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 an OpenClaw setup, each agent is a separate instance running its own model, skills, and channel connections. Agents can reach each other through gateway API calls, shared memory, or by passing structured messages through connected channels. The result is a system where tasks flow between agents automatically, each contributing what it does best.
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.
- Workload distribution — High-volume workflows can be split across agents so no single instance becomes a bottleneck. Requests are handled in parallel rather than queued behind each other.
- 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.
- Fault isolation — If one agent goes down or needs updating, the rest of the team keeps running. No single point of failure.
How It Works in OpenClaw
OpenClaw exposes a gateway API on each running instance. This API accepts incoming messages and returns structured responses, which makes it straightforward for one agent to call another programmatically. There are three primary ways agents communicate:
- Gateway API calls — Agent A sends an HTTP request to Agent B's gateway endpoint with a message payload. Agent B processes it and returns a response. This is the most direct and reliable method for structured inter-agent communication.
- Shared memory — Agents configured to use the same session memory store can read each other's context. One agent writes a summary; another picks it up on the next query without needing an explicit message exchange.
- Channel relay — An agent monitoring a shared channel can detect messages tagged for it and reply. This is useful for human-visible workflows where you want the handoff between agents to be transparent to the user.
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
OpenClaw Launch makes it straightforward to run a team of agents without managing servers. Each instance is independent, so you can configure them separately and connect them through their gateway endpoints.
1. Deploy your first agent
Use the visual configurator on OpenClaw Launch to create your primary (supervisor or front-facing) agent. Choose a model suited for routing and general conversation — a mid-tier model works well here since it mostly classifies and delegates rather than doing deep reasoning.
Give it a clear system prompt that describes its role: it receives requests, determines which specialist to forward them to, and returns the result to the user.
2. Deploy specialist agents
Create a separate instance for each specialist role. On each instance:
- Select a model suited to that specialty (a code model for dev tasks, a reasoning model for analysis).
- Enable the relevant skills from the 12+ available channel plugins.
- Write a focused system prompt that scopes the agent to its domain.
Each instance gets its own gateway URL after deployment. Copy these — your supervisor agent will call them.
3. Connect agents via gateway endpoints
Configure the supervisor agent with the gateway URLs of each specialist. When the supervisor determines a request belongs to a specific domain, it sends an API call to that specialist's gateway and relays the response back to the user.
You can set this up using OpenClaw's built-in skill system or by writing a lightweight routing skill that maps intent categories to agent endpoints.
4. Test the full pipeline
Send a test message through your front-facing channel (Telegram, Discord, or the web UI). Verify that the supervisor correctly identifies the intent, routes to the right specialist, and returns a coherent answer. Check the logs on each instance to confirm the message flow.
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 agents — Each active instance on OpenClaw Launch has its own subscription. Plan your agent count against the value each specialist provides. The Lite plan at $6/mo per instance keeps costs predictable.
- 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.
- Monitor each instance independently — Use OpenClaw Launch's per-instance log view to trace where a request went wrong. A failure in a downstream agent can look like a problem with the supervisor if you're only watching the front end.
Why Use OpenClaw Launch for Agent Teams?
Managing a fleet of agents means more infrastructure to maintain — unless you use a platform that handles it for you. OpenClaw Launch gives each agent its own managed container, auto-restart on failure, and a dedicated gateway URL with no configuration required. You deploy in under 2 minutes per agent, and each one runs 24/7 without you touching a server.
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 instance can run a different model and a different set of skills from 12+ available channel integrations, so your agent team is fully configurable without any shared infrastructure to coordinate.