~/.openclaw/workspace/research/BOOTSTRAP.md
After that the agent answers normally. Nothing else needs to change — in testing, the same question that returned the interview returned the correct answer immediately once the file was gone.
4. Let agents consult each other
No further configuration is needed. Tell your coordinating agent, in its system prompt or a skill, that it can reach a specialist like this:
openclaw agent --agent research --message "Summarize this week's releases" --json --timeout 120
The specialist runs the turn in its own isolated context and returns its answer, which the coordinator can then use or relay.
5. Test the handoff
Send a message through your front-facing channel (Telegram, Discord, or the web UI) that should trigger a consult. Verify the coordinator calls the right specialist and that the answer it reports back genuinely came from that agent — asking the specialist something the coordinator would answer differently is a quick way to confirm the handoff is real.
Use Cases
Agent-to-agent setups solve real workflow problems across a wide range of industries:
- Customer support triage — A front-facing agent greets users, classifies their issue (billing, technical, account), and routes them to the right specialist agent. Each specialist has deep context for its domain and can resolve issues without the user re-explaining themselves.
- Research pipelines — A search agent retrieves relevant documents and data from the web. It passes raw content to a summarizer agent, which distills key points and hands them to a writer agent that produces a polished report. Each step runs concurrently where possible.
- Multi-language support — Deploy a language-detection agent that identifies the user's language and routes them to a dedicated agent for that language. Each language agent is prompted and optionally fine-tuned for its linguistic context, improving response quality over a single multilingual bot.
- Development assistance — A project manager agent breaks down a feature request into subtasks. A coding agent implements the logic, a testing agent writes tests, and a documentation agent drafts the write-up. The manager assembles the outputs and presents a complete package.
- Content workflows — An editorial agent receives a topic brief, assigns research to a search agent, passes findings to a drafting agent, and sends the draft to a tone-checking agent before returning the final version. Entire content pipelines run with minimal human intervention.
Practical Considerations
A few things to keep in mind when building multi-agent systems:
- Latency adds up — Each agent hop introduces response time. A three-agent pipeline will be slower than a single agent. Design your routing to minimize unnecessary hops, and only escalate to a specialist when the supervisor cannot handle the request directly.
- Costs scale with tokens, not agents — Adding agents to an instance does not add subscriptions, since they share one deployment. What does grow is token usage: every consult is a full extra turn, and a coordinator that escalates on every message can quietly multiply your spend. Give each specialist a narrow trigger.
- Clear system prompts matter more — In a multi-agent system, each agent's scope must be unambiguous. Overlap in responsibilities causes duplicated work or conflicting responses. Write precise prompts that define what each agent handles and what it should decline.
- Trace the consult, not just the reply — When an answer looks wrong, check whether the coordinator actually called the specialist and what came back. A failure inside a consulted agent surfaces as a bad answer from the coordinator, so the instance log view is where you confirm which agent produced what.
Why Use OpenClaw Launch for Agent Teams?
Running an agent team normally means infrastructure to maintain — unless the platform handles it. OpenClaw Launch gives you a managed container with auto-restart on failure and a gateway URL with no configuration required. Your whole team lives inside it, so adding a specialist is a single command rather than another deployment to babysit.
The Lite plan starts at $3 for the first month (then $6/mo), and the Pro plan at $20/mo supports heavier workloads with higher resource limits. Each agent on an instance can run a different model and a different scoped prompt, and the instance connects to 12+ channel integrations — so a specialist team costs no more in subscription than a single bot.