How to Build Multi-Agent Workflows in OpenClaw: A Complete Guide to AI Orchestration

Mar 22, 2026

My OpenClaw agent was stuck. I’d asked it to research a security vulnerability, check my code for similar issues, and draft a fix—all in one request. It churned for ten minutes, then produced a mediocre response that touched on everything but solved nothing.

The problem wasn’t the AI. It was me. I was treating a single agent like it could be an expert researcher, security auditor, and code writer simultaneously.

The Single-Agent Trap

I’d built what the r/openclaw community calls “another average bot”—one agent doing everything, badly.

Here’s what I kept experiencing:

Sequential bottlenecks: Research took 5 minutes, then security checks took 3 minutes, then code review took 4 minutes. Total: 12 minutes of waiting.
Context dilution: When I asked my agent to “research security issues and write a report,” it kept forgetting the security context while writing.
No specialization: A general-purpose agent knows a little about everything but excels at nothing.
Coordination gaps: Tasks requiring multiple skills would partially succeed—research done, but security checks skipped because the agent ran out of context.

One Reddit comment hit home: “Setup multi-agents based on workspace structure by project and tasks for your automation workflows if you want an executive AI assistant rather than another average openclaw bot.”

That was the distinction I’d missed: executive assistant vs. average bot.

Understanding Multi-Agent Architecture

The idea is simple: instead of one agent trying to be everything, build specialized agents that collaborate.

Layer 4: Orchestration (The Manager)
    |
    +-- Multi-agent coordinator
    +-- Cron scheduler
    +-- Optimization agent
    |
Layer 3: Capability Agents (The Specialists)
    |
    +-- Web search agent
    +-- Security audit agent
    +-- Code generation agent
    |
Layer 2: Memory & Context (The Foundation)
    |
    +-- Persistent memory agent
    +-- Context retrieval agent
    |
Layer 1: Interface (The Front Door)
    |
    +-- Dashboard agent
    +-- Discord/Slack agent

Each layer has a job. Each agent has a specialty. The orchestration layer coordinates everything.

My First Multi-Agent Setup (And Why It Failed)

I dove in enthusiastically, creating five agents at once:

agents/
├── researcher/
├── coder/
├── reviewer/
├── deployer/
└── reporter/

I configured them all, set up communication protocols, and ran my first workflow.

Nothing worked. The researcher couldn’t pass findings to the coder. The reviewer had no context from the researcher. The orchestrator was sending tasks to agents that hadn’t been initialized.

I’d made the classic mistake: I started with orchestration instead of individual agents.

The Correct Approach: Build Upward

Here’s the order that actually works, based on community recommendations and my own painful trial-and-error.

Phase 1: Start with Visibility (Dashboard Agent)

Before adding complexity, I needed to see what was happening.

projects/
├── dashboard/
│   └── CLAUDE.md
└── (nothing else yet)

The CLAUDE.md file contains the agent’s instructions:

# Dashboard Agent Instructions

## Role
Monitor and display status of all other agents

## Capabilities
- Track agent execution status
- Display recent logs and outputs
- Alert on failures or anomalies

## Output Format
Return structured dashboard data:
- Agent status (running/idle/error)
- Last execution time
- Recent outputs

I built this first and verified I could see agent activity before adding more agents.

Phase 2: Add Memory Persistence

I initially skipped this. The result? Every session started fresh with no context from previous work.

Adding persistent memory changed everything:

agent:
  name: "memory-agent"
  type: "persistent"

storage:
  backend: "postgresql"
  retention_days: 90

capabilities:
  - store_context
  - retrieve_relevant_history
  - summarize_sessions

Now when my research agent finds something important, it persists. When my coding agent starts work, it retrieves relevant context from previous sessions.

Phase 3: Add Specialized Agents One at a Time

I added agents incrementally, testing each one:

Research Agent:

# Research Agent Instructions

## Role
Specialized in web search and information synthesis

## Capabilities
- Query web search APIs
- Summarize and extract key information
- Cite sources accurately

## Output Format
Return structured findings:
- Key insights
- Source citations
- Confidence level (high/medium/low)

Security Audit Agent:

# Security Audit Agent Instructions

## Role
Review code for security vulnerabilities

## Capabilities
- Static code analysis
- Dependency vulnerability scanning
- Best practices verification

## Output Format
Return security report:
- Severity (critical/high/medium/low)
- Vulnerability description
- Recommended fix
- Affected files

Each agent got tested in isolation. I’d run a research query, verify the output made sense. Then run a security scan, check that results were accurate. Only after individual agents worked did I attempt coordination.

Phase 4: Add Orchestration Last

Here’s where everything comes together:

# Conceptual orchestration pattern
class OpenClawOrchestrator:
    def __init__(self):
        self.agents = {
            "research": ResearchAgent(),
            "security": SecurityAgent(),
            "memory": MemoryAgent(),
            "dashboard": DashboardAgent(),
        }
        self.task_queue = TaskQueue()

    async def execute_workflow(self, task):
        # Update dashboard
        await self.agents["dashboard"].log(f"Starting: {task.name}")

        # Run research and security in parallel
        results = await asyncio.gather(
            self.agents["research"].analyze(task),
            self.agents["security"].audit(task),
        )

        # Store in memory for future context
        await self.agents["memory"].persist(results)

        # Update dashboard with completion
        await self.agents["dashboard"].log(f"Completed: {task.name}")

        return self.synthesize(results)

The orchestrator doesn’t do the work—it delegates to specialists and coordinates their outputs.

What Parallel Execution Actually Looks Like

Before multi-agent: I’d request research + security audit + code fix. Total time: 12+ minutes, sequential.

After multi-agent:

Before (Sequential):
  Research:    ████████████ (5 min)
  Security:         ████████ (3 min)
  Code Fix:              ████████████ (4 min)
  Total: 12 minutes

After (Parallel):
  Research:    ████████████ (5 min)
  Security:    ████████ (3 min)     <- Runs in parallel
  Synthesis:         ███ (1 min)     <- Combine results
  Total: 6 minutes

Same work, half the time. The research agent works while the security agent scans. The orchestrator combines results.

The CLAUDE.md Pattern

The key insight from the community: CLAUDE.md files in project folders define agent behavior. Each agent has its own folder with instructions.

projects/
├── dashboard/
│   └── CLAUDE.md          # Monitor and control
├── research/
│   └── CLAUDE.md          # Web search and synthesis
├── security/
│   └── CLAUDE.md          # Code audit and review
├── orchestrator/
│   └── CLAUDE.md          # Task coordination logic
└── memory/
    └── CLAUDE.md          # Persistent state

This structure means:

Each agent has clear, focused instructions
Adding a new agent is adding a new folder
Agents can be tested in isolation
Context doesn’t get muddied across specialties

Common Mistakes I Made

Mistake 1: Building Orchestration First

I created an orchestrator before the agents existed. It’s like hiring a manager before you have any workers.

The right order: Build individual agents, verify they work, then add coordination.

Mistake 2: Skipping Persistent Memory

Without memory, every session starts from zero. Agents can’t learn from previous work. Context is lost between tasks.

Memory is not optional—it’s the foundation that makes multi-agent workflows actually useful.

Mistake 3: One Agent Does Everything

I kept trying to add capabilities to a single agent instead of creating new specialized agents. The result: a bloated, confused agent that did nothing well.

Specialization is the point. Each agent should excel at one thing.

Mistake 4: No Monitoring

I didn’t set up the dashboard agent early enough. When things went wrong, I had no visibility into which agent failed or why.

Build the dashboard first. It’s your window into the system.

Mistake 5: Ignoring Security

In my enthusiasm for automation, I skipped the security audit agent. Later, I discovered my coding agent had been generating code with hardcoded credentials.

The security agent isn’t optional—it catches what you’ll inevitably miss.

The Progressive Setup Path

Based on community recommendations, here’s the order that works:

Gateway dashboard — See what’s happening first
Discord/Slack integration — Notifications before automation
Persistent memory — Foundation for continuity
Web search — External context capability
Security audit — Catch problems early
Agent skills — Specialized capabilities
Multi-agent coordination — Finally, orchestration
Cron scheduling — Automated triggers
Optimization — Refine based on usage

Each step builds on the previous. Don’t jump ahead.

What to Build First

The community recommendation that worked for me: “Have it build a simple dashboard, start with tracking the overnight stuff.”

My first real multi-agent workflow tracked overnight processes:

Dashboard agent displayed status
Research agent checked for updates
Memory agent stored results
Discord agent sent morning summary

Simple. Useful. Taught me the architecture.

Why This Matters

Multi-agent workflows transform OpenClaw from a chat interface into an automation platform:

Parallel execution: Multiple tasks run simultaneously, not sequentially
Specialized expertise: Each agent develops depth in its domain
Scalable architecture: Add new agents without disrupting existing workflows
Continuous operation: Cron orchestration enables 24/7 automated processes
Better results: Specialists coordinated well beat generalists working alone

The difference between “another average bot” and “executive AI assistant” is entirely in the multi-agent architecture.

Quick Implementation Checklist

After the dashboard is working:

The Bottom Line

Building multi-agent workflows in OpenClaw isn’t about having many agents—it’s about having the right agents with clear responsibilities working together.

Start with visibility (dashboard). Build the foundation (memory). Add specialists one at a time. Coordinate them last.

My first attempt failed because I built the manager before the workers. My second attempt succeeded because I built workers first, then coordinated them.

The executive assistant emerges from the architecture, not the other way around.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!