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Multi-Agent Workflows

OpenClaw supports running multiple agents within a single gateway, each with its own identity, workspace, memory, and channel bindings. This guide covers configuration, routing, inter-agent communication, orchestration tools, production patterns, fleet management, and cost optimization.

:::tip Key insight Anthropic's research shows that an Opus lead with Sonnet subagents outperforms a single Opus agent by 90.2% on complex tasks. Multi-agent isn't just about scale — it's about specialization and parallel coverage. :::

Architecture​

Each agent is a fully isolated "brain" with:

  • Its own workspace (SOUL.md, AGENTS.md, USER.md, TOOLS.md, MEMORY.md)
  • Its own state directory (auth profiles, model registry)
  • Its own session store (chat history, routing state)
  • Optionally its own LLM model and provider

All agents share a single gateway process and port.

                     ┌─── Agent "alex" ──── Workspace + Memory + Model
                     │
Gateway (port 18789) ├─── Agent "mia"  ──── Workspace + Memory + Model
                     │
                     └─── Agent "coder" ─── Workspace + Memory + Model
                     
  ▲ Incoming messages routed by bindings (channel, peer, account)

Recent Multi-Agent Improvements​

VersionFeatureImpact
v2026.4.19Per-session nested agent scopingLong-running agent no longer blocks other sessions
v2026.5.22Scoped sub-agent bootstrap contextSub-agents only get AGENTS.md + TOOLS.md by default (saves tokens)
v2026.6.1Workboard orchestration primitivesKanban-style agent coordination with dependency tracking
v2026.6.1Internal namespacesScoped agent/global sessions for tool dispatch isolation

Configuration​

Defining Agents​

~/.openclaw/openclaw.json
{
  "agents": {
    "defaults": {
      "model": "claude-sonnet-4-6"
    },
    "list": [
      {
        "id": "alex",
        "workspace": "~/.openclaw/workspace-alex",
        "model": "claude-opus-4-8"
      },
      {
        "id": "mia",
        "workspace": "~/.openclaw/workspace-mia",
        "model": "claude-haiku-4-5-20251001"
      }
    ]
  }
}

agents.defaults sets baseline settings for all agents. Individual entries in agents.list override specific fields.

Channel Bindings​

Bindings route incoming messages to specific agents based on channel, account, peer, or space:

~/.openclaw/openclaw.json
{
  "bindings": [
    {
      "agentId": "alex",
      "match": {
        "channel": "whatsapp",
        "peer": { "kind": "direct", "id": "+15551230001" }
      }
    },
    {
      "agentId": "mia",
      "match": {
        "channel": "telegram"
      }
    },
    {
      "agentId": "alex",
      "match": {
        "channel": "whatsapp",
        "accountId": "personal"
      }
    }
  ]
}

Binding rules:

  • Most specific wins — peer matches take priority over channel-wide matches
  • Deterministic — same input always routes to the same agent
  • If multiple accounts exist for a channel, use accountId to distinguish
  • Unmatched messages go to the default agent

Per-Agent Identity​

Each agent gets its own workspace with distinct identity files:

FilePurpose
SOUL.mdInternal personality — values, tone, boundaries
IDENTITY.mdExternal presentation — name, emoji, avatar
AGENTS.mdTask instructions — what the agent should do
USER.mdUser context — who the agent serves
TOOLS.mdCapabilities — which tools are available
MEMORY.mdPersistent knowledge — what the agent remembers

Pattern: Share a common AGENTS.md for operating rules, give each agent a unique SOUL.md for personality.

Separate Instances (Alternative)​

For full isolation (different ports, different processes):

# Instance 1 (work)
OPENCLAW_PORT=18789 OPENCLAW_HOME=~/.openclaw-work openclaw gateway

# Instance 2 (personal)
OPENCLAW_PORT=18790 OPENCLAW_HOME=~/.openclaw-personal openclaw gateway

This provides stronger isolation but requires managing two processes.

Common Patterns​

Work + Personal Agents​

Route work contacts to a professional agent, personal contacts to a casual one:

{
  "agents": {
    "list": [
      {
        "id": "work",
        "workspace": "~/.openclaw/workspace-work"
      },
      {
        "id": "personal",
        "workspace": "~/.openclaw/workspace-personal"
      }
    ]
  },
  "bindings": [
    { "agentId": "work", "match": { "channel": "slack" } },
    { "agentId": "work", "match": { "channel": "teams" } },
    { "agentId": "personal", "match": { "channel": "whatsapp" } },
    { "agentId": "personal", "match": { "channel": "telegram" } }
  ]
}

Give each agent a distinct SOUL.md — professional tone for work, casual for personal.

Model Routing by Agent​

Assign different models based on agent role and cost tolerance:

{
  "agents": {
    "list": [
      { "id": "thinker", "model": "claude-opus-4-8" },
      { "id": "responder", "model": "claude-haiku-4-5-20251001" },
      { "id": "coder", "model": "claude-sonnet-4-6" }
    ]
  }
}

Specialist Agents​

Route by topic using separate channels:

AgentChannelSOUL.md Focus
coderGitHub, CLICode review, debugging, PRs
assistantWhatsApp, iMessageCalendar, reminders, messages
researcherDiscord, TelegramWeb research, summaries
homelabHome AssistantSmart home automation

Orchestrator-Worker Pattern​

The pattern Anthropic uses in their own multi-agent research system: one lead agent (expensive, capable) coordinates specialist workers (cheaper, focused):

{
  "agents": {
    "list": [
      {
        "id": "lead",
        "model": "claude-opus-4-8",
        "workspace": "~/.openclaw/workspace-lead"
      },
      {
        "id": "researcher",
        "model": "claude-sonnet-4-6",
        "workspace": "~/.openclaw/workspace-researcher"
      },
      {
        "id": "writer",
        "model": "claude-sonnet-4-6",
        "workspace": "~/.openclaw/workspace-writer"
      },
      {
        "id": "reviewer",
        "model": "claude-haiku-4-5-20251001",
        "workspace": "~/.openclaw/workspace-reviewer"
      }
    ]
  }
}

The lead decomposes tasks, delegates to specialists, and synthesizes results. Workers store output in external systems (files, git) and pass lightweight references back — avoiding context window bloat.

Scaling rules (from Anthropic's research):

  • Simple queries: 1 agent, 3-10 tool calls
  • Moderate tasks: 2-4 subagents in parallel
  • Complex work: 10+ subagents with dependency coordination

Inter-Agent Communication​

File-Based Messaging​

The simplest pattern: agents communicate through shared files.

# Agent "lead" writes a task
echo '{"task": "Review PR #42", "priority": "high"}' > ~/.openclaw/shared/inbox-reviewer.json

# Agent "reviewer" checks its inbox via heartbeat
cat ~/.openclaw/shared/inbox-reviewer.json

This is how Claude Code's built-in Agent Teams work: a shared task list with file-locking to prevent race conditions, plus a mailbox system where teammates message each other by name.

Workboard Messaging​

The Workboard provides structured inter-agent communication through card-based coordination:

# Lead creates a task card for the researcher
openclaw workboard create --title "Research competitor pricing" \
  --assign researcher \
  --parent card-001

# Researcher claims and works on it
openclaw workboard claim card-002

# Researcher marks complete — lead gets notified via lifecycle sync
openclaw workboard update card-002 --status done

MCP-Based Communication​

Agents can call each other's skills through MCP:

Lead agent's config
{
  "mcp": {
    "servers": {
      "researcher-agent": {
        "command": "ssh",
        "args": ["user@server", "openclaw", "mcp", "serve", "--agent", "researcher"]
      }
    }
  }
}

The lead agent can now invoke the researcher's skills as MCP tools — effectively delegating work through a structured protocol.

ClawTeam Communication​

ClawTeam-OpenClaw (1,396 stars) provides purpose-built inter-agent messaging:

  • Point-to-point inboxes — send messages directly to a specific agent
  • Broadcast — send to all agents simultaneously
  • File-based transport (default) — JSON files in ~/.clawteam/, no server required
  • ZeroMQ transport (optional) — P2P for high-throughput, low-latency messaging
# Send a message to a specific agent
clawteam send --to researcher "Analyze the Q2 earnings report"

# Broadcast to all agents
clawteam broadcast "Stand-up: report your status"

# Check inbox
clawteam inbox --agent lead

Workboard Orchestration​

The Workboard (v2026.6.1+) is OpenClaw's official orchestration layer for multi-agent coordination.

Card Lifecycle​

todo → running → review → done
         ↓
      blocked
  • todo — Ready for work, waiting to be claimed
  • running — Agent has claimed and is working on it
  • review — Work complete, pending verification
  • done — Verified and finished
  • blocked — Waiting on a dependency

Agent Coordination Tools​

Agents interact with the Workboard autonomously during execution:

ToolPurpose
workboard_listList cards with claim state
workboard_readRead card details + worker context
workboard_createCreate cards with parent/child relationships
workboard_claimClaim a card for this agent
workboard_linkLink cards into dependency chains
workboard_heartbeatRefresh claim during long runs
workboard_runsReturn persisted run history
workboard_dispatchPromote dependency-ready children, start workers
workboard_reassignMove a card to a different agent
workboard_promotePromote a card to a higher priority

Dispatch Workflow​

The dispatch cycle runs automatically:

  1. Promote dependency-ready children to todo
  2. Block expired claims (stale agents)
  3. Claim next available card
  4. Start worker for the card
  5. Track completion and update lifecycle

Autonomous Sub-Tasks​

An orchestrator agent creates cards, specialist agents claim and execute, and the Workboard tracks everything:

Lead Agent                   Workboard                  Specialist Agents
    │                            │                            │
    ├── create card ────────────►│                            │
    │   "Research competitors"   │◄── claim ──────────────────┤ researcher
    │                            │                            │
    ├── create card ────────────►│                            │
    │   "Write report"           │   (blocked by research)    │
    │   blocked-by: research     │                            │
    │                            │                            │
    │                            │◄── done ───────────────────┤ researcher
    │                            │── auto-unblock ───────────►│
    │                            │◄── claim ──────────────────┤ writer
    │                            │                            │
    │◄── notify ─────────────────│◄── done ───────────────────┤ writer

Self-Organizing Heartbeat​

Add a Workboard check to each agent's heartbeat:

HEARTBEAT.md
## Workboard Check

Every 2 hours:
1. Check for blocked cards you own — investigate and unblock
2. Check for unclaimed cards matching your skills — claim and start
3. Report status of in-progress cards

Orchestration Tools​

Mission Control​

Mission Control (4,032 stars) is a centralized operations and governance platform for running OpenClaw across teams and organizations.

  • Hierarchical orchestration — organizations, board groups, boards, tasks, tags
  • Gateway-aware — connect and manage remote execution environments from one interface
  • Distributed runtime — operate remote gateways without changing operator workflow
  • Real deployments — documented at scale with 5 master instances and 10+ satellite agents on AWS
# Install
git clone https://github.com/abhi1693/openclaw-mission-control
cd openclaw-mission-control && docker compose up -d

Best for: teams and organizations running multiple gateways across environments.

ClawTeam-OpenClaw​

ClawTeam-OpenClaw (1,396 stars, 27 contributors, 453 tests) provides a complete multi-agent framework:

Communication:

  • Point-to-point inboxes and broadcast messaging
  • File-based transport (default) or ZeroMQ P2P
  • Shared kanban task board with dependency chains

Fleet Monitoring:

  • Terminal kanban: clawteam board show
  • Auto-refreshing dashboard: clawteam board live
  • Tiled tmux views: clawteam board attach
  • Web UI: clawteam board serve

Cost Management:

  • Real-time cost dashboard (token/cost by agent, model, and task)
  • Per-agent model assignment with 7-level priority chain
  • Cost tiers: strong / balanced / cheap

Resilience:

  • Circuit breaker patterns (healthy/degraded/open tri-state)
  • Retry with exponential backoff
  • Zombie detection and idle worker identification
  • Shutdown approval workflows

Best for: production teams needing fleet monitoring, cost tracking, and inter-agent messaging.

Antfarm​

Antfarm orchestrates multi-agent workflows with zero external infrastructure:

npx antfarm create my-team
  • YAML-defined teams — plain Markdown and YAML configuration
  • SQLite state tracking at ~/.openclaw/antfarm/antfarm.db
  • Cron scheduling with staggered 15-minute intervals
  • Three bundled workflows:
WorkflowAgentsPurpose
feature-dev7Full feature development pipeline
security-audit7Security review and remediation
bug-fix6Bug triage, fix, and verification

Each agent runs in a fresh session with clean context — no context window bloat. Memory persists through git history and progress files.

Best for: solo developers wanting deterministic multi-agent workflows with no setup.

ClawDeck & ClawController​

ClawDeck and ClawController are commercial dashboards providing visual agent management and monitoring.

DigitalOcean Elastic Scaling​

DigitalOcean App Platform offers elastic scaling for multiple OpenClaw agents with safe defaults and no infrastructure management.

Production Patterns​

Multi-Agent DevOps Pipeline​

Three agents coordinate through the Workboard:

{
  "agents": {
    "list": [
      {
        "id": "reviewer",
        "workspace": "~/.openclaw/workspace-reviewer",
        "model": "claude-opus-4-8"
      },
      {
        "id": "tester",
        "workspace": "~/.openclaw/workspace-tester",
        "model": "claude-sonnet-4-6"
      },
      {
        "id": "deployer",
        "workspace": "~/.openclaw/workspace-deployer",
        "model": "claude-haiku-4-5-20251001"
      }
    ]
  },
  "bindings": [
    { "agentId": "reviewer", "match": { "channel": "discord", "peer": { "kind": "space", "id": "code-review" } } },
    { "agentId": "tester", "match": { "channel": "slack", "peer": { "kind": "space", "id": "ci-cd" } } },
    { "agentId": "deployer", "match": { "channel": "slack", "peer": { "kind": "space", "id": "deployments" } } }
  ]
}

Flow: New PR detected by reviewer's heartbeat → creates Workboard card → reviewer analyzes code → tester claims test card → deployer claims deploy card (blocked until tests pass).

Research Pipeline​

Researcher (Sonnet) → Writer (Sonnet) → Editor (Haiku)
     │                    │                  │
     ├── Web search       ├── Draft report   ├── Grammar check
     ├── Source analysis   ├── Citations      ├── Fact verification
     └── Claim extraction  └── Formatting     └── Final output

Each agent runs in parallel where possible, with the Workboard tracking dependencies.

Customer Support Fleet​

{
  "agents": {
    "list": [
      { "id": "triage", "model": "claude-haiku-4-5-20251001" },
      { "id": "technical", "model": "claude-sonnet-4-6" },
      { "id": "billing", "model": "claude-haiku-4-5-20251001" },
      { "id": "escalation", "model": "claude-opus-4-8" }
    ]
  },
  "bindings": [
    { "agentId": "triage", "match": { "channel": "intercom" } }
  ]
}

The triage agent classifies incoming messages and routes to specialists. Only complex escalations hit the expensive Opus model.

Virtual Company​

Multiple specialized teams coordinated through a leader agent:

TeamAgentModelFocus
DevcoderSonnet 4.6Code, PRs, bugs
MarketingmarketerHaiku 4.5Content, social media
OpsopsHaiku 4.5Monitoring, alerts
LeadleadOpus 4.8Strategy, coordination

The lead agent uses shared memory (via PowerMem or ClawTeam) for cross-agent context.

Memory: Shared vs Isolated​

By default, each agent has completely isolated memory — separate SQLite search index, separate Markdown memory files in its workspace.

When to Share Memory​

ScenarioApproach
Agents need their own contextDefault isolated memory
Agents need to share factsShared filesystem directory
Agents need cross-context searchPowerMem (hybrid + graph memory)
Team-wide managed memoryMem0 (managed platform)
Token-efficient shared memorymemU (3-layer filesystem)

Shared Directory Pattern​

The simplest approach — agents read/write to a common directory:

{
  "agents": {
    "list": [
      {
        "id": "researcher",
        "workspace": "~/.openclaw/workspace-researcher"
      },
      {
        "id": "writer",
        "workspace": "~/.openclaw/workspace-writer"
      }
    ]
  }
}
# Both agents have access to a shared output directory
mkdir -p ~/.openclaw/shared/research-output

# Researcher writes findings
# Writer reads findings to create reports

PowerMem for Multi-Agent​

PowerMem explicitly supports multi-agent memory governance:

  • Isolation between agents by default
  • Cross-agent collaboration when configured
  • Graph-based relationships between memories
  • Hybrid search (vector + keyword + graph)

Dreaming Per Agent​

v2026.6.1 allows scheduling Dreaming (memory consolidation) independently per agent. Each agent can consolidate on its own schedule without affecting others.

Fleet Monitoring​

Built-In Monitoring​

# Check all agents
openclaw status

# Per-agent health
openclaw channel status --agent alex
openclaw channel status --agent mia

# Gateway-wide metrics
openclaw stats tokens --period 7

ClawTeam Fleet Dashboard​

For visual fleet monitoring at scale:

# Terminal kanban — see all agents and their current cards
clawteam board show

# Auto-refreshing dashboard
clawteam board live

# Tiled tmux view — one pane per agent
clawteam board attach

# Web UI
clawteam board serve --port 8080

Mission Control at Scale​

For organizations running multiple gateways:

Mission Control Dashboard
├── Gateway: VPS-1 (US East)
│   ├── Agent: coder (active, 3 tasks)
│   ├── Agent: reviewer (idle)
│   └── Agent: ops (heartbeat running)
├── Gateway: VPS-2 (EU West)
│   ├── Agent: support-eu (active, 12 conversations)
│   └── Agent: translator (idle)
└── Gateway: VPS-3 (APAC)
    └── Agent: support-apac (active, 8 conversations)

Health Check Patterns​

Add multi-agent health checks to the lead agent's heartbeat:

Lead agent's HEARTBEAT.md
## Fleet Health Check

1. Check each agent's channel connectivity
2. Verify Workboard has no stale claimed cards (older than 1 hour)
3. Check for zombie agents (high memory, no recent activity)
4. Report fleet status to Telegram
5. Alert on any disconnected channels or failed agents

Cost Optimization​

Multi-agent setups use approximately 15x more tokens than standard chat. Cost management is critical.

The Token Multiplier​

SetupRelative Cost
Single agent, standard chat1x
Single agent, agentic mode4x
Multi-agent system~15x

Model Tiering​

The biggest lever: assign the right model to each agent's role.

{
  "agents": {
    "list": [
      { "id": "lead", "model": "claude-opus-4-8" },
      { "id": "worker-1", "model": "claude-sonnet-4-6" },
      { "id": "worker-2", "model": "claude-sonnet-4-6" },
      { "id": "monitor", "model": "claude-haiku-4-5-20251001" },
      { "id": "heartbeat-bot", "model": "local/qwen3:14b" }
    ]
  }
}
RoleRecommended ModelWhy
Lead / orchestratorOpusComplex reasoning, delegation decisions
Specialist workerSonnetGood balance of capability and cost
Triage / routingHaikuFast, cheap, good enough for classification
Monitoring / heartbeatLocal (Ollama)Free, runs 24/7

Bootstrap Context Scoping​

Since v2026.5.22, sub-agents only receive AGENTS.md and TOOLS.md by default — not SOUL.md, USER.md, memory, or other files. This prevents token waste on context that workers don't need.

To include additional files for a specific sub-agent, explicitly attach them in the delegation.

Heartbeat Cost Multiplication​

Each agent with a heartbeat sends its full context every cycle. Three agents with 30-minute heartbeats = 144 heartbeat cycles per day.

Mitigations:

  • Route all heartbeats to a cheap/local model via heartbeat_override
  • Stagger heartbeat intervals (agent 1 at :00, agent 2 at :20, agent 3 at :40)
  • Disable heartbeat for agents that don't need autonomous monitoring
  • Use quiet hours on all agents
{
  "agents": {
    "list": [
      {
        "id": "lead",
        "heartbeat": { "interval": 3600 }
      },
      {
        "id": "worker",
        "heartbeat": { "enabled": false }
      },
      {
        "id": "monitor",
        "heartbeat": {
          "interval": 1800,
          "quiet_hours": { "start": "23:00", "end": "07:00" }
        }
      }
    ]
  }
}

Cost Tracking​

# Per-agent token usage
openclaw stats tokens --agent lead --period 7
openclaw stats tokens --agent worker-1 --period 7

# ClawTeam cost dashboard (by agent, model, and task)
clawteam costs show
clawteam costs show --by agent
clawteam costs show --by model

Budget Example​

AgentModelHeartbeatMonthly Cost
Lead (Opus)$15/M tokens1/hour~$180
2x Workers (Sonnet)$9/M tokensdisabled~$60 each (on demand)
Monitor (Haiku)$3/M tokens1/30min~$45
Heartbeat bot (local)$01/30min$0
Total~$345/month

vs. single Opus agent doing everything: ~$500+/month with worse performance.

Security Considerations​

Multi-agent setups multiply the attack surface:

  • Each agent with shell access can execute commands as your user
  • Compromised agents can potentially access other agents' workspaces unless isolated
  • Channel bindings route messages automatically — a compromised channel can reach the wrong agent
  • SOUL.md is the #1 attack vector (persistent personality hijacking)

Mitigations​

  • Use separate OPENCLAW_HOME directories for sensitive vs non-sensitive agents
  • Restrict shell access per agent where possible
  • Use sandbox mode for agents handling untrusted input
  • Monitor each agent's SOUL.md for unauthorized modifications
  • Make identity files read-only: chmod 444 SOUL.md AGENTS.md
  • v2026.6.1 internal namespaces provide scoped isolation between agents

Bootstrap Context Security​

Since v2026.5.22, sub-agents don't receive SOUL.md, USER.md, or customer runbooks by default. This prevents sensitive main-agent context from leaking into worker sessions — a significant security improvement for multi-agent setups.

Choosing an Orchestration Approach​

ApproachBest ForComplexityCost
Channel bindingsSimple routing (work vs personal)LowNone
WorkboardTask coordination within one gatewayMediumBuilt-in
AntfarmDeterministic YAML-defined workflowsMediumFree
ClawTeamFleet management with messaging and monitoringHighFree
Mission ControlMulti-gateway enterprise orchestrationHighFree
Separate instancesMaximum isolationLowMultiple processes

Start simple: Channel bindings with 2-3 agents cover most use cases. Add Workboard when you need task coordination. Add ClawTeam or Mission Control only when managing 5+ agents or multiple gateways.

Quick Start: Your First Multi-Agent Setup​

~/.openclaw/openclaw.json
{
  "agents": {
    "defaults": {
      "model": "claude-sonnet-4-6"
    },
    "list": [
      {
        "id": "assistant",
        "workspace": "~/.openclaw/workspace-assistant"
      },
      {
        "id": "coder",
        "workspace": "~/.openclaw/workspace-coder",
        "model": "claude-sonnet-4-6"
      }
    ]
  },
  "bindings": [
    { "agentId": "assistant", "match": { "channel": "telegram" } },
    { "agentId": "coder", "match": { "channel": "discord" } }
  ]
}
# Create workspaces
mkdir -p ~/.openclaw/workspace-assistant ~/.openclaw/workspace-coder

# Give each agent a personality
echo "You are a friendly personal assistant." > ~/.openclaw/workspace-assistant/SOUL.md
echo "You are a senior software engineer." > ~/.openclaw/workspace-coder/SOUL.md

# Start the gateway — both agents run on one process
openclaw start

See Also​