How to Run Multiple OpenClaw Agents: Multi-Agent Team Setup Guide (2026) | OpenClaw DC

You can run multiple OpenClaw agents on one machine. Each agent gets its own memory, skills, and messaging channel. Use openclaw agents add to create agents, bind them to separate Telegram or Slack channels, and they run independently. Here is the complete setup, from your first two agents to a full team running in parallel.

TL;DR

• Each OpenClaw agent runs as an isolated process with its own memory, credentials, and skill set
• Create agents with openclaw agents add, bind to channels with openclaw agents bind
• Start with 2 agents (orchestrator + specialist), then scale
• v3.24 adds sub-agent support via OpenWebUI for background task delegation
• 4 poorly configured agents are worse than 1 well-configured one

Why Run Multiple Agents?

Multi-agent is the number one question on Reddit and Discord for OpenClaw users, and for good reason. A single agent can do a lot, but there are three situations where splitting into multiple agents makes a meaningful difference.

Context isolation. Every agent maintains its own memory store. A research agent accumulating hundreds of web pages does not pollute the context window of your coding agent. Your personal agent never sees your client work. Isolation means each agent stays focused and does not hallucinate based on unrelated context from another task.

Specialized roles. An agent loaded with coding skills, Git tools, and linter integrations behaves differently from an agent configured for email triage and calendar management. By splitting roles, each agent carries only the skills it needs, which reduces token usage and improves response quality.

Parallel work. Two agents can work at the same time. While one researches a topic and writes a draft, another can be processing your inbox. They do not block each other because they run as separate processes.

Architecture Overview

Here is how a multi-agent OpenClaw setup looks in practice:

                     +-----------+
                     |  OpenClaw |
                     |  Gateway  |
                     +-----+-----+
                           |
            +--------------+--------------+
            |              |              |
      +-----+-----+  +----+-----+  +-----+-----+
      |  Agent 1   |  |  Agent 2  |  |  Agent 3  |
      |  Research   |  |  Coding   |  |  Email    |
      |  Memory: A  |  |  Memory: B |  |  Memory: C |
      |  Skills: web|  |  Skills:git|  |  Skills:mail|
      +-----+------+  +-----+-----+  +-----+------+
            |                |              |
      +-----+------+  +-----+-----+  +-----+------+
      | Telegram    |  | Telegram   |  | Slack      |
      | Chat A      |  | Chat B     |  | Channel C  |
      +-------------+  +------------+  +------------+

Each agent is a separate process. The gateway routes incoming messages to the correct agent based on which channel or chat the message arrives in. Agents never share memory unless you explicitly configure them to.

Step-by-Step: Setting Up Multiple Agents

Before starting, make sure OpenClaw is installed and running. If you have not set it up yet, follow the beginner guide first.

Step 1: Create Your Agents

Use openclaw agents add to create each agent with a name and model:

# Create a research agent
openclaw agents add --name research --model claude-sonnet-4

# Create a coding agent
openclaw agents add --name coding --model deepseek-coder

# Verify both agents exist
openclaw agents list

Each agent gets its own configuration directory, memory store, and skill set. You can assign different models to different agents based on what they do best.

Step 2: Add Messaging Channels

Each agent needs its own channel to receive messages. Add channels with openclaw channels add:

# Add a Telegram chat for the research agent
openclaw channels add --type telegram --name "research-chat" --token "YOUR_BOT_TOKEN_1"

# Add a Telegram chat for the coding agent
openclaw channels add --type telegram --name "coding-chat" --token "YOUR_BOT_TOKEN_2"

You can use any combination of Telegram, Slack, Discord, or the Web UI. For Telegram-specific setup details, see the Telegram setup guide.

Step 3: Bind Agents to Channels

Connect each agent to its channel with openclaw agents bind:

# Bind research agent to its Telegram chat
openclaw agents bind --agent research --channel "research-chat"

# Bind coding agent to its Telegram chat
openclaw agents bind --agent coding --channel "coding-chat"

Step 4: Configure Skills Per Agent

Load only the skills each agent needs:

# Give the research agent web and summarization skills
openclaw skills add --agent research web-search
openclaw skills add --agent research summarize

# Give the coding agent development tools
openclaw skills add --agent coding git-tools
openclaw skills add --agent coding code-review

Step 5: Restart the Gateway

Apply all changes by restarting:

openclaw gateway restart

Send a test message to each channel. Each agent should respond independently.

Real-World Setup: 4 Agents on One Laptop

A Reddit user shared their setup running 4 agents on a single Linux laptop with 16 GB of RAM. Each agent had its own Telegram chat for communication:

• Agent 1 (Personal): Calendar management, reminders, personal email triage
• Agent 2 (Work): Slack monitoring, meeting notes, task tracking
• Agent 3 (Research): Web research, document summarization, source collection
• Agent 4 (Code): Code review, bug fixes, Git operations

The key insight from their post: each agent used a different model optimized for its role. The personal and work agents ran on a local Qwen model to keep costs at zero. The research agent used Claude for quality. The coding agent used DeepSeek Coder for speed. Total API cost was under $15 per month because two of the four agents ran entirely locally.

Common Multi-Agent Patterns

Personal + Work. The simplest split. One agent handles personal tasks with access to your personal email and calendar. The other handles work tasks with access to Slack, Jira, and work email. Memory isolation ensures nothing crosses the boundary.

Research + Coding + Writing. A three-agent team for content-heavy workflows. The research agent gathers sources and creates summaries. The coding agent handles technical implementations. The writing agent produces drafts from research output.

One agent per client. Freelancers and consultants run one agent per client, each with client-specific credentials, knowledge bases, and communication channels. No risk of sending one client’s data to another client’s channel.

Sub-Agents in v3.24

Version 3.24 introduces sub-agent support via OpenWebUI. A sub-agent is a background worker that a parent agent spawns mid-conversation to handle a delegated task.

For example, you ask your orchestrator agent to “research competitor pricing and update the spreadsheet.” The orchestrator spawns a research sub-agent to gather pricing data and a spreadsheet sub-agent to update the file. Both run in the background and report results back to the orchestrator, which consolidates everything and responds to you.

Sub-agents inherit the parent’s credentials by default but get their own memory context. They terminate automatically when their task completes. For full details on v3.24, check the 3.24 release notes.

Start With Two Agents

The community consensus is clear: start with two agents and get them working before adding more. The recommended pair is an orchestrator and a specialist.

The orchestrator handles general conversation, task routing, and anything that does not require specialized skills. The specialist handles one domain deeply, such as coding, research, or email.

Once this pair runs reliably for a week, you have earned the right to add a third agent. Four poorly configured agents that fight over resources, bloat token counts, and duplicate work will produce worse results than one well-tuned agent.

Common Mistakes to Avoid

Token bloat. Loading every skill onto every agent inflates context windows and burns through API budgets. Give each agent only the skills it actually uses. A coding agent does not need email triage skills.

Agents fighting over resources. If two agents share a channel or try to respond to the same message, you get duplicate responses and wasted tokens. Always bind one agent per channel. Use Telegram Topics if you want multiple agents in the same group (see the Telegram guide for topic setup).

Skipping the single-agent phase. If you have not run one OpenClaw agent successfully, do not start with four. Get comfortable with memory, skills, and channel configuration on a single agent first.

Shared memory without intent. Agents sharing a memory namespace can confuse each other. Keep memory isolated unless you have a specific reason to share it, and even then, consider using a read-only shared namespace.

Next Steps

Once your multi-agent system is running, explore these resources:

• How to Use OpenClaw for foundational setup
• OpenClaw Telegram Setup for channel configuration details
• OpenClaw 3.24 Release Notes for sub-agent features and upgrade instructions