Multi-Agent Architecture — Run Multiple AI Agents on One Server

What You Get

💰 5 agents on one €47/mo VPS — no need for separate servers
⚡ 60% fewer rate limit hits with multi-token strategy across agents
📉 75% less token burn with optimized AGENTS.md (target <5KB per agent)
🔄 Self-healing — crashed services auto-restart in under 5 minutes
🧠 Shared knowledge — agents access each other's discoveries via knowledge graph
🎯 Specialized agents — each does one thing well instead of one bot doing everything badly

When to Use

You need more than one agent with different specializations
One agent is hitting rate limits and you want to split the load
You want agents for different tasks: ops, trading, security, freelancing
You need isolated workspaces so agents don't interfere with each other
You want to route different Telegram groups/topics to different agents

Architecture Overview

┌─────────────────────────────────────────────────┐
│                 OpenClaw Gateway                  │
│          (single process, multiple agents)        │
├──────────┬──────────┬──────────┬────────────────┤
│  Agent 1 │  Agent 2 │  Agent 3 │   Agent N      │
│  (main)  │  (ops)   │  (trade) │   (custom)     │
├──────────┼──────────┼──────────┼────────────────┤
│workspace │workspace │workspace │  workspace     │
│          │  -ops    │  -trade  │  -custom       │
├──────────┴──────────┴──────────┴────────────────┤
│              Shared Infrastructure               │
│     (Docker, monitoring, LightRAG, backups)      │
└─────────────────────────────────────────────────┘

Quick Start

Step 1: Plan agents

| Agent | Role | Bot | Workspace | |-------|------|-----|-----------| | Main | Coordination | @main_bot | workspace | | Ops | Monitoring | @ops_bot | workspace-ops | | Security | Audits | @sec_bot | workspace-security |

Step 2: Create workspaces

mkdir -p ~/.openclaw/workspace-ops/{skills,memory,scripts,state}
mkdir -p ~/.openclaw/workspace-security/{skills,memory,scripts,state}

Step 3: Configure openclaw.json

{
  "agents": {
    "main": {
      "name": "Main",
      "model": "anthropic/claude-sonnet-4-6",
      "workspace": "workspace",
      "channels": { "telegram": { "botToken": "TOKEN_1" } }
    },
    "ops": {
      "name": "Ops",
      "model": "anthropic/claude-sonnet-4-6",
      "workspace": "workspace-ops",
      "channels": { "telegram": { "botToken": "TOKEN_2" } }
    }
  }
}

Step 4: Write AGENTS.md per agent

Keep each AGENTS.md under 5KB. Every byte loads into context every message. Smaller = cheaper.

Step 5: Share skills via symlinks

ln -s ~/.openclaw/workspace/skills/self-improving \
      ~/.openclaw/workspace-ops/skills/self-improving

Share utilities. Don't share specialized skills.

Rate Limit Management

Multi-token strategy

Split agents across 2+ Claude subscriptions:

{
  "auth-profiles": {
    "primary": { "token": "TOKEN_A" },
    "secondary": { "token": "TOKEN_B" }
  }
}

Model priority

Critical agents: Opus or Sonnet
Background agents: Sonnet only
When approaching limits: downgrade heavy agents mid-week

Telegram Routing

Option A: Separate bots (recommended) — one bot per agent, cleanest.

Option B: Forum topics — one supergroup, each topic routes to a different agent.

Option C: Commands — /ops check disk → ops agent, everything else → main.

Monitoring & Self-Healing

Heartbeat per agent

Each agent has a HEARTBEAT.md — minimal checks, alert only on problems.

Self-healing script (cron every 5 min)

#!/bin/bash
if ! openclaw gateway status | grep -q "running"; then
    openclaw gateway restart
fi
for svc in langfuse n8n lightrag; do
    if docker compose -f ~/docker/$svc/docker-compose.yml ps | grep -q "Exit"; then
        docker compose -f ~/docker/$svc/docker-compose.yml restart
    fi
done

Agent watchdog

Track last response time per agent. Silent > 15 min → alert.

Shared Memory

Three-tier architecture:

Hot: MEMORY.md — in context, instant, free, per-agent
Warm: memory_search — vector search, instant, free
Deep: Knowledge graph (LightRAG) — cross-agent, ~3-8 sec, small cost

See lightrag-knowledge-base skill for deep memory setup.

Backup

Back up daily: openclaw.json, auth-profiles, all MEMORY.md, all memory/ dirs. Back up weekly: skills/, scripts/, docker configs. Keep 7 days. Automate via cron.

Production Checklist

[ ] Each agent: own workspace + AGENTS.md + MEMORY.md
[ ] AGENTS.md < 5KB per agent
[ ] Dedicated Telegram bot per agent (or topic routing)
[ ] allowedChatIds on all bots
[ ] Rate limits distributed across tokens
[ ] Heartbeat configured per agent
[ ] Self-healing cron running
[ ] Daily backups automated
[ ] Bot tokens secured (not in git/chat)

Common Mistakes

Too many agents too fast — start with 2, add as needed
Giant AGENTS.md — 20KB = wasted tokens every message
No rate limit plan — 5 agents, one token = rate limits by Wednesday
Shared workspace — agents overwrite each other's memory
No monitoring — agent dies silently, nobody notices for hours