title: "How to Build a Multi-Agent System with OpenClaw" date: "2026-02-28" description: "Learn how to build a powerful multi-agent system using OpenClaw. This step-by-step tutorial covers agent configuration, inter-agent communication, and real-world automation patterns." category: "Tutorial" author: "OpenClaw Team" tags: ["multi-agent", "automation", "tutorial"] readTime: "12 min"
Building a multi-agent system might sound complex, but OpenClaw makes it surprisingly straightforward. In this tutorial, you'll learn how to create multiple AI agents that work together to accomplish tasks no single agent could handle alone.
What You'll Build
By the end of this tutorial, you'll have a working multi-agent system with:
- A research agent that gathers information from the web
- A writer agent that creates content based on research
- An editor agent that reviews and polishes the final output
- A coordinator that orchestrates the workflow
Prerequisites
Before you begin, make sure you have:
- OpenClaw installed (installation guide)
- An API key for at least one LLM provider (Claude, OpenAI, or Ollama)
- Basic familiarity with YAML configuration
- Node.js 18+ or Python 3.10+
Step 1: Design Your Agent Architecture
Every multi-agent system starts with a clear architecture. Think about what each agent needs to do and how they communicate.
# agents.yaml
agents:
coordinator:
role: "Project Coordinator"
model: claude-3-sonnet
instructions: |
You coordinate between the research, writer, and editor agents.
Break down tasks and delegate to the appropriate agent.
researcher:
role: "Research Specialist"
model: claude-3-haiku
instructions: |
You gather information from provided sources.
Return structured data with citations.
tools:
- web_search
- url_reader
writer:
role: "Content Writer"
model: claude-3-sonnet
instructions: |
You create well-structured content based on research data.
Follow the style guide provided by the coordinator.
editor:
role: "Content Editor"
model: claude-3-opus
instructions: |
You review content for accuracy, clarity, and style.
Provide specific, actionable feedback.
The key principle here is separation of concerns: each agent has a focused role with specific tools and instructions.
Step 2: Configure Agent Communication
Agents need to share information. OpenClaw supports several communication patterns:
Message Passing
The simplest approach — agents send messages directly to each other:
# communication.yaml
channels:
research_to_writer:
from: researcher
to: writer
format: structured_data
writer_to_editor:
from: writer
to: editor
format: document
editor_feedback:
from: editor
to: writer
format: review_comments
Shared Memory
For more complex workflows, use shared memory so all agents can access common data:
# memory.yaml
shared_memory:
type: redis # or sqlite, filesystem
namespace: content_pipeline
ttl: 3600 # 1 hour
Step 3: Define the Workflow
Now wire everything together with a workflow definition:
# workflow.yaml
name: content_pipeline
description: "Research, write, and edit content"
steps:
- name: research
agent: researcher
input: "{{topic}}"
output: research_data
timeout: 120s
- name: draft
agent: writer
input: "{{research_data}}"
output: draft_content
depends_on: [research]
- name: review
agent: editor
input: "{{draft_content}}"
output: final_content
depends_on: [draft]
- name: revision
agent: writer
input: "{{editor_feedback}}"
output: revised_content
depends_on: [review]
condition: "{{review.needs_revision}}"
Step 4: Implement Error Handling
Multi-agent systems need robust error handling. Here's how to add retry logic and fallbacks:
from openclaw import AgentOrchestrator, RetryPolicy
orchestrator = AgentOrchestrator("agents.yaml")
# Configure retry policies
orchestrator.set_retry_policy(
agent="researcher",
policy=RetryPolicy(
max_retries=3,
backoff_factor=2,
retry_on=["timeout", "rate_limit"]
)
)
# Add fallback agents
orchestrator.set_fallback(
agent="writer",
fallback_agent="writer_backup",
trigger="consecutive_failures >= 2"
)
# Run the pipeline
result = orchestrator.run(
workflow="content_pipeline",
input={"topic": "AI trends in 2026"}
)
print(result.final_content)
Step 5: Monitor and Debug
OpenClaw provides built-in monitoring for multi-agent systems:
# Watch agent activity in real-time
openclaw monitor --workflow content_pipeline
# View agent communication logs
openclaw logs --agents researcher,writer,editor --last 1h
# Get performance metrics
openclaw metrics --workflow content_pipeline
The monitoring dashboard shows:
| Metric | Description | |--------|-------------| | Agent Response Time | Average time each agent takes to respond | | Message Queue Depth | Number of pending messages between agents | | Error Rate | Percentage of failed agent interactions | | Token Usage | LLM tokens consumed per agent |
Step 6: Optimize for Production
Once your multi-agent system works, optimize it for production:
Use Appropriate Models
Match model capability to task complexity:
- Simple tasks (data extraction, formatting): Use smaller, faster models like Claude Haiku
- Creative tasks (writing, brainstorming): Use mid-tier models like Claude Sonnet
- Critical tasks (final review, complex reasoning): Use top-tier models like Claude Opus
Implement Caching
Cache repeated queries to reduce costs and latency:
caching:
enabled: true
strategy: semantic # Cache similar queries, not just exact matches
ttl: 86400 # 24 hours
max_size: 1GB
Set Resource Limits
Prevent runaway costs:
limits:
max_tokens_per_run: 100000
max_agents: 10
max_concurrent_tasks: 5
budget_per_day: $10.00
Step 7: Test Your Multi-Agent System
Before going to production, test each agent individually and then as a system:
# Test individual agents
openclaw test --agent researcher --input "Latest AI developments"
openclaw test --agent writer --input "$(cat sample_research.json)"
openclaw test --agent editor --input "$(cat sample_draft.md)"
# Run the full pipeline with a test input
openclaw run content_pipeline \
--input '{"topic": "Test topic"}' \
--dry-run \
--verbose
Write integration tests that verify the full pipeline produces expected outputs:
import unittest
from openclaw import AgentOrchestrator
class TestContentPipeline(unittest.TestCase):
def setUp(self):
self.orchestrator = AgentOrchestrator("agents.yaml")
def test_research_produces_structured_output(self):
result = self.orchestrator.run_agent(
"researcher",
input="Python web frameworks"
)
self.assertIn("citations", result)
self.assertGreater(len(result["findings"]), 0)
def test_full_pipeline_completes(self):
result = self.orchestrator.run(
workflow="content_pipeline",
input={"topic": "Python web frameworks"},
timeout=300
)
self.assertIsNotNone(result.final_content)
self.assertGreater(len(result.final_content), 500)
def test_editor_provides_feedback(self):
result = self.orchestrator.run_agent(
"editor",
input="This is a poorly written draft with no structure."
)
self.assertIn("suggestions", result)
if __name__ == "__main__":
unittest.main()
Step 8: Scale with Dynamic Agent Spawning
For advanced use cases, spawn agents dynamically based on workload:
# dynamic_agents.yaml
scaling:
researcher:
min_instances: 1
max_instances: 5
scale_on: queue_depth
threshold: 3 # Spawn new instance when 3+ tasks are queued
writer:
min_instances: 1
max_instances: 3
scale_on: queue_depth
threshold: 2
This is particularly useful for batch processing — say you need to research 50 topics at once. Instead of processing them sequentially through a single researcher agent, OpenClaw can spin up multiple instances to process them in parallel.
# Process a batch of topics
openclaw run content_pipeline \
--batch topics.json \
--parallel 5 \
--output results/
Common Pitfalls to Avoid
- Over-engineering: Start with 2-3 agents. Add more only when you have a clear need.
- Vague instructions: Each agent needs specific, detailed instructions. Ambiguity leads to poor results.
- Missing error handling: Always plan for agent failures. Networks go down, APIs rate-limit, models hallucinate.
- Ignoring costs: Multi-agent systems multiply LLM costs. Monitor token usage from day one.
- No testing: Always test agents individually before connecting them. A broken agent in the middle of a pipeline is hard to debug.
- Tight coupling: Design agents that can work independently. If one agent goes down, the others should handle it gracefully.
Real-World Example: Content Marketing Pipeline
Here's a complete, production-ready example that generates blog posts automatically:
# marketing_pipeline.yaml
name: blog_generator
schedule: "0 9 * * MON" # Every Monday at 9am
steps:
- name: trending_topics
agent: researcher
input: "Find 3 trending topics in {{industry}} this week"
output: topics
- name: outline
agent: coordinator
input: "Create blog outlines for: {{topics}}"
output: outlines
- name: write_posts
agent: writer
input: "Write a 1500-word post from: {{outlines[*]}}"
output: drafts
parallel: true # Write all posts simultaneously
- name: review_posts
agent: editor
input: "Review for factual accuracy and SEO: {{drafts[*]}}"
output: reviewed
parallel: true
- name: publish
agent: coordinator
input: "Format and schedule publication: {{reviewed}}"
output: published
tools:
- cms_api
- social_scheduler
This pipeline runs autonomously every Monday, researching trends, writing posts, reviewing them, and scheduling publication — all without human intervention.
Next Steps
Now that you have a working multi-agent system, explore these advanced topics:
- Scheduling AI tasks with cron jobs for automated pipelines
- Security hardening for production deployments
- OpenClaw voice assistant for voice-controlled agents
Multi-agent systems unlock a new level of AI automation. Start small, iterate fast, and let your agents do the heavy lifting.