Architecture

OpenClaw Multi-Agent Architectures: The Complete Guide

Eight battle-tested patterns for orchestrating AI agent teams — from simple solo setups to complex hierarchical swarms. Includes a downloadable reference template and step-by-step implementation guide.

Michel Lacle & Yaneth | ThinkSmart.Life Research

February 18, 2026 · min read

🎧 Listen to this article

~4 min briefing

1. Introduction

The era of the single AI agent doing everything is ending. As AI-powered workflows grow more complex — spanning code generation, research, monitoring, content creation, and customer support — a single agent becomes a bottleneck. It runs out of context, confuses tasks, and can't parallelize work.

Multi-agent architectures solve this by dividing work among specialized agents, each with its own context window, tools, and personality. Think of it as hiring a team instead of overloading one employee. The coordinator talks to you; the coder writes code in a sandbox; the researcher scours the web; the monitor watches your systems overnight.

This guide covers eight architecture patterns for multi-agent systems, with specific implementation details for OpenClaw — an open-source AI agent gateway that natively supports multi-agent routing, sub-agent spawning, cron automation, webhooks, and cross-agent messaging. Whether you're running a personal assistant or orchestrating a production AI team, you'll find a pattern that fits.

💡 What You'll Get From This Guide Eight architecture patterns with text-based diagrams, pros/cons, and real-world use cases. A downloadable reference template (openclaw-team-config.json) for a 3-agent team. A "Getting Started in 5 Steps" section with exact commands. Comparisons with CrewAI, AutoGen, and LangGraph.

2. Why Multi-Agent?

The Limitations of a Single Agent

A single agent has a finite context window — typically 128K to 200K tokens. When you ask it to simultaneously manage your calendar, write a research report, debug code, and monitor your servers, you hit fundamental limits:

Context pollution — code snippets in your calendar chat, research notes in your coding session
No parallelism — one task blocks the next
Tool overload — 50+ tools in one prompt degrade performance
No specialization — one system prompt can't optimize for research AND coding AND monitoring
Blast radius — a mistake in one area affects everything

What Multi-Agent Gives You

Splitting into multiple agents provides:

Isolation — each agent has its own workspace, session history, and tool set
Specialization — custom system prompts, models, and thinking levels per task
Parallelism — multiple agents work simultaneously
Security — sandbox untrusted agents; restrict tools per agent
Cost optimization — use Opus for complex reasoning, Sonnet for routine tasks

Anthropic's own research on building effective agents emphasizes starting simple and adding complexity only when needed. The patterns below are ordered from simplest to most complex — start with Pattern 1 and graduate to more sophisticated architectures as your needs grow.

3. Solo Agent + Cron Automation

Description

The simplest architecture: one agent handles everything, augmented with scheduled cron jobs for recurring tasks. The agent has full access to all tools and manages its own memory, heartbeats, and automation.

┌─────────────────────────────────────────┐ │ Solo Agent (main) │ │ ┌─────────┐ ┌──────────┐ ┌────────┐ │ │ │ Chat │ │ Cron │ │ Memory │ │ │ │ Handler │ │ Scheduler│ │ Files │ │ │ └────┬────┘ └─────┬────┘ └────────┘ │ │ │ │ │ │ ┌────▼─────────────▼──────────────────┐│ │ │ All Tools Available ││ │ │ exec · web · browser · message ││ │ └──────────────────────────────────────┘│ └─────────────────────────────────────────┘ │ │ ┌─────▼─────┐ ┌──────▼──────┐ │ Telegram │ │ WhatsApp │ └───────────┘ └─────────────┘

When to Use It

You're just getting started with OpenClaw
Your workload is moderate (personal assistant, one project)
Tasks are sequential, not parallel
You want the simplest possible setup

OpenClaw Implementation

This is OpenClaw's default. No special configuration needed — just set up your agent workspace with AGENTS.md, SOUL.md, and optionally HEARTBEAT.md for periodic checks. Add cron jobs for recurring work:

# Morning briefing at 7 AM
openclaw cron add \
  --name "Morning brief" \
  --cron "0 7 * * *" \
  --tz "America/New_York" \
  --session isolated \
  --message "Check email, calendar, weather. Summarize." \
  --announce \
  --channel telegram

# Nightly git commit check
openclaw cron add \
  --name "Git status" \
  --cron "0 22 * * 1-5" \
  --session isolated \
  --message "Run git status in all projects. Flag uncommitted work." \
  --announce

Pros & Cons

✅ Zero configuration complexity
✅ Full context continuity in one session
✅ Cron handles scheduled work without bloating context
❌ Context window fills up on complex days
❌ No parallelism — one task at a time
❌ All tools in one prompt = potential confusion

4. Hub & Spoke (Delegator Pattern)

Description

The most common multi-agent pattern in OpenClaw. A main agent handles user conversation and delegates specialized tasks to sub-agents via sessions_spawn. Sub-agents run in isolated sessions, complete their work, and announce results back to the main chat.

┌─────────────┐ │ User │ └──────┬──────┘ │ ┌──────▼──────┐ │ Main Agent │ │ (Coordinator)│ └──┬───┬───┬──┘ ┌──────────┘ │ └──────────┐ │ │ │ ┌──────▼──────┐ ┌────▼─────┐ ┌──────▼──────┐ │ Coding │ │ Research │ │ Monitor │ │ Sub-Agent │ │ Sub-Agent│ │ Sub-Agent │ │ (sandboxed) │ │ │ │ │ └──────┬──────┘ └────┬─────┘ └──────┬──────┘ │ │ │ └──────────────┼──────────────┘ │ ┌──────▼──────┐ │ Announce │ │ Results │ └─────────────┘

When to Use It

Tasks are independent and can run in parallel
You want to keep the main chat clean while heavy work runs in background
Different tasks need different models (Opus for reasoning, Sonnet for routine)
You want sandboxed execution for code tasks

OpenClaw Implementation

The main agent uses sessions_spawn to create sub-agents. Each sub-agent gets its own session, runs the task, and the result is automatically announced back.

// Main agent delegates via sessions_spawn:
sessions_spawn({
  task: "Research the top 5 vector databases. Compare pricing, 
         performance, and ease of use. Write a structured report.",
  label: "vector-db-research",
  model: "anthropic/claude-sonnet-4-20250514"
})

// Sub-agent runs independently, then announces back:
// "✅ Research complete. Top 5 vector databases compared..."

For multi-agent setups with dedicated agent IDs, configure openclaw.json to allow cross-agent spawning:

{
  "agents": {
    "list": [
      { "id": "main", "workspace": "~/.openclaw/workspace" },
      { "id": "coding", "workspace": "~/.openclaw/workspace-coding",
        "sandbox": { "mode": "all", "scope": "agent" } }
    ]
  }
}

Real-World Examples

Content pipeline — main agent receives "write a blog post about X," spawns a research sub-agent and a writing sub-agent in parallel
Code review — main agent spawns a coding sub-agent to analyze a PR, run tests, and report findings
This very article was created by a sub-agent spawned from the main agent using sessions_spawn

Pros & Cons

✅ Clean separation — main chat stays focused
✅ Parallel execution of independent tasks
✅ Sub-agents auto-announce results
✅ Each sub-agent gets a fresh context window
❌ Sub-agents can't spawn further sub-agents (single depth)
❌ No real-time collaboration between sub-agents
❌ Main agent must wait for results to synthesize

5. Pipeline / Assembly Line

Description

Tasks flow through a sequence of specialized agents, each transforming the output for the next stage. Like a factory assembly line — each station adds value. The output of Stage 1 becomes the input of Stage 2.

┌────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────────┐ │ Input │───▶│ Stage 1 │───▶│ Stage 2 │───▶│ Stage 3 │───▶│ Output │ │ │ │ Research │ │ Draft │ │ Review │ │ │ └────────┘ │ Agent │ │ Agent │ │ Agent │ └────────┘ └──────────┘ └──────────┘ └──────────┘ Find sources Write draft QA + polish

When to Use It

Tasks have clear sequential dependencies
Each stage requires different expertise
You want quality gates between stages
Content creation, data processing, code deploy pipelines

OpenClaw Implementation

Use sessions_send to chain agent sessions, or orchestrate via the main agent spawning sequential sub-agents where each reads the previous output from a shared workspace file:

// Stage 1: Research agent writes findings to file
sessions_spawn({
  task: "Research [topic]. Save findings to /tmp/research-output.md",
  label: "pipeline-stage-1-research"
})

// After Stage 1 announces completion, Stage 2 reads it:
sessions_spawn({
  task: "Read /tmp/research-output.md. Write a polished blog draft. 
         Save to /tmp/draft-output.md",
  label: "pipeline-stage-2-draft"
})

// Stage 3: Review
sessions_spawn({
  task: "Read /tmp/draft-output.md. Check for errors, improve prose, 
         verify citations. Save final to /tmp/final.md",
  label: "pipeline-stage-3-review"
})

Pros & Cons

✅ Each stage is specialized and focused
✅ Clear quality gates between stages
✅ Easy to debug — check output at each stage
❌ Sequential = slower total execution time
❌ One slow stage blocks the entire pipeline
❌ Requires careful coordination of inputs/outputs

6. Hierarchical Team

Description

A multi-level management structure where manager agents coordinate groups of worker agents. The top-level agent talks to the user, delegates to department managers, who in turn delegate to specialists. Each level has approval authority over the level below.

┌─────────────┐ │ CEO Agent │ │ (user-facing)│ └──┬────────┬──┘ ┌─────────┘ └─────────┐ ┌──────▼──────┐ ┌────────▼────────┐ │ Eng Manager│ │ Content Manager │ └──┬───────┬──┘ └──┬──────────┬───┘ ┌─────┘ └─────┐ ┌────┘ └────┐ ┌────▼────┐ ┌─────▼───┐ ┌───▼────┐ ┌───▼────┐ │ Backend │ │ Frontend│ │ Writer │ │ Designer│ │ Agent │ │ Agent │ │ Agent │ │ Agent │ └─────────┘ └─────────┘ └────────┘ └─────────┘

When to Use It

Large, complex projects with multiple workstreams
You want approval chains and oversight
Different teams need different contexts and tools
Enterprise-scale AI deployments

OpenClaw Implementation

OpenClaw supports this through multiple agent IDs with per-agent workspaces, tools, and sandbox configurations. The "CEO" agent uses sessions_spawn with agentId to target manager agents:

{
  "agents": {
    "list": [
      { "id": "ceo", "default": true, "workspace": "~/.openclaw/workspace-ceo",
        "model": "anthropic/claude-opus-4-6" },
      { "id": "eng-mgr", "workspace": "~/.openclaw/workspace-eng",
        "model": "anthropic/claude-sonnet-4-20250514" },
      { "id": "content-mgr", "workspace": "~/.openclaw/workspace-content",
        "model": "anthropic/claude-sonnet-4-20250514" }
    ]
  }
}

⚠️ Current Limitation OpenClaw sub-agents cannot spawn further sub-agents (no recursive spawning). To implement true hierarchical teams, the coordinator agent must orchestrate all levels. The "manager" pattern is achieved through separate agent IDs with cross-agent sessions_send rather than nested sessions_spawn.

Pros & Cons

✅ Mirrors real organizational structures
✅ Clear accountability at each level
✅ Managers can aggregate and filter before escalating
❌ Complex to set up and maintain
❌ Communication overhead between levels
❌ Recursive spawning not yet supported in OpenClaw

7. Peer-to-Peer Mesh / Swarm

Description

Agents collaborate as equals, sharing a workspace and communicating through files, messages, or shared state. No single coordinator — agents self-organize based on their capabilities and the work available. Inspired by OpenAI's Swarm framework.

┌──────────┐ ┌──────────┐ ┌──────────┐ │ Agent A │◄───▶│ Agent B │◄───▶│ Agent C │ │ (Code) │ │ (Test) │ │ (Deploy) │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ └────────────────┼─────────────────┘ │ ┌──────▼──────┐ │ Shared │ │ Workspace │ │ (files + │ │ state) │ └─────────────┘

When to Use It

Problems where the optimal decomposition isn't known upfront
Creative/exploratory tasks where agents build on each other's work
Fault-tolerant systems where any agent can pick up dropped work

OpenClaw Implementation

While OpenClaw doesn't have a native swarm protocol, you can approximate this pattern using multiple agents with a shared workspace directory and sessions_send for inter-agent messaging (requires tools.agentToAgent.enabled: true):

{
  "tools": {
    "agentToAgent": {
      "enabled": true,
      "allow": ["agent-a", "agent-b", "agent-c"]
    }
  }
}

Pros & Cons

✅ Highly flexible and adaptive
✅ No single point of failure
✅ Agents can dynamically redistribute work
❌ Hard to debug — no clear chain of command
❌ Risk of agents duplicating work or conflicting
❌ Requires careful state management

8. Specialized Role Teams

Description

Dedicated agents for each organizational function — research, coding, QA, monitoring, customer support. Each role agent has its own channel bindings, tools, and personality. This mirrors how companies organize into departments.

┌─────────────────────────────────────────────────────┐ │ Gateway Server │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Research │ │ Coding │ │ QA │ │ │ │ Agent │ │ Agent │ │ Agent │ │ │ │ │ │(sandboxed)│ │ │ │ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ │ │ │ │ ┌────▼─────┐ ┌────▼─────┐ ┌────▼─────┐ │ │ │ Telegram │ │ Discord │ │ Discord │ │ │ │ Bot 1 │ │ Bot 2 │ │ Bot 3 │ │ │ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────┘

When to Use It

Teams where different people interact with different agents
Multi-person setups sharing one OpenClaw gateway
Clear functional boundaries between tasks
Each role needs different channel access

OpenClaw Implementation

This is OpenClaw's native multi-agent routing at its best. Each agent gets its own Telegram/Discord bot, bound via bindings:

{
  "agents": {
    "list": [
      { "id": "research", "workspace": "~/.openclaw/workspace-research" },
      { "id": "coding", "workspace": "~/.openclaw/workspace-coding",
        "sandbox": { "mode": "all", "scope": "agent" },
        "tools": { "allow": ["read","write","edit","exec"] } },
      { "id": "qa", "workspace": "~/.openclaw/workspace-qa" }
    ]
  },
  "bindings": [
    { "agentId": "research", "match": { "channel": "telegram", "accountId": "research-bot" } },
    { "agentId": "coding", "match": { "channel": "discord", "accountId": "coding-bot" } },
    { "agentId": "qa", "match": { "channel": "discord", "accountId": "qa-bot" } }
  ]
}

Pros & Cons

✅ Clear ownership — each agent is an expert in its domain
✅ Different models/costs per role
✅ Multi-person teams can each talk to "their" agent
✅ Security isolation per role (sandbox coding, restrict QA tools)
❌ Agents don't naturally collaborate without explicit wiring
❌ More bots/accounts to manage

9. Event-Driven Reactive

Description

Agents don't run continuously — they're triggered by external events. A webhook receives a GitHub push, an email arrives, a monitoring alert fires — and the appropriate agent spins up, handles the event, and shuts down. Pure event-driven architecture.

┌──────────┐ ┌──────────┐ ┌──────────┐ │ GitHub │ │ Gmail │ │ Monitor │ │ Webhook │ │ Push │ │ Alert │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ └──────────────┼──────────────┘ │ ┌──────▼──────┐ │ OpenClaw │ │ /hooks/ │ │ Endpoint │ └──────┬──────┘ │ ┌───────────┼───────────┐ │ │ │ ┌─────▼────┐ ┌───▼─────┐ ┌──▼────────┐ │ PR Review│ │ Email │ │ Incident │ │ Agent │ │ Handler │ │ Responder │ └──────────┘ └─────────┘ └───────────┘

When to Use It

DevOps automation — PR reviews, deployment notifications, incident response
Email processing — auto-triage, response drafting
Monitoring — react to alerts with investigation and remediation
Cost-conscious setups — agents only run when needed

OpenClaw Implementation

OpenClaw's webhook system (/hooks/agent and /hooks/wake) is purpose-built for this. You can also use hook mappings for structured payloads:

# GitHub webhook → code review agent
curl -X POST http://localhost:18789/hooks/agent \
  -H "Authorization: Bearer $HOOK_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "New PR opened: #142 Add OAuth support. Review the diff, check for security issues, test coverage.",
    "name": "GitHub",
    "agentId": "coding",
    "deliver": true,
    "channel": "discord",
    "to": "channel:CODE_REVIEW_CHANNEL_ID"
  }'

For Gmail integration, OpenClaw has built-in support via openclaw webhooks gmail setup that watches for new emails and triggers agent runs.

Pros & Cons

✅ Cost-efficient — agents only run when triggered
✅ Real-time response to external events
✅ Clean separation of concerns per event type
❌ Requires webhook infrastructure setup
❌ No persistent state between runs (by design)
❌ Cold start latency for first response

10. Human-in-the-Loop Hybrid

Description

Agents handle routine work autonomously but escalate to humans at decision points. The agent does the research and prepares options; the human makes the call; the agent executes. This is the most practical pattern for high-stakes environments.

┌──────────┐ ┌──────────────┐ ┌──────────┐ │ Agent │────▶│ Decision │────▶│ Agent │ │ Phase 1 │ │ Point │ │ Phase 2 │ │ (research│ │ │ │ (execute)│ │ + draft)│ │ ┌────────┐ │ │ │ └──────────┘ │ │ Human │ │ └──────────┘ │ │ Review │ │ │ └────────┘ │ └──────────────┘ Approve / Edit / Reject

When to Use It

Financial transactions or purchases
Public-facing communications (tweets, emails to clients)
Infrastructure changes (deployments, DNS changes)
Any task where mistakes are costly or irreversible

OpenClaw Implementation

OpenClaw naturally supports this through its channel architecture. Agents can draft messages and present them to the user for approval before sending. The sendPolicy configuration controls when agents can send externally:

{
  "session": {
    "sendPolicy": {
      "rules": [
        { "match": { "channel": "discord", "chatType": "group" }, "action": "deny" }
      ],
      "default": "allow"
    }
  }
}

For explicit approval flows, the agent can present options via inline buttons (on Telegram) or message the user directly and wait for confirmation before proceeding.

Pros & Cons

✅ Human oversight for critical decisions
✅ Best of both worlds — AI speed + human judgment
✅ Builds trust in the system over time
❌ Slower than fully autonomous systems
❌ Human becomes the bottleneck
❌ Requires clear escalation criteria

11. Architecture Comparison Table

Pattern	Complexity	Parallelism	Best For	OpenClaw Features
Solo + Cron	⭐	None	Personal assistant, simple automation	Default setup, cron jobs, heartbeat
Hub & Spoke	⭐⭐	High	Task delegation, content pipelines	sessions_spawn, sub-agent announce
Pipeline	⭐⭐	Low	Sequential processing, quality gates	sessions_spawn chain, shared workspace
Hierarchical	⭐⭐⭐⭐	Medium	Large teams, enterprise workflows	Multi-agent IDs, sessions_send, bindings
Swarm	⭐⭐⭐⭐⭐	Very High	Exploratory, creative, fault-tolerant	Agent-to-agent messaging, shared workspace
Role Teams	⭐⭐⭐	High	Multi-person teams, department splits	Multi-agent routing, per-agent bindings
Event-Driven	⭐⭐⭐	High	DevOps, monitoring, email processing	Webhooks (/hooks/), hook mappings, Gmail
Human-in-Loop	⭐⭐	Low	High-stakes decisions, public comms	sendPolicy, inline buttons, approval flows

🎯 Which Pattern Should You Start With? If you're new to OpenClaw, start with Solo + Cron (Pattern 1). Once you hit context limits or need parallelism, graduate to Hub & Spoke (Pattern 2) — it covers 80% of multi-agent use cases. Only reach for Hierarchical or Swarm patterns when you have a genuine need for complex coordination.

12. Reference Architecture Template

Below is a complete, production-ready openclaw.json configuration for a 3-agent team: a Coordinator (user-facing), a Coder (sandboxed), and a Researcher. Copy this and adapt to your needs.

{
  "agents": {
    "list": [
      {
        "id": "main",
        "name": "Coordinator",
        "default": true,
        "workspace": "~/.openclaw/workspace",
        "model": "anthropic/claude-opus-4-6",
        "description": "User-facing coordinator. Delegates via sessions_spawn."
      },
      {
        "id": "coding",
        "name": "Coder",
        "workspace": "~/.openclaw/workspace-coding",
        "model": "anthropic/claude-sonnet-4-20250514",
        "sandbox": {
          "mode": "all",
          "scope": "agent",
          "docker": {
            "setupCommand": "apt-get update && apt-get install -y git curl python3 nodejs npm"
          }
        },
        "tools": {
          "allow": ["read", "write", "edit", "exec", "web_search", "web_fetch"],
          "deny": ["message", "browser", "nodes", "canvas", "tts"]
        }
      },
      {
        "id": "research",
        "name": "Researcher",
        "workspace": "~/.openclaw/workspace-research",
        "model": "anthropic/claude-sonnet-4-20250514",
        "tools": {
          "allow": ["read", "write", "edit", "exec", "web_search", "web_fetch", "browser", "image"],
          "deny": ["message", "nodes", "canvas", "tts"]
        }
      }
    ]
  },
  "bindings": [
    { "agentId": "main", "match": { "channel": "telegram" } },
    { "agentId": "main", "match": { "channel": "whatsapp" } }
  ],
  "tools": {
    "sessions": { "visibility": "tree" }
  },
  "cron": { "enabled": true },
  "hooks": {
    "enabled": true,
    "token": "${OPENCLAW_HOOKS_TOKEN}",
    "path": "/hooks"
  }
}

AGENTS.md Templates

Main Agent (Coordinator)

# Coordinator Agent

You are the coordinator. Your responsibilities:
1. Talk to the user directly via Telegram/WhatsApp
2. Delegate complex tasks using sessions_spawn:
   - Code tasks → agentId: "coding"
   - Research tasks → agentId: "research"  
3. Synthesize results from sub-agents
4. Manage cron jobs and heartbeats

## Delegation Rules
- Simple questions → handle yourself
- Code/debug tasks → spawn coding agent
- Research/analysis → spawn research agent
- Always provide full context in the task description

Coding Agent

# Coding Agent

You are a specialized coding agent running in a sandbox.
1. Write clean, tested code
2. Follow project conventions
3. Run tests before reporting completion
4. You cannot send messages — your results auto-announce

## Constraints
- Sandboxed environment (Docker)
- No external messaging tools
- Focus on code quality over speed
- Always include test results in your final message

Research Agent

# Research Agent

You are a specialized research agent.
1. Search the web thoroughly (3+ sources minimum)
2. Cross-reference findings
3. Write structured reports with citations
4. You cannot send messages — your results auto-announce

## Process
1. Search via web_search (multiple queries)
2. Fetch and read promising URLs via web_fetch
3. Organize findings with headers and bullet points
4. Include all reference URLs at the end

13. Getting Started in 5 Steps

⏱️ Time Required: ~15 minutes These steps assume you already have OpenClaw installed. If not, see the installation guide.

Step 1: Create Agent Workspaces

# Create workspaces for each agent
openclaw agents add coding
openclaw agents add research

# Verify
openclaw agents list

Step 2: Configure Each Workspace

# Write AGENTS.md for the coding agent
cat > ~/.openclaw/workspace-coding/AGENTS.md << 'EOF'
# Coding Agent
You are a specialized coding agent running in a sandbox.
Write clean, tested code. Run tests before reporting.
EOF

# Write AGENTS.md for the research agent  
cat > ~/.openclaw/workspace-research/AGENTS.md << 'EOF'
# Research Agent
You are a specialized research agent. Search the web thoroughly.
Cross-reference findings. Write structured reports with citations.
EOF

Step 3: Update openclaw.json

Add the agent configurations and bindings from the Reference Template above to your ~/.openclaw/openclaw.json.

Step 4: Restart the Gateway

openclaw gateway restart

# Verify everything is running
openclaw agents list --bindings
openclaw channels status --probe

Step 5: Test the Team

# From your Telegram chat with the main agent, try:
"Research the top 5 project management tools and write a comparison"

# The main agent should spawn a research sub-agent,
# which will announce results back when done.

# Or trigger a coding task:
"Write a Python script that monitors CPU usage and alerts if > 90%"

14. How OpenClaw Compares to Other Frameworks

OpenClaw is not the only multi-agent framework. Here's how it compares to the major alternatives:

Feature	OpenClaw	CrewAI	AutoGen	LangGraph
Approach	Gateway daemon + channels	Role-based agent teams	Conversation-based agents	Graph-based workflows
Multi-agent native	✅ Built-in routing	✅ Core feature	✅ Core feature	✅ Via graph nodes
Chat channels	✅ Telegram, WhatsApp, Discord, Slack, Signal, iMessage	❌ API only	❌ API only	❌ API only
Persistent memory	✅ File-based workspace	✅ Via memory module	⚠️ Limited	✅ Checkpointing
Cron/scheduling	✅ Built-in scheduler	❌ External	❌ External	❌ External
Webhooks	✅ Built-in /hooks/	❌ External	❌ External	❌ External
Sandbox	✅ Per-agent Docker	❌ No	✅ Code executor	❌ No
Learning curve	Medium (JSON config)	Low (Python decorators)	Low (minimal code)	High (graph concepts)
Best for	Personal AI assistants, always-on agents	One-shot team tasks	Conversational agents	Complex stateful workflows

Key differentiator: OpenClaw is the only framework that combines multi-agent routing with native chat channel integration (WhatsApp, Telegram, Discord, etc.), built-in cron scheduling, webhook endpoints, and persistent file-based memory — all running as a single daemon. Other frameworks require you to build the surrounding infrastructure yourself.

15. Conclusion & Next Steps

Multi-agent architectures aren't about using the most complex pattern — they're about matching the right pattern to your problem. Most users will find that Solo + Cron covers 60% of their needs, and Hub & Spoke covers another 30%. Only reach for hierarchical or swarm patterns when you genuinely need them.

The key principles to remember:

Start simple, scale up — don't over-architect from day one
Isolate by concern — each agent should have a clear domain
Use the right model per task — Opus for reasoning, Sonnet for execution
Sandbox untrusted work — code execution should always be sandboxed
Keep humans in the loop — for anything high-stakes or irreversible

OpenClaw makes multi-agent architecture accessible through its native support for agent routing, sub-agent spawning, cron automation, and webhook triggers — all configurable through a single JSON file. Download the reference template, follow the 5 steps, and you'll have a working multi-agent team in under 15 minutes.

📥 Download the Reference Template Get the complete openclaw-team-config.json with 3-agent setup, AGENTS.md templates, and example cron jobs: Download openclaw-team-config.json

References

Anthropic. "Building Effective Agents." anthropic.com/research/building-effective-agents
Microsoft. "AI Agent Orchestration Patterns." learn.microsoft.com
Stack AI. "The 2026 Guide to Agentic Workflow Architectures." stack-ai.com
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